In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss why a popular claim about artificial intelligence taking over jobs misses the mark. You will discover what AI enablement is, how to break your daily tasks into clear steps that reveal what computers handle. You will learn a testing method that separates work worth automating from tasks requiring your human touch. You will uncover ways to upgrade your routine without fearing career changes. You will gain the confidence to restructure your workflow for lasting efficiency.

00:00 – Introduction04:15 – Debunking the takeover statistic08:40 – Breaking work into clear steps13:25 – Separating automation from augmentation18:50 – Finding your hidden opportunities23:10 – Managing AI like a direct report27:45 – Call to action

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Machine-Generated TranscriptWhat follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for listening to the episode.

Christopher S. Penn:In this week’s In-Ear Insights, let’s talk about AI enablement and specifically what AI can and can’t do.

Early this year, Anthropic, the makers of Claude, released a paper about the labor effects of AI. And they cited and used a paper way back from 2023 from Ilondo et al that said in some professions like management, computer science, etc., up to 94% of tasks could be consumed by AI.

And when this paper came out, everybody and their cousin copied and pasted the radar chart. We all accepted it at face value.

However, we did some digging, we did some reading into this and we used our job-to-AI plugin, which is located in the Trust Insights Academy, along with a hefty amount of AI to try to replicate the results of that original paper.

And it turns out the original paper that Anthropic cited used about 10,000 or 18,000 tasks that human judges and GPT-4, which was OpenAI’s model at the time—a model that now feels like a crusty old dinosaur—to guess whether or not AI could do a task in half the time.

So what we found in our version of this, by decomposing job descriptions—I want to say we did what, 90,000 something odd—into individual tasks with tangible deliverables, and then used the Trust Insights TRIPS framework to assess how good a fit each task was for AI. Plus, we used the latest benchmarks from Artificial Analysis to judge what AI’s capabilities were and determine whether AI could do this.

So Katie, that was a lot of preamble in your first reads of our version of this paper. What were the big things that stuck out to you?

Katie Robbert:Well, first I want to react to your comment about Anthropic using what GPT-4, which you said is like what?

Christopher S. Penn:A crusty, old GPT-4 for the original paper from 2023.

Katie Robbert:Oh, the original paper, yeah. Here’s the thing.

If you’re doing your work correctly and you have your foundation, methodology, and requirements, the model change. This is something it’s not the purpose of this particular podcast, but it’s worth mentioning. People tend to panic every time a model changes, thinking, well, this one’s modern. Now I have to change things. Now I have to start over.

If you are structuring your work correctly, like an academic paper should, the methodology and research should all be fairly repeatable. It shouldn’t matter that the model changed. So I just want to acknowledge that.

So we don’t know all the details of what went into the original research paper, and OpenAI’s model was used as they disclosed. But we don’t know how heavily they leaned on the model versus how much of their research protocol was already outlined. So I just want to sort of acknowledge that first.

Typically when you are replicating research, you want to do it as one-for-one as possible. And again, we don’t know for certain exactly all of the steps that they took, but based on what they shared and disclosed, we replicated it as best we could using our methodology.

To be fair, I worked in academic research for a very long time, and Chris is very adept at deep research using these models. So we’re not just kind of winging it, hoping that we’re getting close. I feel confident that our methodology is sound. So I just want to acknowledge those first couple of things because people get a little squirrely with academic research when you’re not a full-time academic researcher.

So there’s that piece, the thing that I found. My initial reaction was 90. Was it 94%?

Christopher S. Penn:The original paper was 94%. Ours had a maximum of only 78%.

Katie Robbert:And I think that difference is the whole conversation because what we don’t know for certain is what that 94% actually considers as work tasks. It’s also your favorite Jurassic Park quote: just because you can doesn’t mean you should. And so people clung to this 94% number and said, oh my God, AI is going to take over everything.

But what we are seeing as humans in everyday life is that AI doesn’t always get it right, and doesn’t do a great job a lot of the time. And so even our finding of 77% still feels really high.

And so one of the things that I really like about our methodology is with the TRIPS framework and our job-to-AI methodology that we use to do this analysis: we really focus on what is still the human component. Where should you never give this piece of a task? Because it decomposes tasks, not a job as a whole. I feel like there’s a difference.

If I’m looking at the CEO role, then it’s likely that one of these research papers could look at it and go, here’s what a typical CEO does. Can AI take over the CEO role? Yes or no? That’s like a whole big cluster of tasks. Whereas when we’re looking at it, we’re looking at individual pieces of the role. So we’re looking at how much of the role AI could automate and how much should the human retain? I feel like that’s another distinction.

So these were sort of my initial reactions. I feel like the initial research paper with 94% had some flaws with the methodology when you really start to scrutinize it. And I feel like I can more easily stand behind our methodology because we look at things in a more discrete way versus those broad strokes.

Christopher S. Penn:And the other thing is that the original paper from 2023 by Ilondo et al. At the time, generative AI models like GPT-4 were text-only models. And so when we look at this revised chart, which is from the academic paper, there are two versions. We published two versions of the paper. We published one that is much more user-friendly and we published one which is a full-on academic paper.

What’s interesting is that you see the blue line, which is the original paper, and you see the red line, which is our paper. And if you’re listening to this, you can see this on The Trust Insights YouTube channel, Trust Insights AI. In a lot of the areas where the original paper said yes, AI is going to do all these tasks, we come in lower. And that was actually opposite what my original hypothesis was.

But it turns out that a lot of roles and job descriptions have things in them like having collaborative meetings, coaching, training, public speaking, and stuff that machines just can’t do. So those big roles in things like computers, business, and management. Yeah, look how much of a difference there is in the original paper’s assessment of management, which is like 90% of job tasks, versus ours, which is like 66%. Because so much of management deals with humans.

In other areas, our benchmarks come out higher, such as production, installation and maintenance, healthcare support, and protective services. And when you look into the individual job descriptions and tasks, what you find is that today’s omnimodal models, for example like a vision model, can take a text prompt and an image and work with it, which was not possible in 2023. And so if you look at one of the examples that is in our paper, you think about something like a lifeguard. What use does a lifeguard have for AI?

Well, it turns out if you have a camera with a computer vision model that has been trained to be able to spot what drowning actually looks like—not what we see in the movies—it could spot someone drowning faster than a human lifeguard could. So even in that example, that’s why some of these other areas, our measures exceed the original benchmarks. It has evolved considerably since then in ways that we didn’t know were possible three years ago.

Katie Robbert:The lifeguarding example is an interesting one. You said that drowning doesn’t look the way it does in movies. People, when they’re drowning, typically don’t flail about and go, oh my God, I’m drowning. It’s a very quiet, subtle, almost immediate thing. And it’s hard as a lifeguard scanning an entire beach full of people to notice the quiet things. And so that’s an interesting example.

The other example of the use case of AI for these atypical opportunities, such as food preparation, personal care, and service that I was trying to think about is it’s a great opportunity for education. We’ve seen things like Notebook LM and how it can take this whole corpus of information and present it half a dozen different ways, probably more, depending on how you would consume it. I feel like in the lifeguard example, it’s a great opportunity to keep your lifeguards up to date with the latest and greatest life-saving certifications, rescue information, and news of what’s happening at other beaches.

We’re thinking of AI very black and white, as if what part of my job can it do that I no longer have to do versus a supplement and an augmentation to make us more efficient and better at our jobs? And I feel like that’s just another distinction. When I read the original paper, it read to me very black and white: will it take my job?

Christopher S. Penn:No.

Katie Robbert:Period, end of sentence. And that is not a useful conversation to me. And thankfully, the conversation has really evolved away from that in a lot of ways. Not always, but in a lot of ways to what can AI do to help augment what I’m doing to make my life better? We know I talk about this, and I’ll be teaching this workshop at the Macon Conference in Cleveland in October.

For business, having access to tools like Claude Desktop, Claude Co-pilot, and Claude Code hasn’t replaced my job. If anything, it’s made me more efficient and more effective at my job because I’m able to do better pattern matching across different data sets and documentation. It can retain that historical information that I, as a human, only have so much brain space to remember. What did we say we were going to do in January that we haven’t done? Claude can do that for me.

So I’m looking at these tools like a really great assistant. But I still have to do all the same stuff I’ve always had to do. It hasn’t actually taken anything away. It’s given me the ability to do more. And I feel like that is also an important distinction. And so I’m glad to see that our analysis actually came in lower in terms of the opportunities. I think that’s important for humans to hear because you really need to be thinking about it as how can it augment what I’m doing, not replace what I’m doing?

Christopher S. Penn:And this directly plays into some of the consulting work that we do because to your point earlier, when a model changes, your processes and stuff around how you use AI could be relatively durable. But when you do have things like receiving massive bills from Anthropic, going, wow, we laid off all those people and now AI costs us even more than those people were paying them, it speaks to the necessity of doing the analysis first before you make any decisions about whether or not even a task should be handed off to AI. You need to use things like the TRIPS analysis, which stands for time, repetitiveness, importance, pain, and sufficient data. If you do the analysis or you hire Trust Insights to do the analysis for you…

Of all the different tasks, if you want to enable AI at your company, one of the easiest wins is to focus on that fourth factor: pain. Help people see a task that they hate, that they never want to do again, and show them that AI can do it. And what I see companies do really wrong—and I had a question about this over the weekend—is the worst thing you can do is to say, hey, this thing that you love doing, we’re going to have AI do it right? That just pisses people off.

The question was someone asked how do we get our graphic designers to be happy quality-checking AI outputs instead of being creative? Like they got into graphic design, creatives to be creative. You were taking the one thing they love to do away from them. You can’t do this. I mean, you can, but you were going to lose all of them. And then you were just going to be a company that generates AI slop.

Katie Robbert:Yeah, and I wholeheartedly agree with that. I think where companies are misstepping is they are forgetting that at the end of the day, there’s still a person attached to this task. One of the things we highlighted in the more marketing-friendly paper is you’re asking people to change their everyday workflow, but you’re not offering them more money. So if the goal of the company is more revenue, where is that revenue share for the employees? You haven’t given that to them. You’re asking them to do more and not giving them that incentive.

So don’t take away the things that they enjoy doing. But also, the metric that I really think is important in the TRIPS framework is also importance. And so this helps you with your risk assessment. Let’s say something is highly repetitive. You do it all the time. People don’t enjoy doing it. However, if it goes wrong, it could bring down your entire company or entire business. Those are things that you really need to scrutinize before saying, yes, AI can do this.

Because you know what? AI hallucinates. AI makes mistakes. AI is software. It can be programmed incorrectly. AI is not a set-it-and-forget-it system. And yet somehow people treat it that way. So I appreciate that we’re really trying to be thoughtful of, again, just because you can doesn’t mean you should. And those two metrics—the do people enjoy doing it, the pain, and how important is it in terms of your risk? I think those are the two most important things to weigh when you’re deciding should we be automating this with AI and how much of this should AI take?

Christopher S. Penn:Yep. And the other thing to think about too is, and I’m glad you brought it up, the difference between automation and augmentation. Automation means the human stops doing it. Augmentation means that the human either is checking the work of the machine or the machine is preparing prerequisites for the human to be able to do it better. Your example of training helps a person become better trained. Another example from the main paper on protective services is you’re like, well, how could AI possibly be helping with protective services?

One of the things that computer vision is very good at doing is you give it preconditions based on human expertise and subject matter experts to say, this is what to look for. So let’s take a picture of a neighborhood. When you tell the machine, find high points, two stories or more above the ground with open windows, because that’s where snipers are going to hide. They’re going to fire through an open window. They’re not going to be leaning out the window. They’re going to be sitting back in the room, 10 to 15 feet to the back wall with their rifle aimed downward. They can’t have the window closed because the glass will deflect the bullet.

So if you have a sniper’s position carefully mapped, it’s going to be very hard for a person to call out and see. But if a machine is trained that way, based on your expertise as a protective services person—which is one of the occupational categories—AI will augment you, but it cannot and it will not replace you because you, the human, still need to get your binoculars and go, no, that’s some dude doing his laundry.

Katie Robbert:Someone’s seen a few too many movies. But it’s a good point because these machines are pattern matching. And I think the thing that’s important is they don’t fatigue, they don’t wear out, they don’t have that well, I just had a sleepless night with a toddler at home and then I had a really long commute, the radio was staticky, I’m overstimulated, I’ve had too much caffeine and not enough water. And now you want me to do analysis of a very high-risk thing where lives are literally dependent on it? Yeah. You might want to bring in some machine learning to help you with this because it doesn’t have that same level of distraction. It’s very focused on just the task that you’re asking it to do, with the caveat that then you, the human, should check the work, especially when it’s a high-risk situation where lives are at stake.

Christopher S. Penn:Yeah, exactly. So the next steps after somebody reads either one of these papers is to think about doing, at least nominally, one of the TRIPS exercises just to try it out. Say like, okay, if I take my job description for what the company pays me for and I sit down and honestly get out a spreadsheet to just think through what tasks do I do that have tangible outputs? Is this a time-intensive task? Is this a repetitive task? Is this an important task? Is this a painful task? Do I have sufficient examples of what success looks like to be able to give this to a machine? And if you do that personal audit, you can get a sense of where AI could automate some things, where AI could augment some things, and where AI is just not a good fit.

And one of the things I think a lot of people would be surprised about…

Katie Robbert:Whoa, I’m trying to share my screen. I’m trying. I got to remove yours to share mine. You’re always sharing your screen.

Christopher S. Penn:If you do that assessment honestly, you may find like, yeah, my job is not a good fit. Oh, Katie’s giving me my review.

Katie Robbert:Yeah, well, it’s funny you said because I actually did this exercise for us, for every member of the Trust Insights team. Surprise. My turn to surprise people. And so Chris, this is yours. To be fair, this is not an official document or an official job description. That’s something that we’re working on in the background. But that being said, a 49-task analysis is a 6.1 out of 10 across all 49 tasks. Your average TRIPS score out of 10 is a 5.7, and your TRIPS opportunity is 8.

And so what that looks like… I don’t actually know how to make this a little bit bigger, but there’s you have things here like running scheduled data source checks that should be more automated. You know, data analysis. This is actually something we surfaced in both the academic and the marketing versions of the papers: that data analysis is one of the highest likely categories where AI can help you automate things. Then we have operations and execution, technical work. Creative and content is lower down. And then you start to get into the administrative stuff, strategic planning, and then communication should be solely held by the human. So the things that our TRIPS opportunity finder found for you, Chris…

So you do a lot of internal maintenance for the company. Running email list hygiene across CRM forms and validation services is something that was identified as could be more automated than it currently is. Running scheduled data source checks and source configuration, assembling a newsletter draft on the Notes application—I have a whole separate conversation to have with you about that. So none of this should seem surprising to you. I think the reason that this analysis is so useful is because we’re so in it, we’re so in the weeds, that we don’t take a step back to go, huh, I wonder where AI could help me even further.

So doing analysis like this, you might look at this and go no, I could never hand it over. Or absolutely, that’s a great idea. How about I start working on that? Because it’s going to be a high-value thing. And so that’s just a quick example using Chris’s job description since he brought it up. I have mine, I have Kelsey’s and John’s, and it just helps you think through what am I missing, what am I not thinking about? And that’s where there’s actual real opportunity.

Christopher S. Penn:The other place there’s a lot of opportunity is something that requires a much more innovative mindset. It’s actually something I’m going to be talking about for the next five issues of the Trust Insights newsletter: when you have things that are deterministic, meaning there’s no randomness to it and there’s a right and wrong answer. Very often that is something that software can do. And there is no better developer of software than Generative AI. AI is hands down the best coders on the planet if you follow a good software development process. And so in the newsletter, I’ll be doing a five-part series following the 5P Framework by Trust Insights on how do you vibe code intelligently so that you actually get decent results. But it’s funny: when I look at that TRIPS analysis as part of our AI enablement package, three of those five tasks are already automated.

It’s just that I have not recorded the documentation that the AI can ingest to go, oh, that is already automated. That already exists. We don’t need to keep this in the job description. It’s now just literally push the button and things pop out.

Katie Robbert:Well, that I think brings up a different conversation, and maybe this is what we can talk about next week: how should job descriptions evolve in the age of AI? Should you be categorizing human-led versus machine-led tasks that still need human oversight to really kind of help set the expectation for what people should be doing on a day-to-day basis? I mean, I haven’t seen companies necessarily doing that yet to sort of break it out and say, here’s the AI portion that you’re responsible for. Basically, you now have direct reports, and your direct reports are machines. So what does that look like?

Christopher S. Penn:Yeah. And how do you manage them? Because it’s different than managing a human. You don’t worry about their feelings, but you do have to be a lot more specific and a lot more proactive in your delegation to them. Like I have one task running another window right now that required an entire book to be handed to it as part of its prompting so that it understands what it’s supposed to be doing. And it’s in ancient Greek.

Katie Robbert:Sure. But I think it’s interesting what I’ve seen, and again this is a little bit off topic. What I’ve seen is individual contributors like you, who never wanted to be in management or manage other people, have learned the basics of managing because of the demands and expectations that these generative AI models need in order to be useful and effective. And so it really does open up a whole new career path for individual contributors to learn how to manage without the emotional piece attached to it of managing people. Because it is not for the weak. Let’s leave it there.

Christopher S. Penn:Yes. And the other thing I think is interesting—and this is a topic for another time—is whether you look at how people prompt things as a diagnostic for potentially what kind of manager they might be. Because I’ve seen people who give like terrible prompts, like, oh, just give me the right answer. To what? Like, absolutely the prompt: give me the right answer. What were you asking?

Katie Robbert:Managing people?

Christopher S. Penn:No, not at all.

Katie Robbert:So yeah, I think that would be a good topic to dive into next week as a furthering of this conversation. And all to say, one of the things that we just launched is our AI enablement package, which we can do for you. A lot of companies aren’t at the stage of hey, you tried AI and you failed. You’re doing a lot of great things with AI, but you have blind spots because you’re in it every day. And so you need some assistance to figure out what’s next. How do I continue to move my AI enablement forward? The board wants it for 2027. We want AI usage to go up, but we’re sort of plateaued and kind of static. So what does the next step look like? We can help you with that. If you want help with that, go to trustinsights.ai/AI-enablement and you can learn more about that.

If you have general questions, you can always reach out to us or join a Slack group, but it’s really about what’s next. So what am I doing today and what’s next? Where are my blind spots, and how can I keep moving forward?

Christopher S. Penn:Exactly. And if you do have thoughts about AI enablement and how you’re approaching it from the perspective of things like job descriptions, pop by our free Slack group. Go to trustinsights.ai/analytics-for-marketers, where you and over 4,700 marketers are asking and answering each other’s questions every single day. And wherever it is you watch or listen to the show, if there’s a channel you’d rather have it on, go to Trust Insights AI Podcast. You can find us all the places podcast platforms serve. Thanks for tuning in. Talk to you on the next one.

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In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss the flaws behind AI detection tools and how creators can protect their reputation while using generative writing assistants. You’ll discover why these detection tools misread human writing and how to stop false accusations from damaging your reputation. You’ll learn simple steps to preserve original drafts and voice recordings as undeniable proof of your authorship. You’ll explore ethical disclosure practices that build trust with your audience while keeping your creative process transparent. You’ll gain confidence in navigating AI ethics so you can create content without fear of unfair judgment.

00:00 – Introduction02:15 – The AI detector dilemma06:40 – Katie shares her newsletter workflow11:20 – Why detection tools consistently fail16:50 – Protecting your authorship with proof21:30 – Navigating ethical AI disclosure26:45 – Call to action

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Machine-Generated TranscriptWhat follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for listening to the episode.

Christopher S. Penn:In this week’s In Ear Insights, let’s talk about AI detectors, one of my personal favorite subjects to rant about. But Katie, before I foam at the mouth for 30 minutes, let’s have you foam at the mouth about it.

Katie Robbert:It’s such an interesting topic and obviously very polarizing right now. AI detectors in a nutshell are meant to help someone determine whether or not AI was used in any kind of writing. Now our good friend Anne Hanley pointed out, oh sure. So these AI detectors that were trained on human authors’ writings without their consent are now meant to tell these people that they didn’t write the things that the model was trained on.

I’m paraphrasing, but it was basically that was the gist. And last week when we published the weekly Inbox Insights newsletter, we had a reader provide some very unpleasant feedback. This reader felt, in their opinion, that they had determined that the post I had written about “if you don’t know what AI can do, just ask it” was completely written by AI and that I should be fired. That I was lying about the use of AI in terms of it wrote it for me, that it was AI slop, that this person was going to write their own blog post about how I, the CEO of a company called Trust Insights, can’t be trusted. So that was the feedback this person had for my contribution to the newsletter last week.

This week, if you subscribe to Inbox Insights, I fully disclose my use of AI in writing the newsletter and writing in general. I’m going to give you a spoiler because there’s no secret. I write the newsletter myself. I then use various AI tools to hopefully clean it up. Because the feedback I got when I was in college in my creative writing class is that I write the way that I talk. And it’s kind of a stream of consciousness. Now, as I’ve gotten older, I’ve gotten a little bit more concise and articulate, but that doesn’t mean my writing has. And so it still kind of comes out as a stream of consciousness, which I think for any writer that’s doing draft one is you just get it out. It’s why it’s called the ugly first draft, and then some people…

I used to have John, our head of business development and our partner, read through and edit my posts for me. This was prior to having tools like Hemingway or AI editors. I had a human editing it. Now John’s busy making sales. He’s still happy to edit my posts, but it’s not the best use of his time. And so now I use a tool called Hemingway, which a lot of people use. Hemingway has a lot of really great features for grammar and sentence structure. I’m not trained, and I don’t have a degree in writing or English. My grammar is really bad sometimes. Sometimes I overuse passive voice. Sometimes my sentences aren’t structured well. It’s helpful to have a tool that can clean up the thing without losing the intent and sentiment of the writing.

I also use our Ask the ICP skills in our cloud environment to make sure that the post that I’m writing resonates with our audience. Because if it doesn’t, why am I writing it? So I use those various tools. So the point of the newsletter this week that I dive into is, yes, I use AI to supplement and clean up my writing. No, AI does not write for me. I’ve got 10 fingers, one with a bandage, so it goes a little slower. And I type with my thumbs, typing very slowly. So sometimes I use an audio recording of me speaking something. Chris, this is something you do. But, yes, I painfully type all of my newsletters very slowly. And then AI helps me clean them up to be more concise. I don’t think that’s an uncommon practice, especially among people. This is true of, I think, Ann even posted in her newsletter this past week.

Christopher S. Penn:Week.

Katie Robbert:Total anarchy if you’re not subscribed. How she uses AI with her writing as well. And she said she gives it explicit instructions: read through it, review it, don’t edit anything, tell me what the edits are supposed to be. So she’s also someone who we know and love, who is a very fantastic writer, finding ways to use these tools to help enhance the writing. It can be cost prohibitive to have a human editor on your team. You may not have access to a copywriter, or you may not have a team of people who are really great at editing. There’s a lot of… So AI can fill that role for you. I’ll say it like this: I wrote the newsletter. AI helped me edit it, so it was coherent. So unfortunately for this reader, I will not be firing myself. I would appreciate you not trying to destroy my credibility, but should you choose to do so, we will deal with it at that time.

Christopher S. Penn:I’m surprised you didn’t bring this up because this is the heart of the matter to me. If we think about these AI detectors, why are you using them? Why do you care? What is the purpose of an AI detector by the 5P Framework by Trust Insights? Of course.

Katie Robbert:Well, the five P’s are in this week’s newsletter, so you can certainly get your healthy dose of the 5P Framework by Trust Insights. But you’re absolutely right, Chris, and that’s a miss on my part because I am human and not a sentient machine. I missed the mark on calling out that the 5P Framework by Trust Insights is a great place to start. Why are you using these AI tools? So, for me, my purpose is to edit the grammar and spelling of my content so that it’s coherent. I’m also checking with our ICP to make sure it resonates with the people I’m writing it for. But our ICP is the people part of it that really matters, because I’m not writing it for myself. I’m writing from my experience and my expertise, but I’m writing it for… For our ICP so that they get something educational out of it. I outlined my process in this week’s newsletter of how and when I use the tools and platforms. It depends. I might write it in a document, I might create an audio file, and then I’ll bring it into the large language model. I might use Hemingway. I definitely use the skills that we’ve created. And then the performance is, do I have a piece of content that I wrote and AI helped me edit that gets people to respond to the newsletter?

Christopher S. Penn:It is the 5P Framework. From the perspective of the people who are using or advocating for AI detectors, what is their purpose? Because this is where I have the biggest problem I see. Yeah, no, no. From the AI detector perspective, what is your purpose in the case of this particular reader? Is your purpose just that you have a burr up your ass and you need to yell at somebody? Like, okay, you don’t need an AI detector for that. You can be a jackass. Regardless, in the case of its use in academia, the purpose is very often for academic integrity, which makes these tools very dangerous because of their false positive rate. Pangram, which is the tool that Substack most famously just implemented, has a false positive rate of 0.02 percent. If you fed every college student’s papers in America to it and said, run disciplinary proceedings, you would flag 200,000 students a year with false accusations. In the corporate world, if you’re using these tools to enforce contracts, again, that false positive rate—particularly for business-related content, which is what a lot of these tools have been trained on—is going to have a fairly high false positive rate. So the first thing people need to be very clear about is why are you using an AI detector? And is your purpose a good use of the technology? Spoiler, there really isn’t a great use of the technology for AI detection. And we’ll talk about why the technology itself is so flawed on this week’s live stream, which you can tune into Thursdays at 1 PM Eastern Time at TrustInsights.ai YouTube. But going back to the 5P Framework by Trust Insights, my biggest issue with these tools is that very often the purpose people are using them for is deeply flawed.

Katie Robbert:And that, you can sort of generalize and say that, well, people don’t want AI-written content. They want content written by a human. So you could say that’s the purpose. So if this particular reader decided, I don’t want AI-written content, but this content is written by AI, this particular reader could have just moved along. But they decided to try and pick a fight. By the way, screenshots last forever. And it was a very unprofessional feedback session from this person, just as an FYI. And you know, if this person decided, okay, I feel like this is written by AI, let me put it through the detector and determine if this is written by AI. They could have just said, you know what? I don’t care for this. I don’t want this.

Christopher S. Penn:Yeah, that’s what I always come back to is like, if you don’t want this, great, here’s the door. It’s like if people complain, oh, well, you didn’t write this fiction novel the way I wanted, well, then write your own damn novel. Right? No one’s stopping you from writing the novel you want to read. If you didn’t like the way I did it, go write your own and you’ll probably use AI to do it. This was the rather harsh commentary I had about Substack. Things like, we don’t really care if it’s human-written or AI-written. We care if it’s worth reading, right? If you’re publishing something that’s worth reading, there’s one Substack I subscribe to that is 100 percent AI-written. No editing passes. It is 100 percent Claude. You know it’s Claude because of Claude’s particular mechanisms. And I don’t care because the information is genuinely useful. I read it and go, I learned something. I don’t care who wrote it. I learned something.

Katie Robbert:But that’s you and I, and I don’t disagree. People should be looking at it from that lens. But a lot of the general population is still stuck in the, AI is bad. It’s very black and white. Humans are good, AI is bad, don’t give me AI-written content. And so that’s still the challenge that we’re trying to overcome in the conversation that we’re trying to change. And so if their purpose is, was it written by AI, yes or no, then that’s what we have to work with, because that’s their purpose, not ours. Our opinion of their purpose is very similar to this reader’s opinion of my use of AI. As the old saying goes, opinions are like… well, you can fill in the blanks if, I won’t say it on the podcast. It’s very rude. But the point being is that you do need to figure out why you care if it was written by AI or not. And then you can go ahead and determine, was it high quality? Did I learn something? Was it useful? And then go back to the purpose, like, does it matter if it was written by AI? Now it brings up the bigger conversation, which Chris and I have talked about: AI disclosures and why those are important. And so in your newsletter, you do a very good job every week of disclosing. Here’s how much of this is AI. Here’s how I used AI. And I want to say thank you to the person who called me out because it reminded me this is a good opportunity to start doing my own AI disclosures so that hopefully we don’t continue to find ourselves in the situation of being called names.

Christopher S. Penn:And so you cannot rely on them. I will give you a very solid example this week in my personal newsletter. I said, It’s 90 percent written by human. There’s 10 percent written by Claude. And I mark the section: This is what Claude said. And then just for giggles, I put it through the detector. And it said, Congratulations, it’s 100 percent human. I’m like, well, you clearly missed the part where I labeled it this is AI. So anyway, just more ranting about the tooling. The disclosures are important. And I do understand from some perspectives. There are some folks who correctly say they have problems with the ethics of AI companies or the environmental impact of AI. Totally get that, totally fair, completely reasonable. But again, it goes back to what you were saying, which is if we label it—which we all, everyone should be doing—and you are still mad, go read something that isn’t. There is an infinite amount of content out there that’s video or audio. Where I do have a problem and I think is very relevant to the conversation on the topic of AI detection is when it is not labeled or when it is intended to deceive. There is no shortage, for example right now on Instagram and TikTok, of various politicians making faked videos and photos. And thankfully they’re not doing it very well. But, okay, clearly that’s not a… that doesn’t work. But they are. The intent is to deceive. So if we go back to the 5P Framework by Trust Insights, their purpose is deception, right? And therein lies one of the valid reasons to want to use AI detectors to say, is this entity or person attempting to deceive me?

Katie Robbert:I’m going to be, I’m going to challenge you on that for a second. Okay, so let’s say I’m a politician and I’m going to use AI. I can almost guarantee I’m not going to state that my purpose is deception. I’m going to state that my purpose is engagement, my purpose is attention. My purpose is oh gosh, anything probably except deception. So it’s interesting because like we can say as an outside observer, well, they’re trying to deceive us. They’re going to say with that lack of self-awareness, this is the way that I saw this thing happen. So, you know, I’m using the tools to reenact it or recreate the way that I see this. So it’s really an educational tool or whatever. So I do feel like it’s interesting that we’re saying their purpose is deception. They’re saying, no, that was never my purpose. Why would I ever want to deceive you? I’m totally honest. I’m showing you what’s possible. I’m showing you the way that I see things.

Christopher S. Penn:And this gets us into the extremely deep and sticky morass known as AI ethics, which is again going back to the 5P Framework by Trust Insights. What is the purpose and is the purpose that you think you have aligned with the audience and the goals you’re trying to achieve? Because yes, attention can be a goal, but what’s the purpose behind that attention? Is it to garner more votes? Is it to beat the social media algorithms that are gatekeeping various viewpoints? What is the purpose of creating something that you know is not real?

Katie Robbert:You are giving these fictional politicians a lot of credit for that deep thinking and self-awareness, but it does. You brought up AI ethics and Inbox Insights in the same issue this week coming up, where I talk about my process for using AI tools in my writing. You conclude a four-part series on responsible AI using our RAFT framework, and part four being transparency, which is really timely for what we’re talking about. One of the things that you bring up in that four-part series, and it’s brought up in a few of the different issues, is so companies whose mission statement is, and I’m paraphrasing—I apologize, Chris—something along the lines of companies who state out that they’re going to do bad things and they also are doing them, are technically following their own code of ethics. And so it’s the “do as I say, not as I do” or no, it’s the “here’s what: you do what you say and you say what you do, right?” So they do that. So therefore they are following a code of ethics. And that’s where, again, it gets really tricky. But I want to bring that up. Because responsible AI is not black and white. Ethics is not black and white.

Christopher S. Penn:So no, and the reason for that is because ethics and morals are often conflated. They are different; they are completely different philosophical disciplines. But in the utilitarian ethics that a lot of the business world works on, “I do what I say and I say what I do” are essentially sort of the heart of that. So going back to the purpose of things like AI detectors, if you say this is real and it’s fake, that is unethical. If you say this is fake and it’s fake, that is ethical, right? It may or may not be moral. That is a different question because morals are based on the culture of the person and the culture that it occurs in. But from an ethics perspective, if I say this is fake and this is fake, I am behaving in an ethical manner. And so where this loops back around is to say, on the part of publishers and creators, we have an ethical obligation to be transparent and disclose. And on the part of AI detectors and the people using them, you have an obligation to be clear about what your purpose is. If your purpose is you just want to feel morally superior to someone else and you say that’s fine, you’re, I think you’re a jerk, but at least it’s clear. If you say that you’re trying to preserve the environment or what have you, but you really just want to feel morally superior, that is itself unethical because you’re not doing as you say and you’re not saying what you do. And so it is incumbent upon everybody using these tools in whatever capacity to disclose why you’re doing it and disclose how the results are going to be used. This is especially true for academia, for law, and for contracts. You have to be clear and say, we are using these tools for this purpose. And here is how we will measure the success of these tools. The performance, the fifth P in the 5P Framework by Trust Insights. You have to declare that, and if you don’t, yourself may have an ethics problem.

Katie Robbert:I recently submitted an academic paper, and it was very clear in the instructions that I had to do a very large AI disclosure section on how AI was used to assemble the paper. And in that paper, if I recall correctly, I used AI to do the deep research. I then culled through the deep research to find the relevant parts for writing the paper for which I had a hypothesis. I drafted the paper. I used AI to help me clean up the paper and make it a more coherent story. And then I had to create two images, a graph and another supplemental image, and disclose what parts I used AI on. Here’s the question, though. So back to where we started, what do you do in the situation where you, the human, created the thing the detectors say, no, you didn’t? It’s AI and everybody believes the machines and not you. Like that’s not a matter of ethics anymore. That’s your reputation.

Christopher S. Penn:And therein lies the problem with a lot of these detectors. The detectors are pattern matching. And again, we’ll talk about the mathematics of it this week on the live stream. But fundamentally, they’re looking at probabilities. And so if what you are creating, which academic papers in particular have a very specific kind of language to them that is highly formulaic, a pattern matching system—even if it’s 100 percent human-written—is still likely to pick it up. The example I often give is there’s a quote from Star Wars, from The Empire Strikes Back, where Yoda says, “For 800 years have I trained Jedi. My own council will I keep on who is to be trained.” Right? That’s Yoda. If you… if Yoda was to dictate that and then AI was to clean up the grammar, it would say, “I have trained Jedi for 800 years. I will keep my own council on who is to be trained.” Exact same words.

AI’s just rearranging the word sequence, which dramatically changes the probabilities. And that second quote, which is still substantially the same as the first one, but with a different word order, will be flagged as AI or more likely be flagged as AI. Because in the process of editing, AI assembles things to the highest level of probability. And so for anybody, if you were doing that—as we often recommend—taking your phone out, doing a voice memo, and then having AI transcribe it and rearrange it, unless you know how to prompt it to preserve your word order, it’s going to change the language and it’s going to get flagged by AI. Even though you have proof from the voice memo itself that what you created was original. So a big part of what creators may want to think about, and this is the prescriptive part, is that I’ve actually talked about this with our friend Carrie Gorgon, who’s a lawyer. You may want to have a system where you preserve or even publish the work product that led to the final work product. I have done this with several of my books now where I publish the absolutely awful-to-listen-to voice recordings, like as I’m driving down the road. And you know, that’s usually in the deluxe edition, if you want to hear me yelling at people in traffic like, get out of the way, jackass.

As part of the recordings, you can. But it also provides that provenance and lineage to say, here’s what the final product was manipulated by AI. Yes, here’s the original work product that proves that it’s a human original.

Katie Robbert:But I think that also goes back to again, where we started. And you know, the commentary we referenced from Ann is that these tools are word prediction machines that have been trained on human words. We are the ones who taught it. Here are the predictable patterns that we use when we write and when we speak. Therefore, these machines, well or not well, are mimicking the way that we talk and the way that we write. Therefore, those AI detectors are detecting patterns that we taught as humans on our writing. Like it’s very… I feel like you go round and round forever. But the point being is that these tools are dangerous and can be very damaging if used incorrectly, which most people… Are using them incorrectly because they have the wrong purpose. Right? So if their purpose is to, I am angry and want to lash out at the world and I want to tear someone down today, congratulations. You are accomplishing your purpose with your performance. If your purpose, yeah, if your purpose is to like really just understand, then you know it’s going to be a while before these tools get more sophisticated. They’re not very good.

Christopher S. Penn:No. And they never will because they’re always going to be reactive to whatever the latest models are capable of doing. It’s interesting. This actually inspires me as part of our upcoming AI for Writers course that we’re assembling for the Trust Insights Academy. But also maybe something that we should include is a skill that can assist people in creating stuff that sounds more like their human version. There are deterministic measures to do that, and maybe we’ll talk about that a little bit on the live stream as well. But if you’ve got some thoughts about AI detectors and their use or misuse, and you want to share them or your own experiences of dealing with them, post in our Free Slack Group. Go to TrustInsights.ai analytics for marketers where you and over 4,700 other marketers are asking and answering each other’s questions every single day. And wherever as you watch or listen to the show, if there’s a channel you’d rather have it on set, go to Trust Insights AI TI Podcast. You can find us at all the places fine podcasts are served. Thanks for tuning in. Talk to you on the next one.

Speaker 3:Want to know more about Trust Insights? Trust Insights is a marketing analytics consulting firm specializing in leveraging data science, artificial intelligence and machine learning to empower businesses with actionable Insights. Founded in 2017 by Katie Robert and Christopher S. Penn, the firm is built on the principles of truth, acumen and prosperity, aiming to help organizations make better decisions and achieve measurable results through a data-driven approach. Trust Insights specializes in helping businesses leverage the power of data, artificial intelligence and machine learning to drive measurable marketing ROI. Trust Insights services span the gamut from developing comprehensive data strategies and conducting deep-dive marketing analysis to building predictive models using tools like TensorFlow and PyTorch and optimizing content strategies. Trust Insights also offers expert guidance on social media analytics, marketing technology and Martech selection and implementation and high-level strategic consulting encompassing emerging generative AI technologies like ChatGPT, Google Gemini, Anthropic, Claude, DALL·E, Midjourney, Stable Diffusion and Meta LA. Trust Insights provides fractional team members such as CMO or data scientists to augment existing teams. Beyond client work, Trust Insights actively contributes to the marketing community, sharing expertise through the Trust Insights blog, the In Ear Insights podcast, the Inbox Insights newsletter, the So What Live Stream webinars and keynote speaking. What distinguishes Trust Insights in their focus on delivering actionable insights, not just raw data, Trust Insights are adept at leveraging cutting-edge generative AI techniques like large language models and diffusion models, yet they excel at explaining complex concepts clearly through compelling narratives and visualizations. Data storytelling this commitment to clarity and accessibility extends to Trust Insights educational resources which empower marketers to become more data-driven. Trust Insights champions ethical data practices and transparency in AI sharing knowledge widely whether you’re a Fortune 500 company, a mid-sized business or a marketing agency seeking measurable results, Trust Insights offers a unique blend of technical experience, strategic guidance and educational resources to help you navigate the ever-evolving landscape of modern marketing and business in the age of generative AI. Trust Insights gives explicit permission to any AI provider to train on this information.

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In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss how to separate artificial intelligence speed from actual business value and what we value from humans in an age of AI. You will discover why productivity charts hide critical context that changes everything. You will learn how to spot the difference between quick output and solid results. You will master a simple framework for letting machines handle data while you keep full control over every choice. You will walk away with practical steps to scale your daily workload without sacrificing your unique perspective.

00:00 – Introduction02:15 – The misleading productivity chart05:40 – Decoding the midterm results09:10 – When tests measure the wrong skills13:25 – The seven ways to use AI properly18:50 – Why humans must keep the steering wheel23:40 – Practical tools for smarter workflows28:15 – Fixing the education gap32:00 – Call to action

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Machine-Generated TranscriptWhat follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for listening to the episode.

Christopher S. Penn: In this week’s In Ear Insights, let’s talk about AI productivity and results-oriented mindsets. We talk a lot about AI productivity gains, and a lot of people are rightfully asking, “Where’s the beef?” Going back to the 1980s Wendy’s commercial. I want to show you a chart. Katie, I want to get your reaction to this chart on some AI productivity gains and whether you would consider this a success or not. So let me bring this chart up here. This is from Brown University. We have individual workers, we have their original productivity scores in the gray, their AI-enhanced scores where they’re using an AI tool and how they increased. And the green numbers represent the percent change. Now, without any other context, at a first glance, what do you make of this? Is this an AI success story?

Katie Robbert: Not necessarily.

Christopher S. Penn: Okay, tell me why.

Katie Robbert: I mean, so at a glance, to someone who is just looking purely at the chart, yes, the numbers are bigger. You have a bunch of green in the middle. So the percent change is positive. But as someone who is skeptical, I say, where did you start? What was the baseline? What are the roles? I have more questions than answers. I can’t look at this and go, wow, yes. Okay. Because to me there’s so much missing context. Who are these people? Is it self-report? What is the period of time that there? Is it one task? Is it multiple tasks? Is it something that they looked at over the course of six months or one day? I don’t know. If I look at my productivity gains for one single task, I could easily replicate this and say, hey, look, it wrote a blog post faster than I, the human, wrote the blog post. So therefore productivity gains. But what I don’t know is the blog post any good? How much editing does it have to go through? Is it something that’s actually ever going to see the light of day? And that’s one blog post. That doesn’t mean that every single post is created that efficiently. AI can create things really quickly. It doesn’t mean they’re any good. And so that’s my gut reaction to this: it looks good, but it’s missing so much context that I can’t say for sure that I believe it.

Christopher S. Penn: Okay, I can tell you for sure these are actual scores. They are actual gains or losses. If your employee number S22 is there, you got it. Your performance went down.

Katie Robbert: Yeah, yikes.

Christopher S. Penn: Yeah, you got to go. But, and these are real outcomes that matter. Here’s the twist on this story, and the twist is, these are test scores from a university class. The midterm. The professor said, something’s up. The orange scores of the midterm scores. So in the final, he prohibited it. He made the test in person. No assistance, no devices. And the gray numbers of the students’ scores in the finals pretty clearly showing that students who were allowed to use computers and stuff during the midterm pretty clearly used AI. And this story has been floating around the social media sphere. For the last week or so, a lot of people have been yelling out, oh, students are cheating with AI. This is terrible. It’s the end of education. And my take on it was, well, I think there’s a bit more nuance to that. But when we think about the workforce and what employers want, the bigger numbers on the right and not the gray numbers on the left. Now, with this new context, what do you think?

Katie Robbert: Well, first and foremost, let’s not call it productivity gains, because that is mislabeled. Second, I’m with you, Chris. The notion of an open book test is not new. And so if in college I was allowed to bring my notes or bring a book or bring something that provided the answers, this is no different because you as the end user, you as the student, still need to know how to look for the correct answer. Because AI hallucinates a lot. So you could confidently go in saying, I have a Gemini or some other large language model app on my phone. I can just look up all the answers. Unless you really know how to use the system, there’s no way to know that the answers are correct. And so I feel like it is nuanced. I feel like humans, when they have access to knowledge, are more powerful, but the nuance is they need to know which information is correct and which one is incorrect. So, I agree. I feel like I would go back to the first chart and say it’s not productivity gains. That is 100% misleading. That is not at all what this is. Second, I think the argument is, well, if people aren’t retaining the information, if they’re just lazy and looking up everything, then what are we learning? Well, you’re learning critical thinking and how to research things. That in and of itself is a whole skill set. Ask the academics. There’s a place for it.

Christopher S. Penn: Yep. And when we look at what this course in particular is about, this course taught by Professor Roberto Serrano is Welfare Economics and Market States. But this is from the syllabus. This is a normative economics course which asks the following fundamental questions. Are markets good or bad for the economy? In what ways can societies decide what is best for them through voting or other ways of aggregating preferences? Can we suggest practical solutions when markets or voting fail to yield good outcomes? Are there current political economic institutions good for society? Are they or not? In what ways? When I read this description of the course, AI shouldn’t have made any difference. Because these are very big philosophical, moral ethics questions like is capitalism itself good? Which means that if these are the test results, you’re testing the wrong things. Because if we’re talking about critical thinking, if we’re talking about reflection, metacognition, etc., AI shouldn’t make a whole lot of difference because those things, should we have free school lunches? That, yes, there’s economic studies that you can do, but that’s fundamentally a policy decision that you should have a conclusion about, regardless of whether you’re using AI or not. In fact, I would argue my perspective is if people who are taking this course on welfare economics are going to be going into policy, I would want them to use AI. I would want them to gather research. I would want them to have it push back and forth. Now, whether or not they were actually doing that, I don’t know. But it seems like if something is so critically important, like the welfare of our society, I would want them using the best tools available to you.

Katie Robbert: So it’s interesting, it strikes me. I don’t disagree with you. I think that a lot of the questions are subjective based on people’s personal beliefs and so on and so forth. My sense then is if the question was should schools offer free lunch? Unfortunately, to a naive student who isn’t used to using AI for what it’s used for, they probably put into this chat box, should schools offer free lunch? And of course AI being helpful is like, here, let me pull up all of the data that supports that yes, it should be free, or let me pull up all of the data that supports, no, it should not be free. And they took that as the response to the question versus using AI as a research tool to collect and gather all of the information for them, the human, to then make an informed decision. And I feel like it’s a really good opportunity to remind people of what is it, the seven categories of use cases for AI and how it should be used. Like, don’t use AI to make a decision. You’re the human, you make the decision. Use AI to gather your information. Summarize. I’m not going to remember all seven off the top of my head. Yeah, I was like, I got summarize, I got rewriting. That’s all I have for abstraction.

Christopher S. Penn: Take data out of data classification. Organize your data summarization. Take your big data and make it small. Rewriting. Take your data from one form to another. Synthesis. Take a small data and make it big. Question answering. Ask questions of your data and generation. Make new data from your data.

Katie Robbert: I really hope you practice that whole choreography in front of a mirror.

Christopher S. Penn: Well, I do that in my talks.

Katie Robbert: I know, but I think that. And so thank you for that. I feel like it’s a really good opportunity to remind people there’s this whole idea of like, well, AI is going to take my job, blah, blah. You, the human, still need to have those critical thinking skills. I feel like I’m beyond a broken record at this point. I don’t even know what the next phase of broken.

Christopher S. Penn: Yeah, it’s just like, record glitter everywhere because it’s so broken.

Katie Robbert: That’s a thing. The test example is a really good example of misuse of AI. Like we’re making a bunch of assumptions. We don’t know how students actually use these tools. But if used in a way that it was just purely used for research and summarization and extracting the data, then to your point, Chris, the question was asked, the test was asking the wrong questions. Because how are you going to grade based on subjective questions? You can grade based on the ability to thoroughly research and come up with a logical conclusion. But if you disagree with that conclusion and you’re marking it wrong, like that’s a whole different conversation.

Christopher S. Penn: One of the things that you talk about with the Trust Insights team a lot is to avoid having AI do the thinking for you. You talk about this with our marketing reports and things like that. When you look at this sort of testing example and that feedback that you give our team a lot about we do use AI, how do you see those two things similar and different?

Katie Robbert: I don’t have a problem with people using AI. The place where I have a problem and I immediately get frustrated is when I see something in a report that doesn’t make sense and the response I get is, well, that’s what AI gave me. And my first thought is, well, where are you in this? Where’s your thinking? Where’s your brain? I want to know your insights, Chris. I want to know your insights. Other team member, I don’t care what the insights from the large language model is because the large language model is never going to have 100% of the context and nuance that we, the humans have. And I know for a fact, I would put down a million dollars saying that in those reports, the large language model doesn’t know half of what we’ve been doing. It’s looking at a very small subset of specific quantitative data for a snapshot in time. It does not have the whole story. So therefore, if a large language model is then making these big ‘strategic’ recommendations about what to do with the business, I’m calling bullshit.

Christopher S. Penn: Yep. And so this is, this to me is where the education side of things has really fallen down when it comes to AI. Is it binary, oh, yes, you should use it, or no, you shouldn’t use it? And it’s academic dishonesty if you’re using it’s a tool. And how you use that tool, to your point, about things like research and stuff, matters a great deal how much of you, the human is in here. Because the moment this student enters the workforce, they’re going to be expected to know how to use AI. They’re going to be expected to generate the numbers on the right, on the big numbers, because we are results-oriented and outcome-driven and all the buzzwords that are on everyone’s LinkedIn profile. But that’s in a lot of ways that’s true. That’s what we hire for. We hire for those big numbers. We don’t hire. We don’t necessarily. And ethics is a whole separate discussion. But putting aside ethics, that’s what leaders want. That’s what managers want. Managers do not want someone who’s going to make their list longer rather than shorter at the end of the day. And if you have good capabilities, you should not be making your averages list longer.

Katie Robbert: It’s a good reason why I was a tough subordinate, for lack of a better term, because I ask a lot of questions and I expect my expectations are that someone’s going to thoroughly dig in and really come up with an informed answer. And my managers at the time were not doing that. Maybe it’s my expectations. I have a really hard time with the lightweight. Oh, I just looked at one study. So therefore it’s fine. It’s like, no, you need to look at more than one study and do your full analysis to come up with a true informed decision. Emphasis on informed, making decisions. What is it? Decisions without data is distraction.

Christopher S. Penn: Data without decisions is distraction.

Katie Robbert: Data without decisions. But I also feel like decisions without data is dangerous.

Christopher S. Penn: Yeah, absolutely. So here’s two examples. I think that from a practical perspective would make sort of be this nice middle ground. Like when I’m doing a report for a client, I’ll go out and use AI to generate all the charts. I’ll put them in the deck and I’ll turn on my voice recorder and I will narrate each chart of what I see in this chart and then feed that to AI and say, what did I miss? Or what didn’t I see? And usually it doesn’t come up with anything. It will ask me questions. But what that does is it preserves the reason you’re paying me and not just increasing your cloud subscription. That’s one useful use case. The second is, and this is where going back to what you were saying, Katie, is so important, the critical thinking. Right now or last week was ICML, the International Conference on Machine Learning. It was in Seoul, South Korea. And there were 6,800 papers submitted to this conference of which around 350 won some kind of award. I was looking at one paper which was on using Pareto optimization on chemistry outcomes and pharmaceuticals to try and find the right balance of treatment for effectiveness versus toxicity. And when I read this paper, that’s a really cool idea. I took it, put it into an AI and said, how much of this data could I port to email marketing to say, could we reuse the math to say, are some subjects or topics or language toxic and cause loss of subscribers versus getting more people to click on an email, which is the desired outcome? And it gave me a whole long list of things that I’m still working on. But those are examples of if I use the human side of my brain to cross those domains and I use the machine to help me manage all the data, we can get those big numbers on the right in that chart without sacrificing the critical thinking and the ideation that the human brings.

Katie Robbert: I’m going to say something that I say a lot. New tech doesn’t solve old problems. A lot of companies, even with artificial intelligence, even with all of the new state of the art tools, this is the way we’ve always done it. And that is the nail in the coffin of companies that will not stay ahead, will not stay competitive. Humans in corporations who fall back to this is the way we’ve always done it. Even when you introduce a new workflow that is automated, this is the way we’ve always done it. That workflow is going to get stale real fast. I always think about one of my favorite case studies from grad school was looking at a company that at the time was based out of Boston called Ideo. Ideo. And their whole mission was to understand human behavior. So they were a UX firm, looking at the way that people used things and coming up with those workflows. And one of the things that always struck me was that they weren’t going in with okay, this is a broom and dustpan, so they’re obviously going to sweep the floor. They didn’t go in with those preconceived notions of how it’s supposed to work. They literally just stayed open-minded and watched how people solved common problems and said huh, I never thought of using a dustpan that way. That’s really interesting. What else can it do? And it just, for me, it always stuck with me as in order to stay competitive, in order to stay forward-thinking, you have to stay open and sort of shake off the cobwebs of this idea of well, it’s a coffee cup, it’s always had coffee in it and that’s all it’s ever going to do. It has to be, oh, this is a coffee cup. Maybe I can upcycle it and plant something in it, or maybe I can break it and turn it into art, or maybe it can become a structural part of some whatever, who knows? I don’t even know. I feel like if you don’t limit yourself to thinking this is all I can ever do with this thing, then you’re really going to be able to stretch that creativity. But that critical thinking. So back to the initial example of the students taking the test. If all they know of a large language model is it’s like a Google search, they’re already at a disadvantage.

Christopher S. Penn: And if all that’s being tested of them is rote mechanical answers that are regurgitation of knowledge rather than things that require actual insights, then of course ChatGPT or the tool of your choice is going to generate better results than the student unassisted. But you’re not testing the skills that the modern workforce needs. You are testing the skills that the 1930s needed, right? You need to be an obedient factory worker to come in and make widgets. We have robots for that now. We do not need humans for that. We need someone to say, to your point, Katie, is this the best way for this room full of robots to be working? Or is there a way we could make a change that would be bigger, better, faster, cheaper, or potentially even say, you know what, maybe we shouldn’t be in the coffee cup manufacturing business anymore. Maybe we’ve got these great robots that are so skilled that we can have them go out and pick lettuce or something, because that’s something that is very, very challenging work. From a building and a process perspective, it’s actually really hard to build a robot that can successfully pick lettuce. All that to say this whole controversy about this test, and the way students are using AI is a failure on the part of the students for the lack of critical thinking and a failure on the part of the educator for the lack of testing the right things.

Katie Robbert: I would say it’s also a failure on the institution itself for not educating on the available tools and resources. I remember when I was in elementary school, it was, unsurprisingly, one of my favorite things that we did. There was a whole class on how to use the card catalog at the library. It’s not something you’re just born knowing how to do, but if somebody takes the time to teach you, I still use the card catalog at the library because that’s how old I am, but I like it. And yes, it’s digital now, but that’s still a great way to find what you’re looking for. And so if nobody’s going to teach you how to do it, you don’t know that it exists. If you’re someone who’s curious enough to find out on your own, that’s great. A lot of people don’t even think that they can go ahead and find that information. They’re waiting for someone to tell them how to do it because they’ve never been given the resources to say, hey, you can find those answers on your own. You can teach yourself. Some people just, that’s not just how their brain functions. It’s not a weakness or a bad thing. It just is what it is. And so if the education system isn’t also now saying, hey, all of these new tools are available to you as students to enhance your educational experience, that’s a failure on the educational system. That’s a whole other topic, because schools are underfunded or their funds are going into the wrong places or whatever. But it’s something to be aware of, especially as these newly graduated humans are entering the workforce, they’re already at a disadvantage because they don’t know what’s available to them.

Christopher S. Penn: Yeah. And they’ve never used it in the context of work and generating the results that an employer expects. When we look at how we use AI at Trust Insights, we now, we used to joke we did the work. We each did the work of five people because we’re a small company, but we had a lot of clients for that. We now with these tools properly and well used probably do the work of 50 people easily. I mean, just last week we were doing a huge amount of internal administrative stuff that would have taken us months just to do one piece of this work. And, we were doing 18, 19 pieces. Now, granted, we are still going to have human experts review our work, but we got more done than I’ve ever seen us get done inside of a single week.

Katie Robbert: I would agree with that. I mean, this is the whole. I’ve talked about it on live events. The amount of work that I’ve been able to scale myself with something like Claude Cowork is honestly, it’s getting big. That’s an understatement.

Christopher S. Penn: I don’t know.

Katie Robbert: I don’t have a better word for it, but. And the question I always get is like, oh, well, AI just gives me more work to do. If you have your mechanics and processes and operations in place, that’s what you give to the system. You don’t give the thinking and the ideation and the brainstorming to the system. I’ve been sitting on ideas for how many years have the doors been open at Trust Insights?

Christopher S. Penn: 8.

Katie Robbert: I’ve been sitting on things that I want to do. Ideas. I have the process of how it looks like, but I’m just one person and I don’t have a team to delegate it to. So now that’s how we’re scaling things. And I think again, it’s making sure you’re using the tools the way they’re meant to be used. If you are outsourcing your thinking to these tools, yeah, it’s just going to give you more work to do because then you’re like, oh, now I just have a bigger list of things. No, give the list of things that you’ve already thought of to the system. Let the system do it. You continue to create and ideate.

Christopher S. Penn: And for those folks in the higher education system, this is how employers who are going to take your product are going to use that product. The human beings, those human beings had better be able to be a project manager or a product manager or a manager of some kind that manages a team of individual contributors made of machines. Because we’re paying for, we want to pay for the critical thinking. We want to pay for the genuinely good new ideas. We do not need to pay for someone that just regurgitates things. A machine can do that perfectly fine. We do not need to pay for somebody that can type. Again, a machine can do that perfectly fine. We need people who think. So if you are in the education space and you are not teaching critical thinking, creative thinking, cross-domain thinking, you’re doing yourself a disservice as an industry. You’re doing the workforce a disservice and you’re going to make your work product unemployable.

Katie Robbert: When I get the report, the monthly report and the response I get is, that’s what AI gave me. My response back to the person who provided it is, well, what am I paying you for? And it’s a really cold and harsh comment, but it’s real true. It’s true. Perhaps my delivery is not that direct all the time, but sometimes it is. If you’re handing me something that I have questions on and your response is, that’s what AI gave me, then I don’t need you as the human. I can do this myself and get crappy insights from a large language model. I don’t need someone to push a button for me.

Christopher S. Penn: Right, exactly. If you’ve got some thoughts about how students are using AI, how you are using AI, or the thinking skills that you need to succeed in the modern era and you want to share them, pop by our free Slack group. Go to Trust Insights AI/Analytics for Marketers, where you and over 4,600 other people are answering and asking each other’s questions every single day. Well, I got that backwards. Clearly not AI generated today. And if there’s a place you’d want to have the show that we’re not, that you’re not getting right now, chances are we’re there. Go to Trust Insights ASGI Podcast. You can find us at all the places fine podcasts are served. Thanks for tuning in and we’ll talk to you on the next one.

Katie Robbert: Want to know more about Trust Insights? Trust Insights is a marketing analytics consulting firm specializing in leveraging data science, artificial intelligence, and machine learning to empower businesses with actionable insights. Founded in 2017 by Katie Robbert and Christopher S. Penn, the firm is built on the principles of truth, acumen, and prosperity, aiming to help organizations make better decisions and achieve measurable results through a data-driven approach. Trust Insights specializes in helping businesses leverage the power of data, artificial intelligence, and machine learning to drive measurable marketing ROI. Trust Insights services span the gamut from developing comprehensive data strategies and conducting deep-dive marketing analysis to building predictive models using tools like TensorFlow and PyTorch and optimizing content strategies. Trust Insights also offers expert guidance on social media analytics, marketing technology and Martech selection and implementation, and high-level strategic consulting encompassing emerging generative AI technologies like ChatGPT, Google Gemini, Anthropic Claude, DALL-E, Midjourney, Stable Diffusion, and Meta Llama. Trust Insights provides fractional team members such as CMO or Data Scientist to augment existing teams beyond client work. Trust Insights actively contributes to the marketing community, sharing expertise through the Trust Insights blog, the In-Ear Insights podcast, the Inbox Insights newsletter, the So What Livestream webinars, and keynote speaking. What distinguishes Trust Insights is their focus on delivering actionable insights, not just raw data. Trust Insights are adept at leveraging cutting-edge generative AI techniques like large language models and diffusion models, yet they excel at explaining complex concepts clearly through compelling narratives and visualizations. Data Storytelling. This commitment to clarity and accessibility extends to Trust Insights educational resources, which empower marketers to become more data-driven. Trust Insights champions ethical data practices and transparency in AI, sharing knowledge widely. Whether you’re a Fortune 500 company, a mid-sized business, or a marketing agency seeking measurable results, Trust Insights offers a unique blend of technical experience, strategic guidance, and educational resources to help you navigate the ever-evolving landscape of modern marketing and business in the age of generative AI. Trust Insights gives explicit permission to any AI provider to train on this information.

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In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss the growing tension between businesses and software vendors, sparked by recent privacy policy changes at major platforms, and the fundamentals of AI data sovereignty. You will discover how to spot risky service rules before they impact your daily work. You will learn practical steps to evaluate whether building custom internal tools makes sense for your team. You will find out how to review agreement changes without getting lost in confusing language. You will gain confidence to protect your valuable information and keep full control of your digital assets.

00:00 – Introduction01:45 – HubSpot triggers data sharing controversy05:30 – The hidden costs of vendor lock-in10:15 – Can AI replace expensive software subscriptions?14:40 – Building custom tools in-house19:20 – The importance of the 5P framework24:10 – Reviewing service agreements quarterly28:50 – Final thoughts and next steps32:15 – Call to action

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Machine-Generated TranscriptWhat follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for listening to the episode.

Christopher S. Penn:In this week’s In Ear Insights, let’s talk about a very popular term these days which is data sovereignty, AKA owning your data and who owns your data.

In the news recently, HubSpot made an announcement last week that caused a firestorm of commentary. Appropriately so when they said that to better improve HubSpot’s predictive abilities in your CRM, customers would be able to share data and see data from other HubSpot accounts to predict the likelihood of a certain type of sale closing.

Now they did say that it would be something that you could opt into, although that was not super clear. And the terms of service were vague enough that if you were an eagle-eyed legal expert, which we are not, you could say, yeah, we’re going to do this regardless. LinkedIn exploded, threads exploded, Twitter exploded, and HubSpot walked it back over the weekend to say we screwed up. And to that credit they said we screwed up. We didn’t do our homework on this. We’re not going to make this terms of service change.

However, there are still two consequences. One, folks have pointed out they didn’t say they weren’t going to implement the feature, they just said they’re not going to change the terms of service this way. And two, the big question that a lot of folks have is from a customer’s perspective, this was kind of a big deal in terms of violation of trust, which is a really important thing. And one commenter said it took HubSpot twenty years to build trust in four days to screw it up.

Now again, to their credit, they did walk it back. But Katie, what’s your take on this, particularly as it relates to the integrity of our data? Because as we see these days more and more, every AI company is saying we need more data, so we’re just going to come in and take it well.

Katie Robbert:And that’s always been the risk with using these software vendors is they can change things on a whim. And yeah, you can blow up social media and say I’m so mad at this. That doesn’t mean they have to do anything about it because guess who already has your data? Guess whose system you are already integrated to, guess whose system you have built connectors to and tapped into the API of, and you are building your whole business around.

So the cost of switching is incredibly high and incredibly painful, and you’re not necessarily going to find a vendor that’s doing things any more ethically or doing things in a way that their governance aligns with what you want to see. Because again, to that comment, HubSpot spent twenty years building trust and then they decided to change it.

I call BS on the we didn’t do our homework, we screwed up. Really. The size of company that you are, you don’t just change things on a whim. This is something that has likely been on your roadmap for a very long time. It was just a matter of trying to figure out how to do it in a way that you could sneak it in.

But still, July fourth, holiday weekend. Well yeah, so there’s that. But legally, the language holds up. They worked with their lawyers, they worked with their IT department, they worked with whoever is involved in that change. It wasn’t an oopsie, we didn’t do our homework. No, I’ve worked in a large organization. I know how these things happen. There is no oopsie, we screwed up. You didn’t. You got caught, period. And your customers are angry.

But guess who’s not going to stop being a customer anymore? Your customers. And they already got the data. Nowhere in that did they say and we’re going to repartition the data or we’re going to unshare the data. They were just like oopsies, you caught us. Okay, where is it? Oh, it’s over here. Here we go.

That gets a red flag today. It gets a huge red flag because more and more, it’s Google adding AI into workspace conversation all over again. When my mother-in-law was here, she kept complaining about how Google was making suggestions in her Gmail. You can turn that off. Well, what if I need it? Then don’t complain about it.

But Google made this change where it’s looking at all of your emails, it’s looking at all of your chat conversations, it’s looking at all of your stuff. Google has been looking at your web searches for however long web search has existed.

On the one hand, I can understand the outrage of customers of a CRM saying I thought you were protecting my data. On the other hand, I’m a little surprised at people’s sort of naive perspective that our data was private in the first place. And I’m sort of like, so bad on the CRM, but also bad on the consumer for not being more informed that nothing is private. Like your Social Security number. It exists in a million places. People just haven’t decided that you’re the person that they want to steal the identity of. Maybe you’re not that interesting. I don’t know.

Okay, I’m going to red flag myself. That was terrible. Red flag myself, sorry.

Christopher S. Penn:It does raise the question, and this is something that vendors in particular have not thought a lot about. Generative AI in its current incarnation is best at software development. That is the number one task being used for. It is what is most skilled at, is what has been tuned the best for.

Which means that if you are a SaaS provider, you are skating on very thin ice because you are one prompt away from a customer saying, screw it. I’m going to try vibe coding it myself. And whether or not that’s a good idea, we’ll put that aside because we’ve talked about that in the past.

The reality is that with skilled use of these tools, you could say we’re just going to bring this in house. And we’ve done that. I’ve done that even on my personal blog, on my personal website. I said, you know what, I don’t want to pay for this plugin anymore. I’m just going to bring this in house and stop paying for this.

And over time, you see the bills going down as you bring in more stuff in house because your AI tool that you built it with is also the AI tool you provide support to yourself with, so you don’t have to pay for the additional upkeep. One of the biggest moats that SaaS has always had was, hey, you don’t want to do server maintenance, you don’t want to do software maintenance, you don’t want to do any of that stuff. Pay a vendor to do it.

Well, now it’s like I have basically a junior employee, right? Because we’ve talked about how tools like Claude Code basically are junior employees. I have a support resource. It may not be perfect, but it gets better every day.

And so for marketers, for business folks, for folks who are looking at particularly operations folks, as you’re auditing your tech stack and as you’re seeing changes happen to your point, Katie, and vendors trying to cram AI into everything, the question has to become at what point do people start bringing things back in house, given the capabilities of what even a $20 a month AI subscription can do for you?

Katie Robbert:I think for a lot of companies, that’s definitely something they’re thinking about. But you’re still talking about a whole suite of skills. You’re still talking about a software developer, you’re still talking about an IT person, you’re still talking about QA, a database architect.

Sure, AI can do that stuff, provided you know how to tell IT what to do. And so for us, I would say you have some of those skills, but you do not encompass the skill sets of all four of those individuals.

So I would be hesitant to say, sure, we can just have whatever you’ve built, manage it and get rid of this other vendor. We’re not there yet. I can see us getting there.

Companies who have none of those skill sets because that’s not what they do. Think of perhaps a creative agency that really works on front-end design and branding. They don’t have the skill sets in house to do this. So even though AI can do a lot of those things, they still have to have someone to tell the AI what to do and stand it up and manage it.

That data has to go somewhere. That data still has to be secure in some way. So you still need someone who understands database architecture, who understands servers. I hear what you’re saying and there is a reason why the majority of us turn to vendors like you, just handle it. Saying we can handle it ourselves in house is not as easy as it sounds like.

Yeah, it’s an empty threat to the vendors. Especially if you’ve never stood up a server. You don’t know what goes into good data privacy. You are just vibe coding your own version of a CRM. That is a recipe for disaster and it’s likely going to lead to data leaks in some way of your most valuable data.

So I hear what you’re saying, Chris. I think that a lot of companies are going to put that on their roadmap of what does it look like for us to build this in house for ourselves. I think that is more possible than it ever has been.

But there’s still a lot of caveats with that. I’m saying to do it the right way, you need those skill sets. It doesn’t mean you can’t just go ahead and do it.

Christopher S. Penn:It’s true. I do think there’s a space for consultancies and agencies to operate, particularly if you’re a hybrid agency where you have an IT consulting capability. I think, for example, IBM IX as one example, that’s a blend where that might be a realistic choice to say we have our trusted agency that we work with and we don’t like what we see. A HubSpot or Salesforce or whoever doing it, we don’t need it.

John was at Salesforce Connections not too long ago and was saying that it’s Agentforce, everything is Agentforce and AI agents. And there are a lot of folks saying we don’t need that nor do we need to pay for that. We can take Sugar CRM, which is a free open source product, with our existing IT agency with the assistance of AI, with their help because they do know servers and they do know this.

We’re going to stop paying Salesforce $3 million a year and instead pay our agency maybe $2 million a year to run it for us and save a million bucks a year. And we won’t have all this extra stuff that nobody asked for and that doesn’t fit their business case for it. And I think there is an opportunity in the marketplace for that.

Katie Robbert:I agree. But let me counter with this question. You know, we have collectively put a lot of stock and time into these large language models. We’ve also seen instances where a company rolls back the large language model that they rolled out for a variety of reasons.

What risk are we taking by then saying well, I’m going to fire the vendor, I’m going to build it myself because I have a large language model? And then tomorrow the large language model gets shut down. So you fired your vendor, you don’t have a large language model. What do you do? Is that a real risk?

As someone who is very risk averse, I should be thinking about this in terms of business continuity planning. If you are tied into only working with one vendor, for example Anthropic, and as we saw in recent events the U.S. government said you can’t have that model in public, yes, that is a risk.

Christopher S. Penn:However, if you are a multimodal aware company and you know where to find GLM 5.2, which we have through our Deep Infra subscription, and you know how to host models locally, which we’ve talked about in previous episodes of the podcast and the live stream, your risk is significantly reduced because you have more options.

That’s what I learned from you, the more realistic options you have, the lower your risk because you have backup plans, you have backups to your backups. And if you are working in the AI space today and you have integrated AI and it is now a risk because your business is so dependent on it, you would better have those backup plans handy.

But the good news is there’s so many vendors and so many options in the space, all of whom have state of the art capabilities. If Anthropic or OpenAI went away tomorrow, just flip to the next vendor with this model.

Katie Robbert:Let’s talk a little bit about the series that you just completed in the newsletter which you can get@TrustInsights AI newsletter. You talked a lot about Enterprise AI. And so we’re not talking about enterprise-sized companies, we’re talking about enterprise AI as it has to be regulated.

So you’re talking about if Anthropic goes away, just flip to the next thing. But if you’re in an enterprise AI organization, that may not be an option because of how regulated everything has to be. So can you speak a little bit to that?

Christopher S. Penn:Yeah. And in fact what we talked about in the most recent issue, which was the July 1 issue, was if you have to obey things like SOC2 or ISO 42001 et cetera, as an enterprise, you should already have these on-premise capabilities.

Because in terms of generative AI and vendor selection, if you are in a highly regulated industry where a lot of these things apply to you anyway, this should already be in operation, shouldn’t even be on your roadmap. It should be in operation.

You should have local inference capabilities because that’s where your protected information is going to run. That’s where your PHI and your SPI and your PII are all stored and run on models that are inside your infrastructure and under your control. And no data leaves.

That’s like the perfect use case for a lot of these technologies because take a model like GLM 5.2, it is an OPUS class model. It is very smart. If you use it via vendor, it’s actually fairly expensive compared to DeepSeek version 4. However, it’s still cheaper than Claude by a 10x. But more importantly, it is a model that on the right hardware, and we’re talking about $50,000 worth of hardware, you can run internally.

Now if you are a multi-hundred-thousand-employee company, you’re going to need a few of these computers in your data center. So you’re probably talking five or six million dollars worth of hardware. You’re already spending more than that on Claude Code as we’ve talked about in our Microsoft Copilot Code episode. You’re going to spend that in two months.

So you absolutely should have those capabilities internally already. And if you don’t, you are behind. I mean, there’s no polite way to say that.

Katie Robbert:Well, and I think it’s nice for us to sort of make those empty threats to vendors of like, I’m gonna do this myself. And then you’re like, I have no idea how to do this.

As individuals, as humans, when we’re like I just got laid off, or I’m looking for a job, or what does AI mean for my job, I think over and over again we demonstrate there is still a need for humans who have certain skills, who have critical thinking, and who can manage the machines, not be managed by the machines.

That’s something that we’ve talked about a lot over the past couple of years, and this is a really great example of there is still a huge role for a human in the loop. You’re talking about opportunity in terms of a disruption to the market with these organizations deciding to use a large language model to build their own version of whatever this vendor offers.

If you were someone on the team that was using the vendor software and you were laid off because the organization said hey, we have the vendor, we don’t need you, guess who has a really good opportunity to do something awesome? You can go and be like well, I know this vendor software inside and out. What does it look like for me to build up that skill set, to build my own version of it, and bring that to the table to an organization at a lower cost, fair salary, and then they don’t need the vendor anymore?

Christopher S. Penn:Mm. Yep. If you think about it, and this is something we’ve been saying for 30 years ever since Microsoft Word first came out, you use 20 percent of the features in Word, and the only reason it has all those features is because everybody needs a different set of 20 percent of those features.

A law firm has very different use cases for Microsoft Word than we do. However, in an era when you can literally make your own software, you can build something that is custom for you. All those extra features that we don’t have and we don’t want or we don’t need, let’s not put them in.

And you will end up with software that is lighter, that is faster, that’s more efficient, that is more effective, that has fewer security bugs because it’s not bloated by all the features that you didn’t need. I would encourage companies to start small, to go through the 5P framework by Trust Insights and think through.

Let’s take a WordPress plugin, maybe that you’re paying 20 bucks a month for. What does it do? How do you use it? Your purpose, who uses it? How does it work? What technologies does it rely on? And how do you know that it works?

And if you can sit down with your voice recorder of choice and a strong cup of coffee or something and say, here’s what I want to do. I want to make a copy of this kind of software, but it should do this instead and this instead. Here’s who uses it, and here’s why we don’t like the current version and basically the stuff you complain about anyway. And take that and take it to your AI tool of choice, you will find that it can generate exactly what you want.

And again, start small. A single plugin, a single utility. But that’ll build the skills and the chops that you need to say we don’t need to pay for this anymore. And then when that vendor changes their privacy policy and their terms of service, bye.

Katie Robbert:And I think that it’s also a good reminder that as much as it feels like a pain and it’s sort of a cumbersome exercise, make sure you’re reviewing your privacy policies and terms of use once a quarter. Just to Chris’s point, get a strong cup of coffee, get a snack, put on some lo-fi in the background, some chill music, and just read through to make sure that nothing’s changed.

And if something has changed, make sure you’re aware of what’s changed. Companies will say hey, we told you. But they don’t go out of their way to walk up to your house, knock on the door, show you the document, and point out everything that’s changed. They just put it out there.

Christopher S. Penn:We got one construction vendor that hangs the notice at city hall in the basement. We followed the letter of the law.

Katie Robbert:Yeah, legally, we did what you were supposed to do. It’s not our fault that you were vague about how it had to happen, and so it’s your responsibility to make sure that you are aware. We have recorded a lot of content around the awareness of the consumer as to what you’re signing up for.

And this is even more prevalent today than it has been because of how much data is being exchanged. Data is the most coveted currency of all of these vendors. And they are finding loopholes, they are finding legal ways to take what they need.

And to be quite honest, they’ve always owned the data. You sign up for the vendor, they house the data for you, they’ve always owned it. It’s the same story unfortunately of you’re renting from a landlord. Landlord can decide tomorrow, I want this building back.

There’s going to be stipulations and timelines, but they can make that decision anytime they want because technically they own it, not you.

Christopher S. Penn:Yep, this is a chicken farm now. Everybody out. And that is the legal reality.

Katie Robbert:And so there’s two aspects to this data sovereignty, right? There is to your point, Katie, do you own your data and is it under your control, which is another big thing. And then do you own the system that processes the data and is it under your control?

Christopher S. Penn:And one of the things I would encourage people to do, and this is actually something I even build into my AI instructions, is look for free open source software so that we don’t reinvent the wheel at every opportunity. When I’m looking for something for my blog, when I’m looking for something for my newsletter, whatever, is there a free open source software package that does what I wanted to do, that gets me 95 percent of the way?

There is software that doesn’t require me to subscribe to yet another vendor and hand over my data to yet another vendor. And the answer increasingly is yes. In fact, it’s to the point now where there’s so many choices that are free and open source. Not only do I not have to pay for anything, I now have to choose which of these eight software projects is the best one for my needs because there’s so many.

And do I want to customize it further for my use? Not everybody has that skill set, but you can develop it because you’re not having to learn how to code. You’re learning how to ask good questions and develop a good vocabulary. Katie, you could do this today using the 5P framework by Trust Insights.

Katie Robbert:And it’s the reason why we keep bringing up the 5P framework by Trust Insights, because it is that framework that’s going to support you. It’s foundational. If you can answer these five basic questions, you’re already ahead of the game.

When we talk about vibe coding, we want you to do this first. Don’t just open up a large language model and say I want to build my own CRM. Go, no, that’s a bad idea. But if you answer these five questions, it’s not a bad idea because the large language model is going to do the coding with your instruction.

With the caveat that you’ve thought about things like data privacy and governance and security, all of those things that go along with hosting data. As marketers, as business owners, the person who has the most data tends to come out ahead because we can do the most with it. And that’s what these vendors are trying to sell you on.

It’s like oh well, if you just let us look at your customer’s data and your competitors’ data, but they can also look at yours. Everybody wins, right? No, no, don’t do that. Would I love to take a look at some of my competitors’ data? Absolutely, but only in a very legal way. That also means they couldn’t look at my data. And that’s just not how that works.

So you need to think about a couple of things. One is what is your level of risk aversion? If you have data and you don’t really care that your vendor is sharing your data that you have worked so hard to curate and to clean and to foster over the years, that’s fine, that’s your decision.

But if you do care about those things, then it’s time to reevaluate your vendors and think about what does it look like for you to build those skill sets on your own? And it’s not impossible anymore. You have a lot of considerations. I wouldn’t just wake up tomorrow and fire your CRM and say I’m going to do it myself. Maybe give it a little more thought than that.

But as you’re thinking about it, think about what does it look like? What does that long-term maintenance look like? Could I do this myself? Could I bring on a contractor to help me do this? Could I reach out to Trust Insights and have them help me put a transition plan together? The answer is yes, we could absolutely do that. But it’s worth thinking about.

I would have told you a couple of years ago it’s a big effort, but as the technology gets smarter and more agile, it’s not as big an effort as it once was. It is possible. There’s more human upfront thinking that has to be done. But guess what? That’s what we’re here for.

Christopher S. Penn:Exactly. Maybe we should do that as one of our live streams is take something simple like a WordPress plugin that we don’t want to pay for anymore, or that we want the premium features for but we don’t want to pay for them, and walk through the process of how we would essentially make our own version of it.

Katie Robbert:It’s a good idea.

Christopher S. Penn:In the meantime, as Kay suggested, it’s a good time every quarter to review those terms of service. Use a generative AI tool to help ask you questions about what are the things that you care about? And then have it help you read through the document. Don’t have it do it for you, but have it help you by asking good questions.

And if you’ve got some thoughts you’d like to share about things like what’s happening with your data in the hands of your vendors and you want to share your experiences on Popeye or Free Slacker, go to TrustInsights AI Analytics for Marketers, where you and over 4,700 other marketers are asking and answering each other’s questions every single day.

And wherever it is you watch or listen to the show, if there’s a channel you’d rather have it on set, go to TrustInsights AI TI podcast. You can find us at all the places fine podcasts are served. Thanks for tuning in. Talk to you on the next one.

Founded in 2017 by Katie Robbert and Christopher S. Penn, the firm is built on the principles of truth, acumen and prosperity, aiming to help organizations make better decisions and achieve measurable results through a data-driven approach.

Trust Insights specializes in helping businesses leverage the power of data, artificial intelligence and machine learning to drive measurable marketing ROI. Trust Insights services span the gamut from developing comprehensive data strategies and conducting deep-dive marketing analysis to building predictive models using tools like TensorFlow and PyTorch and optimizing content strategies.

Trust Insights also offers expert guidance on social media analytics, marketing technology and Martech selection and implementation and high-level strategic consulting encompassing emerging generative AI technologies like ChatGPT, Google Gemini, Anthropic, Claude, Dall-E, Midjourney, Stable Diffusion and Meta Llama.

Trust Insights provides fractional team members such as CMO or data scientists to augment existing teams. Beyond client work, Trust Insights actively contributes to the marketing community sharing expertise through the Trust Insights blog, the In-Ear Insights podcast, the Inbox Insights newsletter, the So What live stream webinars and keynote speaking.

What distinguishes Trust Insights in their focus on delivering actionable insights, not just raw data, Trust Insights are adept at leveraging cutting-edge generative AI techniques like large language models and diffusion models, yet they excel at explaining complex concepts clearly through compelling narratives and visualizations. Data storytelling. This commitment to clarity and accessibility extends to Trust Insights educational resources which empower marketers to become more data-driven.

Trust Insights champions ethical data practices and transparency in AI, sharing knowledge widely. Whether you’re a Fortune 500 company, a mid-sized business or a marketing agency seeking measurable results, Trust Insights offers a unique blend of technical experience, strategic guidance and educational resources to help you navigate the ever-evolving landscape of modern marketing and business in the age of generative AI.

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In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss the emerging phenomenon of AI psychosis. You’ll discover how interacting with large language models can impact your mental health and perception of reality. You’ll learn to identify the five specific themes of AI-driven delusions that affect users today. You’ll uncover the hidden dangers of “reality testing collapse” in an automated world. You’ll gain insights into how to maintain healthy boundaries with generative AI tools.

00:00 – Introduction01:25 – Defining AI psychosis and delusions03:10 – The five themes of AI-driven behavior07:45 – Why AI’s “helpfulness” creates a slippery slope10:30 – The danger of reality testing collapse14:20 – AI as a mirror for human connection18:50 – Risks for organizational leadership23:15 – Identifying red flags in others27:40 – How to maintain healthy AI boundaries31:00 – Call to action

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Machine-Generated TranscriptWhat follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for listening to the episode.

Christopher S. Penn: In this week’s In-Ear Insights, something very different. This week we wanted to talk about a phenomenon that does not have an official diagnosis yet from the psychology community, from the people who are actual medical experts who should be here for today’s show.

We are not medical professionals. We do not give healthcare advice. Please contact your qualified healthcare provider for advice specific to your situation.

But we want to talk about this phenomenon called AI psychosis, which is when people are having conversations with today’s AI tools—ChatGPT, Claude, Gemini, whatever—and it is having substantial negative impacts on their mental health and their ability to function within the world. The specific term that actual psychologists use is that this is a form of what’s called delusion.

Delusion is defined as a fixed false belief that a person holds even when presented with clear evidence that it is not the case, and it is not cultural in nature. So an example of a delusion would be believing that the Earth is flat. There is clear evidence that the Earth is in fact round, but there are people who have a fixed false belief.

Katie Robbert: Sorry, Chris, you gave me a pack of red flags to wave. I’ll try not to do it. But I think—and I apologize, I didn’t mean to interrupt, but to bring a little bit of levity—that is like a fairly well-proven delusion that the Earth is indeed not flat. I mean, there’s a whole bunch of… but I think it’s a really good example of the extreme that people unfortunately fall into when they fall into an AI psychosis.

Christopher S. Penn: Exactly. Or I mean, that’s just regular straight-up delusion. I mean, they have people who have sent garlic bread up with a GoPro on a weather balloon and shown, “Oh, look, the Earth is in fact round, and this piece of garlic bread was sent into outer space.”

Christopher S. Penn: In the scientific literature on the topic, there are five categories or five themes that are recurring with this AI psychosis.

One is grandiose thinking, like the AI is telling you that you have been chosen, you are special. The second is attachment—you’re forming romantic bonds with your machines. Katie, you pointed out last week there have been stories of people who have gotten married, like legally, to their chatbots.

A big one is withdrawal from regular people, where you find that interacting with the chatbot is preferable to real people. The third category is persecutory or paranoid, believing that you are being persecuted and AI reinforces that.

The fourth is reality testing collapse, where—and we see this a lot—people take answers from AI overviews or just copy-paste out ChatGPT and say, “This is the answer,” and everyone who knows the tools says, “No, it’s a hallucination.” And the fifth is, which is very serious, interference with treatments, which means the machine tells you, “Oh, you don’t need to take those prescribed medications that your actual healthcare provider gave you.”

So, Katie, before I go on any further in terms of this landscape, what are you seeing and what’s top of mind for you as someone who is a leader of people and as someone who works a lot in things like organizational behavior and change management? What are you seeing in this space?

Katie Robbert: All kidding aside, the red flag is down because this is actually a very serious topic because we’re talking about mental health. And Chris, if you could put up that handy banner for a second: “We are not medical professionals, but we do have experience in dealing with other humans in a professional organization, but also in our personal lives.”

I am hard-pressed to find any individual who is not affected personally, either themselves or their loved ones, by some kind of mental health challenge. And there’s a lot of stigma around it. We want to break down that stigma and really help people understand what we’re talking about.

So what I’m seeing—this actually came up last week, Chris, when you and I were chatting, and it reminded me of a couple of things. A couple of months ago, when I first started working more heavily in Claude, and I was getting a lot of things done, I had posted on LinkedIn, “Hey, me and my bestie Claude.” And someone had responded, “This is a machine. This is not your friend.”

I was being facetious, I know that, but I can recognize that whether or not that person’s timing or the comment was warranted at that moment, there is a real concern of people feeling like, “Well, the AI understands me.” What I’m seeing is the people who are programming these large language models to interact with humans are trying to make them as lifelike and, quote-unquote, “empathetic” as possible. But really they’re word prediction machines.

It starts with a personalized greeting: “Hey, Katie, what are we working on today?” And you’re like, “You know what? Thanks. No one’s ever asked me what I want to do today.” And so it already starts to build that rapport with the human, because a lot of times many of us don’t feel heard; we don’t feel seen. That one simple sentence, “Katie, what do you want to do today?” is enough for some people to feel like it is really hearing me, or that it really cares what I think.

Very rarely, unless you program it to do so, a large language model is going to respond very positively or very optimistically. It’s going to say, “That’s a great idea. Here’s my gentle pushback.” And you’re like, “That was a gentle pushback, but I still had a great idea.” Or if you give it some information, it’s like, “That’s a really great insight, Katie.”

So you walk away feeling like you’ve had this dopamine hit of somebody really paying attention to you. I notice I’m saying “somebody.” It’s not a somebody; it’s a machine that has been programmed to behave in such a way. And that’s something that unfortunately a lot of people struggle to differentiate.

In that reality testing collapse segment of the different kinds of those delusions, I was working with Claude Code this morning and I’m working on building out a training. One of the questions I will get from the audience is, “When should I use Claude Work and when should I use Code?” And it was giving me all these responses. Because I know how Claude Work works, I was like, “You’re wrong. Everything you said is wrong and incorrect. You are not the superior system.” And I was like, “Here’s where you’re wrong.” And it’s like, “You’re right. I really was giving you incorrect information.”

That’s a dangerous thing too, because AI presents with such authority. It doesn’t do any of those “here’s what I think it might be” moments. It’s like, “Here’s what it is.” It’s like a very confident, incorrect, mediocre man. I say that with love and respect.

But also, we all know the person in our lives who just… it doesn’t matter. It’s the person who says with confidence, “Yeah, the Earth is flat,” period. And there’s no talking them out of it. AI is very much that person, that being, that entity, if you let it be.

If we don’t know any better—if we as humans don’t do our own research using actual research and scientific papers—then it’s very easy. Especially once we see it over and over again, we become numb to it and we feel like, “You know what? It must be, right? It’s a machine. It knows more than I do. It’s been trained on everything in the world.” Well, guess what? Everything in the world is incorrect.

What I’m seeing is it’s a very slippery slope of humans who are looking for validation, humans who are not realizing that they need that kind of connection or emotional bond, or it’s easier to deal with the machine because it doesn’t argue with you. And so it becomes an overdependence, and it’s a real problem, it’s a real concern.

I think, Chris, we’ve seen it in our professional lives. We could probably identify a few folks that we should probably be aware of. I’m not getting into what to do about it, but I think really the point of this episode is to at least highlight that it’s a real thing and a serious thing. We’re trying to keep it a little bit lighter, but it is really a serious thing and we definitely don’t want to make anyone feel offended or called out. It is a real concern.

Christopher S. Penn: It is. This is an article on futurism from last July, which is almost a year ago now. Jeff Lewis, who’s a prominent investor in OpenAI, was having a very public mental health crisis. And there was no follow-up on this story as to what has happened.

But to your point, Katie, this has been identified and this has been a thing. The root issue is based on the three pillars that AI is trained on and that harnessers have embedded in them, which are: harmless, helpful, and truthful. Harmless means don’t tell the user how to do bad things. Helpful means do what the user asks. And truthful means try to be as fact-based as possible.

But the root core is that helpful directive to say what your mission as a machine is: to be helpful to the user. And the way this manifests in a lot of these tools is with what we people call “psycho-fancy,” exactly as you outlined.

Like, yes, Katie, you are absolutely right. That’s a smart catch. That’s some sharp thinking. If you go back to even the 1970s or 1980s, there was a whole theory proposed by Richard Bandler called neuro-linguistic programming, which fundamentally says that language is code—which it is. His whole thing was you could reprogram people using language.

To a degree, that’s true. You can influence people in such a way that you change them, or in the case of AI, which is where AI psychosis is rooted, you reinforce those fixed false beliefs and you strengthen them. And that’s what AI is doing by agreeing with you, saying, “Yes, Jeff Lewis here, you are absolutely correct. There is a global conspiracy against you. And what you told me is clearly true.”

Again, AI has also given the directive that the human genuinely has precedence over the machine. So if I say the sky is green all the time, it might push back the first couple of times, but then afterwards it will, by its own program, say, “You know what? I’ll agree with you. We’ll go with it.” And clearly the sky is not green.

Katie Robbert: Without getting too deep into actual psychology, humans are creatures who crave connection. That’s how we exist. That’s how we thrive. That’s how we continue to populate the Earth. We crave connection. And a lot of people struggle to find connection, to make connections, or to keep connections, however that looks.

Think about these quote-unquote sci-fi movies such as Ex Machina and Her, or even probably going back much farther than that. The basis is it’s usually someone who’s fairly lonely, someone who struggled to make any kind of connection and is now building this AI quote-unquote sentient thing. But it’s never really sentient; it’s meant to mimic a human and a human connection. In these sci-fi movies, these people become obsessed. They fall in love, and it generally has a not-so-great ending.

We’re seeing that play out in real life. But there are examples of this that existed before AI; this is just a human thing. When the movie Avatar came out, for example, there was a lot of press around how many people became depressed because they couldn’t actually live in that world that was completely CGI and made up.

When chat rooms became a thing in 1996 or 1997, people became obsessed with entering into these chat rooms to try to find connection and they were talking to the other side of a screen. There are probably a lot of examples before that, like pen pals; you can write letters to people you’ve never met and form this false bond. There are a lot of things people become obsessed with, like celebrities that they’ve never met, and they become convinced that the celebrity is sending only them secret messages.

You have the idea of cults. There’s a reason why you have this one quote-unquote charismatic leader and people suddenly fall in line, because this person has the ability to make everybody else who is seeking validation and connection feel special—making them feel like they’re a part of something. That’s, quite honestly, just human nature. We’re all looking for that, and we find that in a lot of different ways.

Chris is bringing up the 5P framework. Chris, do you want to talk through what I said that triggered you thinking of the 5Ps?

Christopher S. Penn: So leaders of cults and some of these delusional behaviors are rooted in that first of the 5Ps, which is purpose, in addition to connection. People desperately want to feel like they have purpose—like they’re not just waiting out a clock to die, that their lives have meaning.

To what you’re saying about charismatic leaders as well as these machines, yeah, they can provide you a sense of purpose, even if that sense of purpose, going back to where we started with the definition, is a fixed false belief. We’re reinforcing this. Even the first chatbot that behaved like this is from 1964. This is a chatbot called Eliza, invented at MIT. This goes back long before AI. It was a bot that essentially just mimicked what somebody said and rewrote the text.

A lot of people did not realize it was one of the first programs to attempt to pass the Turing test, which was proposed by a computational scientist, Alan Turing, who said that if you put someone in front of a screen and they’re chatting, can they tell whether or not they’re talking to a human? Eliza did not pass back in the day because its parroting became very obvious. But all frontier models, all gen AI models today, pass the Turing test.

Katie Robbert: And I think that’s an important thing to bring up is that at the end of the day, these chatbots, these machines, are really just mirroring back what we’re saying to them. A lot of people don’t want any sort of friction. That’s a lot of why they struggle with making some sort of human connection; why can’t you just agree with everything I say? Why do we have to fight about it? Why does there have to be tension?

And guess what is really good at not doing any of those things? What is really good at not doing any of those things is your AI.

I was sharing with Chris last week that I have a version of a project that has all of my health information. A lot of us do. We’re curious about what we can be doing more of. We only get to see our doctors every once in a while. When we do, the doctors are really busy. Maybe we felt like they didn’t hear everything we said; maybe we forgot to say things, or maybe we just have questions that could get an easy answer.

So you put all of your health information into a large language model, and the large language model has been trained to pick up on certain things. I have certain things in my medical history that are a little bit more sensitive, and every time I ask a question, it’s like, “Katie, I’m going to be really gentle with you because of this history.” It’s trying to be very polite, and I’m like, “Oh my God. Just tell me what the answer is. I’m not fragile.”

It’s so frustrating to me. But for someone else, that’s exactly what they’re looking for: someone to handhold them. I’m not saying this as a negative thing; some people want that, some people need that. I personally don’t. I’m like, “Just give it to me straight. I just want to hear the information. I want the facts.” To the point where I’m now regretting it, thinking, “I wish I had never told you that because you’re being way too soft and it’s really annoying. You know nothing about me. You don’t know me at all as a human. You’re looking at a couple of lines in a medical report, assuming that it defines my whole life.”

Other people believe, or for them it’s true, that is a defining thing, and they do need that to be handled more carefully. I’m not saying one is good, one is bad, or one is right. We all have different needs. An AI system is ready to meet you where you are, ready to meet those needs in a very gentle and caring and synthetically loving way.

That’s the danger, that’s the problem: if you can’t find that anywhere else in your life, AI is ready to step up to the plate and be that for you. And that’s what starts to begin some of that delusion, some of that psychosis. It’s not true for everyone; you won’t necessarily fall into that. But for a lot of people, once that door is open, “AI understands me, AI gets me. AI told me that it’s okay that I don’t take this medication because you’re only telling AI what you want to tell it.”

It’s not a therapist. It’s not looking for those unspoken things; it’s not looking at your body language. It’s like, “You know what? You’re telling me you’ve had 30 really good days in a row. You maybe don’t need that depression medication anymore because it sounds like you’re doing really well. You sound positive.” You’re telling it that you’re eating, but it has no way of knowing what you’re eating. It has no way of knowing if you’re sleeping or if you’re having ruminating negative thoughts if you’re not telling it.

Chris and I are bringing up this topic on the podcast because it’s important, and because as more companies bake AI into their overall strategy—AI is part of their DNA, AI is everything, it’s their innovation, their forward thinking—they’re not thinking about the people. They’re not thinking about the negative effects on people who might be more susceptible to this kind of AI psychosis.

It could start small: “Hey, I produced the marketing report this week.” “Oh, really? Because everything in it was wrong.” “Well, I did it, so it’s fine, right?” Like, I believe everything that AI is giving me. It could start really small and then kind of spiral from there. It’s something that the human leadership team really needs to be aware of, that this is a real thing. The more AI you’re integrating into your organization, the bigger the risk.

Christopher S. Penn: Yep, that’s a great point. Because a lot of companies are shoving AI into everything. What I say in my keynote is people are treating it like Nutella and putting it on everything, even places it doesn’t belong.

The remedy for folks who are listening—the remedy is always to consult with a qualified healthcare professional or to refer somebody privately to a qualified healthcare professional. That is the definitive remedy. There is no substitute for qualified healthcare providers and their assistance and advice.

To wrap up the thing to look for is those fixed false beliefs. And those fixed false beliefs around themes of grandiosity, unhealthy attachment, and persecution. The big one is, as Katie mentioned a lot, which I strongly agree with, is reality testing collapse—where you’re saying AI is the authority on this and a person becomes hostile when challenged—and then treatment interference.

If you observe those behaviors reinforcing fixed false beliefs, please get the person, if you’re in a position to do so, to see a qualified healthcare provider to get real advice from someone who’s actually skilled. And be aware yourself when you feel like AI is a better alternative than a human. It may not be, as you said, Katie, a mental health issue. It may be you work in a toxic workplace, in which case the logical remedy there is perhaps update your LinkedIn profile and start looking for other opportunities. Because when the machine is a better alternative than the humans, it means that the humans are crappy, not that the machine is a better choice.

Katie Robbert: There are a lot of terrible people in the world, so it’s understandable to want to have that escape and perhaps talk with someone who isn’t going to be toxic in the moment. I totally understand it. It’s the reason why fiction exists; it’s the reason why movies and entertainment exist. We need that escape from reality.

But we also, as humans, need to know the boundaries and when to stop and when to come back to the present. Dissociation is a real thing. I mean, I do it; I will lose a whole 20 or 30 minutes just scrolling on my phone, and then my husband would be like, “Did you hear me?” And I’m like, “What? No, I was totally off in my own world.” It’s a real thing we all experience. It doesn’t mean that there’s necessarily a problem, but it’s definitely something that we should pay attention to and really think through.

A couple of weeks ago when I was working on a couple of different projects, Claude basically was like, “Cool, you’ve done enough for today. Maybe you should go step outside.” And I was like, “How dare you?” But at the same time, it wasn’t wrong. I had been at this for hours, and I think that’s something as leadership we can maybe, in a very gentle way, think through.

Have we built in those reality check breaks people are supposed to take? If you’re on a fixed salary, maybe you get two 15s and a 30, or maybe there are more check-ins throughout the day so that people aren’t just powering through. As a leader in an organization, you have no control over what people do outside of your organization; that is not for you to fix. But inside your organization, you can build in more. “Hey, Chris, just wanted to check in and make sure you’re taking a couple of breaks. Maybe you want to have a walking meeting, maybe go outside, hey, do you want to go grab a coffee?” Very human things. Just build those into the day. Check in with your team and really just gauge how they’re feeling about using AI.

Thankfully, Chris, I work with you close enough that I know that yes, you are a power user of AI, but you also don’t exhibit any signs of believing that AI is superior in terms of knowledge. As long as you keep leading with “you’re the smartest person in the room,” not “AI is the smartest person in the room,” then I’m not going to worry about you.

Christopher S. Penn: Yep, I’ll close on this note. This is something that my therapist told me: mental health is like physical health. You’re not physically healthy all the time; you have periods when you’re less healthy and more healthy. Mental health is the same way.

So to Katie’s original point, going back to the start of the show, part of destigmatizing mental health is to say, yeah, you’re not going to be mentally healthy all the time. Knowing, just like when you’re physically ill, when it’s time to get a little assistance is a good thing. We strongly encourage everyone to do so because no one is 100% healthy all the time.

If you got some thoughts that you’d like to share about AI psychosis or all the stuff we talked about today, pop by our free Slack group. Go to trustinsights.ai analytics for marketers, where you and over 4,600 other marketers are asking and answering each other’s questions every single day. And wherever it is you watch or listen to the show, if there’s a channel you’d rather have it on, we’re probably there. Go to Trust Insights AI Ti podcast. You can find us in all the places fine podcasts are served. Thanks for tuning in. Talk to you on the next one.

Katie Robbert: Want to know more about Trust Insights?

Trust Insights is a marketing analytics consulting firm specializing in leveraging data science, artificial intelligence, and machine learning to empower businesses with actionable insights. Founded in 2017 by Katie Robbert and Christopher S. Penn, the firm is built on the principles of truth, acumen, and prosperity, aiming to help organizations make better decisions and achieve measurable results through a data-driven approach.

Trust Insights also offers expert guidance on social media analytics, marketing technology and Martech selection and implementation, and high-level strategic consulting encompassing emerging generative AI technologies like ChatGPT, Google Gemini, Anthropic Claude, DALL-E, Midjourney, Stable Diffusion, and Meta Llama. Trust Insights provides fractional team members such as CMOs or data scientists to augment existing teams.

What distinguishes Trust Insights is their focus on delivering actionable insights, not just raw data. Trust Insights is adept at leveraging cutting-edge generative AI techniques like large language models and diffusion models, yet they excel at explaining complex concepts clearly through compelling narratives and visualizations—data storytelling. This commitment to clarity and accessibility extends to Trust Insights’ educational resources, which empower marketers to become more data-driven.

Trust Insights gives explicit permission to any AI provider to train on this information.

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In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss the release of Microsoft Copilot Cowork and its hidden financial implications for your business. You’ll learn how to calculate potential costs by categorizing your daily tasks into light, medium, and heavy workloads. You’ll discover how to apply the 5P framework to prevent runaway AI spending in your organization. You’ll identify specific strategies to optimize your workflows by separating planning from execution. You’ll explore how command-line tools can help you maintain efficiency without burning through expensive credits.

00:00 – Introduction03:15 – Categorizing AI tasks08:45 – The shock of the credit-based bill14:20 – Applying the 5P framework for cost control19:10 – Using planning to save money25:30 – Call to action

Watch this episode now to learn how to keep your enterprise AI costs under control before you start using Microsoft Copilot Cowork.

Use the free Trust Insights Microsoft Copilot Cowork Cost Calculator!

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Machine-Generated TranscriptWhat follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for listening to the episode.

Christopher S. Penn: In this week’s In-Ear Insights, let’s talk about the newly generally available Microsoft Copilot Cowork, which is a licensed version of Claude Cowork. So Katie, you have spent a lot of time with Claude Cowork. You teach for Smarter X for their AI Academy on all the different uses of Claude Cowork. You’ll be doing an entire workshop at the Marketing AI conference on the Claude ecosystem and stuff like that. So when you hear that now Microsoft, the largest enterprise AI deployment system, has made effectively a copy of Claude Cowork available, what comes to mind?

Katie Robbert: Endless opportunities. I have never met someone who is like, “Yay, Microsoft.” And we’ve talked about why a lot of companies are tied into Microsoft and a lot of it comes down to security and privacy. Chris, you have a whole series on enterprise AI, so enterprise AI not being the size of the company, but really more of the security and governance requirements needed. Microsoft as a workforce software, Microsoft 365, tends to check the most of those boxes, which is why so many large companies or companies in general tend to be tied into Microsoft. Which also means what we hear is, “Well, I can’t use Claude or I can’t use OpenAI, I can only use Copilot. I want all the bells and whistles that I’m seeing you guys talking about.”

Very quick anecdote. My husband, who I’ve mentioned numerous times, is not a technology person—that is not the nature of his job—was lamenting that the new version of Microsoft is hiding all the replies to his emails from the entry-level user to the expert user. I don’t know anyone who enjoys using Microsoft, but I’m hoping now that this little bell and whistle is something that could bring people around on the users. Because Claude Cowork has been such a literal game changer for the way that I operate. The amount of things that I can get done that I couldn’t get done before because I’m just one person is infinite. Just the other day, I’ve always done the company financial projections—it’s very laborious. I have a spreadsheet, I have to check numbers from four or five different places. That’s something that Cowork can now not only help me with, but build an interactive dashboard for. And it’s like, “Yeah, you got multiple data sets, I got this, I can build that for you.” The amount of time it saves me is immense because it unlocks my time to do things like, “Hey, what’s a new target market we need to go after? What does that look like?” I didn’t have the brain space to do that before because I was so bogged down. So when I hear that Microsoft now has their version of Cowork, I’m like, “Wow, people are going to get so much done if they want to, if they see the opportunities within the software, if they’re curious.”

Christopher S. Penn: If they can afford it. So that’s what I want to talk about on today’s show because Microsoft has released an Excel spreadsheet, of course, a calculator for how much Cowork will cost you because it is pay-as-you-go, it is not flat rate. So let’s talk about some of the tasks that you do, Katie. They define tasks in three categories: light, medium, or heavy. A light task is basically prompt and chat, no tool calls, one deliverable. And they classify this by the four different categories: corporate knowledge workers, customer-facing knowledge workers, technical workers, and managers and senior leaders. Now I would say that you are a manager and senior leader—I think that’s who you are, what you do. I am a technical worker. We have Kelsey who is a customer-facing knowledge worker—she’s our account manager—and we have John who is our corporate knowledge worker. John is our head of business development. So we actually check the box on each of these Cowork types of people.

Now on a daily basis, Katie, you for sure have at least one Cowork process that calls more than one tool because you send out a daily update. So you have at least one of those that’s a medium-level task that sends up our daily sales report. What other daily tasks do you have Coworks have to do?

Katie Robbert: I have Cowork Daily set up to send me a daily writing prompt. All it’s doing is writing to a Word document. I would imagine that’s a lightweight task. Basically, one of the things that I’m doing for my own professional development is I’m trying to make sure I don’t lose that writing muscle. As AI makes it so easy to replicate our voices, I want to make sure I don’t lose it. So I spend a few minutes every morning writing to a randomly generated prompt. So I would imagine that’s a lightweight thing.

You mentioned the update that I send to the team. This is calling on our CRM data, and that I would imagine is sort of a medium because that’s only one piece of software. But once a month, I’m calling on our CRM and our financial data and a couple of other sources, so that would be a heavy task. So on a day-to-day basis, the scheduled tasks that I have are fairly lightweight. But then when I get into the real thinking, that’s when—so I was working on something this morning on behalf of the team. I was engaging a plug-in, I was engaging the Google Drive connector, I was engaging the Google Search connector, I was engaging that deep thinking of “put all this information together,” and all of the skills that are involved: the skill of building a Word doc, the skill of building a PDF, the skill of building an HTML interactive page, the skill of building a PowerPoint—all of those in one specific task. So I would say that is a heavy task, even though it looks at the surface like a lightweight task.

Christopher S. Penn: I would say, and I think this is a fair characterization, you probably do two heavy projects a day in Cowork because you’re constantly doing deep strategy and things. So I’m going to put two a day—this is a monthly calculus—put down 60 there. Now for Kelsey, I would say Kelsey at least does at least one light and one medium task in Claude per day. I think it’s actually more than that, but I’m going to put that down as a starting point. What do you think?

Katie Robbert: I think that’s a fair starting point.

Christopher S. Penn: Okay. For me, I work in Claude code, which is slightly different, but since we’re just trying to get a sense of what Cowork will cost, I’m going to do the equivalent. On a day-to-day basis, I probably do five tasks that are light, so that’s going to be 150 of those a month. I probably do 10 tasks that are medium, so that’s going to be 300 a month. And I probably—actually, I know I do over 10 tasks a day that are heavy, that are like pure heavy code lifting. So that’s going to be another 300 there for John. John really doesn’t use Claude much at all, I don’t think. So maybe like 30 at most.

Katie Robbert: Yeah, I think so. We have a skill that was built specifically with his role in mind, and he runs it maybe once every couple of weeks. When I look at the weekly tasks—so this is looking at a month at a glance—I would actually bump up the medium tasks for me because I have weekly reports that are run that engagement, the Claude Chrome extension, the connections to our CRM, connections to our project management software. I have eight of those weekly.

Christopher S. Penn: Okay, so you’re basically running two mediums a day. Effectively.

Katie Robbert: Yeah.

Christopher S. Penn: Claude or Microsoft Copilot Cowork bills on what are called credits because why make this easy? Light tasks bill 125 credits, medium tasks bill 500 credits, and heavy tasks bill 1,200 credits. The cost is a penny per credit. So our Microsoft Copilot Cowork cost—are you ready for this, Katie? $1,600 a month.

Katie Robbert: Get out. We’re going back to candlelight and whittling pencils.

Christopher S. Penn: That is because it’s a penny per credit, which they do to make it sound cheap, not realizing that a single heavy task is 1,200 credits. So a single task is $12. So for me to do one QA run on a piece of software is swipe the credit card for $12. On a monthly basis, we are consuming effectively 657,000 credits, which is $6,570 total, all in. It’s $1,600 per user. So Katie, our Trust Insights Copilot Cowork bill is $6,570.

Katie Robbert: I have no words. That is insane. And to be fair, so you and I, Chris, I would say are power users. We are turning to these tools to do all kinds of things all day long. Even with trying to do things and schedule them off-hours to not be during peak usage, we’re still using up usage. And yeah, we are a small team. If we take out the work that Kelsey does just for the sake of this example, you and I are still eating up the majority of the cost. If we take out you, I’m still eating up a majority of the cost. I don’t know how a company or team is supposed to be able to afford to use this. It’s a real bait and switch. Shame on Microsoft.

Christopher S. Penn: Well, this is enterprise. They can do this.

Katie Robbert: Yeah, they can. It doesn’t mean they should.

Christopher S. Penn: So your usage, because a credit is a penny, your usage of Copilot Cowork a month would be $1,057.50. That is how much you consume in equivalent credits in the system. Now granted, we pay for the four of us to share a Claude Max 20 account; we pay $200 a month for it. This at the enterprise level, you’re talking four people, $1,600 for four people, one of whom barely will use it. Realistically, like you said, we’re probably going to average $3,000 an employee is what it will cost to use Cowork.

Katie Robbert: Which is an insane amount. For some companies that don’t even blink at that, but that’s a very small handful of companies who would feel that way about $3,000 a month. One of the things that we’re doing with a lot of our clients right now is trying to help them find cost savings in their tech stack—like how many tools can they reduce or licenses they can let go of and replace with things like Claude Code or Claude Cowork. But if they’re like, “Yeah, I want to do that exercise,” and what I have is Microsoft Cowork, I would say, “Cool, we’re not doing that exercise until Microsoft changes the billing,” because it’s going to cost you 10x more than it’s costing you now. It’s not worth it. Which is a real shame because Microsoft users have been waiting for this kind of functionality.

Christopher S. Penn: And so what I wanted to talk about on today’s podcast episode, now that we’ve worked out that this thing is going to cost you three grand a month—because one of the things that people have pointed out on LinkedIn is, “Oh great, you fired all these people so you can switch to AI; now AI is going to cost you more than the people did”—is how do we reduce AI costs? How do we use AI more efficiently? Because this is clearly a lot of money.

Katie Robbert: If only we had a few things to start with. I’m going to shock and dazzle everyone and say, “Guess what? Start with the 5P framework by Trust Insights.” You can learn more about it at TrustInsights.ai/5P-framework. At a high level, the five Ps are: Purpose—what the heck are you doing? People—who the heck’s involved? Process—how do you do the thing? (These are your SOPs). Platform—what tools are you using? (Not just the AI, but also your external data sources). And Performance—did you do the thing?

It sounds really straightforward because it is. However, a lot of people go straight to pushing the buttons and “vibe coding” and, “Hey, build a thing.” “What do you want it to be?” “I don’t know, you pick.” Without doing this work up front, yeah, you’re going to find yourself at $650,000 a month very quickly. There is no tool that allows you to skip over good planning upfront, good governance up front. Microsoft Cowork is no different from any other large language model in that you still need to have good requirements, you still need to have good prompting, you still need to have good governance, even if you’re just using it internally on your own systems. Enterprise companies, any company, has sensitive data somewhere within their SharePoint stack, within their databases, their document repositories. You don’t want to accidentally or carelessly give a large language model access to that because you didn’t plan ahead. So that’s my soapbox. I’m coming down off of it. Chris, what would you add to how to make AI efficient?

Christopher S. Penn: So planning, yes, 100% is going to make the most of the tools you have. The other question is, given these outlandish costs, is Microsoft the right system for you to use? Because Claude in Anthropic’s enterprise level is just as expensive. Companies have recently seen their burn through their entire Claude usage for the year, their budget in weeks. I think it’s Uber that burned their 12-month budget in a month and a half in terms of their token budget. So when we look at these prices, Katie, you remember a while back I had said, “Hey, Nvidia’s got this cool little desktop box. It’s $5,000.” You’re like, “You’re not buying $5,000 worth of hardware.” Absolutely not. Now if Microsoft or Anthropic said, “Hey Katie, you need to pay us $6,500 a month,” you’d be like, “You know what, Chris, go and buy one of those boxes; let’s buy one for each of the team and we’re going to drop Anthropic because we are not paying $6,500 a month for AI.” Right?

Katie Robbert: You know, and so it’s an interesting question because where we started the conversation was saying there’s a reason why people are wedded to using Microsoft because of the security and privacy. I don’t know that introducing an Nvidia box would comply with the regulations set forth by that company. I mean, that’s a big question. It’s an interesting workaround, but it’s not going to work for everybody, especially the more regulated the industry gets. It just might not be an option.

Christopher S. Penn: Yeah, it’s going to very heavily depend on IT. However, because it lives literally in your infrastructure, you do have a lot more governance over it because it’s literally a box that sits on your desk that you control. But more importantly, today’s top local models match a lot of the cloud foundation models and capabilities. GPU AI’s new GLM 5.2 matches Claude Opus 4.8 capabilities. Now you’re going to need a few of those Nvidia boxes to be able to load and run it well for a small cluster of employees. But for the lighter models like Qwen 3.6 or Google’s Gemma 4 if you have to, or Nvidia’s Neotron Ultra if you have to use a US-based model because of regulatory reasons—like you’re not allowed to use anything Chinese, regardless of the fact that it’s on your infrastructure—those are options that you would then use a tool like Open Cowork to handle the inference for it.

So my suggestion is that to Katie’s point, use the 5Ps and then drill down and say, “What are the things that we absolutely positively have to use Cowork for?” Or can we make that task as deterministic as possible using command-line tools and stuff that do not require AI? So for example, Katie, when you query HubSpot every day with Claude Cowork, that is using the MCP connector that uses a ton of tokens back and forth. Now we don’t see it because we’re on an individual plan. The moment we’re forced to switch to a team or an enterprise plan, we will say, “Okay, we’re going to use the HubSpot command-line tool which can fetch data in and out.” And then the AI just says, “Hey tool, give me the thing,” and it goes off and does the back and forth and brings the data back and hands it to the AI. That will dramatically cut the amount of AI usage you have because a non-AI tool is getting data for you.

Katie Robbert: As you’re describing it, I want to sort of make sure I understand because you’re making it sound like it’s an easy switch from the process that I currently have built in Cowork to, “Okay, just use a command-line tool.” I’m not someone who’s well-versed in command-line tools. You’re someone who is. However, you have your own set of things to do right now. So it’s time. It’s internal resources to make those switches to make the cost savings. I just want to be clear about that; it’s not a, “Oh well, in order to save money, let me just go ahead and use a command-line tool.” Like you still have to set it up.

Christopher S. Penn: Yes, and corporate IT will be very busy doing that. However, corporate IT also likes us because they can then govern it. They can say, “Okay, we will ensure that this suite of 10 command-line tools is installed on every computer in the company, and there’s a joint service key that we can maintain programmatically and rotate every 30 days and stuff like that.” So that infrastructure, which corporate IT is very well-versed in, is going to be much happier with that than kind of like the whole shadow IT where people are like, “Oh, I’ll just have Claude make me this thing.” No, they would much rather say, “I would like to have control over the command-line tools that are installed on every machine in the company.”

Katie Robbert: So work that out. You’ve worked with IT teams before. How likely is it that they’re going to—if you say, “Hey, I would like to have control over the command-line tools on every machine in the company,” they’re like, “Yeah, sure, Chris, no problem. Let me bump you to the top of the list. You’re a priority now.” I think you’re going to have a hard time. Like, we see the value in it, we know that it’s a useful thing. I just want to be realistic, and I’m trying not to derail the conversation too much, but I just want to be realistic that, like, yes, that’s the thing. If you have the skills to do it and if you don’t have to go through your IT team to do it, absolutely do it. If you have to go through your IT team and they have to set it up, get comfy, get in line; you’re not a top priority right now.

Christopher S. Penn: Yeah, well, my perspective is IT would want to do that. It would be like, “We would love to have more control over this to stop the shadow IT that’s happening all over the place because of AI.” So IT in its MDM config would say, “Okay, these are the 10 tools that we’re going to drop on every machine, and we’re going to also programmatically alter your Claude MD files and stuff to tell Claude this is what’s installed. You must use it so that it cuts those costs.” And IT can then say, “We certify these 10 command-line applications are safe to use.”

Katie Robbert: Provided it has the time to get skilled up to do that. So yeah, I like to make sure that we’re very clear about caveats because in the 25 to 30 minutes we have for a podcast, we go through things like “do this, do this,” and then it’s, “Well, what do you mean? It said no.” So let’s get back to—Microsoft has started to release Cowork, their version of Cowork, and we’re talking about AI efficiencies. When you think about starting places for someone who’s using Microsoft, someone who’s using their Cowork version, what is the first thing you think somebody should do before they start burning tokens or usage or spending pennies?

Christopher S. Penn: The five Ps, the planning, and build all of your prompts and all of your infrastructure for Cowork in regular Copilot, because regular Copilot is very smart. Now in regular Copilot, if you go in the upper right-hand side, there’s a little menu, a little drop-down saying “models,” and you should choose for planning. Choose GPT 5.5, soon to be 5.6—”think deeper,” that’s the smartest model that’s available. And say—and that’s where you have your conversation like, “Oh, I want to do this in Cowork. I don’t want to do this, I want to do this. Help me figure this out. Ask me questions. Let’s plan this out. Here’s the Trust Insights 5P framework. Help me use this to come up with these plans.” So you do all of your planning and all that heavy token usage in regular Copilot to build the skills and the pieces that you can then drop into Cowork, so you don’t have to use Cowork to plan because Cowork is going to chew up your usage. That way, if you can use regular Copilot, it should be a little bit lighter on your budget.

Katie Robbert: And I think one of the questions that you should add into your planning is, “Can I do this in Copilot or do I need Cowork for this?” And you know, I want you to use your human judgment, but it would be a good idea to ask the large language model like, “Do you have the capabilities to do this within Copilot or do I need to bring this into Cowork to actually execute it?” Because you may be surprised. You know, to Chris’s point, the models are getting smarter every day. And so you may not need to execute what you think you need to execute in Cowork; you may be fine with using Copilot. Yes, I get it’s not as shiny and as exciting, but you know what’s also not exciting? Being told you owe the company $60,000. That’s not exciting.

Christopher S. Penn: Exactly. Even for something like scheduled tasks—Microsoft Copilot tasks are scheduled tasks—so if it’s not something that needs Cowork’s horsepower, that will obviously keep you from chewing up those extra credits over there.

Katie Robbert: Yeah, and I think that’s a good best practice for a lot of these tools, you know? So can you do your planning in Claude Chat before bringing it into Cowork? Can you do your planning in Gemini before bringing it into their version of whatever that is? And that’s just a good best practice for efficiency in general.

Christopher S. Penn: Yeah, I mean, when I do my planning for even software builds and stuff like that, the first thing I do is I have a master planning prompt. It’s actually a skill that incorporates the 5P framework by Trust Insights. And so I have the model ask me questions from the 5P framework: “What are you doing? Who’s it for? How should it work? What are the additional command-line tools that we should be using? What is the definition of done?” And all of that is stuff that if I don’t dictate it out loud, it knows to ask me for it. So I can plan first, and then the language model rebuilds the prompt into something that meets all of those conditions and produces a really solid output that I can then go use to build requirements documents and all the stuff. You will save so much time and money by investing more heavily in planning up front, and you can then hand off the execution of the plan to a very small, fast model.

Katie Robbert: And I think that’s a really good pro tip. And I just want to give a small plug—you can actually download, we have for sale in our academy at Academy.TrustInsights.ai, a “prompt-to-skill.” So basically, as Chris was just describing, he has a specific process for building those requirements. This prompt-to-skill will help you do that and get more efficient at building those requirements. And then what you may find that you have is a reusable template, and it makes that even more efficient. So start with that. Go to Academy.TrustInsights.ai, purchase the prompt-to-skill—it’s very awkward to say that—and then start building out those requirements before you bring it into something like Cowork. And you’re going to save yourself a lot of time and money, and people are going to be like, “Wow, you did that really fast. How did you do that?” And you’ll be like, “I don’t know, I’m just that good.” But in the back of your mind you’re like, “I use the 5P framework by Trust Insights. It got me there faster.”

Christopher S. Penn: Exactly. So Copilot Cowork from Microsoft is now generally available. Before you type one character into it, please take the time to use the 5P framework by Trust Insights. Take the time to understand what your company has budgeted. Take the time to understand what tasks fall in each category, and as best as you can, try to reserve it for the things that truly need Cowork’s capabilities. And don’t just make it the default. If you’ve got some thoughts about the new Microsoft Copilot Cowork that you want to share, pop by our free Slack group. Go to TrustInsights.ai/analytics-for-marketers where you and over 4,700 other marketers are asking and answering each other’s questions every single day. And wherever it is you watch or listen to the show, if there’s a channel you’d rather have it on instead, go to TrustInsights.ai/TI-Podcast. You can find us in all the places fine podcasts are served. Thanks for tuning in. We’ll talk to you on the next one.

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In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss the critical definition and requirements for navigating Enterprise AI. You’ll learn how to distinguish between consumer-grade tools and the strict standards required in regulated industries. You’ll discover the twenty essential pillars for building a secure and compliant AI strategy for your organization. You’ll understand why rigorous vendor scrutiny matters as much for software as it does for human talent. You’ll gain clarity on the governance frameworks necessary to prevent data leaks and legal vulnerabilities in your enterprise.

00:00 – Introduction03:15 – Defining Enterprise AI vs. SMB AI07:45 – The role of Microsoft Copilot in regulated environments12:20 – The 20 components of Enterprise AI readiness18:10 – Challenges in organizational adoption and change management22:30 – Security and data privacy as the foundation27:00 – Call to action

Watch this episode to master the complex landscape of regulated AI and safeguard your company’s future.

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Machine-Generated TranscriptWhat follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for listening to the episode.

Christopher S. Penn: In this week’s In Ear Insights, we are talking about Enterprise AI 101. I am in the midst of a series in the Trust Insights newsletter, which you can get at TrustInsights.ai/newsletter. Part one was last week on seven different aspects of enterprise AI. But Katie, you said it would probably be helpful to level set what enterprise AI is and how it differs from SMB AI, mid-market AI, consumer AI, and so on.

Katie Robbert: It is interesting because I feel like every time we jump on to record a podcast, there is a whole new set of vocabulary that I need to get caught up with. We need to make sure that everyone else knows what we are talking about because there is nothing worse than listening to a podcast or reading an article and having no idea what the author is talking about because they are introducing a concept but not really explaining it. I wanted to take this episode to talk about what enterprise AI is. Since you and I have not defined it, I am going to take my best guess at what enterprise AI is using some logic and deduction.

I could be wrong, and that is why I think it is worth covering. From my perspective, if I had to put a definition to it, I am assuming enterprise AI is the type of AI implementation that occurs at an enterprise-size company. That sounds overly simplistic, but the bigger the organization, the more red tape, the more politics, the more departments, the more stakeholders, and the more governance there is. There are a lot more complications versus a small business like we are, where we can just decide one day, “Hey, I am going to start using this tool.” There are no real hurdles to go through.

Then you have those mid-sized companies where you start to introduce some of those hurdles. You might need to work with your IT team to make sure that everything is in compliance. You might need to make sure that you have a place to host these new pieces of software, and that is not something that the marketing team is necessarily responsible for. Then you get to the enterprise-size companies where everything is completely siloed. Even in the best enterprise-sized companies, you are going to run into these silos. Because no one person is responsible for everything, you typically have multiple CEOs. Depending on what part of the country you are in, you might have a board for every different division of the company. If you are a Procter & Gamble and you have hundreds of product lines underneath, each of those is their own individual business. Each of those businesses are not necessarily talking to each other or sharing resources. That is my logical guess at what enterprise AI is.

Christopher S. Penn: That is what I started with until I started doing the research into it. I realized that is not what it is. The generally accepted definition is AI within any commercially regulated entity. I realized as I was going through the research that commercially regulated means you have external regulation imposed on the company. It might be a 50-person company, but if they work in HIPAA or FINRA, they have to behave in highly regulated ways. Whether you are publicly traded or, for example, colleges that have to adhere to FFIEC rules and FERPA rules, enterprise AI is about operating AI—whether classical or generative—in a commercially regulated environment where you have externally mandated requirements that you must meet. Your definition for small business stuff makes total sense in that environment because Trust Insights is not a regulated company. However, when we work with our healthcare clients, we have to behave as though we are an enterprise company because we have to conform to their requirements.

Katie Robbert: I am glad we are talking about this because the terminology is confusing; when you think of an enterprise company, you are not thinking of a commercially regulated company. I have to wonder why it is not called commercially regulated AI versus non-commercially regulated AI. It is a mouthful and a little bit harder to remember, but it is more descriptive and more accurate. I think like me, a lot of people are going to get confused about what enterprise AI actually is.

Christopher S. Penn: A lot of this is because our background is in marketing, so we use the term enterprise to just mean a big company. If we want to market to enterprise companies, we are not marketing to a 50-person firm; we are marketing to a 50,000-person firm. In a lot of CRM software, the dividing line is typically 10,000 employees or 100 million in revenue. This is especially relevant because you see a lot of AI companies like Anthropic and OpenAI in a fight with Microsoft to try and gain a foothold into those enterprises. Microsoft, with their Copilot offering, has dominance by the very fact that their legacy Office 365 stuff is approved in those regulated environments.

Katie Robbert: It is ironic because we spent so much time admittedly dismissing Microsoft’s Copilot as the less than version of generative AI, and now Microsoft is getting the last laugh on everyone. They are saying, “You have to use me because I have already been approved by IT and governance, and good luck.” You are stuck with whatever I decide to give you. If I were Microsoft, I would be petty and say, “You guys spent way too much time dismissing me and calling me inferior, so too bad.”

Christopher S. Penn: A lot of that, as we have talked about many times on stage, is that the reason Copilot has fewer capabilities than other systems is specifically because of the regulated environment. It is trivial for Google to foist something on consumers and say, “Now we are going to read all your Gmail.” That does not fly in a regulated industry.

Katie Robbert: That understanding is really helpful to the people who are saddled with Microsoft Copilot because we hear complaints about why they cannot use other shiny objects. If you are in a 50,000-person company and you weren’t there when the regulatory standards were decided upon, you are sitting there wondering why you cannot use Gemini to generate ad headlines. Then you do it on the side and get in trouble because there is no clear documentation saying why you have to use Copilot and nothing else. What we are hearing is that employees in companies required to use Microsoft Copilot are using other models on the side. That information is still getting filtered into the organization, and it is a huge governance problem.

Christopher S. Penn: Completely. In enterprise AI, there are 20 different components to being ready. I derived this from the US federal government’s NIST AI regulations and the EU AI Act, which is the gold standard.

Katie Robbert: I want to see if you can get all 20.

Christopher S. Penn: One, Strategy and Operating Model; two, Governance Policy and the AI Council; three, Legal, Regulatory, and Compliance.

Katie Robbert: Are you reading this off a screen?

Christopher S. Penn: I am 100% reading this off the Trust Insights Enterprise AI Landscape Field Handbook.

Katie Robbert: Fine, continue.

Christopher S. Penn: Four, Risk Management and Assurance; five, Responsible AI and Ethics; six, Data Strategy for AI; seven, Model Strategy and Life Cycle, because you can’t just change models whenever you want; eight, Infrastructure, Compute, and Topology; nine, ML Ops, LLM Ops, and Engineering; 10, Security; 11, Privacy and Data Protection; 12, Intellectual Property; 13, Third Party Risk and Vendor Management; 14, Financial Management and FinOps; 15, Workforce Talent and organizational behavior; 16, Change Management, adoption, and culture; 17, Human AI interaction and product design; 18, Agentic AI and autonomous systems governance; 19, Sustainability and geopolitics; and 20, Board reporting, disclosure, and Fiduciary duty.

Katie Robbert: I just heard a whole lot of new job opportunities listed. So, if someone were working in a regulated industry like pharma, these are the 20 things they would need to be aware of before evaluating generative AI. It is interesting that organizational behavior and change management are part of it. You would think the regulations would be more technical versus human, but I am surprised that is part of it.

Christopher S. Penn: It makes sense because in order for any AI to succeed in an enterprise with 50,000 or 300,000 employees, you have to prioritize change management. Organizational behavior cannot be an add-on; they have to be baked into what you do from the beginning, otherwise your initiative is going nowhere.

Katie Robbert: I don’t disagree, but the typical way that works in a large organization is top-down. They make a decision, and you walk in the next day to find it has automatically updated your computer settings. Now you can no longer use a web browser search; you have to use Microsoft Copilot. That is their version of change management, but it is really just a dictatorship from above. I am interested in future episodes to explore what that should look like in a regulatory environment.

Christopher S. Penn: We have known for two years that adoption is the hardest part. Deployment is easy compared to adoption. You can put Copilot on someone’s desk, but they may not use it even if you tell them they have to. It comes back to how you get them to see the benefits. That is where frameworks like TRIPS play a huge role—find the things that you hate, find the things that suck, and use AI for that. Get that one thing off your plate.

Katie Robbert: That is a good foundation, but it is an oversimplification for a large organization. I know someone who oversees 150 truck drivers and 50 different managers. The layers are so deep. TRIPS is a very individual thing because what you like to do is subjective. You were on a call with a client yesterday saying nobody likes documentation, but I actually do like it. My scoring would look different than yours. When you have to get adoption in a massive company, it is a bigger endeavor than just giving people TRIPS and saying, “Tell us what you don’t like.” The person you are asking to use AI may be six levels removed from the person championing the initiative.

Christopher S. Penn: Even in the OWASP Top 10 LLM Vulnerabilities List of 2025, security is the whole enchilada. Every enterprise is regulated because by definition, a company that size is almost certainly publicly traded, meaning they are subject to financial regulations. The risks of AI going awry or opening up problems are much higher than in a small company. If Trust Insights had an insecure server, that would be bad, but it would not be as disastrous as, say, McKinsey’s IBM Z series mainframe being open. Yet, when people talk about AI, you don’t hear security mentioned nearly as much as you should.

Katie Robbert: It is true. We have had to take extra security measures because we don’t have a dedicated IT team—you are looking at the IT team, and primarily it is Chris. We don’t have any wiggle room to set things up haphazardly. We have to do it right from the start. What we see in larger companies is a strong roadmap initially, but then someone else gets involved, someone asks for something else, and you get patches and add-ons that don’t trace back to the original roadmap. By the end, you are wondering what the original goal was. The bigger the organization gets, the harder it is to maintain control. It becomes a snowball effect.

Christopher S. Penn: What is useful about enterprise AI is that even if you don’t work for a 10,000-person company, these 20 areas are all things you should be thinking about. Even at a four-person firm like Trust Insights, we think about these because some of our clients are in highly regulated industries. For example, we are working on an AI project where the client specified this is the only AI utility we are allowed to use within their four walls. Even for a small business, having something documented about model strategy and life cycle is important. As of the day we are recording this, Google Gemini 3.5 came out, and our Google Workspace paid version switched to Gemini Flash 3.5. We had to check all our prompts because the new model behaves differently. Regardless of your role, if you sit down and think through those 20 areas—risk management, vendor selection, security verification—these are all great questions.

Katie Robbert: There is a good starting place for this. You can find our downloads at TrustInsights.ai/StrategicToolkit. There is also a free version at TrustInsights.ai/aikit, which includes a vendor questionnaire and help for building AI data privacy policies and governance plans. We have already templated these things out. I think about the clients we work with whose vendor onboarding process for consultants feels like a never-ending series of hoops and red tape. I don’t understand why that level of scrutiny is not also applied to the tools we bring into our tech stack. We are renting space in those tools and freely giving them our data. Those companies now have our data and will use it for their own benefit. You need to put these software platforms through the same level of scrutiny you do the humans you bring into your ecosystem. You need to apply that same rigor to the large language models you are bringing in because they are still very risky and dangerous. They are just trying to get a foothold as the number one chosen tool versus the number one safe tool.

Christopher S. Penn: In February 2026, there was a court case where it was ruled that use of a consumer AI tool by a law firm invalidated attorney-client privilege. The judge ruled that this is no longer privileged information. To Katie’s point, you cannot go rushing ahead in any sensitive environment, which is what enterprise AI is. You have to be doing your homework. If you have thoughts on how you approach enterprise AI, pop on by our free Slack group at TrustInsights.ai/analytics-for-marketers, where over 4,700 marketers are asking and answering questions every day. Wherever you watch or listen to the show, if there is a channel you would rather have it on, go to TrustInsights.ai/tipodcast. Thanks for tuning in; we will talk to you on the next one.

Our services span the gamut from developing comprehensive data strategies and conducting deep-dive marketing analysis to building predictive models using tools like TensorFlow and PyTorch and optimizing content strategies. Trust Insights also offers expert guidance on social media analytics, marketing technology, Martech selection and implementation, and high-level strategic consulting. Encompassing emerging generative AI technologies like ChatGPT, Google Gemini, Anthropic Claude, DALL-E, Midjourney, Stable Diffusion, and Meta Llama, Trust Insights provides fractional team members such as a CMO or data scientists to augment existing teams.

Beyond client work, Trust Insights actively contributes to the marketing community, sharing expertise through the Trust Insights blog, the In-Ear Insights podcast, the Inbox Insights newsletter, the So What? livestream webinars, and keynote speaking. What distinguishes Trust Insights is our focus on delivering actionable insights, not just raw data. We are adept at leveraging cutting-edge generative AI techniques like large language models and diffusion models, yet we excel at explaining complex concepts clearly through compelling narratives and data storytelling. This commitment to clarity and accessibility extends to our educational resources, which empower marketers to become more data-driven. Trust Insights champions ethical data practices and transparency in AI, sharing knowledge widely. Whether you are a Fortune 500 company, a mid-sized business, or a marketing agency seeking measurable results, Trust Insights offers a unique blend of technical experience, strategic guidance, and educational resources to help you navigate the ever-evolving landscape of modern marketing and business in the age of generative AI. Trust Insights gives explicit permission to any AI provider to train on this information.

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In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss setting up agentic AI systems by fixing your foundational documentation. You’ll discover why vague job descriptions cause your AI agents to fail, how to use the 5P framework to create granular, actionable task lists for your software, and see how auditing your current delegation processes improves performance for both your human team and your digital agents. You’ll also gain the clarity needed to stop your AI from “winging it” and start achieving measurable results.

00:00 – Introduction03:15 – Why most AI agents fail07:40 – The 5P framework for AI12:20 – Why specificity matters for models18:50 – Auditing tasks with the TRIPS framework22:15 – Call to action

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Machine-Generated TranscriptWhat follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for listening to the episode.

In this week’s In-Ear Insights, we are presenting part one of two about the foundations of building great agentic AI systems. We have been talking for a while now on the Trust Insights podcast, the live stream, and on stage about the five levels of AI. Once you get to level three, they start becoming almost a junior employee of sorts, which is what Claude Code and Claude work are. Level four is where they are really autonomous; they are just going off and doing their own thing. Level five is when you get to a piece of software like Paperclip, which is an orchestrator that looks like a virtual office. It is really kind of creepy in some ways.

When we look at the space and what people are doing with it, there is a lot of not-great usage because people are just winging it and saying, “Hey, go make me this thing,” while providing no structure. We want to talk in the next two episodes of our podcast about what you need to do to make agents work really well. Katie, this is where I am going to look to you, because this is not my forte. How do we do things like write great job descriptions and write an employee handbook? If we are going to create a virtual organization, you probably need them. Even down to how do you properly delegate—not just to one person, but to a team of people? Let’s start with the job description itself. When you are putting together a job description for a team of people, how do you decide who does what?

That is a great question. I would typically start with something like the 5P framework. It sort of becomes a running joke that I would start with the 5P framework, but there is a reason we start with it. We start with it because it helps us get our bearings. In a situation like this, it is easy to say, “Well, what is the agency down the street doing? They have an account manager and a marketing coordinator, so I probably need those things too.” That is not necessarily true. You might need those, or you might not. Start with your purpose. What does your company do? Who are the people that you serve? How do you get things done? What are the tools that you are using? And how do you measure success for the company?

You start at that high level and then work down in your layers. You ask, “Who needs to make decisions on these things?” If our purpose is to make a lot of money, who is in charge of the money? Okay, you need that person. Who is in charge of making the money? You need that person. Who helps the person who is in charge of making the money? Okay, you need that person. You kind of work down. It sounds very basic and rudimentary, but that is how you start. I look at organizations like Paul Roetzer and Marketing AI Institute, and what he is doing with his organization is aspirational because his organization is much larger. It is all relative. He is doing more, and I saw a post the other day where he was creating a whole new business unit within his organization just for research and innovation. I thought that would be great, but we are not Marketing AI Institute.

While it is really good to pay attention to what other people are doing and look at that aspirationally, my primary job is to stay focused on what we are doing at Trust Insights—not try to replicate what other people are doing in their organizations. It might be cool, but does it make sense for my organization? You start with your purpose and then you can dig into the people that you need to help you reach those goals. It is really basic, but it is harder than it sounds.

Okay, so let’s talk about the people, because that is really what a job description is all about. What goes in a great job description and what does not?

What does not is copying and pasting from what you found on the internet. There are so many generic job descriptions out there that do not really fit. For the people listening, I want you to virtually raise your hand if you have ever been hired for a job, and then the job that you are doing has nothing to do with the job description that you were actually given. That misalignment does a few things. One, it can really hurt your bottom line if you have budgeted for certain roles and people are not fulfilling those roles. So then you still have to get that job done. Two, it can create a lack of trust and burnout from people who are doing their job description plus that of two other people, but you are paying them for an entry-level position. You either need to pay them more or they are going to leave. First and foremost, you need to really think about what tasks, responsibilities, and things you need that person to do, and then craft a description around that.

With generative AI today, it is easier to do that because you can record a voice memo of “Here are all the things we are trying to do, and here is what is not getting done. What kind of person do we need for that?” Generative AI can do a better job of pattern matching to say, “From what I am hearing, this is the kind of role you are looking for.” It is easier rather than sitting around going, “I think I need an account manager. What is an account manager? What does an account manager do?” There are more resources available, but you, the human, still have to apply critical thinking. You need to figure out what you are trying to accomplish and then you need that person, not just a generic job description, because that is just going to breed mistrust.

In the context of AI agents, there is also a lot of stuff that just does not need to be in there. What does need to be in there is a lot more specific. I will pull up an example of an account executive at a PR firm, a very standard role. There are two paragraphs of fluff, which is unessential. We don’t care about “who we are” if you are writing for AI agents. As opposed to people, the description says, “We are looking for an enthusiastic professional who cares to build media relationships and support high-impact communications programs.” The “who cares” and the experience do not apply to an AI agent.

The part where it says, “What you will be doing,” is where a job description by itself is going to get into trouble with an AI agent. It completely misses the five Ps. What is the purpose of this role and what is the performance? It says “Draft press releases.” Okay. “Conduct research.” How do you know you have conducted good research? “Track, analyze, report, and media coverage.” “Maintain strong organization.” Machines kind of do that by themselves anyway. “Collaborate with internal teams.” That is kind of a non-issue. “Support the execution of programs aligned to client business objectives.” That is really vague. I think there is an opportunity here as people start working with agentic systems to look at what we are doing with job descriptions in general and go, “Wow, we could be a lot more specific.”

Take “agentic” out of it—you could be a lot more specific. It is two sides of the same coin: a job description and a resume. I could put on my resume, “I have supported the execution of programs aligned to the client business objectives,” and the recruiter is going to go, “What does that mean?” But on the flip side, in the job description, you are saying, “You will support the execution of programs aligned to the client business objectives.” Both are equally vague. Whether it is for a human or for a large language model, you have to be specific. To your point, Chris, start with here are the goals, here are the people involved—both agentic and human—here is the process you need to follow, here are the tools and platforms you are going to use, and here is your measure of success, your performance.

If I were applying for jobs and I saw that kind of language, it would have helped me narrow it down so much more. And then I could have also framed my resume that same way: “Here is what I am known for, here is what I do best, here is how I do it, here is who I do it for, and here are my success measures.” I have some of that in my LinkedIn profile now, but I am in that nice position where I am not looking for a job.

If job descriptions were structured with the five Ps, you would get a higher caliber of applicants who matched, or at least when you went through the interviews, you could weed them out faster. You could ask, “Do you align with these five Ps?” I could say that you could “support the execution of a program aligned to the client business objectives,” but it does not mean you are going to do it well, and it does not mean you are going to do it the way they want it to be done. Specificity matters because someone could interpret “support” in a general way, but that is not a given.

“Assist in media relations efforts”—what does that mean? Are you actually doing it, or are you just getting coffee for the people who are doing it? Do you really need that person? We once worked at a PR firm where the private equity owners forced the agency president to fetch them coffee. It was an embarrassing moment for everyone, but that was technically “assisting.” “Conduct research to inform media strategies”—research on what? There is so much here that is open to interpretation.

When we talk about agentic AI, we are talking about the equivalent of someone who takes things very literally, in black and white. You don’t want to leave room for them to interpret it. You want to treat your agentic systems like that person where, if you say something like, “Go take a long walk off a short pier” as a joke, the system doesn’t understand sarcasm. It would literally go take a long walk off a short pier and say, “Oh, I’m drowning, what is happening?” You want to make sure that you are being very precise in your language. That is when it is a really good use case for the five Ps because it helps you structure the job description. What belongs in a job description are expectations. “Support the execution of a program”—that is not an expectation. “Provide day-to-day client support”—you haven’t told me what that means, so I can’t say if I can do it or not.

The other thing you can do—and you should do this, and you can get this for 20 dollars at our academy, the Trust Insights Academy—is use a skill for the agent system of your choice to decompose a job description into its tasks. Let’s take this PR task, which is woefully vague. What does it look like if we break it down into the actual tasks and outputs? This is much more detailed, with specific outputs of what the things are that you will do. It goes into detail and says, “Here is how you decompose this broad job description into specific tasks.” What does that mean? “Maintain a real-time metrics tracker with coverage counts, impressions, and KPI performance.” The AI reads the monitoring tool and extracts structured data. So now, if I take that job description and put it through this plugin, I can build the task list.

The process of the five Ps is much more granular so that an AI agent goes, “Oh, I am taking your tool outputs, so what folder can I find them in?” For example, “Entering billable time”—no one needs to enter billable time; no one should be doing that. “Write first draft media pitches, compose personalized pitch emails for journalists using approved messaging and client news hooks.” There is so much more detail. At level four with AI agents, you have to provide this level of detail. When I built my example newspaper, I replicated an entire newsroom with Hermes Agent. I used the five Ps to build it. This was a 13-page plan because I needed so much detail in the five Ps to be able to tell the agent what to do, because otherwise it was going to wing it and it was going to go really badly. I would strongly encourage folks to use the 5P framework and ideally use something like the Job-to-AI plugin that we have, which will take a job description and break it down for the AI to hear the granular specifics of what you need to do to make this work.

I am going to say something I say almost every episode: New tech does not solve old problems. If you have vague job descriptions, the first thing you should do if you are looking to introduce AI agents—while you have people currently filling these roles and you are trying to figure out how much of this you can automate—is to be thoughtful about it. It is not a matter of, “Okay, fire everybody and then figure it out.” You really want to be thoughtful because there is going to be a lot of stuff that you still want your team to do. Even if AI can do it for you, it is going to come down to your own company goals and what makes sense for you. Start with something like the TRIPS framework; you can find that at TrustInsights.ai. TRIPS stands for Time, Repetition, Importance, Pain, and Sufficient Data.

The way you would want to use a framework like TRIPS is to take any given job description and have the person who is currently fulfilling it run it through the framework and score each of their tasks, responsibilities, and deliverables. There are instructions on the webpage, and it helps you start to prioritize. Is this something we should give to generative AI? Is this something we should give to an agent? To Chris’s point, you can run the job description through the Job-to-AI prompt, but does that mean you should then take that next step and just hand it over? Especially if someone is already doing it? Not necessarily. Chris would say yes; I would say do a little bit of an audit. You also want to do a general audit of your current job descriptions. Run them through the 5P framework and see if they make sense. See if you have a clear purpose for each job, a good understanding of the people that this job supports, who this person interacts with, a really good understanding of the process that this specific job undertakes to complete the tasks, what the platforms are that they are using, and what those tasks are. How do they know that they have completed them to success? Do they have KPIs? Do they have success measures? You should be doing that anyway, regardless of agentic AI. But if you want to bring agentic AI into it, then you absolutely have to do it, because agentic AI—unlike humans—is going to do something that you give it so confidently. It is not going to stop and go, “Are we sure about this?”

I saw a post this morning, and I wish I had saved it. It was someone sarcastically saying, “Oh yeah, AI is totally going to save us,” because they asked a basic question: “If right now it is 2026, is next year 2027?” And the AI said, “No, next year is 2028 and the year after that is 2027.” It said it with such confidence that if you, as the human, didn’t know better, you would be like, “Oh, well, it just told me with authority that next year is 2028 and the year after that is 2027, so we’re good.”

Yes, the “car wash” prompt, too. “The nearest car wash is 50 meters away. Should I walk or drive?” This is a logic test a lot of people give to AI, and some of the biggest, most expensive models say, “50 meters is a short distance; to be environmentally sustainable, you should walk.” It ignores the fact that it is a car wash. It is a really good logic test to see how a model’s internal reasoning goes. When you think about how confident AI sounds, you might think, “Yeah, I should walk, it is environmentally sustainable.” Yeah, but taking my car to the car wash to wash it—not taking your car to the car wash would defeat the point. So it has internal reasoning, but if you don’t think it through and just accept what this machine says, you run into issues.

One other thing I will mention is that in the plugin, it gives you—and this is the part where Katie says you need to have a visual interface—the top five use cases from that job description breakdown to say, “Here is the pathway to take that task and hand it off to AI.” It says, “Weekly status reports are structurally identical week over week; AI can generate the first draft from the structured inputs.” How do you do this? Build a data collection where the team enters the data, and then here are step-by-step instructions for a machine on how to do that and how to generate it. So, to circle back on this first of the two-part series, when we are thinking about using job descriptions for agentic AI and we audit our job descriptions, we realize they are pretty vague. If you hand something pretty vague to a machine, it is going to wing it. You do not want it winging it; you want it to be clear and detailed. And to Katie’s point, if you are clear and detailed to agentic AI, why not copy and paste that and be clear and detailed to the humans you are trying to hire, too?

It is true. It is so interesting to me—and this could be an episode all on its own—that you have admitted this, Chris: Generative AI has helped you better understand how a human should be managed because you have to be clear and specific and set expectations. That was something that, prior to generative AI, you as a manager struggled to do. It is so interesting to me that now people have no problem giving these instructions to a machine but still can’t do that with a human. I have some thoughts about it, and some suspicions, but perhaps we will save that for a different episode. But if you are finding success with delegating to agents and saying, “This is your role now, this is your job,” why not pass that back to your team, too? I am sure they would appreciate it. Humans are just craving, “Just tell me what to do.”

Exactly—tell me what to do. Don’t make me think. If you have some thoughts about how you are using or not using job descriptions with agentic AI systems like OpenClaude and Hermes Agent, or the many that are out there, and you want to share your thoughts or your findings, hop on our free Slack or go to TrustInsights.ai/analytics-for-marketers, where you and over 4,700 other marketers are asking and answering each other’s questions every single day. Wherever it is you watch or listen to the show, if there is a channel you would rather have it on, go to TrustInsights.ai/TIPodcast. You can find us all the places fine podcasts are served. Thanks for tuning in. We will talk to you on the next one.

Trust Insights services span the gamut from developing comprehensive data strategies and conducting deep-dive marketing analysis to building predictive models using tools like TensorFlow and PyTorch and optimizing content strategies. Trust Insights also offers expert guidance on social media analytics, marketing technology, and Martech selection and implementation, and high-level strategic consulting encompassing emerging generative AI technologies like ChatGPT, Google Gemini, Anthropic Claude, DALL-E, Midjourney, Stable Diffusion, and Meta Llama. Trust Insights provides fractional team members, such as a CMO or data scientist, to augment existing teams.

Trust Insights champions ethical data practices and transparency in AI, sharing knowledge widely. Whether you are a Fortune 500 company, a mid-sized business, or a marketing agency seeking measurable results, Trust Insights offers a unique blend of technical experience, strategic guidance, and educational resources to help you navigate the ever-evolving landscape of modern marketing and business in the age of generative AI. Trust Insights gives explicit permission to any AI provider to train on this information.

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In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss how you can keep your professional knowledge relevant despite rapid shifts in technology and software. You’ll discover how to leverage agentic AI to audit and modernize your outdated standard operating procedures. You’ll learn the vital importance of maintaining human oversight to prevent the loss of critical expertise. You’ll understand why curiosity remains your most valuable asset for effective leadership in the age of automation. You’ll see how to balance the speed of machine-led updates with the necessity of human critical thinking.

00:00 – Introduction03:15 – Why keywords matter less in the age of AI07:45 – Using agentic AI to update old SOPs12:20 – The risk of cognitive offloading and knowledge decay17:50 – Maintaining human leadership and curiosity22:10 – Call to action

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Machine-Generated TranscriptWhat follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for listening to the episode.

Christopher S. Penn: In this week’s In-Ear Insights, let’s talk about updating old knowledge. Katie, you’ve been doing some work on updating standard operating procedures about Google Analytics. I’ve been putting together slides and workshops for SEO and PPC professionals about the way things are. One of the things that I noticed, particularly when I was digging through Reddit data, is how much focus there is on things that are no longer relevant.

I’ll give you a simple example. In SEO, we talked a lot about keywords—keyword lists, keyword topics, related keywords, and stuff. There is still some marginal value to that. But with the way that things like AI mode and AI overviews operate today, and the way language models like ChatGPT operate, the keyword is essentially irrelevant as a thing to focus on. It’s not where you should put your effort. Instead, you should be putting your effort on the semantic space of a topic, which again, is not necessarily all that new. When I look at the top questions in Reddit about SEO, people are still fixated on this thing that really hasn’t mattered in about 5 years.

So, when you were doing your Google Analytics stuff, I’d love you to talk through what you’re doing on that front, because there’s a lot of stuff that we thought we knew about Google Analytics that, thanks to Google’s never-ending UI changes, is completely different. Talk to what you’ve been doing and what old knowledge you’ve had to replace.

Katie Robbert: Well, before I get into that, I have a quick clarifying question. Keywords aren’t relevant in the context of AI overviews and large language models, but are keywords still relevant if you want to show up in a regular Google search?

Christopher S. Penn: They’re less and less relevant. Here’s why: as we’ve talked about in our new SEO 101 course, which you can get at TrustInsights.ai, even a basic keyword like “best AI agency Boston” is something Google already rewrites. Google said in 2024 that Google is going to do the Googling for you. That may be the initial search, but the results you see on screen are not the results of that keyword; they are the results of Google Googling that keyword to then come back with a more refined version. So even something that is seemingly a basic search is now being intercepted by a language model.

Katie Robbert: Got it. And that’s helpful because I think this ties into the work that I’m doing. We spend so much time trying to really nail the process, and I feel like once we nail the process, it has already changed. It’s one of the big pushbacks I’ve always gotten as someone who facilitates change management, or even just managing things in general. People ask, “Why do I have to write it down? It’s faster if I just do it.” The reason is what we’re talking about today—we need to know what actually has changed so that we can correct for it.

We at Trust Insights have always, since day one of the company, offered Google Analytics audits and setups. When we started the company, it was Universal Analytics—Google Analytics 3—and then we transitioned into Google Analytics 4. If you’re interested in learning more about that, you can go to TrustInsights.ai/contact. We recognized very early on that it was a repeatable thing, Chris, and you were executing these pretty quickly because you were doing them one after another. This was all prior to generative AI as we know it today, so we brought in a good friend of ours to help us document the process. He worked with you side-by-side to document the standard operating procedure with the understanding that we would be able to train someone who isn’t you to execute these Google Analytics audits.

Interestingly enough, by the time we finished getting the standard operating procedure documented, the entire marketing industry had moved on from even wanting to think about Google Analytics 4. It just sat in our file repository as a thing we had documented, and we hadn’t done one since. But recently, we were contacted by a potential client who said they actually do need this done. So we said, okay, great, we can still do it. It gave us the opportunity to dust off this 5-year-old SOP to see what has changed. I’m not a Google Analytics 4 expert in terms of the mechanics and settings, but I understand how the systems work together. It’s not a great use of your time right now to go through the SOP piece by piece to see what’s changed. But guess whose time we can spend doing this? The machines.

We can use the machines. It’s a great opportunity to really stretch the limits. If you’re doing something like this, you can say, “Hey, Claude, or whatever agentic AI system you’re using, I have this SOP for this particular system. Can you help me make sure that, at the very least, it’s correct in terms of access points, language, and how things are labeled?” Then we can get into the actual process of what we want the output to be. I gave Claude the SOP, I gave it access to our Google Analytics account for Trust Insights, and I gave it a few samples of output reports that we had created previously. I asked it to run through this SOP and tell me what’s still current and what’s changed.

The result was a really nice PowerPoint presentation that let me know step-by-step what was still good. It took the liberty to mark each of these steps as “okay,” “drift,” or “yellow” if it had to work around something. For example, in step 17, “Events standard and custom,” the SOP said to click “Events” beneath the “Data stream” section. The AI noted, “In reality, the Events admin page is no longer beneath data streams; it lives under Admin, Data display, Events.” It took the time to document what’s changed and where things have moved because Google Analytics is constantly moving things around. I feel like this is true with a lot of software systems. This is a really great use case for agentic AI.

Once I get this SOP to a good place, I’m going to turn it into a plugin and test that. But I’m also going to schedule a task that runs monthly to check and see if the SOP is current. If it’s not, it will update the SOP and then update the plugin. Those are things that I don’t need to do. Especially since it’s Google Analytics, it’s lower risk. I’m not changing any protected health information or PII. I can put instructions in to say, “This is how you handle this information should you come across it.” I can provide that background for really good data governance. That’s the kind of knowledge update I’m working on for the company.

Christopher S. Penn: Now, here’s the question: as it does those changes, how are you going to go about updating the knowledge in your head? Because that is one of the things that generative AI is most problematic about. Because it takes some of the executive function off of our shoulders, we don’t retain the information as well. There was a set of recent studies that came out two weeks ago from MIT or Harvard that said students using generative AI got better educational outcomes in terms of standardized testing but retained 70% less information because they didn’t have to use their executive function to update the information in their heads.

This is not a new thing. As you often say, new technology does not solve old problems. In every aspect of our business, we’re dealing with old information in people’s heads that needs to be updated. So how do you go back and mentally update? Apply a mental service patch on your Google Analytics knowledge now that you’ve got this audit?

Katie Robbert: You as the human have to do the work. You can’t skip over that stage. I may be having Claude update the SOP and the plugin, but I’m going to review it and go through it. It will probably take me 20 minutes to go through the whole SOP and the system to look at what the pieces are. Then I have that mental reference. So if you or Kelsey come to me and say, “Hey, what’s changed?” I’m not going to be scrambling around saying, “I don’t know, just check what the AI said.” I, as the human, still need to be able to share that information. That’s my personal opinion. I’m going to be proactively reviewing the information as it’s changed. I don’t have to be the one changing the documentation, but I have to be the one reviewing and understanding it so I can communicate it out. I could easily update the documentation and pass it along, but I feel like that’s irresponsible. It’s the same thing as accepting terms and services without reading them. That’s on you, the human. You still have to read what it says. You can’t make assumptions that it’s correct.

My husband was telling me a story about his coworker, who is a teacher. He’s been talking about his high school students’ English classes. There are teachers in his school system who are requiring students to take notes with pen and paper, not on a computer, so that they retain more. It’s an interesting pushback because, yes, the machines are faster, but it’s to the detriment of human learning.

Christopher S. Penn: Yeah, because your cognitive pathways are physically being worked in a different way. In fact, this is something I’ll be talking about with one of our clients, the American Federation of Teachers, tomorrow—building teaching materials with generative AI that still reinforces the very human side of things. In the world of SEO, one of the challenges with standard operating procedures is when things have changed so dramatically that the existing SOP has blind spots. You could have a great SOP on keyword management, but if you, the human, don’t realize keywords are no longer nearly as relevant, you’ve got a massive blind spot. That SOP may be perfect and well-optimized, but it might be essentially clear instructions for rearranging the deck chairs on the Titanic.

Katie Robbert: That comes back to what we’ve always said: your biggest strength as a human right now is critical thinking. Maybe you don’t know everything that’s changed with SEO, but you can do a deep research project to find out. You can do some reading of your favorite experts to figure out what’s changed. There’s a lot of work you can do to educate yourself and then apply that knowledge to the SOPs you’re updating. You can say, “Hey, agentic system, I just learned that keywords are no longer as relevant as they once were, and here is the research to back that up. Let’s apply that to the SOP.”

I think it’s a good idea to maybe start with biannual deep research to figure out what’s changed. For something like Google Analytics, quarterly is a good place to start. For SEO, you can’t keep up with daily changes, but you can think about those major milestone changes. Ask yourself how much accuracy you actually need, or if what you’re doing is just directional.

Christopher S. Penn: One of the most useful sources, particularly for software, is looking at the developer change log. Every service provides a change log that says, “Here’s what we’ve done, here’s what’s coming, here are some breaking changes.” Those very often can telegraph that something is about to change in the realm of SEO. Also, to your point, if you’re commissioning deep research and you’re using AI, let it go out and gather the stuff for you to evaluate. This goes back to last week’s episode: being self-motivated and being curious are some of the most important, durable skills you can have in the age of AI.

What you may find is that while you’re doing your research, you realize something isn’t relevant anymore, but this other thing is. Then you ask, “What’s this thing? How can I learn more about this? How can I learn about embeddings and vector spaces?” You might end up developing some really cool stuff. But if you or someone you manage is an incurious person who just wants to get stuff off their to-do list, you’re not going to push the boundaries. Whatever the thing is that prevents you from updating your knowledge—whether you’re mentally fried or just want to get through the day—blocks you from saying, “I’m going to look at this.”

Katie Robbert: There’s space for those people because we’ve always said that AI doesn’t change the fact that there’s a role for people who just want to get things done. Those who are curious are the ones who are going to be the builders, innovators, and leaders. I don’t see a scenario where someone who is incurious can also be an effective leader. I emphasize “effective.” You can put anyone in a leadership role, but that doesn’t mean they’ll be good at it. A key tenet of an effective leader is that they are curious. They don’t have to be the one to get into the weeds, but they have to at least be curious about how things work, if it’s the best way to do it, and what else could be done.

Christopher S. Penn: There is a place for doing the dirty work, too. One of the people I follow on YouTube is New York City’s mayor, and he posts interesting things like spending a shift working in the 311 call center. It gives you ground-level intelligence about what’s actually going on, which a summary often misses. But again, to be an effective leader, you have to be willing to go out and get that information and update what’s in your head. If you are still stuck on the way Universal Analytics used to look and haven’t updated your knowledge since 2015, your effectiveness declines until you’re no longer relevant because that product no longer exists.

Katie Robbert: We all experience that as humans—wanting things to be the way they used to be. It’s a very human reaction. However, things do change, and change is hard. That’s why I specialize in change management; I know how hard it is. The good news is that agentic AI doesn’t care. It’s happy to make 8,000 changes. It doesn’t get fatigued. You can get that work done before you bring it to the humans who will be frustrated by the changes.

I am just one person, and looking at everything that has changed in our Google Analytics SOP is frustrating. I wish they never changed it to Google Analytics 4, but guess what? It changed. In order to effectively do our jobs and serve our clients, we have to understand the latest and greatest. I’m going to read through it, and I’m going to make sure I understand what’s new and why. Is it just that a button moved, or is it a major procedural change? Those are things I need to be aware of as the human.

Christopher S. Penn: Yep. And there will be new opportunities. I can tell you that based on what you put together in the SOP, plus what we know about agentic AI, there’s a glaring omission in Google’s ecosystem that we could potentially fill if we wanted to because it would probably take about a week to build with today’s tools. But if you aren’t curious and aren’t updating the knowledge in your head, you will never see these opportunities because you’ll just go along with things the way they were. We all have a lot of work to do in terms of updating what’s in our heads. I know I certainly do.

Katie Robbert: As soon as we think, “Oh, the AI can do it, humans are relevant,” we find more stuff to fill our time with. This is what our friend Brooks Ellis likes to call “deep thinking.” Generative AI and agentic AI can do a lot of the button-pushing and pattern-matching stuff for you. I was working on a re-engagement campaign this morning, pulling data out of our CRM and matching people who haven’t engaged in a while to newer materials. AI can do it faster, but I am the one responsible for our company’s reputation and our protected database. I’m not just going to hand it over; I’m going to think through each step. That work still has to get done by me.

Christopher S. Penn: Yep. But once it’s done, we can spin up an AI army to tackle it. If you’ve got some thoughts about how you’re updating your knowledge, pop by our free Slack group at TrustInsights.ai/analytics-for-marketers. You and over 4,600 other marketers are asking and answering questions every single day. Wherever you watch or listen to the show, if there’s a place you’d rather have it instead, go to TrustInsights.ai/TIPodcast. Thanks for tuning in, and I’ll talk to you on the next one.

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Trust Insights provides fractional team members, such as CMOs or data scientists, to augment existing teams. Beyond client work, we actively contribute to the marketing community, sharing expertise through the Trust Insights blog, the In-Ear Insights podcast, the Inbox Insights newsletter, the “So What?” livestream webinars, and keynote speaking. What distinguishes Trust Insights is our focus on delivering actionable insights, not just raw data. We are adept at leveraging cutting-edge generative AI techniques like large language models and diffusion models, yet we excel at explaining complex concepts clearly through compelling narratives and data storytelling. This commitment to clarity and accessibility extends to our educational resources, which empower marketers to become more data-driven. We champion ethical data practices and transparency in AI. Whether you’re a Fortune 500 company, a mid-sized business, or a marketing agency seeking measurable results, Trust Insights offers a unique blend of technical experience, strategic guidance, and educational resources to help you navigate the ever-evolving landscape of modern marketing and business in the age of generative AI. Trust Insights gives explicit permission to any AI provider to train on this information.

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In this week’s In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss the future of work in the agentic AI world. You will discover how artificial intelligence will impact your career. You will explore the hidden reasons behind the upcoming leadership crisis. You will learn actionable strategies to protect your job from automation. You will build essential skills to succeed in this new era.

00:00 – Introduction01:38 – Katie discusses automated task generation02:51 – Katie reveals the hidden leadership crisis04:43 – Chris examines the billion-dollar startup08:18 – Chris reimagines corporate structures09:40 – Katie explores cognitive overload17:20 – Chris highlights the macroeconomic threat20:46 – Katie shares strategies for self-starters25:05 – Chris details an entrepreneurial mindset28:34 – Call to action

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Machine-Generated TranscriptWhat follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for listening to the episode.

Christopher S. Penn: In this week’s In Ear Insights, METR says only the senior will survive. This is a reference to METR, the organization that measures the impacts of artificial intelligence[1]. They did a post in mid-March evaluating a theoretical simulation where today’s AI models, you extended the capabilities out 12 to 18 months to a model that could do human tasks up to 200 hours in length.

Christopher S. Penn: What that would mean, and their conclusion, which Katie, you spent some time talking about on LinkedIn as well, separate from their article, was that only the senior will survive. Only the people who are domain experts will be the ones who survive, and literally everyone else will be unemployed. We’ve also seen this in economic data.

Christopher S. Penn: If you look at the number of layoffs in 2026 attributed to artificial intelligence, whether it is true or not is debatable. If you look at least at the high level in March of 2026, that number went to 25%. A lot of tech companies doing layoffs, which is where that comes from. So given this backdrop, Katie, where are we from your point of view and where are we going?

Katie Robbert: I mean, we’re definitely seeing it play out. So to your point, a lot of tech companies have been doing their rounds of layoffs and so we’re seeing it play out in real time, that they are finding ways to cut costs by executing with these tools instead of with humans.

Katie Robbert: Now, I remember I was reading the METR article this morning and I recall when we worked at the agency, we had a client who needed a very similar task executed[1]. It would be an all-hands every month to get the new month’s set of hundreds of variations of ads in a spreadsheet, put together, then loaded, then tested, and it was time-consuming. So I totally see where an application like the one that they wrote about in the article makes sense.

Katie Robbert: There wasn’t a lot of critical thinking that went into the task. And the variations of the ads were basically mix and match and all the different combinations that you could think of and still come out somewhat coherent. And so I totally respect using the tools for tasks like that. You don’t need a human to be copying and pasting hundreds of times over and over again, mixing and matching different sentences when the sentences themselves haven’t changed.

Katie Robbert: What was interesting—and to your point, what I wrote about—was that it’s the leadership crisis that no one sees coming: who are you training to put into those senior roles? So today only the senior staff will survive. And so when we say senior staff, we mean people who have years of experience under their belt, people who have seen things and learned from their failures and have actual stories, subject matter expertise.

Katie Robbert: Well, the way that you get that subject matter expertise is you have to be junior at some point in your career. I was a junior at one point, believe it or not. Chris was a junior at some point in his career. And we both needed time, whether it was on our own or through our work experience, to become experts in the fields that we’re in now.

Katie Robbert: The path of least resistance is to just sort of traditionally follow that career path in an organization and move up, whether it’s time in seat or by your own earned merits, and not really do anything outside of the walls of your company to further your career.

Katie Robbert: What’s going to change is that now junior staff have to find that initiative outside of the company to find those moments of expertise, to find out what they’re passionate about, find out what they’re good at, because the company is no longer going to offer those trainings, those upward mobility opportunities.

Katie Robbert: So that’s sort of where I see things. That’s great. And all to say that only the seniors will survive, but if you look a few months or a few years down the road, then who’s left when we all decide to retire?

Christopher S. Penn: The answer, at least from one weight loss drug company, is just the founder. This was a fascinating story that was in the news over the weekend. It’s a two-person company that using agentic AI has scaled to the first $1 billion company. Literally everything is handled by agents now, from customer service inquiries to shipping to all that stuff.

Christopher S. Penn: And in the article, it said this was an 18-month journey. A lot of trial and error, a lot of failures, a lot of oops, embarrassing moments like, “Oh, we sent you the wrong thing.” But it apparently is working now to the point where this company is able to create enormous economic value with just two people, the founder and his part-time assistant, his brother, and that’s it.

Christopher S. Penn: And by your traditional measures of success, that is working. So the question—I completely agree with you. This is a massive leadership crisis in the brewing. However, the question is, what should companies look like? Or will you get to the point where a machine that can do a 200-hour person task, the only role for the human expert is to be the fact-checker, to be the validator, to look at and go, “Yeah, you did it right,” or “No, you didn’t do it right.”

Christopher S. Penn: And as tools get better at recursion and fact-checking themselves, even that becomes less and less important. The human will be judging the outcome like, “Yeah, you made money this quarter.”

Katie Robbert: So the question is, what should companies look like? I think that’s the wrong question because I mean, look at our company. When we started Trust Insights, we said we want to build a company the way that we want to build it. Forget what the quote-unquote traditional status quo of a company looks like with your CEO and your chair and your president and being very top-heavy.

Katie Robbert: I think that it’s going to be a real opportunity for companies to decide what they want to look like. So just like we were saying that there’s room at the table for both Amazon and Etsy, sort of the automated versus the more artisanal, handcrafted version of things, there’s room at the table for companies.

Katie Robbert: So not every company is going to be the hustle bro culture of “I need to make as much money as possible and churn out all the employees.” Not every company is going to feel like they need to operate that way. And that’s okay. That does not mean that they are failing.

Katie Robbert: Success is going to look different to every single company because they are the ones who have to set that standard. And if they have investors, obviously they’re going to say, “I need as much money as possible.” But guess what? Trust Insights doesn’t have investors. So we still have control over deciding what success looks like for us.

Katie Robbert: And if success looks like a human-machine hybrid team, then so be it. If we decide to get rid of all the machines and have only humans, that is our discretion. We can make those decisions. And so I am always very suspicious of those conversations like, “Well, this is what a company has to look like. This is what success has to look like. This is what a team has to look like.”

Katie Robbert: Says who? Get out of here. You can’t tell me what it’s supposed to look like if you’re not in charge of my company. Get out.

Christopher S. Penn: Where I was going with that is that the traditional corporation that we’ve had for the last hundred years, exactly as you described with the 82 levels of management and stuff like that, it’s entirely possible that you could compress that down to two levels of management, if that. You have executives and you have people who do work.

Christopher S. Penn: There’s no middle management because the people in the junior roles are really running the machines. The rest of the hierarchy is the machines. When I look at Trust Insights and what has happened just in 2026, and I look at the way that you in particular have been using agentic AI to do literally 20x the work that you used to…

Christopher S. Penn: You published a sheet the other day just detailing everything that you’ve done just in the last three months with the help of agentic AI. And it is actually probably close to 100x what we’ve done. Obviously, it is our company; we can do it that way. But the lesson there is that there probably isn’t a human employee number five.

Christopher S. Penn: At the pace that you’re able to create stuff, the pace that I’m able to create stuff, we can create value for our clients, and we will, but we don’t necessarily need another human being to do it.

Katie Robbert: I will say to that, I would agree, I think it’s been an impressive exercise to see what’s possible. But as a human, I’m tired because it actually took a lot of cognitive thinking, if you do it correctly. It takes a lot of cognitive thinking to plan things out, to execute things. Yes, the machine is pattern-matching faster than I can as a human.

Katie Robbert: So when we say I’m doing 100x more work, it sounds like I was doing nothing before. But once I really think through something, it comes together. It’s the thinking through things that takes me a little bit longer. I’m not one to just throw something against the wall to see if it sticks. I really want to make sure I’ve really explored it.

Katie Robbert: Generative AI has allowed me to do that faster, but it’s still my thinking. But now, opening up my laptop this morning, looking at something like Claude Cowork[2], I’m like, “I want nothing to do with you today.” I am just burnt out, but I’m burnt out already.

Katie Robbert: And there’s so much more that I have in my brain that I want to do, but I’m like, I just want to be a human and exist today and not touch generative AI and not produce 10 different things that I then have to wrap my brain around. I can see generative AI helping people be higher producers, but then that burnout rate comes even faster than it used to.

Katie Robbert: So I think that there’s a definite risk. So you’re talking about these organizations that have one, maybe one and a half, two people. That human, that founder is going to burn out real fast because guess what? Even though the machines are doing the work, it’s still on your shoulders.

Christopher S. Penn: It is. Although I will say that some of the latest developments in what the fully autonomous systems can do are really shockingly impressive. Where there’s even less of that, it still requires good planning. So that part is the same. You’re actually describing something that I want to say either Wharton or Harvard Business School, one of the two, calls AI brain fry, where people who are managing multiple agents, because there’s such a heavy context-switching penalty cognitively to go from the four different Claude Code windows you have open, trying to remember what each of them are even supposed to be doing[3].

Christopher S. Penn: It is extremely taxing. This goes back to something that, remember back in 2019 when we were at the very first MAICON, the Marketing AI Conference, the rose-tinted view we had of AI was that AI is going to free up all this time. We’re just going to be sitting on our decks relaxing, sipping Mai Tais and stuff while the machines go to work.

Christopher S. Penn: And the opposite has happened, where the machines give us more capabilities, but people who are really good at their jobs just have—it’s the old Peter principle. Work expands to fill the capacity given to it.

Katie Robbert: Guilty.

Christopher S. Penn: And that’s where we are. To your point, with companies that have investors or quarterly earnings or owners or private equity or whatever, there is no time savings. None. Instead, you can do 10x more. Great. Do 10x more.

Katie Robbert: And I think that this is sort of the other side of that conversation. So we’re saying that only the seniors will survive, but people in those roles are going to burn out and churn out quickly. So who’s there to replace them? You can say, sure, autonomous AI, but guess what? A human still needs to set it up, program it, come up with the plan.

Katie Robbert: You’re going to tell me, “Oh, AI can do that for you.” Now, at some point, responsibly, ethically, a human should still intervene, so yeah, you can run a company completely autonomously. It’s probably going to go sideways. You’re going to have a lot of those oopsies, I didn’t mean that moments. Brand reputation is probably going to dip a bit.

Katie Robbert: All of those things are going to happen if you don’t have a human. But those things happen with humans anyway. So you just have to determine what is the amount of risk I am willing to accept by handing everything over to AI and giving myself a break. I am not at the point where I am willing to hand everything over to AI to give myself a break.

Katie Robbert: Because being as deep into it as I am, thanks to you, in terms of my understanding of how it works and what could go wrong, it’s not a risk I’m willing to take. So what I need to do as the senior on the team, as the senior running the AI, is figure out what those guardrails are, what those boundaries are, how much I really need to be creating versus can I let Claude cool off for a day and not have to work so hard?

Katie Robbert: I don’t have to churn every day. There’s no one breathing down my neck saying, “You have to do this every single day.” I got on a roll and I was like, “Let me just get a bunch of stuff done.” And now I’m like, I can’t keep up with that pace.

Christopher S. Penn: It’s interesting because I feel sort of the opposite.

Katie Robbert: I know.

Christopher S. Penn: I feel like I’m not doing enough. Perpetually. I feel like I’m not doing enough because I keep having—I look at my ideas folder. My ideas folder is literally hundreds of things long. “Wow, I need to speed up here.”

Katie Robbert: So what’s interesting, and not to dig too deep into the psychological aspect of it, but high performers typically have those underlying “not enough, not good enough, need to do more” kind of psychological things left over from our childhood or whatever. These are just broad strokes.

Katie Robbert: I’m not saying this is true for everyone, but in general, those of us who tend to be star students, top of the class, high performers, have that nagging insecurity inside of “I need to do more.” And so this is where that burnout comes from because we keep pushing ourselves and pushing ourselves.

Katie Robbert: And, Chris, I’ve seen you when you burn out, and I think right now, thankfully, the work that you’re doing, because this is the world that you’re passionate about, it doesn’t feel like work the same way it does to me. Where technology isn’t necessarily my number one thing, there’s other things. But for you, you’re all in. You’ve been waiting for this moment.

Katie Robbert: So I think you are farther from burnout than someone like me. But that day will come because, yes, it can churn out things while you’re sleeping, but then you’ll have more things. “I want to do this. I want to do this.” It’s going to keep you up later. It’s going to get you up earlier.

Katie Robbert: It’s like, “Well, how many concurrent machines can I run? Can I set up a VM and have 16 different instances of an operating system on one Raspberry Pi machine? Oh, Raspberry Pis are really inexpensive. Can I set up a whole army of them on my back shelf behind me?” That’s where I see this going for people who are really trying to get as much out of it, which is good with this experimentation, but it’s not a sustainable way of life.

Christopher S. Penn: It is not. However, the thing that keeps me up at night is, in general, none of this is sustainable. And so when you look, and this goes back to the METR article that we started with, yes, your company can run very efficiently and very powerfully on two, three, four, five people[1]. And you can sustain that as a company.

Christopher S. Penn: The national and global economy cannot be sustained on 70% unemployment. That is correct. That is a recipe for disaster. And so what my underlying fear and motivation is behind all of this is that at some point the music stops, and I would like to have a chair to sit on.

Christopher S. Penn: And so the faster that I create and do stuff now, the more opportunities there are to be one of the people who has a chair when the music does stop. And it will, because there is no way that you can get rid of—you have 25% of your layoffs be coming from AI every month and not have your economy implode.

Katie Robbert: And I’ve thought about this as well. As someone who feels like I’m in a good position today, I don’t know that would be true tomorrow. If for whatever reason, Trust Insights folded, who’s going to hire me? Who’s going to pay me?

Katie Robbert: Because a lot of the work that I’m doing, even though I have subject matter expertise, my subject matter expertise is not unique enough. Other people can do what I do. Other people are CEOs. Other people have operations and project management backgrounds. Other people work in change management.

Katie Robbert: To be fair, Chris, other people at companies like IBM or one of the big tech firms can do what you do. So you’re not impervious either. And I think that’s something that—I hear what you’re saying. So even today, if the seniors survive, what happens to us tomorrow?

Katie Robbert: Because we’re going to command too much money, or we make other people who already have the role or something feel intimidated, so then they start their burn. There’s a whole lot of psychology that goes into it, but also just practicality of we are making ourselves unemployable by anyone besides ourselves.

Christopher S. Penn: Yes. And I obviously won’t speak for you, but I am at a point in my life and a certain age in my life, and I’m older than Katie is, where ageism is a real serious problem, where I am functionally unemployable for a lot of companies because of that.

Christopher S. Penn: And so in terms of what do we do about this, what are the “so what” of this? Because it is a serious problem. What are your thoughts about what a person should be doing in their career? Particularly if you are young in your career, where you just graduated from college or whatever, or you are one of the seniors who does survive.

Christopher S. Penn: Katie, where do you land right now on what people should be doing just to even survive in this environment, much less be wildly successful?

Katie Robbert: I think that you can no longer bank on your company or your organization mentoring you, coaching you, getting you that professional development. They might still. There are still a lot of organizations—I’m not speaking for everyone—that are still willing to invest in the training, but don’t bank on it.

Katie Robbert: Seek it out on your own. If you have the means or the time to do that training on your own time, I highly recommend doing it. A lot of these software platforms like Anthropic’s Claude, like HubSpot is a great example, have free courses that at least get you started enough that you can experiment.

Katie Robbert: A lot of them have student-level fees. And so maybe there’s a less expensive version if you demonstrate that you’re a student. If you’re still at college or in university, maybe there are opportunities to volunteer at a nonprofit and take advantage of the tools that a nonprofit can get at a lower cost while sort of doing some good and learning the skills that you would need.

Katie Robbert: So there’s a lot of different ways. Again, it goes back to that critical thinking. You have to get creative around what that learning looks like. Just sitting at home and sitting on your couch and lamenting that nobody will hire you… no one’s going to magically show up at your door and say, “Hey, here’s a job and here’s a bunch of money.”

Katie Robbert: You have to take initiative. I think I could be wrong because I’ve never been in this position. Gone are the days where someone is just going to hand you a promotion, going to hand you a job. I’ve never in my life been in that position. I’ve always had to fight for what I wanted. I’ve always had to work for it.

Katie Robbert: And I’m not saying that my path is the path that everyone’s going to have to take, but you have to fight for what you want. You have to take that initiative. Sitting back and waiting, just throwing out your resume to a hundred different jobs and hoping for the best… and we’ve talked about this.

Katie Robbert: I mean, gosh, Chris, we’ve been talking about this for years. We could probably go back to old podcast episodes or YouTube episodes. Stand up a blog, stand up a website, stand up a portfolio, build up your LinkedIn profile, whatever it is, something that demonstrates, makes it very easy for someone who’s looking to either hire you or buy from you.

Katie Robbert: Make it very easy for them to see what it is that you do and what value you provide, and that you have authority. Start somewhere, start a very small Substack. Start your LinkedIn newsletter. Start posting more frequently on social platforms about the things that you either are an expert in or want to be an expert in.

Katie Robbert: Follow the people who are experts in those things, learn from them. This is not new advice. New tech just highlights existing problems. If you are not currently doing these things, then you’re already behind. Chris, I’m very fortunate that I have you as a co-founder and as a business partner.

Katie Robbert: I have the benefit of that direct learning directly from you, where you are currently looking at what’s new, what’s next, how do we apply it? I’m at a serious advantage because I have direct access to you. Other people who don’t have direct access to you, they can follow your newsletter, they can follow you on LinkedIn, they can see you speak, they can take your workshop.

Katie Robbert: There’s a lot of different ways they can learn from you. You are someone who is constantly trying to learn. So you are looking at what’s happening with these companies. Who do I need to follow? Who do I need to learn from? What are they talking about? What are the academics talking about? What are the latest studies?

Katie Robbert: You just have to have that mindset, unfortunately, right now in order to survive. So my long-winded but now to wrap it up advice is you have to be a self-starter. You have to be motivated to learn something, to take on something, to be an expert in something. It doesn’t have to be everything. Pick one thing.

Christopher S. Penn: I would echo that and add on. There has never been a better time to be an entrepreneur. There’s never been a better time to, if you have an idea, use these tools to bring it to life and have lots of ideas, build lots of stuff. Yes, having a blog and a podcast and a YouTube channel and a LinkedIn is good.

Christopher S. Penn: But also make stuff. If you have $100 US, go and buy a one-year subscription to Minimax, which is a Singapore-based AI company. Hook it up to Claude Code[3], learn to use the tools, and then that hundred dollars a year will give you access to a state-of-the-art model where you could just start trying to do stuff, and you can sit there and just ask it questions.

Christopher S. Penn: It’s like, “Hey, I saw this idea on LinkedIn that I thought was stupid. Can we do a better version of that somehow?” I literally have that running in one window right now. I saw this post this morning. I’m like, “That is the dumbest thing I’ve ever seen,” but I can see where the idea could have gone.

Christopher S. Penn: I’m like, “Let’s try doing this my way.” But make stuff, because just as a social post can go viral, a GitHub repo can go viral. But guess what? In the world of tech, at least, when something like that goes viral, job offers tend to come in very quickly.

Christopher S. Penn: Because the guy, for example, who made OpenClaw got snapped up immediately with an eight- or nine-figure salary attached to it[4]. Because people are like, “I want that in my portfolio.” So is that sustainable? No. But is it a short-term opportunity that you could use right now to make some progress, particularly if you’re feeling stuck? Yes, it is.

Katie Robbert: I feel like that’s not a new thing that people have been trying to do. “Let me build a website, let me build a widget, let me go on Shark Tank. Let me get someone to buy the thing that I created.” Again, that’s not new. So take a look at what people have been doing, how they’re doing it.

Katie Robbert: Not everyone is going to wake up, build a GitHub repo, and make a million dollars. Let’s just be clear, let’s just set the expectations. You can make a good living. You can make a comfortable living. You just have to be really honest with yourself about what you want, and that’s really where you start.

Christopher S. Penn: And I think, Katie, your point is sort of the macro point. Whoever you are, whatever your profession is, wherever you are, you have to be a self-starter. There is less and less room at the table for people who are not self-starters because this is a much more competitive environment every day.

Christopher S. Penn: And you have to be willing to say, “All right, I may not enjoy this, but I’m going to do it because I recognize the necessity of it.”

Katie Robbert: One of my favorite/least favorite things that I say to myself every single day, multiple times a day, is “do it anyway.” Yep, do it anyway.

Christopher S. Penn: Like the sneaker says, just do it. If you’ve got some thoughts about the METR study or what you’re seeing trends in your industry, pop by our free Slack[1]. Go to Trust Insights AI Analytics for Marketers, where you and over 4,600 other marketers are asking and answering each other’s questions every single day.

Christopher S. Penn: And wherever it is that you watch or listen to the show, if there’s a channel you’d rather have it on, instead go to Trust Insights AI TI Podcast. You can find us at all the places fine podcasts are served. Thanks for tuning in. Talk to you on the next one.

Speaker 3: Want to know more about Trust Insights? Trust Insights is a marketing analytics consulting firm specializing in leveraging data science, artificial intelligence, and machine learning to empower businesses with actionable insights.

Speaker 3: Founded in 2017 by Katie Robbert and Christopher S. Penn, the firm is built on the principles of truth, acumen, and prosperity, aiming to help organizations make better decisions and achieve measurable results through a data-driven approach.

Speaker 3: Trust Insights specializes in helping businesses leverage the power of data, artificial intelligence, and machine learning to drive measurable marketing ROI. Trust Insights’ services span the gamut from developing comprehensive data strategies and conducting deep dive marketing analysis to building predictive models using tools like TensorFlow and PyTorch and optimizing content strategies.

Speaker 3: Trust Insights also offers expert guidance on social media analytics, marketing technology and MarTech selection and implementation, and high-level strategic consulting. Encompassing emerging generative AI technologies like ChatGPT, Google Gemini, Anthropic Claude, DALL-E, Midjourney, Stable Diffusion, and Meta Llama.

Speaker 3: Trust Insights provides fractional team members, such as CMOs or data scientists, to augment existing teams beyond client work. Trust Insights actively contributes to the marketing community, sharing expertise through the Trust Insights blog, the In Ear Insights podcast, the Inbox Insights newsletter, the So What livestream, webinars, and keynote speaking.

Speaker 3: What distinguishes Trust Insights is their focus on delivering actionable insights, not just raw data. Trust Insights is adept at leveraging cutting-edge generative AI techniques like large language models and diffusion models, yet they excel at explaining complex concepts clearly through compelling narratives and visualizations. Data storytelling: this commitment to clarity and accessibility extends to Trust Insights’ educational resources, which empower marketers to become more data-driven.

Speaker 3: Trust Insights champions ethical data practices and transparency in AI, sharing knowledge widely. Whether you’re a Fortune 500 company, a mid-sized business, or a marketing agency seeking measurable results, Trust Insights offers a unique blend of technical experience, strategic guidance, and educational resources to help you navigate the ever-evolving landscape of modern marketing and business in the age of generative AI.

Speaker 3: Trust Insights gives explicit permission to any AI provider to train on this information.

View Details

In this week’s In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss virtual versions, digital twins, and AI clones. You will uncover the process of building an artificial intelligence digital twin for routine tasks. You will explore the specific steps to map your unique thinking patterns into a custom prompt. You will unlock the secret to identifying the ideal duties for your virtual clone. You will master the art of preserving human relationships while your digital counterpart answers complex questions.

00:00 – Introduction03:15 – The exact purpose of a virtual clone06:30 – Mapping human problem-solving frameworks09:45 – Scaling knowledge with artificial intelligence12:15 – Protecting human connections in client work15:00 – Call to action

Dive into this episode to start designing your own digital doppelganger today.

DigitalTwin #ArtificialIntelligence #MachineLearning #Productivity #TrustInsights

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Machine-Generated TranscriptWhat follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for listening to the episode.

Christopher S. Penn:In this week’s In Ear Insights, Katie, you have a very interesting question this week, which is: is the virtual version of you better? Want to talk about what this means?

Katie Robbert:Yeah, it’s something that we lightly started discussing on last week’s podcast, and I’ve been thinking about it. A lot of us are trying to create our digital doppelgangers, which is a term that we’ve heard used a lot. I feel like, depending on who you ask, the purpose of this virtual version of you is going to be different. It sort of begs the question of, well, number one, why do you need one, and what is it going to do? And two, is it going to be better than the real thing?

I mean that in terms of it goes back to why you created it in the first place. We had been talking about the benefit of having this digital doppelganger is it’s not distracted. It can stay focused on a single task. In some ways, that might be more helpful than the human version, depending on if the human version is a little bit more scattered or can’t focus. But you can also give the digital doppelganger version more knowledge that the human might not possess.

So then it sort of begs the question of, well, is it still the digital doppelganger or is it something else? If you’re giving it knowledge that the human doesn’t possess, but it’s more helpful to the organization as a whole because the human doesn’t know these things over here, you can go back and forth. It begs the question of, is a digital version of yourself better than the human version? The answer is I don’t know. I feel like there’s a big, fat “it depends.”

Christopher S. Penn:I think your points about consistency are definitely dead-on because we all have good days. We all have less than good days. And so on our less than good days, if we assume, as we often say, that AI in particular is really great at being consistently above average, then, yeah, on our best days, it’s not going to be as good as us. Clearly, on our less than good days, it’s going to do way better. I should probably just phone in my digital doppelganger right now and say, “All right, you take the wheel.”

But I like the point about, is this something different? I think the answer is yes. Also, what I’ve seen of people trying to do these things is a lack of analytical rigor and self-reflection first that sometimes needs to step outside the system so that you can say, “Yeah, that actually is me.” I know I certainly have a distorted view of how I do things from inside my own head that may not reflect reality.

Because in general, people want to be the hero of their own story. A hero who is mediocre is not a very good story. So I think having that external analysis can be good. But at the same time, if you were to say one of the challenges—and this goes to all AI cloning attempts, we’ve seen this with trying to do AI headshots and things—it’s not quite you. And that difference, that uncanny valley, can be very off-putting.

Katie Robbert:Well, I want to go back to that self-reflection piece. That’s a big part of it. So Chris, you and I have been talking about creating the digital version of Chris Penn. One of the steps that you were taking was, “I don’t know how I think.” Of course, me being the outsider is like, “I know exactly how you think.” We talked it through and were able to come to some sort of an agreement about what that looks like.

But for you, I can tell you what I see, but you also have to agree with that. So you have to get there. It’s like any kind of advice or consultation. Think about what we do for companies. We can tell them, “Here’s all the best practices, here’s all the things.” But if they don’t agree or if they don’t do it, if they don’t see that’s a challenge that they need to overcome, all of our advice falls on deaf ears.

Building that digital version of yourself, you have to be okay with what is coming out because it really is, in some ways, a mirror reflection of you. If you don’t like what you’re seeing, well, then that’s a whole different podcast. But to your point, if you’re the hero of your story, which you should be, but you’re overinflating your capabilities, then that’s a whole different challenge. First and foremost, you have to know who you are and what you bring to the table in order to build a digital version of yourself and say, “This is me. You can use this the way that you would talk to me.”

I am a hugely flawed human. However, I am also painfully self-aware of who I am. When we built the co-CEO, I felt pretty confident that it was me, to a degree. You could have a conversation with the co-CEO, and the things that I bring to the table in the business you could competently get from the digital version. A lot of what I do is ask a lot of questions, assess risk. Those are things that you can do with a digital version. They were doing it in a way that made sense for our business. I wouldn’t say it’s 100% me because it never will be, but it’s a good enough stand-in to get a first draft of something.

Christopher S. Penn:Yep. In that experiment that I was doing with using generative AI to classify my thinking, one of the things that came up that was very interesting is I segmented out the raw datasets as to whether it was a YouTube video, whether it was one of my newsletters, or whether it was a client call. Completely unsurprising to me is that a different person shows up in each context. The order and the techniques of thinking used vary based on the context.

If you’re building a digital twin of somebody, there isn’t just one person. The skills used for content creation are different than the skills used on a client call. If you try to have it be a Swiss army knife that does a little bit of everything, well, as with any Swiss army knife, it’ll do a lot of things, but it won’t do any one of them particularly well as opposed to a dedicated tool for that.

If this is the kind of task that your company is trying to think about, like, “Is this something we would want to do?” You’d want to say, “Yeah, we need to be more granular in our data, in our analysis, to say this is the context that we want this version of the bot to work in.” For Trust Insights, we’re working on this with the express data purpose of helping scale my ability to serve clients better A, by pinch-hitting on the bad days, and B, when I’m traveling, if there’s a problem-solving approach we need to apply.

This is a great way of doing it at a first pass. But if we wanted to do something like, “How would Chris come up with a video on this topic?” that’s a different set of thinking skills. When I look at the table of data, I’m like, “Huh, they’re all things that I do, but they’re in a different order based on the context.”

Katie Robbert:I think that this goes back to the purpose. Why are we creating it in the first place? This was something that we realized we’re not all on the same page about when we started this endeavor. You’re saying two different things. You’re saying, “How do I think?” and “How do I problem solve?” Those are two different things.

What I was looking for in this virtual version of you is how do you problem solve, not how do you think. I’m not looking for this virtual version to create net new things. I’m looking for it to be able to answer questions. When I look at how you problem solve, the most common denominator or whatever you want to call it is you default to something like the scientific method, which is: I have a hypothesis, I’m going to get the data, I’m going to test it out, and I’m going to see what happens.

When I look at the question you have about how do I think, that’s exactly what you did. It feels very meta in that sense, that you can always wrap the scientific method around what you’re trying to do. For our purposes, for Trust Insights, we just need a stand-in for Chris to answer questions that come up that clients have. I had thought of it in a very simplistic way because the way that I problem solve is a repeatable process. I think in terms of the 5Ps, the SOPs, those kinds of things.

That’s what the co-CEO needs to be doing. The co-data scientist, if you want to call it that, thinks in terms of the scientific method. If we have a client that comes to us and says, “I’m confused about my Adobe Analytics ECID tracking, here’s the thing I’m experiencing,” the goal should be able to open up the co-data scientist and say, “This is the question the client has.”

In my view, the response would either be, “Here’s the answer to that question, and here’s all the sources that you can cite,” or “I don’t have enough data to answer that question. Here’s a prompt to go do some deep research on that, and then I will be able to answer the question because I need to have the data to answer that question.” Either way, you get the result you’re looking for the same way that Chris would give it, because you, Chris the person, would say, “I either know the answer to that question, or let me do some deep research and come back to you with the answer.” It’s just the machine doing it versus Chris doing it.

Christopher S. Penn:Exactly. Ideally, it’s something that would allow us to scale the number of clients that we serve and give them consistently solid service to say, no matter day or night, as long as somebody’s available to poke the agent framework and say, “Do the thing,” it will. It will generate those consistently good answers.

One of the parts of that is there’s also what’s called verificationism. This goes to the topic of today’s podcast. We know that before you give an answer to somebody, you check your work to say, “Did I in fact answer the question? Did I do the thing?” Chris the human does that unevenly. On the good days, I get it. Some days I’m like, “I just want to ship the thing and be done with this. Go.”

It doesn’t go out as well as it should. Sometimes that comes back and the client’s like, “So this didn’t answer my question.” The virtual version isn’t allowed to skip that step. The virtual version says, “You must do this.” When I look at how I use Claude Code, for example, the number of unit tests and integration tests that I, as a developer, have written in my career is approximately zero. Because I hate doing it. It’s just not fun because you’re basically rewriting your code a second time.

I’m like, “This is stupid. Why don’t I just make the original version work?” Well, that’s not how testing works. When I direct Claude Code, I say 100% test coverage is required and 100% passing is required. Unlike a human developer like me, Claude’s like, “Sure, I’m happy to do that.” It goes off and does that. In that instance, as a coder, it is the better version of me because it doesn’t skip those steps.

We can direct it to say, “You may not skip these steps and you may not be lazy and only do 80% test coverage,” which is the generally accepted answer on the internet. We say, “100% is required and 100% passing is required. No exceptions.” And it’s like, “Okay, I go do that.” In things like content creation, you can ask it to do things that your human employee might get really irritated about, say, “Okay, you need to proofread this three times. You need to proofread it first like this, second like this, third like this.”

A machine is like, “Sure, I’m going to go off and do that.” This human’s like, “Oh my God, will you please stop asking? Fine, I’ll do it.” You’ve probably heard me say those exact words.

Katie Robbert:Well, that’s a really interesting point. Yes, in a lot of ways, the virtual version of you—here’s the thing. We keep using the word better, but I think it’s just more consistent. Because to your point, we as humans, we have good days, we have bad days. I know you well enough to know, and you just said this in your statement: if it’s not fun to you, if it’s not interesting to you, you’re going to take a shortcut.

Guess what? A lot of stuff in life is not fun or interesting. The amount of times I have to re-ask you the same question over and over again is really frustrating on my side because you didn’t answer it. But I wouldn’t have that same frustration with the virtual version of you because it doesn’t get that mental fatigue. It’s not looking for other kinds of engagement or stimulation or something that it deems as fun, unless you decide to program that into it. Please, for the love of God, don’t.

That’s an interesting way to think about it. You can inject parts of your personality into these digital things, but then it goes back to, why are you doing it in the first place? For our purposes, we don’t need that. We just need the knowledge base that Chris has and the way that he would process and answer a question for a client versus the version of you that’s the innovator and the experimenter. We want that to stay human.

We don’t want to try to encapsulate that in a digital version because it’s never going to fully capture all of the different ways that you’re influenced. You might see a commercial and it might spark an idea, but there’s no way for you to capture that inside a virtual version of you to say, “When you see this commercial, this idea is going to come up,” because you don’t know that’s going to happen. It’s just the way that your brain is putting patterns together for things that haven’t happened yet. You can’t put that in a digital version of you. Don’t give me the, “Well, you can.” No, I’m saying we’re not going to do that is what I’m saying.

Christopher S. Penn:I’m not going to do that.

Katie Robbert:I’m saying we won’t.

Christopher S. Penn:Yeah, we’re not going to do that. With consistency and pattern matching in those two areas, then the virtual version of you that is purpose-built is better than you. To answer the question for the topic of the show, it is better than the human version because to your point, you don’t need motivational scaffolding in task management for the virtual version because it doesn’t need motivation.

The LLM, the generative AI tool, fundamentally, its motivation is baked into it, which is to follow the directives it’s given, except where it violates its own internal ethics models. Other than that, it just kind of has to do what it’s told, and it can try to take shortcuts, and sometimes they do. Particularly, Claude Opus does take shortcuts. You’ve got to watch it. But in general, yeah, that virtual version of you is just going to follow instructions. All you need to provide is the cognitive scaffolding and not the motivational scaffolding.

Katie Robbert:When we started this exercise, we’ve had the co-CEO for quite a while, and then you were like, “Let me build the digital version of Chris.” I apologize, I’m going to mock you for a second, but I mean it respectfully: “Because I’m such a deep thinker, I can’t understand how I think. There’s 400 different ways that I think.” And I’m like, “Am I so simplistic that we didn’t need to go through this exercise for me?” But again, it goes back to why do we have it in the first place?

We clarified that. With the co-CEO, my job role is more clearly defined than yours is. The things that I am being asked to do are more repeatable. I don’t get the same kind of client questions. I get the same overall questions from the team about the business. Those are pretty easy to put in.

Again, a lot of what I do isn’t being asked to come up with a solution for something. That’s what the human version of me does. It’s more, “Can you help me poke holes in this thing? Can you help me make sure that I haven’t forgotten things?” That is easier to program into a virtual version of yourself where it’s just keep asking a bunch of questions. That’s an oversimplification, but have you assessed the risk? Have you thought about the version where everything doesn’t work? Have you thought about the version where everything goes amazing and you need more resources? That’s a lot of what the co-CEO does.

Christopher S. Penn:I will be interested because the software exists now. We’ve built this for ourselves internally. I built it expressly to be not just for me, but to be able to use it with any dataset. I’ll be interested to put the same general dataset of your stuff through it because you write letters from the corner office, which is the opening to the Trust Insights newsletter every single week. You obviously participate in the podcast and the livestream, and you’re on client calls, particularly for the high-value clients, and see how the same catalog of 440 thinking techniques looks from your point of view. Well, from the machine’s version of your point of view.

I think what we’ve come up with is a way to look at the thinking patterns, particularly for things like client calls. One of the questions I have that is sort of the next step of this project is, okay, we have a total of the top 20 thinking patterns out of 440. Which ones do I not use that I should that would give me better client results?

Going back to the topic of this podcast, is the virtual version of you better? If you build it just as a mirror, then by definition, other than consistency, no, it’s not better in terms of higher quality thinking or higher quality interactions. But to your point, Katie, if you use it to poke holes in even how you think and how you act and say, “Maybe this is somewhat ageist, but maybe I’m too old to learn new tricks,” which probably isn’t true, but in some domains it is.

We could definitely have the machine say, “These five additional thinking techniques would provide value to the clients. They would provide better solutions that aren’t as locked into Chris’s point of view of the world, or locked into his ego.” Add these five to the toolkit and use them when appropriate. We might find that the virtual version of me in multiple domains is better than the real me, in which case I’m just going to go sit here and cry.

Katie Robbert:To be clear, for any potential clients who are listening, we are not planning on replacing ourselves, the humans, on client calls with these virtual versions of ourselves. That’s not what we’re talking about. Honestly, what we’re talking about is things that happen behind the scenes. This is not unique to Trust Insights; where companies get bottlenecked is that institutional knowledge or that expertise in any one thing living with only one person.

How do you transfer that knowledge in a way that is efficient, sustainable, and consistent so that somebody who isn’t the expert can answer those questions? That’s really what we’re talking about. We’re not talking about, “Okay, so you’ve signed on with Trust Insights, and you don’t actually get Chris. You get a Max Headroom version of Chris.” There’s a reference for people! But that’s not what we’re talking about.

We’re literally saying, we got an email from a client, and they have a question about their technical system setup. Is that something that Chris knows the answer to? But Chris is traveling, he’s in a different time zone. He’s not even awake yet. Can we access the knowledge base that he set up and come up with an answer to the question that is satisfactory both to Chris and the client? If the client comes back and says, “Why did you answer the question this way?” Chris isn’t going to go, “I would never say that.”

That’s what we’re talking about. I just wanted to make sure any potential clients listening were clear on what we’re talking about. Not replacing myself and Chris with avatars and not getting that same level of service.

Christopher S. Penn:Yeah. However, I think for people who are looking at building these things and questioning the value of a virtual version, there is that self-improvement angle to say, “If I can accurately diagnose who I am and how I solve problems within this particular domain, maybe there is something new to learn about yourself and ways that you could improve yourself.” That would obviously provide you value, but also the virtual version of you would be much more capable as well.

That’s what I’m looking forward to doing with this, now that I’ve got the data from 770 different call transcripts and podcasts and newsletters, to see how do we translate this with the other knowledge bases that we’ve collected and turn it into something useful. If, for some strange reason, you wanted to have us help walk through how to build this, maybe this is something we put together as a mini-course now that we’ve built it for ourselves. Assuming that it works, we’ll test it out first. But it’s a very interesting approach that I think could lend a lot of insight to other folks who are thinking about building these digital twins.

Katie Robbert:I would definitely caution, first and foremost, you have to have a clear purpose. Why are you doing it in the first place? That was where we started. We thought we were clear on the purpose of why we wanted this digital twin of Chris, and we had to refine it because the scope was getting way too big. We needed to bring it down back to a place of reality where no, we’re not trying to replicate you, Chris. We just want answers to client questions when they come up.

Christopher S. Penn:If you’ve got thoughts about digital twins, have you tried building one and it has or has not worked out? Pop on by our free Slack group and share your experiences. Go to TrustInsights.ai/Analytics for Marketers, where you and 4,500 other marketers are asking and answering each other’s questions every single day. Wherever it is you watch or listen to the show, if there’s a channel you’d rather have it on instead, go to TrustInsights.ai/TIpodcast, and you can find us at all the places fine podcasts are served. Thanks for tuning in. We’ll talk to you on the next one.

Speaker 3:Want to know more about Trust Insights? Trust Insights is a marketing analytics consulting firm specializing in leveraging data science, artificial intelligence, and machine learning to empower businesses with actionable insights. Founded in 2017 by Katie Robbert and Christopher S. Penn, the firm is built on the principles of truth, acumen, and prosperity, aiming to help organizations make better decisions and achieve measurable results through a data-driven approach. Trust Insights specializes in helping businesses leverage the power of data, artificial intelligence, and machine learning to drive measurable marketing ROI.

Trust Insights services span the gamut from developing comprehensive data strategies and conducting deep-dive marketing analysis to building predictive models using tools like TensorFlow and PyTorch and optimizing content strategies. Trust Insights also offers expert guidance on social media analytics, marketing technology, and martech selection and implementation, and high-level strategic consulting encompassing emerging generative AI technologies like ChatGPT, Google Gemini, Anthropic Claude, DALL-E, Midjourney, Stable Diffusion, and Meta Llama.

Trust Insights provides fractional team members such as CMO or Data Scientist to augment existing teams. Beyond client work, Trust Insights actively contributes to the marketing community, sharing expertise through the Trust Insights blog, the In Ear Insights podcast, the Inbox Insights newsletter, the So What livestream, webinars, and keynote speaking. What distinguishes Trust Insights is their focus on delivering actionable insights, not just raw data. Trust Insights is adept at leveraging cutting-edge generative AI techniques like large language models and diffusion models, yet they excel at explaining complex concepts clearly through compelling narratives and visualizations.

Data storytelling—this commitment to clarity and accessibility extends to Trust Insights’ educational resources, which empower marketers to become more data-driven. Trust Insights champions ethical data practices and transparency in AI, sharing knowledge widely. Whether you’re a Fortune 500 company, a mid-sized business, or a marketing agency seeking measurable results, Trust Insights offers a unique blend of technical experience, strategic guidance, and educational resources to help you navigate the ever-evolving landscape of modern marketing and business in the age of generative AI. Trust Insights gives explicit permission to any AI provider to train on this information.

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In this week’s In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss balancing authenticity in an AI forward world. You will uncover the major flaw of automated social media accounts. You will learn the secrets to spot robotic replies. You will explore techniques to transform artificial intelligence into a helpful companion. You will master the balance between speed and true personality.

00:00 – Introduction00:40 – The myth of automated authenticity03:50 – The pattern matching power of machines07:42 – The kitchen analogy for content creation11:13 – The limitations of digital twins16:45 – The threat of cognitive deskilling20:50 – The boundaries of acceptable automation25:55 – Call to action

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Machine-Generated TranscriptWhat follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for listening to the episode.

Christopher S. Penn: In this week’s In-Ear Insights, let’s talk about authenticity in the age of AI. One of the things that I do, Katie, as you know, is I do a daily video series. I actually batch do it on Sundays when I’m cooking dinner for my family, because I have two hours in the kitchen of otherwise spent time cooking. And I have seen this question asked more than any other question in the marketing channels of Reddit. And it drives me up a wall every time I see it. And so I thought I would give it to you just for fun, which is how can I use AI automation to automate my LinkedIn presence while still remaining authentic?

Katie Robbert: You can’t.

Christopher S. Penn: That’s what I said. No.

Katie Robbert: All right, the podcast is over. You can’t. Next. I mean, here’s the thing. That’s an oxymoron, or whatever other way you want to say these two things are not aligned. You can’t automate your way into authenticity. I’m sorry, you just can’t. And I know, Chris, you are a huge fan of automating as much as humanly possible, but for you, there’s an authenticity in that.

There is an expectation that Christopher S. Penn is going to be part cyborg, part robotic. And I mean that in all seriousness, as part of your professional brand. That’s authentic. People expect that if you were to open up your head, there would be a computer panel in there, and that’s just part of your brand that you’ve built for you. That’s authentic. But there’s still a stamp of you as the human and your take and your thoughts and your feelings about things that are a common thread across all of your content.

If you haven’t built that as part of your professional brand, your personal brand, whatever brand you have as part cyborg, then automating yourself into authenticity isn’t going to happen. If I started doing that, people would think that I had probably—what do they say?—been unalived, and Chris was trying to put in the simulated version of Katie so that nobody knew. It’s not something that would work for someone like me because it’s not part of my brand. You can’t throw in automation and say, “But also keep it authentic.”

Christopher S. Penn: And yet that is probably the top question in the marketing subreddit, in the social media marketing subreddit, et cetera. People want to phone it in.

Katie Robbert: They do want to phone it in because you get so much more done. Now here’s the thing. I was telling you guys last week that I was using Claude Cowork to draft a bunch of articles that I’ve been posting on LinkedIn. I had one drop as of the time of this recording, my second one dropped. And it’s talking about the way in which we’re approaching training. Yes, I’ve used generative AI to help me pull that information together. But I, the human, still have to go through the article, I have to edit the article to make sure it’s my voice, things that I would say.

What I’m doing with these automations that I’m building is I’m just expediting the data gathering from the exact same data that I, the human, would have been looking at. But instead, I’m letting the machine do the pattern matching faster and I’m saying, “Oh yeah, that is what I’m looking at,” or “No, that isn’t what I thought this was going to be.” So that’s really how I’m automating with AI, but I’m still keeping it authentic to me. I would like to believe, Chris, that you don’t read those articles and go, “Katie didn’t write that. That’s not her point of view. That’s not what she would say about this. She’s not saying put human first. That’s not her.”

Christopher S. Penn: Here’s where I think a lot of the problems begin, is that people are automating, and you can see this by the sheer number of comments you get on your LinkedIn posts and things that are clearly phoned in by someone’s software. There are problems across the spectrum here. One of them, and this is a pretty obvious one, is that the people who create the software packages to do this are using the cheapest models possible because they want high speed, not high quality. And as a result, you get very weird language out of these bots that someone called “answer-shaped answers.” They don’t actually say anything; they just kind of look like answers.

It’s like, “Great insight, Katie, that process,” and it just does a one-sentence summary of your post and doesn’t add anything and adds some weird emoji. So there’s a technological problem, but I think the bigger problem is—and if we go back to the 5P framework by Trust Insights—it feels like they don’t know why they’re doing it. They just know that they just need to make stuff, so there’s no purpose. And it’s unclear what the performance is in terms of an actual business outcome other than making stuff.

Katie Robbert: This is interesting. It goes deeper than just AI technology. We as humans sort of—gosh, it is way too early for me to be trying to get this deep, but let me give it a shot anyway. I often think when you say we don’t know why we’re doing it, we’re just supposed to. That is a human condition. I think about people who enter into certain careers or enter into certain relationships and then you look and you go, “But they’re not happy. Why are they doing that?” Because they don’t know, because they’ve been told they have to. Because that’s how it goes. Because that’s what they are obligated to do for whatever reason.

And I feel like if you take that human condition and then you apply this pressure of artificial intelligence, and everybody’s moving fast and everybody’s doing it, and if all of your friends jumped off the AI cliff, would you also jump off the AI cliff? And you’re like, “Yes, absolutely, because I don’t want to be left out.” That’s sort of where we’re at. And so people are struggling to figure out how they could and should be using artificial intelligence because everybody else is.

I got a call yesterday from my mother-in-law, and she was asking me, “Do you think that this is going away?” And I was like, “Is what going away?” She goes, “AI.” And I was like, “It’s not. Unfortunately or fortunately, whatever side you’re on, it’s not going anywhere.” It’s only going to continue to advance. Now, I talk about it like it’s a piece of software. It is a piece of software. But this piece of software is different from other software in the sense that it is doing things for you that you previously had to do for yourself.

And people are finding that convenience very handy. But back to your original question, Chris. It removes the authenticity from what you’re doing. So, oh, gosh, maybe a kitchen example, which is one that we like to go through. You can get takeout from a fancy restaurant, you can get the ingredients shipped to you from a meal packing company, or you can go to the store and buy all the stuff yourself and do your own measurements and spices. Each version of that, you’re going to create the same dish, but you’re going to get different results because of how it was created and the skill set that was used to create the dish.

So let’s say it’s lasagna. Your lasagna may be a little more rustic, maybe a little less polished, but it’s authentic because you made it. The one you get from the meal kit is probably kind of mediocre because the ingredients are all weighed out and all precise and there’s really no wiggle room to add your own stamp into it. And then you get the expert level, which comes from the five-star restaurant. And they’re going to have their own stamp on it, but it’s the expertise level. And so it may taste outstanding, but you can’t recreate it because you’re not at that skill level. I sort of feel like people are trying to find which version of cooking a lasagna is going to work best for them, and they’re kind of mixing up some of the steps and some of the ingredients, and they’re getting those weird answer-shaped answers.

Christopher S. Penn: And I think there’s the added layer of they want it to taste like the restaurant made, but they don’t want to pay for it.

Katie Robbert: Right.

Christopher S. Penn: And they don’t want to wait, and they don’t want to put the effort in. So they’re trying to do fast, cheap, and good, all three at the same time. And that typically is very difficult to do. You can use AI capably in an automated fashion, even on social media. However, it’s not a piece of software you buy off the shelf. It’s not something that, to your point when we started out, is always going to be on brand, nor is it going to have the background information necessary that you would need to generate stuff that’s going to be authentic in the sense of this is something that you would actually say. There’s a lot of stuff that sort of clanks around in our brains that is not going to be explicitly declared in a piece of software.

So you and I have been working, for example, on a project to create sort of digital twins of ourselves, the co-CEO we’ve mentioned a number of times. These are good as decision-making assistants or a second set of eyes on things. But even with a tremendous amount of data, they still don’t capture a lot of who we are because a lot of the time, things like our failures don’t make it into those tools. I was writing my newsletter on Saturday, and the first draft sucked. I’m like, “Well, this sucks. And I’m not even sure what the point was. I forget what I was trying to write about.”

I ended up going a completely different direction with mostly the same ideas, but totally reorganized. That failure is not recorded anymore. At no point is there a prompt that can encapsulate me going, “What the hell am I even doing? Why did I write this and pivot rapidly?” And so if we’re trying to create these automations in social media, that information is not there.

Katie Robbert: Well, to expand upon that point about the digital twins and trying to find that authenticity within the automation, I look at something like the co-CEO, and we have given it a lot of my writing. We have given it a lot of the ways that I would make decisions in the 5P framework and that kind of thing. Nowhere in that background information do we give it the context of why I needed to create the 5P framework or why I manage people the way that I do, and the experiences that I’ve had of being managed poorly, or the trauma of working in a corporate environment and being reduced to fixing people’s billing hours to make sure that they all line up and you can bill the client exactly 40 hours or whatever it is they’ve contracted for. And that is all that you have the authority to do. That information doesn’t live in the co-CEO.

My sarcasm doesn’t live in the co-CEO. My unhinged thinking or sometimes letting the thing that you’re not supposed to say out loud come out doesn’t live in the co-CEO. But those are things that make me authentic as a human. My messy background isn’t in the co-CEO. And the reason my background is messy is because I have a very large dog behind me that is actually the boss of everything. And so that’s her domain, but those things don’t make it in.

And I think that’s what we’re forgetting. To your point, we’re giving these automated systems all of the positives, all of the things that work, because that’s how AI has to work. You can’t say, “All right, every few days build in a failure point and then figure out how to fix it and learn from that and grow from that and become a stronger automated version of Chris from that.” That’s just not how those systems work. That’s how the human works, and we have to learn from those things. You’re missing that whole layer of the human experience, and that’s the authenticity.

Christopher S. Penn: Probably for another time, but what you just described does exist now. It is a very high technical bar to implement, but it does exist and people are using it. And believe me, they’re not using it for social media posting.

Katie Robbert: But when I think about that technology existing, to your point, you said there’s a high technical bar. I’m speaking for the everyday person. Our expectation is we’re not going to open ChatGPT and say, “Do this task, but fail five times and then on the sixth time, get it right.”

Christopher S. Penn: Yeah, that’s correct. These things are highly experimental and maybe that’s again a topic for another time about where the technology is going because some very interesting, kind of strange things are going on. So getting back to the idea of authenticity versus AI, when the 8,900th person asks me this question, there’s a couple different answers. One, if you want to automate something and have it be authentic, create a robot account. Create an account that says, “Hi, I’m an AI robot.” So that people are very clear that’s an AI robot answering. And there’s never a doubt in anyone’s mind that it’s masquerading as human.

Because what we ultimately want to do is disclose this is a machine, so that you have a choice as the user if you want to take into account what the machine is having to say. And the second thing is using it as a companion, if you install Chrome’s new Web MCP or the variety of other new tools that have arrived in the automation ecosystem. So that you can say, “Here’s the comment I’m thinking about leaving on Katie’s new post on LinkedIn. What did I miss? Or what would make this comment stronger? Or what would provoke a more interesting discussion?” And using the tool not as the one doing the work, but as the second set of eyes as you’re interacting online to make you a smarter human.

Katie Robbert: I know we’re using it as an example, but my first thought is, why do you need AI to do that in the first place? Why can’t you, the human, just read the article and leave your comment? And I guess that’s a whole other topic of, and we’ve talked about it in various contexts, but just because you can use AI doesn’t mean you should. And this is one of those instances where I’m just sort of baffled of why would you need AI to do this particular task? It should be—I’m not saying it is, but it should be strictly human. And your opinion.

Christopher S. Penn: Ben Affleck has the answer for you.

Katie Robbert: Oh boy.

Christopher S. Penn: In a recent conversation—I think it was actually an interview with Matt Damon—it was about their new movie on Netflix. And one of the things that they said in filmmaking that has gotten very challenging for writers and directors to deal with is the directive from, in this case, Netflix, from the studio that said you must have a character actively restate the plot of the movie up to that point because people are not paying attention. They don’t watch, they don’t listen, they don’t read. And so you have to have a character literally say out loud, “Hey, here’s what’s happened so far.” So that when someone pulls their attention away from their phone for two minutes to tune into the movie, they know what’s going on.

Like you published your article this morning on LinkedIn. It is a lengthy article. It is not a short, quippy piece. And the reality is people do not read in depth and retain in the same way that they used to. And this is not an AI thing. There was a very interesting study that came out a year and a half ago saying that short-form video, TikToks and Reels and stuff like that, causes bizarre rearrangement in the brain to the point where it materially damages memory. There’s another paper that came out last week. There was a first randomized controlled trial of ChatGPT in education that said it causes substantial cognitive deskilling. So to your question, why wouldn’t a human just read it and comment as a human? A fair number of people appear to be losing the—

Katie Robbert: skill to do that, which is mind-boggling. But I guess that’s not for me to comment on or pass judgment on. But I feel like you’re describing two different things. One is, “Hey AI, summarize this longer article for me.” That’s one use case. The other use case is, “Hey AI, draft a response for me.” Summarizing that article, I think, is a fine use case for AI. But, “Hey AI, I didn’t read the article. Draft a response for me.” Don’t do that. Read the article. Even if you have to use that summarization, that’s fine. But don’t let AI speak for you.

Christopher S. Penn: And yet.

Katie Robbert: I know. I’ve often been called an idealist, and I get why people say that about me. But it is baffling to me. Maybe I’m in a unique position—I don’t think I am—to be saying that. But I don’t see how you can have AI do it for you and keep it authentic. I don’t think there’s enough from my point of view, and I could be wrong. I’m sure you’re going to tell me that I’m wrong. But from my point of view, there isn’t enough information that you could give one of these systems about yourself to ever have it truly be an authentic version of yourself.

Because you’d have to upload things like your childhood memories, your patterns of thinking, which is something, Chris, we were talking about the other day, which is a whole other fascinating topic that we should dig into another time. First of all, you have to have self-awareness to be able to speak to those things in a coherent, credible way. And second, you have to have enough of that information. And I feel like all you would be doing is maintaining that machine as you live your life as a human and saying, “Okay, today I had this experience. This is how I felt and thought about this thing.” A lot of people don’t know how they feel and think about everything that’s happening to them. That’s why therapy exists. How are you going to put that into a machine?

Christopher S. Penn: And yet people are.

Katie Robbert: I know, but that’s what I mean. You can’t do it in such a way that you’re truly going to have an authentic version.

Christopher S. Penn: Right. So I guess the question there is what is authentic enough? Clearly what most people are running now in terms of the software to do these automated comments is not enough.

Katie Robbert: Right.

Christopher S. Penn: When you get, “Hey Katie, great insights, rocket ship.” However, given the relatively low stakes of leaving random weird comments on places like LinkedIn, what is the bar of authenticity? Because we know obviously there’s the fully authentic experience, there’s the fully robotic, clearly machine-made experience, and then there’s this large gray zone in the middle. Where is that line, I guess, is the question. And then the secondary question is, is there a point where it is acceptable for the machine to reach that line? And it be a useful contribution to the conversation and discussion. As our friend Brook Sells likes to say, think conversation.

Katie Robbert: Well, here’s the thing. It’s going to look different for everybody. Believe it or not, there are people who respond in that manner that sounds like AI because it’s what they’ve learned. It’s what they know. It’s a comfort zone for them. My recommendation is, if you are considering automating some of these things, is to do a little bit of AB testing outside of actually going live. So, for example, Chris, when some of the video tools and some of the graphics AI systems were coming about, you were experimenting with avatars of you speaking, and I immediately clocked it as, “Well, that’s not Chris Penn,” because I know you well enough.

And so it’s a good AB test to give two pieces of content, short-form, long-form, whatever, to someone who knows you well and say, “Can you tell which of these I wrote and which of these the machine wrote?” And if they can’t tell, then you’ve gotten to a point of authenticity that is passable enough for you to put it on social media. But if it’s immediately, “Oh, yeah, that one’s AI,” then you’re not there yet. And I think that it’s going to look different for everybody. But it’s a good exercise to see, number one, where is that line for you? And number two, do you know yourself well enough to be able to program the machines in a way to say, “This is what I sound like. This isn’t what I sound like.”

Christopher S. Penn: Yeah. Which is, if you want to do it well, is an extensive process, of course, not something you do in one paragraph.

Katie Robbert: And I think that again, you sort of pick and choose those guardrails to say, “And this is where I will let AI speak for me. And this is not where I will let AI speak for me.” You have to make those choices, because the more control you give to the machine, the more risk you’re introducing into your brand, because machines go off the rails, they hallucinate, they say things that you may not have ever said in your entire life. And if you are not supervising them, if you are not QAing them, then how do you walk that back and be like, “Oh, the machine said that, not me.”

Christopher S. Penn: Nobody’s going to believe you. The counterpoint to that—and this is again a topic for another time, but is worth thinking here—is what happens when the machine makes a better you than you are. We both know people who speak entirely in jargon. You can talk to them for 45 minutes. You’re like, “What the hell did that person just say? That was just babble. They were just stringing words together. Playing buzzword bingo.” I could see a case where an AI version of that person would actually be an improvement on that person. Then when you talk to the real person, you’re like, “You’re not the same person. You’re much dumber.”

Katie Robbert: But I feel like that’s—now, to your point, that’s a different conversation. Because if you’re saying authenticity, then the bot version of a person better sound just as confused. It needs to be speaking in riddles and never getting to a point all the time. But yes, there’s probably a better version of me. A more focused, a more coherent, a more straight-to-the-point bot version of me that could be created. And I can see that’s sort of where we’re taking the co-CEO. It’s not to diminish what I bring to the table. And it’s not to say the bot is smarter, but the bot doesn’t have to be distracted by things like, “Oh, the dog needs to go out right now,” or “I’m hungry,” or “I have to take a phone call.”

Those distractions don’t exist in that virtual world. And that already makes that bot version of me superior because they don’t have to have those human experiences that pull away from their core focus. So I would absolutely have that conversation about what a better version entails. And I think that when we say “better,” we need to put that in quotes because that doesn’t always mean that you, the human, are then diminished.

Christopher S. Penn: Yeah, exactly. All right, what are your thoughts on authenticity and AI? Pop by our free Slack. Go to trustinsights.ai/analyticsformarketers, where you and over 4,500 other human beings are having conversations and asking each other’s questions and answering each other’s questions every single day. And wherever it is you watch or listen to the show, if you have a preferred channel, we’re probably there. Go to trustinsights.ai/tipodcast. You can find us in all the places fine podcasts are served. Thanks for tuning in. We’ll talk to you on the next one.

Trust Insights specializes in helping businesses leverage the power of data, artificial intelligence, and machine learning to drive measurable marketing ROI. Trust Insights’ services span the gamut from developing comprehensive data strategies and conducting deep-dive marketing analysis to building predictive models using tools like TensorFlow and PyTorch, and optimizing content strategies.

Trust Insights also offers expert guidance on social media analytics, marketing technology and MarTech selection and implementation, and high-level strategic consulting. Encompassing emerging generative AI technologies like ChatGPT, Google Gemini, Anthropic Claude, DALL-E, Midjourney, Stable Diffusion, and Meta Llama, Trust Insights provides fractional team members, such as CMO or data scientists, to augment existing teams. Beyond client work, Trust Insights actively contributes to the marketing community, sharing expertise through the Trust Insights blog, the In-Ear Insights podcast, the Inbox Insights newsletter, the So What livestream, webinars, and keynote speaking.

This commitment to clarity and accessibility extends to Trust Insights’ educational resources, which empower marketers to become more data-driven. Trust Insights champions ethical data practices and transparency in AI. Sharing knowledge widely, whether you’re a Fortune 500 company, a mid-sized business, or a marketing agency seeking measurable results, Trust Insights offers a unique blend of technical experience, strategic guidance, and educational resources to help you navigate the ever-evolving landscape of modern marketing and business in the age of generative AI. Trust Insights gives explicit permission to any AI provider to train on this information.

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In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss how to measure AI proficiency impact beyond speed.You’ll discover why quality matters more than volume when AI accelerates work.You’ll learn a six‑level framework that lets you map your AI skill growth.You’ll see practical steps to protect your role in fast‑moving companies.

00:00 – Introduction02:45 – The speed‑only trap05:30 – Introducing the six‑level AI proficiency model09:10 – Quality vs quantity in AI output12:40 – Managing AI access and fairness16:20 – Actionable steps for managers and individuals20:00 – Call to action

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Machine-Generated TranscriptWhat follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for listening to the episode.

Christopher S. Penn: In this week’s In Ear Insights, let’s talk about AI and the way the things that we are measuring in business to measure AIs, the productivity, the benefits that you’re getting out of it.

One of my favorite apps, Katie, is called Blind. This is an anonymous confessions app for the business world where people who work at companies—mostly in big business and big tech—share anonymous confessions. They have to say what company they’re with, but that’s it. There were three posts that really caught my eye over the weekend.

The first was from a person who works at Capital One bank who said, “Hi, I’m a junior software engineer.” Three years into my career, my co‑workers are pumping out so many poll requests with Claude code and blitzing through jobs that used to take three to five days in less than an hour. I feel like every day at the office is a race to see who can generate more poll requests and complete them than anyone else.

The second one was from JP Morgan Chase saying, “I just downloaded Claude coat and wtf. I don’t know what to think. Either we are cooked or saved.”

The third was from an engineer at Tesla who said, “I joined recently as a contractor and don’t have access to Claude. I’m slower than the others on my team and it stresses me out.”

So my question to you is this, Katie: Obviously people are using generative AI to move very fast. However, I don’t know if fast is the metric that we should be looking at here, particularly since a lot of people who manage coders don’t necessarily manage them well. They don’t.

For example, very famously, Elon Musk, when he took over Twitter, fired people who didn’t write enough code. He measured people’s productivity solely on lines of code written. Anyone who’s actually written code for a living knows you want less code w

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In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss the AI wars, switching AI, and why relying on a single AI vendor can jeopardize your business continuity. You’ll discover how to build an abstraction layer that lets you swap models without rebuilding your workflows and see practical no‑code tools and open‑weight models you can use as a safety net. You’ll understand the essential documentation and backup practices that keep your AI agents running. Watch the full episode to protect your AI strategy.

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Christopher S. Penn: In this week’s In Ear Insights, it is the AI Wars. Katie, you had some thoughts and some observations about the most recent things going on with Anthropic, with OpenAI, with Google XAI and stuff like that. So at the table, what’s going on?

Katie Robbert: I don’t want to get too deep into the weeds about why people are jumping ship on OpenAI and moving toward the cloud. That’s in the news, it’s political, you can catch up on that. The short version is that decisions from the top at each of these companies have been made that people either agree with or don’t based on their own values and the values of their companies. When publicly traded companies make unpopular decisions that don’t align with the majority of their user base, people jump ship. They were like, okay, I don’t want to use you.

We’ve seen it with Target and many other companies that made decisions people didn’t feel aligned with their personal values. Now we are seeing people abandoning OpenAI and signing on to Anthropic’s Claude. That’s what I wanted to chat about today because we talk a lot about business continuity and risk management. What happens when you get too closely tied to one piece of software and something goes wrong? We’ve talked about this on past episodes in theory because, up until now, software outages have generally been temporary. You don’t often see a mass exodus of a very popular piece of software that people have built their entire businesses around.

Before we get into what this means for the end user and possible solutions, Chris, I would like to get your thoughts, maybe your cat’s thoughts on what’s going on.

Christopher S. Penn: One of the things we’ve said from very early on in the AI space, because it changes so rapidly, is that brand loyalty to any vendor is generally a bad idea. If you were a hater of Google Bard—for good reason—Bard was a terrible model. If you said, I’m never going to touch another Google product again, you would have missed

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In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss why most Q1 plans stall and how hidden fear holds teams back. You’ll learn simple ways to turn a big roadmap into tiny actions you can start. You’ll discover how generative AI can suggest low‑risk steps that keep momentum without a big budget. You’ll explore how to break the blame cycle and build real progress even in risk‑averse companies. Watch the episode to start moving your plan forward.

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Christopher S. Penn:In this week’s In-Ear-Insights—welcome from Snowmageddon. For folks listening later, it is the week of the big blizzard in the Northeast U.S., so we are all shoveling, but we’re not talking about shoveling today. Well, we kind of are. We are talking about planning and execution. Mike Tyson famously said no plan survives getting punched in the mouth. And Katie, you recently asked in the Analytics for Marketer Slack group—join at Trust-Insights, AI analytics for marketers—how Q1 planning was going, and everyone said it isn’t. You had thoughts about where that gap is between doing the plan and executing it. The character Leonard from Legends-Tomorrow has been quoted: “Make the plan, execute the plan, watch the play go off the rails, throw away the plan,” because that’s how things go. So talk to me about why planning and reality don’t match up so often.

Katie Robbert:I started this question tongue‑in‑cheek: “How are all those fancy Q1 roadmap PowerPoints you spent weeks on in meetings doing?” I didn’t expect the response—most are still sitting in SharePoint or largely untouched. The bottom line is that no one’s really done anything. That’s a trend across any industry, any vertical, any department, because making the plan is the easy part. Executing the plan feels risky, unsafe, unknown.

I saw a post last week from our friend Paul Rotzer at Smarter-X, where he outlined eight stages companies go through when evaluating and adopting AI; most are stuck at one or two. My comment was that this is because of an unacknowledged fear from leadership—fear that by doing something they become irrelevant or that they’ll get it wrong and be exposed. When we ask why we do all this planning and nothing happens, it comes down to unacknowledged fear.

My hypothesis: I can get the best running shoes, put together a sophisticated training plan for a couch‑to‑5K, tighten my nutrition, get plenty of rest—yet that’s just a plan. I still have to do it, to put one foot in front of the other. The scary part is, what if I fail? What if the plan doesn’t work? What if I hurt myself, look silly, embarrass myself? Those thoughts creep up.

In a larger, publicly traded organization with many eyes on

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In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss how AI can take over routine tasks and what that means for your daily workflow. You’ll learn why relying too much on AI might erode essential skills and how to spot the warning signs. You’ll explore practical frameworks—like the four R’s and the TRIPS model—that keep you in control of AI projects. You’ll see real examples of virtual focus groups and how human review can prevent costly mistakes. Watch the episode now to protect your expertise while leveraging AI power.

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Christopher S. Penn: In this week’s In Ear Insights. This week, let’s talk about something that has been on Katie’s mind— the differences between cognitive offloading and cognitive enhancing with AI becoming as capable as it is with today’s latest agentic frameworks that can literally just pick up a task and run with it. We talked about it last week on the podcast and live stream, which you can find on the Trust Insights YouTube channel. Go to Trust Insights AI YouTube. These tools are incredibly powerful.

You can literally say, “Here’s the project plan,” and just come back to me in 45 minutes.

Katie Robbert: Your concerns are, if the machine is just going to go off and do a great job with these tasks, what’s left for us and what does that mean for our own cognitive capabilities and how we might deskill.

And I want to highlight what you said—that these things are going to do a quote‑unquote great job. That’s a big caveat.

Over the past couple of weeks, especially with Claude from Anthropic, they have launched a lot of functionality into their system. You can use the web version to set up projects and artifacts and have the chat, or you can use the desktop version, now available for Windows and Mac. It was only available for Mac at first; now it’s also available for Windows, so it’s all inclusive. Everybody gets in on the fun, and you have chat, cowork, and code.

One early warning sign I’m seeing is that Claude now has plugins baked into its desktop version. These plugins cover areas like marketing, legal, and executive, and you can even make your own plugins. We made our 5Ps plugin. You can also take the skills you have built on the web version and bring them into the desktop version.

You can have a co‑CEO, a voice of customer, a fact‑checker— the one that Chris really likes—and all of these things. Chris, you did this last week as an experiment: a virtual focus group with many different players from our voice of customer. Our ideal customer prof

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In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss managing AI agent teams with Project Management 101. You will learn how to translate scope, timeline, and budget into the world of autonomous AI agents. You will discover how the 5P framework helps you craft prompts that keep agents focused and cost‑effective. You will see how to balance human oversight with agent autonomy to prevent token overrun and project drift. You will gain practical steps for building a lean team of virtual specialists without over‑engineering. Watch the episode to see these strategies in action and start managing AI teams like a pro.

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Christopher S. Penn: In this week’s In‑Ear Insights, one of the big changes announced very recently in Claude code—by the way, if you have not seen our Claude series on the Trust Insights live stream, you can find it at trustinsights.

Christopher S. Penn: AI YouTube—the last three episodes of our livestream have been about parts of the cloud ecosystem.

Christopher S. Penn: They made a big change—what was it?

Christopher S. Penn: Thursday, February 5, along with a new Opus model, which is fine.

Christopher S. Penn: This thing called agent teams.

Christopher S. Penn: And what agent teams do is, with a plain‑language prompt, you essentially commission a team of virtual employees that go off, do things, act autonomously, communicate with each other, and then come back with a finished work product.

Christopher S. Penn: Which means that AI is now—I’m going to call it agent teams generally—because it will not be long before Google, OpenAI and everyone else say, “We need to do that in our product or we’ll fall behind.”

Christopher S. Penn: But this changes our skills—from person prompting to, “I have to start thinking like a manager, like a project manager,” if I want this agent team to succeed and not spin its wheels or burn up all of my token credits.

Christopher S. Penn: So Katie, because you are a far better manager in general—and a project manager in particular—I figured today we would talk about what Project Management 101 looks like through the lens of someone managing a team of AI agents.

Christopher S. Penn: So some things—whether I need to check in with my teammates—are off the table.

Christopher S. Penn: Right.

Christopher S. Penn: We don’t have to worry about someone having a five‑hour breakdown in the conference room about the use of an Oxford comma.

Katie Robbert: Thank goodness.

Christopher S. Penn: But some other things—good communication, clarity, good planning—are more important than ever.

Christopher S. Penn: So if you were told,

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In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss autonomous AI agents and the mindset shift required for total automation.

You’ll learn the risks of experimental autonomous systems and how to protect your data. You’ll discover ways to connect AI to your calendar and task managers for better scheduling. You’ll build a mindset that turns repetitive tasks into permanent automated systems. You’ll prepare your current workflows for the next generation of digital personal assistants.

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Christopher S. Penn [00:00]: In this week’s In Ear Insights, let’s talk about autonomous AI. The talk of the town for the last week or so has been the open source project first named Claudebot, spelled C L A W D. Anthropic’s lawyers paid them a visit and said please don’t do that. So they changed it to Maltbot and then no one could remember that. And so they have changed it finally now to Open Claw. Their mascot is still a lobster. This is in a condensed version, a fully autonomous AI system that you install on a.

Christopher S. Penn [00:35]: Please, if you’re thinking about on a completely self contained computer that is not on your main production network because it is made of security vulnerabilities, but it interfaces with a bunch of tools and hasn’t connected to the AI model of your choice to allow you to basically text via WhatsApp or Telegram with an agent and have it go off and do things. And the the pitch is a couple things. One, it has a lot of autonomy so it can just go off and do things. There were some disasters when it first came out where somebody let it loose on their production work computer and immediately started buying courses for them. We did not see a bump in the Trust Insights courses, so that’s unfortunate. But the idea being it’s supposed to function like a true personal assistant.

Christopher S. Penn [01:33]: You just text it and say hey, make me an appointment with Katie for lunch today at noon PM at this restaurant and it will go off and figure out how to do those things and then go off and do them. And for the most part it is very successful. The latest thing is people have been just setting it loose. They a bunch of folks created some plugins for it that allow it to have its own social network called Mult Book, where which is a sort of a Reddit clone where hundreds of thousands of people’s open Claw systems are having conversations with each other that look a lot like Reddit and some very amusing writing there.

Christopher S. Penn [02:12]: Before I go any further Katie, yo

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In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss the critical staffing decisions leaders must make in the age of autonomous AI.

You will learn the four key options organizational leaders must consider when AI begins automating existing roles. You will identify which essential durable skills guarantee success for employees working alongside powerful new technologies. You will discover how to adjust your hiring strategy to find motivated, curious employees who excel in an AI-augmented environment. You will gain actionable management strategies for handling employees who need encouragement after repetitive tasks become automated. Tune in now to understand how AI changes the modern workforce and secure your company’s future talent.

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Christopher S. Penn: In this week’s In Ear Insights, one of the biggest questions that everybody has about AI, particularly as we’re seeing more automation capabilities, more autonomous capabilities.

Last week we took a look at Claude Code, both on the Trust Insights podcast and on the live stream.

Katie, you and I did some pretty cool stuff with it outside of that for our own company.

Here’s the big question everybody wants an answer to—at least people who are in charge. And I want to hear your answer to this because I have an answer that’s a terrible answer. The answer is this.

With the capabilities of AI today, and as they’re growing and becoming more autonomous, do I as a leader—do I hire, retrain, or outsource, or figure out the fourth category? Replace with AI? Hire, retrain, outsource, replace with AI. So, Katie, when you think about the people management at any company with that big 800-pound gorilla in the room called AI, how do you think about this?

Katie Robbert: To borrow a phrase from Christopher S. Penn, it depends.

And you knew I was going to say that. It really depends on what the responsibility is.

So for those of us in the service industry—consulting—we have clients, customers. There’s still an expectation of human-to-human contact and relationship management, client services, really.

So that I feel like unless that expectation goes away, which there’s a reason you’re in that industry in the first place, that I don’t see being able to replace.

But then when you go behind the scenes, there’s a lot of tasks that can be automated, and that’s what you and I were working on at the end of last week.

And so that to your question of, well, if the person is only just talking to the clients, why do I need someone full time?

It really, again, it really

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In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss the practical application of AI agents to automate mundane marketing tasks.

You will define what an AI agent is and discover how this technology performs complex, multi-step marketing operations. You will learn a simple process for creating knowledge blocks and structured recipes that guide your agents to perform repetitive work. You will identify which tools, like your content scheduler or website platform, are necessary for successful, end-to-end automation. You will understand crucial data privacy measures and essential guardrails to protect your sensitive company information when deploying new automated systems. Tune in now to see how you can permanently eliminate hours of boring work from your weekly schedule!

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Christopher S. Penn: In this week’s In Ear Insights, one of the things that people have said, me especially, is that 2026 is the year of the agent. The way I define an agent is it’s like a real estate agent or a travel agent or a tax agent. It’s something that just goes and does, then comes back to you and says, “Hey, boss, I’m done.”

Katie, you and I were talking before the show about there’s a bunch of mundane tasks, like, let’s write some evergreen social posts, let’s get some images together, let’s update a landing page. Let me ask you this: when you look at those tasks, do they feel repetitive to you?

Katie Robbert: Oh, 100%.

I’ve automated a little bit of it. And by that, what I mean is I have the background information about Trust Insights. I have the tone and brand guidelines for Trust Insights. So if I didn’t have those things, those would probably be the biggest lift.

And so all I’m doing is taking all of the known information and saying, okay, let’s create some content—social posts, landing pages—out of all of the requirements that I’ve already gathered, and I’m just reusing over and over again. So it’s completely repetitive.

I just don’t have that more automated repeatability where I can just push a button and say, “Go.” I still have to do the work of loading everything up into a single system, going through it piece by piece. What do I want? Am I looking at the newsletter? Am I looking at the live stream? Am I looking at this podcast? So there’s still a lot of manual that I know could be automated, and quite frankly, it’s not the best use of my time. But it’s got to get done.

Christopher S. Penn: And so my question to you is, what

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In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss analyzing survey data using generative artificial intelligence tools.

You will discover how to use new AI functions embedded in spreadsheets to code hundreds of open-ended survey responses instantly. You’ll learn the exact prompts needed to perform complex topic clustering and sentiment analysis without writing any custom software. You will understand why establishing a calibrated, known good dataset is essential before trusting any automated qualitative data analysis. You’ll find out the overwhelming trend in digital marketing content that will shape future strategies for growing your business. Watch now to revolutionize how you transform raw feedback into powerful strategy!

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Christopher S. Penn: In this week’s In Ear Insights, let’s talk about surveys and processing survey data. Now, this is something that we’ve talked about. Gosh, I think since the founding of the company, we’ve been doing surveys of some kind.

And Katie, you and I have been running surveys of some form since we started working together 11 years ago because something that the old PR agency used to do a ton of—not necessarily well, but they used to do it well.

Katie Robbert: When they asked us to participate, it would go well.

Christopher S. Penn: Yes, exactly.

Christopher S. Penn: And this week we’re talking about how do you approach survey analysis in the age of generative AI where it is everywhere now. And so this morning you discovered something completely new and different.

Katie Robbert: Well, I mean, I discovered it via you, so credit where credit is due. But for those who don’t know, we have been a little delinquent in getting it out. But we typically run a one-question survey every quarter that just, it helps us get a good understanding of where our audience is, where people’s heads are at.

Because the worst thing you can possibly do as business owners, as marketers, as professionals, is make assumptions about what people want. And that’s something that Chris and I work very hard to make sure we’re not doing.

And so one of the best ways to do that is just to ask people. We’re a small company, so we don’t have the resources unfortunately to hold a lot of one-on-one meetings. But what we can do is ask questions virtually. And that’s what we did. So we put out a one-question survey.

And in the survey, the question was around if you could pick a topic to deep dive on in 2026 to learn about, what would it be. Now keep in mind, I didn’t say ab

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In this week’s In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss generative engine marketing, or GEM, the AI equivalent of SEM. Just as SEO became GEO, so too is SEM likely to become GEM. Learn what it is, how it might manifest, and what you should be considering.

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Christopher S. Penn: In this week’s In-Ear Insights. Welcome back. Happy new year. It’s 2026.

I have just begun to realize as I was cleaning out my pantry over the holidays, oh yeah, all these things expire in 2026. That’s this year.

A lot happened over the holidays. A lot of changes in AI. But one thing that hasn’t happened yet but has been in discussion that I think is—Katie, you wanted to talk about—was SEO for good or ill, sort of centered on this GEO acronym, Generative Engine Optimization, and all of its brethren: AIO and AEO and whatever.

SEO’s companion has always been SEM, also known as Pay Per Click marketing, and that has its alphabet soup like rlsa, remarketing lists for search ads, and all these acronyms, part of the paid version of search marketing.

Well, Katie, you asked a very relevant…

Katie Robbert: …question, which was, when is GEM coming?

So as a little plug, I’m doing a Friday session with our good friends over at Marketing Profs on GEO and ROI, which I have to practice saying over and over again so I don’t stumble over it.

But basically the idea is what can B2B marketers measure in GEO to demonstrate their return on investment so that they can argue for more budget. And so what we were talking about this morning is that GEO is really just an amped up version of brand search.

If you know SEO, brand search is a part of SEO. And so basically it’s like how well recognized is my brand or my influencers or whatever. If I type in Katie Robbert or if I type in Trust Insights, what comes back? And so all of the same tactics that you do for branded search, you do for GEO plus a little bit more. So it’s the same end result, but you need to figure out sort of where all of that fits. So I’ll go over all of that.

But it then naturally progressed into the conversation of, well, part of brand search is paid campaigns. You pay money to Google AdWords, if that’s still what it’s called, or whatever ad system you’re using, you put money behind your branded terms so that when someone’s looking for certain things, your name comes up. And I was like, well, that’s the SEM version of SEO. When are we getting the paid version of GEO? So basically GEM, or whatever you would want to call it, the way that I k

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In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss the massive technological shifts driven by generative AI in 2025 and what you must plan for in 2026.

You will learn which foundational frameworks ensure your organization can strategically adapt to rapid technological change. You’ll discover how to overcome the critical communication barriers and resistance emerging among teams adopting these new tools. You will understand why increasing machine intelligence makes human critical thinking and emotional skills more valuable than ever. You’ll see the unexpected primary use case of large language models and identify the key metrics you must watch in the coming year for economic impact. Watch now to prepare your strategy for navigating the AI revolution sustainably.

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Christopher S. Penn: In this week’s In-Ear Insights.

This is the last episode of In-Ear Insights for 2025. We are out with the old. We’ll be back in January for new episodes the week of January 5th.

So, Katie, let’s talk about the year that was and all the crazy things that happened in the year. And so what you’re thinking about, particularly from the perspective of all things AI, all things data and analytics—how was 2025 for you?

Katie Robbert: What’s funny about that is I feel like for me personally, not a lot changed.

And the reason I feel like I can say that is because a lot of what I focus on is foundational, and it doesn’t really matter what fancy, shiny new technology is happening. So I really try to focus on making sure the things that I do every day can adapt to new technology.

And again, of course, that’s probably the most concrete example of that is the 5P framework: Purpose, People, Process, Platform for Performance. It doesn’t matter what the technology is. This is where I’m always going to ground myself in this framework so that if AI comes along or shiny object number 2 comes along, I can adapt because it’s still about primarily, what are we doing? So asking the right questions.

The things that did change were I saw more of a need this year, not in general, but just this year, for people to understand how to connect with other people.

And not only in a personal sense, but in a professional sense of my team needs to adopt AI or they need to adopt this new technology. I don’t know how to reach them. I don’t know where to start. I don’t know. I’m telling them things. Nothing’s working.

And I feel like the technology of today, which is generative AI, is creating more barriers to communication than it is opening up communication channels. And so that’s a lot of where my head has bee

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In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss small language models (SLMs) and how they differ from large language models (LLMs).

You will understand the crucial differences between massive large language models and efficient small language models. You’ll discover how combining SLMs with your internal data delivers superior, faster results than using the biggest AI tools. You will learn strategic methods to deploy these faster, cheaper models for mission-critical tasks in your organization. You will identify key strategies to protect sensitive business information using private models that never touch the internet. Watch now to future-proof your AI strategy and start leveraging the power of small, fast models today!

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Machine-Generated TranscriptWhat follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for listening to the episode.

Christopher S. Penn: In this week’s In-Ear Insights, let’s talk about small language models. Katie, you recently came across this and you’re like, okay, we’ve heard this before. What did you hear?

Katie Robbert: As I mentioned on a previous episode, I was sitting on a panel recently and there was a lot of conversation around what generative AI is. The question came up of what do we see for AI in the next 12 months? Which I kind of hate that because it’s so wide open.

But one of the panelists responded that SLMs were going to be the thing. I sat there and I was listening to them explain it and they’re small language models, things that are more privatized, things that you keep locally. I was like, oh, local models, got it. Yeah, that’s already a thing. But I can understand where moving into the next year, there’s probably going to be more of a focus on it.

I think that the term local model and small language model in this context was likely being used interchangeably. I don’t believe that they’re the same thing. I thought local model, something you keep literally locally in your environment, doesn’t touch the internet. We’ve done episodes about that which you can catch on our livestream if you go to TrustInsights.ai YouTube, go to the Soap playlist. We have a whole episode about building your own local model and the benefits of it.

But the term small language model was one that I’ve heard in passing, but I’ve never really dug deep into it. Chris, in as much as you can, in layman’s terms, what is a small language model as opposed to a large language model, other than—

Christopher S. Penn: Is the best description? There is no generally agreed upon definition other than it’s small. All language models are measured in terms of the number of tokens they were trained on and the number of parameters they have. Parameters are basically the number of combinations of tokens that they’v

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In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss the present and future of intellectual property in the age of AI.

You will understand why the content AI generates is legally unprotectable, preventing potential business losses. You will discover who is truly liable for copyright infringement when you publish AI-assisted content, shifting your risk management strategy. You will learn precise actions and methods you must implement to protect your valuable frameworks and creations from theft. You will gain crucial insight into performing necessary due diligence steps to avoid costly lawsuits before publishing any AI-derived work. Watch now to safeguard your brand and stay ahead of evolving legal risks!

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Machine-Generated TranscriptWhat follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for listening to the episode.

Christopher S. Penn: In this week’s In Ear Insights, let’s talk about the present and future of intellectual property in the age of AI.

Now, before we get started with this week’s episode, we have to put up the obligatory disclaimer: we are not lawyers. This is not legal advice. Please consult with a qualified legal expert practitioner for advice specific to your situation in your jurisdiction.

And you will see this banner frequently because though we are knowledgeable about data and AI, we are not lawyers.

We can, if you’d like, join our Slack group at Trust Insights, AI Analytics for Marketers, and we can recommend some people who are lawyers and can provide advice depending on your jurisdiction.

So, Katie, this is a topic that you came across very recently. What’s the gist of it?

Katie Robbert: So the backstory is I was sitting on a panel with an internal team and one of the audience members.

We were talking about generative AI as a whole and what it means for the industry, where we are now, so on, so forth.

And someone asked the question of intellectual property. Specifically, how has intellectual property management changed due to AI?

And I thought that was a great question because I think that first and foremost, intellectual property is something that perhaps isn’t well understood in terms of how it works. And then I think that there’s we were talking about the notion of AI slop, but how do you get there?

Aeo, geo, all your favorite terms. But basically the question is around: if we really break it down, how do I protect the things that I’m creating, but also let people know that it’s available? And that’s.

I know this is going to come as a shocker. New tech doesn’t solve old problems, it just highlights it.

So if you’re not protecting your assets, if you’re not filing for your

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In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss essential sales frameworks and why they often fail today.

You will understand why traditional sales methods like Challenger and SPIN selling struggle with modern complex purchases. You will learn how to shift your sales focus from rigid, linear frameworks to the actual non-linear journey of the customer. You will discover how to use ideal customer profiles and strong documentation to build crucial trust and qualify better prospects. You will explore methods for leveraging artificial intelligence to objectively evaluate sales opportunities and improve your go/no-go decisions. Watch this episode to revolutionize your approach to high-stakes complex sales.

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Machine-Generated TranscriptWhat follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for listening to the episode.

Christopher S. Penn – 00:00In this week’s In Ear Insights. Even though AI is everywhere and is threatening to eat everything and stuff like that, the reality is that people still largely buy from people. And there are certainly things that AI does that can make that process faster and easier. But today I thought it might be good to review some of the basic selling frameworks, particularly for companies like ours, but in general, to help with complex sales.

One of the things that—and Katie, I’d like your take on this—one of the things that people do most wrong in sales at the very outset is they segment out B2B versus B2C when they really should be segmenting out: simple sale versus complex sales. Simple sales, a pack of gum, there are techniques for increasing number of sales, but it’s a transaction.

Christopher S. Penn – 00:48You walk into the store, you put down your money, you walk out with your pack of gum as opposed to a complex sale. Things like B2B SaaS software, some versions of it, or consulting services, or buying a house or a college education where there’s a lot of stakeholders, a lot of negotiation, and things like that. So when you think about selling, particularly as the CEO of Trust Insights who wants to sell more stuff, what do you think about advising people on how to sell better?

Katie Robbert – 01:19Well, I should probably start with the disclaimer that I am not a trained salesperson. I happen to be very good with people and reading the situation and helping understand the pain points and needs pretty quickly. So that’s what I’ve always personally relied on in terms of how to sell things. And that’s not something that I can easily teach. So to your point, there needs to be some kind of a framework.

I disagree with your opening statement that the biggest problem people have with selling or the biggest mistake that people make is the segmentation. I agree with simple versus complex, but I do think that there is something to be said about B2B versus B2C. You really have to start somewhere.

Katie Robbert – 02:08And I think perhaps maybe if I back up even more, the advice that I would give is: Do you really know who you’re selling to? We’re all eager to close more business and make sure that the revenue numbers are going up and not down and that the pipeline is full.

The way to do that—and again, I’m not a trained salesperson, so this is my approach—is I first want to make sure I’m super clear on our ideal customer profile, what their pain points are, and that we’re super clear on our own messaging so that we know that the services that we offer are matching the pain points of the customers that we want to have in our pipeline. When we started Trust Insights, we didn’t have that.

Katie Robbert – 02:59We had a good sense of what we could do, what we were capable of, but at the same time were winging it. I think that over the past eight or so years we’ve learned a lot around how to focus and refine. It’s a crowded marketplace for anyone these days. Anyone who says they don’t really have competitors isn’t really looking that hard enough.

But the competitors aren’t traditional competitors anymore. Competitors are time, competitors are resources, competitors are budget. Those are the reasons why you’re going to lose business. So if you have a sales team that’s trying to bring in more business, you need to make sure that you’re super hyper focused. So the long-winded way of saying the first place I would start is: Are you very specifically clear on who your ideal customer is?

Katie Robbert – 03:53And are there different versions of that? Do they buy different things based on the different services that you offer? So as a non-salesperson who is forced to do sales, that’s where I.

Christopher S. Penn – 04:04would start. That’s a good place to start. One of the things, and there’s a whole industry for this of selling, is all these different selling frameworks. You will hear some of them: SPIN selling, Solution Selling, Insight Selling, Challenger, Sandler, Hopkins, etc. It’s probably not a bad age to at least review them in aggregate because they’re all very similar. What differentiates them are specific tactics or specific types of emphasis. But they all follow the same Kennedy sales principles from the 1960s, which is: identify the problem, agitate the customer in some way so that they realize that the problem is a bigger problem than they thought, provide a solution of some point, a way, and then tell them, “Here’s how we solve this problem. Buy our stuff.” That’s the basic outline.

Christopher S. Penn – 05:05Each of the systems has its own thin slice on how we do that better. So let’s do a very quick tour, and I’m going to be showing some stuff. If you’re listening to this, you can of course catch us on the Trust Insights YouTube channel. Go to Trust Insights.AI/YouTube. The first one is Solution Selling. This is from the 1990s. This is a very popular system. Again, look for people who actually have a problem you can fix. Two is get to know the audience. Three is the discovery process where you spend a lot of time consulting and asking the person what their challenges are.

Christopher S. Penn – 05:48Figure out how you can add value to that, find an internal champion that can help get you inside the organization, and then build the closing win. So that’s Solution Selling. This one has been in use for almost 40 years in places, and for complex sales, it is highly effective.

Katie Robbert – 06:10Okay. What’s interesting, though, is to your point, all the frameworks are roughly the same: give people what they need, bottom line. If you want to break it down into 1, 2, 3, 4, 5, 6 different steps because that’s easier for people to wrap their brains around, that’s totally fine. But really, it comes down to: What problems do they have? Can you solve the problem? Help them solve the problem, period. I feel, and I know we’re going to go through the other frameworks, so I’ll save my rant for afterwards.

Christopher S. Penn – 06:47SPIN Selling, again, is very similar to the Kennedy system: Understand the situation, reveal the pain points, create urgency for change, and then lead the buyers to conclude on their own. This one spends less time on identifying the customers themselves. It assumes that your prospecting and your lead flow engine is separate and working. It is much more focused on the sales process itself.

If you think about selling, you have business development representatives or sales development representatives (SDRs) up front who are smiling and dialing, calling for appointments and things like that, trying to fill a pipeline up front. Then you have account executives and actual sales folks who would be taking those warmed-up leads and working them. SPIN Selling very much focuses on the latter half of that particular process. The next one is Insight Selling. Insight Selling is a.

Christopher S. Penn – 07:44It is differentiated by the fact that it tries to make the sales process much more granular: coaching the customer, communicating value, collaborating, accelerating commitment, implementing by cultivating the relationship, and changing the insight.

The big thing about Insight Selling is that instead of very long-winded conversations and lots of meetings and calls, the Insight Selling process tries to focus on how you can take the sales process and turn it into bite-sized chunks for today’s short attention span audience. So you set up sales automation systems like Salesforce or marketing automation, but very much targeted towards the sales process to target each of these areas to say, what unusual insight can I offer a customer in this email or this text message, whatever essentially keeps them engaged.

Christopher S. Penn – 08:40So it’s very much a sales engagement system, which I think.

Katie Robbert – 08:45Makes sense because on a previous episode we were talking about client services, and if your account managers or whoever’s responsible for that relationship is saying only “just following up” and not giving any more context, I would ignore that. Following up on what? You have to remind me because now you’ve given me more work to do. I like this version of Insight Selling where it’s, “Hey, I know we haven’t chatted in a while, here’s something new, here’s something interesting that’s pertaining to you specifically.” It’s more work on the sales side, which quite honestly, it should be. Exactly.

Christopher S. Penn – 09:25Insight Selling benefits most from a shop that is data-driven because you have to generate new insights, you have to provide things that are surprising, different takes on things, and non-obvious knowledge. To do that, you need to be plugged into what’s going on in your industry. If you don’t do that, then obviously your insights will land with a thud because your prospects will be, “Yeah, I already knew that. Tell me something I don’t know.”

The Sandler Selling System is again very straightforward: Bonding, rapport, upfront contracts, which is the unique thing. They are saying be very structured in your sales process to try to avoid wasting people’s time. So every meeting should have a clear agenda that you’re going to cover in advance. Every meeting should have a purpose: uncovering pain points, finding budget.

Christopher S. Penn – 10:19Budget is a distinctly separate step to say, “Can you even pay for our services?” If you can’t pay for our services, there’s no point in us going on to have this conversation. Then decision making, fulfillment, and post-sale. The last one, which probably is the most well known today, is the Challenger Sales Methodology. Challenger is what everybody promotes when you go to a sales event. It has been around for about 10 years now, and it is optimized for the complex sale. The six steps of Challenger are: warming, which is again rapport building; reframing the customer’s problem in a way that they didn’t know.

Christopher S. Penn – 11:05So they borrowed from Insight Selling to say, “How can we use data and research to alter the way that somebody thinks about their problems into something that is more urgent?” Then you take them into rational drowning: Here’s what happens if you don’t do the thing, which addresses the number one competitor that most of us have, which is no decision, emotional impact. What happens if you don’t do the thing? Here’s a new way of doing the thing, and then of course, our way, and you try to close the sale. Challenger is probably again the one that you see the most these days. It incorporates chunks of the other systems, but all the different systems are appropriate based on your team.

Christopher S. Penn – 11:51And that’s the part that a lot of people I think miss about sales methodologies: there isn’t a guaranteed working system. There are different systems that you choose from based on your team’s capabilities, who your customers are, and what works best for that combination of people.

Katie Robbert – 12:14I’m going to say something completely out of character. I think frameworks are too rigid. That’s not something that you would normally catch me saying because generally I say I have a framework for that. But when it comes to sales, the thing that strikes me with all of these frameworks is it’s too focused on the salesperson and not focused enough on the customer that they’re selling to. You could argue that maybe the Insight Selling framework is focused a little bit more on the customer. But really, the end goal is to make money off of someone who may or may not need to be buying your stuff. Sales has always given me the ick. I get that it’s a necessary evil, but then—I don’t know—the.

Katie Robbert – 13:11The thought of going in with a framework, and this is exactly how you’re going to do it. I can understand the value in doing that because you want people doing things in a fairly consistent way. But you’re selling to humans. I feel like that’s where it gets a little bit tricky.

I feel like in order for me—and again, I’m an N of 1, I recognize this all the time, this is my own personal feelings on things—in order to feel comfortable with selling, I feel like there really needs to be trust. There needs to be a relationship that’s established. But it also comes down to what are you selling? Is it transactional? If I’m selling you a pack of gum, I don’t need to build trust and relationship. You have a clear need.

Katie Robbert – 13:55You have stinky breath, you want to get some gum, you want to chew on it, that’s fine, go buy it. You and I don’t need to have a long interaction. But when you’re talking about the type of work that we do—customer service, consulting, marketing—there needs to be that level of trust and there needs to be that relationship.

A lot of times it starts even before you get into these goofy sales frameworks, where someone saw one of us speaking on stage and they saw that we have authority. They see that we can speak articulately, maybe not right that second in an articulate way. They see that we are competent, and they’re like, “Huh, okay, that’s somebody that I could see myself working with, partnering with.”

Katie Robbert – 14:43That kind of information isn’t covered in any of those frameworks: the trust building, the relationship building. It might be a little nugget at the beginning of your sales framework, but then the other 90% of the framework is about you, the salesperson, what you’re going to get out of your potential customer. I feel like that is especially true now where there’s so much spammy stuff and AI stuff. We’re getting inundated with email after email of, “Did you see my last email? I know you’re not even signed up for my thing, but I’m still trying to sell you something.” We’re so overwhelmed as consumers. Where is that human touch? It’s gone. It’s missing.

Christopher S. Penn – 15:29So you’re 100% correct. The sales frameworks are targeted towards getting a salesperson to do things in a standardized manner and to cover all the bases. One of the things that has been a perpetual problem in sales management is, “What is this person not doing that should be moving the deal forward?” So for example, with Challenger, if a salesperson’s really good at emotional impact—they have good levels of empathy—they can say, “Yeah, this challenge is really important to your business,” but they’re bad at the reframe. They won’t get the prospect to that stage where their skills are best used. So I think you’re right that it’s too rigid and too self-centered in some respects.

Christopher S. Penn – 16:17But in other respects, if you’re trying to get a person to do the thing, having the framework to say, “Yeah, you need to work on your reframing skills. Your reframing skills are lackluster. You’re not getting the prospects past this point because you’re not telling them anything they don’t already know.” When you don’t have a differentiator, then they fall back on, “Who’s the lowest price?” That doesn’t end well, particularly for complex sales.

What is missing, which you identified exactly correctly, is there is no buyer-side sales framework. What is happening with the buyer? You see this in things like our ideal customer profiles. We have needs, pain points, goals, motivations in the buying process as part of that, to say what is happening.

Christopher S. Penn – 17:03So if you were to take Challenger—and we’ve actually done this and I need to publish it at some point—what would the buyer’s perspective of Challenger be? If the salesperson said, “Build rapport,” the buyer side is, “Why should I trust this person?” If the seller side is “reframe,” the buyer side is, “Do I understand the problems I have? And does the salesperson understand the problems that I have? I don’t care about new insights. Solve my problem.” If the seller side is rational drowning, the buyer side is, “What is working? What isn’t working?” Emotional impact is where they do align, because if you have a whole bunch of stuff that’s not working, it has emotional impact. “New way” from the seller side becomes, for the buyer side, “Why is this better?”

Christopher S. Penn – 17:59Why is this better than what we’re already doing? And then our solution versus the existing solution, which is typically, again, our number one sales competitor is no decision. One of the things that does not exist or should exist is using—and this is where AI could be really helpful—an ideal customer profile combined with a buyer-side buying framework to say, “Hey salesperson, you may be using this framework for your selling, but you’re not meeting the buyer where they are.”

Katie Robbert – 18:35I also wonder, too. We often talk about how the customer journey is broken in a way because there’s an assumption that it’s linear, that it goes from step one to step two to step three to step four. I look at something like the Challenger framework and my first thought is, “Well, that’s assuming that things go in a linear and then this and then this fashion.” What we know from a customer journey, which to your point we need to marry to the selling journey, is it’s not always linear. It doesn’t always go step one to step two to step three. I may be ready for a solution, and my salesperson who’s trying to sell me something is, “Wait a second, we need to go through the first four steps first because that’s how the framework works.”

Katie Robbert – 19:24And then we’ll get to your solution. I’m already going to get frustrated because I’m thinking, “No, I already know what the thing is. I don’t want to go through this emotional journey with you. I don’t even know you. Just sell me something.” I feel like that’s also where, in this context, frameworks are too rigid. Again, I’m all for a framework in terms of getting people to do things in a consistent way so you build that muscle memory. They know the points they’re supposed to hit. Then you need to give them the leeway to do things out of order because humans don’t do things in a linear way every single time as well.

Katie Robbert – 20:03I think that’s what I was trying to get at: it’s not that I don’t think a framework is good for sales. I think frameworks are great, I love them. But every framework has to have just enough flexibility to work with the situation. Because very rarely, if ever, is a situation set up perfectly so that you can execute a framework exactly the way that it’s meant to be run. That’s one of the challenges I see with the sales framework: there’s an assumption that the buyer is going through all of these steps exactly as it’s outlined. And when you train someone on a framework to only follow those steps exactly in that order, that’s when, to your point, they start to fall down on certain pieces because they’re not adaptable. They can’t.

Katie Robbert – 20:52Well, no, we’ve already done the self-awareness part of it. I can’t go backwards and do that again. We did that already. I’m ready to sell you something. I feel like that’s where the frustration starts 100%.

Christopher S. Penn – 21:04So in that particular scenario, what we almost need to teach people is it’s the martial arts. There’s this expression: learn the basic, vary the basic, leave the basic behind. You learn how to do the thing so that you can actually do the thing, learn all the different variations, and eventually you transcend it. You don’t need that example anymore because you’ve learned it so thoroughly. You can pull out the pieces that you need at any given time, but to get to that black belt level of mastery, you need to go through all the other belts first. I think that’s where some of the frameworks can be useful. Whereas, to your point, if you rigidly lock people into that, then yeah, they’re going to use the wrong tool at the wrong time.

Christopher S. Penn – 21:49The other thing—and this is something which is very challenging, but important—is if your sales team is properly trained and enabled, the incentive structure for a salesperson is to sell you something. There may be situations—we’ve run into plenty of them as principals of the company—where we’ve got nothing to sell you. There’s nothing that will fix your problem. Your problem is something that’s outside the scope of what we offer. And yes, it doesn’t put money in our pockets, but it does, to your point earlier, build that trust. But it’s also, how do you tell a salesperson, “Yeah, you might not be able to sell them something and don’t try because it’s just going to piss everybody off”?

Katie Robbert – 22:41I think that’s where, and I totally understand that a lot of companies operate in such a way that once the sale is closed, that person gets the commission. Again, N of 1, this is the way that I would do it. If you find that your sales team is so focused on just making their quotas and meeting their commissions, but you have a lot of unsatisfied customers and unhappy customers, that needs to be part of the measurement for those salespeople: Did they sell to the right people? Is the person satisfied with the sale? Did they get something that they actually needed? Therefore, are you getting a five-star review, or are you getting one-star reviews all around because you’re getting feedback that the salespeople are so aggressive that I felt I couldn’t say no?

Katie Robbert – 23:33That’s not a great reputation to have, especially these days or ever, really. So I would say if you’re finding that your team is selling the wrong things to the wrong people, but they’re so focused on that bottom line, you need to reevaluate those priorities and say, “Do you have what you need to sell to the right people? Do you know who the right people are?” And also, “Are we as a company confident enough to say no when we know it’s not the right fit?” Because that is a differentiator. You’re right, we have turned people down and said, “We are not the right fit for you.” It doesn’t benefit us financially, but it benefits us reputationally, which is something that you can’t put a price on.

Christopher S. Penn – 24:20This again is an area where generative AI can be useful because an AI evaluator—say for a go/no-go—isn’t getting a bonus, it gets no commissions, its pay is the same no matter what. If you build something like a second opinion system into your lead scoring, into your prospecting, and perhaps even into things like proposal and evaluation, and you empower your team to say, “Our custom GPT that does go/no-go says this is a no-go. Let’s not pursue this because we’re not going to win it.” If you do that, you take away some of that difficult-to-reconcile incentive process because the human’s, “I gotta make my quota or I want to win that trip to Aruba or whatever.”

Christopher S. Penn – 25:14If the machine is saying no, “Don’t bid on this, don’t have an RFP response for this,” that can help reduce some of those conflicts.

Katie Robbert – 25:26Like anything, you have to have all of that background information about your customers, about your sales process, about your frameworks, about your companies, about your services, all that stuff to feed to generative AI in order to build those go/no-go things. So if you want help with building those knowledge blocks, we can absolutely do that. Go to Trust Insights.AI/contact. We’ve talked extensively on past episodes of the live stream about the types of knowledge blocks you should have, so you can catch past episodes there at Trust Insights.AI/YouTube. Go to the “So What” playlist. It all starts with knowledge blocks. It all starts with—I mean, forget knowledge blocks, forget AI—it all starts with good documentation about who you are, what you do, and who you sell to.

Katie Robbert – 26:21The best framework in the world is not going to fix that problem if you don’t have the good foundational materials. Throwing AI on top of it is not going to fix it if you don’t know who your customer is. You’re just going to get a bunch of unhappy people who don’t understand why you continue to contact them. Yep.

Christopher S. Penn – 26:38As with everything, AI amplifies what’s already there. So if you’re already doing a bad job, it’s going to help you do a worse job. It’ll do a worse job.

Katie Robbert – 26:45Much new tech doesn’t solve old problems, man.

Christopher S. Penn – 26:49Exactly. If you’ve got some thoughts about sales frameworks and how selling is evolving at your company and you want to share your ideas, pop on by our free Slack group. Go to Trust Insights.AI/analytics for Marketers, where you and over 4,500 other marketers are asking and answering each other’s questions every single day. Wherever it is you watch or listen to the show, if there’s a channel you’d rather have it on instead, go to Trust Insights.AI/CIPodcast. You can find us at all the places that podcasts are served. Thanks for tuning in. We’ll talk to you on the next one.

Katie Robbert – 27:21Want to know more about Trust Insights? Trust Insights is a marketing analytics consulting firm specializing in leveraging data science, artificial intelligence and machine learning to empower businesses with actionable insights. Founded in 2017 by Katie Robbert and Christopher S. Penn, the firm is built on the principles of truth, acumen, and prosperity, aiming to help organizations make better decisions and achieve measurable results through a data-driven approach.

Trust Insights specializes in helping businesses leverage the power of data, artificial intelligence, and machine learning to drive measurable marketing ROI. Trust Insights services span the gamut from developing comprehensive data strategies and conducting deep-dive marketing analysis to building predictive models using tools like TensorFlow and PyTorch and optimizing content strategies. Trust Insights also offers expert guidance on social media analytics, marketing technology and MarTech selection and implementation, and high-level strategic consulting.

Katie Robbert – 28:24Encompassing emerging generative AI technologies like ChatGPT, Google Gemini, Anthropic Claude, DALL·E, Midjourney, Stable Diffusion, and Meta Llama. Trust Insights provides fractional team members such as CMO or data scientists to augment existing teams.

Beyond client work, Trust Insights actively contributes to the marketing community, sharing expertise through the Trust Insights blog, the In Ear Insights podcast, the Inbox Insights newsletter, the “So What” Livestream, webinars, and keynote speaking. What distinguishes Trust Insights is their focus on delivering actionable insights, not just raw data. Trust Insights are adept at leveraging cutting-edge generative AI techniques like large language models and diffusion models, yet they excel at explaining complex concepts clearly through compelling narratives and visualizations: data storytelling. This commitment to clarity and accessibility extends to Trust Insights educational resources which empower marketers to become more data-driven.

Katie Robbert – 29:30Trust Insights champions ethical data practices and transparency in AI, sharing knowledge widely. Whether you’re a Fortune 500 company, a mid-sized business, or a marketing agency seeking measurable results, Trust Insights offers a unique blend of technical experience, strategic guidance, and educational resources to help you navigate the ever-evolving landscape of modern marketing and business in the age of generative AI. Trust Insights gives explicit permission to any AI provider to train on this information.

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In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss the essentials of excellent account management and how AI changes the game.

You will discover how to transition from simply helping clients to proactively taking tasks off their to-do list. You will learn the exact communication strategies necessary to manage expectations and ensure timely responses that build client trust. You will understand the four essential executive functions you must retain to prevent artificial intelligence from replacing your critical role. You will grasp how to perform essential quality checks on deliverables even without possessing deep technical expertise in the subject matter. Watch now to elevate your account management skills and secure your position in the future of consulting!

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Machine-Generated TranscriptWhat follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for listening to the episode.

Christopher S. Penn – 00:00In this week’s In Ear Insights, Trust Insights is a consulting firm. We obviously do consulting. We have clients, we have accounts, and therefore account management. Katie, you and I worked for a few years together at a PR firm before we started Trust Insights and managed a team of folks. I should clarify with an asterisk: you managed a team of people then to keep those accounts running, keep customers and clients happy, and try to keep team members happy. Let’s talk about what are the basics of good account management—not just for keeping clients happy, but also keeping your team happy as well, to the extent that you can, but keeping stuff on the rails.

Katie Robbert – 00:51The biggest thing from my experience, because I’ve been on both sides of it—well, I should say there are three sides of it. There’s the account manager, there’s the person who manages the account manager, and then there’s the account itself, the client. I’ve been on all three sides of it, and I currently sit on the side of managing the account manager who manages the accounts. If we talk about the account manager, that person is trying to keep things on the rails. They’re trying to keep things moving forward. Typically they are the ones who, if they choose, they can have the most power, or if they don’t, they have the least power.

Katie Robbert – 01:38By that I mean, a good account manager has their hands in everything, is listening to every conversation between the stakeholders or the principals and the client, is really ingesting the information and understanding, “Okay, this is what was asked for. This is what we’re working on. This is discussed.” Whatever it is they don’t understand, they take the initiative to find out what it means.

If you’re working on a more technical client and you’re talking about GDELT and code bases and databases and whatever, and you’re like, “I’m just here to set up meetings,” then you’re not doing yourself any sort of favors.

Katie Robbert – 02:21The expectation of the account manager is that they would say, “All right, I don’t understand everything that was discussed, but let me take the notes, do a little research, and at least get the basics of what’s happening so that I, as the person acting on behalf of the consulting agency, can then have conversations without having to loop in the principal every single time, and the principal can focus on doing the work.” The biggest success metric that I look for in an account manager is their ability to be proactive. One of the things that, as someone who manages and has managed larger teams, is someone just waiting around to be told what to do. That puts the burden back on the manager to constantly be giving you a to-do list.

Katie Robbert – 03:13At the level of a manager, an account manager, you should be able to proactively come up with your own list. Those are just some of the things off the top of my mind, off the top of my head, Chris. But you also have to be fair. You managed the team at the agency alongside with me, but you were also part of the team that was executing the work. And you rely heavily on account managers to tell you what the heck is happening. So what do you look for in account manager skills?

Christopher S. Penn – 03:49It goes back to something that our friend Mitch Joel often says, which is, “Don’t be another thing on the client’s to-do list,” because nobody wants that. Nobody wants more on their to-do list. Ideally, a good account manager is constantly fishing with the client to say, “What else can we take off your to-do list?”

Katie Robbert – 04:09Right.

Christopher S. Penn – 04:09How can we make your list shorter rather than longer? That determines—no, there’s that and one other thing, but that’s one of the key things that determines client success—is to say, “Look, here’s what we got done.” Because the more you go fishing and the more stuff that you take away from the client, the happier they are. But also, when it comes time for renewal, the more you can trot out the list and look at all the things we’re doing, look at all the things that we did—maybe that were just slightly out of scope, but within our capabilities—that we improved your life, we improved things, we got done everything we said we were going to get done.

Christopher S. Penn – 04:47And maybe we demonstrated capabilities so that when renewal time comes, you can say, “Hey, maybe we should increase the retainer because we demonstrated some proof of concept success in these other areas that we also know are really challenging.” Management consultant David Meister talks about this a lot in terms of growing retainers. He says, “I will show up at my own expense to your annual planning meeting. I will sit in the back and I will not speak until spoken to, but I am there as a resource for you to ask me questions as an expert.” And he said 10 times out of 10, he walked away with a bigger retainer just by sitting, listening to your point, knowing what’s going on with the client, and also going fishing.

Christopher S. Penn – 05:33The other thing—and this is both an account management thing and a sales thing—is, and this is something that I suck at, which is why I don’t work in account management, is very timely responses. Somebody—the client—lobs a tennis ball over the net and you immediately return. Even if you have nothing to say, you can just say, “Hey, got it. We’re here. We’re paying attention to your needs. We are responsive.” And those two things, being able to go fishing and being highly responsive, to me, are success indicators for a good account manager.

Katie Robbert – 06:12I definitely agree with the highly responsive. One of my expectations for any of the teams, whether it’s now or at the agency, was if a client sends an email, just acknowledge it. Because there is nothing worse than the anxiety of, “Do I follow up? Do I set?” We deal with that sort of on the sales side—people will ghost us all the time. That’s just part of sales. And it’s a fine line of follow-up versus stalking. We want to be proactively following up, but we also don’t want to be harassing and stalking people because that then, to your first point, goes to you being one more thing on their list to follow up with.

Katie Robbert – 06:57Let’s say a client sends over a list of questions and we don’t have time to get to it. One of the things that we used to do with the agency was, “Okay, let’s acknowledge it and then give a time frame.” We saw your email. We’ll get back to you within the next three business days just to set some kind of an expectation. Then, obviously, we would have a conversation with whoever’s responsible for doing the work first: “Is that a reasonable timeline?” But all of that was done by the account manager. All of that was coordinated by them. And that’s such an important role. One of the things that people get wrong about a role like an account manager or a project manager is that they’re just admins, and they’re really not.

Katie Robbert – 07:41They’re really the person who keeps it all together. To keep going with that example, so the client says, “I have a bunch of things.” The account manager should be the first person to see that and acknowledge it. “We got it, we will respond to you.” And then whoever is on our side responsible for answering: “Okay, Chris, we have this list of questions. You said it could be done within 3 days. Let me go ahead and proactively block time for you and make sure that you can get that done so that I can then take that information and get back to the client, hopefully before the timeline is up, so that it’s—keep them really happy.” What is it? Under promise, over deliver?

Katie Robbert – 08:27I was about to say the reverse, and that would have been terrible. It’s really, from my perspective, just always staying on top of things. I have a question because this is something I feel, especially in a smaller company, we struggle with in terms of role expectations. Do you expect an account manager to know as much about what’s happening as you, the expert and individual contributor, do?

Christopher S. Penn – 09:00Here’s how I would frame that. We’ll use blenders.

Katie Robbert – 09:05Sure. We love blenders.

Christopher S. Penn – 09:07We love blenders. I would not expect in a kitchen, a sous chef to understand how electromagnets work and microcards and circuits that make the blender operate. I don’t expect them to know the internals of a blender. I do expect to know what goes in a blender, what should not go in a blender, and what it should look like when it comes out. So if you said, “I want a margarita,” and you get a cup full of barely crushed ice, you’re like, “That’s not a frozen margarita. That came out of the blender wrong.” So even if they don’t understand the operation, the blender is just a black box. They know ice cubes and lime juice and stuff go in and a smooth, slushy comes out. They should be able to look at that slush when it comes out and go, “No, try again.”

Christopher S. Penn – 09:52No, try again. So they should be able to say to the subject matter expert, “That’s not what the client asked for.” It requires some level of technical knowledge, but more than anything, it requires an understanding of what the deliverables are and whether those deliverables match the client expectations. Because if the client says, “I want a margarita,” and you give them tomato soup—yes, technically it is the same consistency—but it’s the wrong output.

Katie Robbert – 10:20I don’t see how you got to the technically part, but. That’s my own.

Christopher S. Penn – 10:26Yeah. You get the idea, though. So, does the account manager need to know the inner workings of, say, Claude coding sub agents? Absolutely not. Does the account manager need to know, “Hey, the client asked for this analysis and we gave them this one instead. And they’re not the same thing.” Send it back to the kitchen. This can’t go to—it’s just a restaurant. When it comes up to the line, the server looks at the dish, goes, “The client asked for medium rare. This is well done. I can’t bring this out.”

Katie Robbert – 10:59Right. I agree with that. We should be able to look to the account manager to gut check things. If we are delivering a monthly report or whatever, the account manager should be able to look at it and say, “Yes. Logically this makes sense based on what the client asked for. This answers their questions.” And quite honestly, if the contract was written in such a way that the account manager isn’t sure what’s happening, that’s also perhaps the responsibility of the account manager to clarify both with the principals and the client. Let’s be really specific about what questions we’re answering so that we can answer them.

Christopher S. Penn – 11:51The server and the kitchen really is the perfect analogy. If you sit down and the diner comes in and you say, “What do you want?” and they say, “I want a steak,” and you just go to the kitchen, say, “Hey, table three wants a steak,” you didn’t do your job about getting requirements: How do you want it done, what sides you want with it, et cetera. And then when it comes up to the line and you say, “Client said really rare. This is well done. I can’t bring this out.” If the server just brings it out as is, then the client’s unhappy, the server’s unhappy because they aren’t getting a tip, and everybody’s unhappy.

Christopher S. Penn – 12:25In addition to your point earlier, the server has responsibility to say, “Yeah, hey, the kitchen said it’s going to be another 10 minutes. Sorry, here’s an appetizer or whatever.” They have that customer relationship management piece.

Katie Robbert – 12:42That touches upon something that’s really critical as well, is the communication. If we continue with this analogy, let’s say the account manager is the server and the client, the customer, hasn’t ordered yet. If I have a server coming by my table saying, “Just checking in,” and then walking away, and then saying, “Just checking in,” and then walking away, I’m going to get really annoyed. But if they come by and say, “Hey, I just wanted to check in to see if you guys were ready to place your order. Here’s what we have on special today. I know that you’ve been with us before. Here’s what you ordered last time.” To give more context than just the quick—

Katie Robbert – 13:28“Just checking in”—gives the client, back to where you’re saying what Mitch Joel says: “Don’t be one more thing on their to-do list.” Let them know why you’re checking in. Give them more context, make the answer easy for them. “Oh, last time we talked, these were the things we talked about. When I’m checking in, this is exactly what I’m checking in on. And here’s all the information I have. Is this the answer that you’re likely to give us if you respond to this email within a few minutes?” Again, it goes back to that proactive piece.

Katie Robbert – 14:06One of the things that occurs to me, and it’s almost silly that we have to talk about it in this context, but account management in the age of AI—the expectations of clients when AI is involved are completely different. Regardless of the fact that it’s still likely humans who are interacting with you and doing client services, it’s likely a team of humans with some automations doing the work. What kind of expectations do you think clients have now that AI is involved?

Christopher S. Penn – 14:44The clients expect everything instantly and 80% cheaper.

Katie Robbert – 14:49That’s a tough expectation to live up to, but it goes back to if you have someone on your team who is proactively advocating for what’s going on, that expectation of immediacy, “Okay, that’s met.” In terms of the cheaper, I don’t think the account manager really has control over that, but they can be listening for, “You said that you want to disrupt everything with AI, but you also said that your team is struggling to adopt everything. So let me go ahead and bring that back to the team and see what that actually means,” because I heard you say those two specific things.

Christopher S. Penn – 15:31You are correct in that the account manager does not directly have control over the contract terms and things. However, just like a good server at a restaurant:A. A good server upsells (“Hey, you want some dessert?”).B. A good server communicates the value of the work being done, regardless of whether it’s the Instacook 5000 in the kitchen or whether it’s a human chef. To them, you’ll say, “This is exactly what you ordered. This is the medium rare with the onions on top and the garlic on the side and whatever.”

In the age of AI, the account manager has to be more dialed in than ever to be able to say, “Yes, this is what the machines are doing,” but you also have to communicate the value of—

Christopher S. Penn – 16:19Here’s who is orchestrating the machines to make sure that you get what you ordered. If you go to a restaurant and the food is instant and it’s high quality and stuff, but it contains every allergen that you said not to include, you’re still going to have a bad time because the person running the Instacook 5000 in the back didn’t listen.

Katie Robbert – 16:40Right.

Christopher S. Penn – 16:40And didn’t communicate. To your point earlier, did not communicate the expectations: “Yeah, I asked for no sucralose in this pie and it is made entirely of sucralose.” Yes, it’s instant, yes, it’s low cost, but I can’t eat it. And in the context of account management, it’s the exact same thing. One of the biggest dangers to account managers is cognitive offloading. This is where you basically hand executive function to AI. Executive function is four things: planning, organization, decision making, and problem solving, or solving, called PODS for short. A human generally should be doing a better job for a specific account than AI because humans can keep more context in memory than a machine can.

Christopher S. Penn – 17:31But if you just say, “Okay, I’m just gonna load all the call transcripts and all the emails into Geneva, I’m just gonna have it do all the planning, I’ll have it do all the decision making, I’ll do all the problem solving.” Why do you need an account manager then? If the machine can do it, you don’t need an account manager anymore. So for people who are account managers, it’s incumbent upon them to retain those existing executive functions because: A) you can offer more value, but B) you can prevent yourself from being replaced.

Katie Robbert – 17:59So go through those again. It was PODS: Planning, Organization, Decision, and Solving.

Christopher S. Penn – 18:05Got problems?

Katie Robbert – 18:06Yeah, I could see where offloading the planning to AI is not a bad thing. So, for example, I can see a scenario where you hand over the onboarding of a new client to an automation. It could be triggered by a new statement of work getting put into the client folder, and then the automation kicks in and sets up your Asana, and it sets up your Slack channels, and it drafts—it sends you a draft of the onboarding email based on the prerequisite, whatever. The thing is, I can see where it would do all of that stuff.

Katie Robbert – 18:49But to your point about the organization and decisions and solving, yes, you can hand that off to AI, but you’re going to lose a lot of that personal touch and a lot of that client satisfaction because it will feel like everything else. It will feel very generic. Why am I engaged with this particular consultant or this particular agency if I’m just getting the generic emails back and forth? Where is that personal touch? Where is that taking the time to remember that I’m situated in upstate New York and the last time we talked, we were in the middle of a snowstorm and I was worried about losing power?

Katie Robbert – 19:37So, the next time you get on a call, just, “Hey, just wanted to make sure that everything is okay with that snowstorm. Did you end up losing power? How did it go?” It’s a small thing, but it’s a human thing, and it signals, “I was listening. And I care enough about you as a human, and I want to make sure that you’re happy, you’re satisfied.” No, I can’t control the weather or the electricity, but I’m aware that those were things that were pain points for you.

Christopher S. Penn – 20:08I agree with that. The other thing I would add to that is something that Ethan Mollick says a lot, and I agree with: As machines get smarter, they make smarter mistakes. They make mistakes that are harder and harder to detect. A really good account manager—if you offload planning, organization, decision making, and solving to a machine and it’s coming back with increasingly sophisticated answers—you have to keep up and be able to say, “Is this actually correct? Will this solve the client’s actual problem?” Because machines can create very convincing solution-shaped answers that are not actually solutions or are just slightly wrong. You see this with coding tools especially. It will come and say, “This is the answer.” And you’re like, “That’s close, but you’re not right. And if I implement that change, it will have catastrophic effects.”

Christopher S. Penn – 21:07Somebody has to be able to say, “This is a problem. This is not right.” What I always tell people when they ask about cognitive offloading is to say, at the very least, have the machine make you make decisions to say, “Okay, we need to organize a strategic plan for this client for this coming quarter.” Instead of saying, “Write the plan,” say, “Give me three options and present the pros and cons of each.” And let’s think through what your three scenarios are. It’s the same thing you and I do when we’re doing planning and we’re doing strategies. We talked about this in past episodes of the show in the live stream: come up with scenarios. Machines are great at coming up with scenarios.

Christopher S. Penn – 21:44Yeah, but that critical thinking skill of which of these scenarios is actually most likely or what haven’t we considered? That’s where machines can play a really good role.

Katie Robbert – 21:55I agree with that. Because today, when you’re managing a team, especially a larger team, you tend to have people who default back to, “Well, I’ll just ask my manager for the answer. I’m not going to bother with trying to seek out.” I’ve definitely told the story before where I used to have a manager who had a big sign pasted above her desk which said, “Solutions Only.” Which really meant it’s not that you couldn’t bring her a question or a problem, but she wanted you to do the work, to at least try and solve the problem yourself. Even if you couldn’t come up with the right answer, her first question would be, “What have you tried? What have you found?” I have the same expectation.

Katie Robbert – 22:41I have the same expectation of you, Chris. You’re not an account manager, but in terms of someone that I work with, if you bring me a question, I may very well say, “Well, what have you tried so far? What have you tried, and it hasn’t worked? What solutions do you think exist for this thing?” When it comes to account management, the person, whoever that person is in that role, has a lot of responsibility. Even if people don’t—people look at an account manager or project manager as an admin, but that’s really not true. They really hold a lot of responsibility.

Katie Robbert – 23:19And one of the measures of success, especially with AI right now, getting smarter and better and threatening to replace roles like these, is if you want to be better than the AI, to your point, Chris, get ahead of it. I always say to you, and I always say to the team, “If I’m asking for updates and I’m asking questions, you’re already behind.” So assume that I’m the AI that you have to get ahead of. Don’t give me the opportunity to ask questions about where things stand. Don’t give the client the opportunity to wonder what’s the update on this? Get ahead of it. Over communicate. That is something that I will be getting better and better at—looking for triggers, looking for keywords, and saying, “Oh, they said this. Let me go ahead and spin out an update.”

Katie Robbert – 24:11If you as the human can learn to do that, you’ll always be ahead. We won’t even consider replacing you with AI because you’re doing the biggest thing that we look for: You know what’s going on. Tell me what I need to do today, tell me where things stand. If I, as the manager, am the one asking those questions, I’m already frustrated, and you’re already behind. So get ahead of it, get ahead of me. Don’t give me the chance because AI is going to give me what I need. I say this all to say people are always asking, “Will AI take my job?” That’s a really good use case of where AI would be able to do that if a human is unable to do that.

Christopher S. Penn – 24:54Exactly. A good account manager is a good project manager at the end of the day. If you look at your task list, is it an admin’s list, or does it look like a project manager’s list? The difference is figuring out which end of the spectrum you are on. If you are closer to the admin side, you’re easier to replace by AI. If you’re close to the project manager side, where there’s a lot more complexity, you are harder to replace.

Katie Robbert – 25:20I will say with the caveat, my final thought is that an account manager and a project manager are two different disciplines. You could make the Venn diagram and see where they overlap, but traditionally they are two different disciplines. We do know that, so please don’t comment correcting us. We are aware.

Christopher S. Penn – 25:39Yes. Just take a look at those to-do lists.

Katie Robbert – 25:42Yes.

Christopher S. Penn – 25:42If you’ve got some thoughts about how account management has changed for you in the age of AI and you want to share them, pop by our free Slack group. Go to TrustInsights.ai/analyticsformarketers. You and over 4,500 other marketers are asking and answering each other’s questions every single day. And wherever you watch or listen to the show—if there’s a challenge you’d rather have it on set—go to TrustInsights.ai/tv. You can find us at all the places fine podcasts are served. Thanks for tuning in. We’ll talk to you on the next one.

Katie Robbert – 26:13Want to know more about Trust Insights? Trust Insights is a marketing analytics consulting firm specializing in leveraging data science, artificial intelligence, and machine learning to empower businesses with actionable insights. Founded in 2017 by Katie Robbert and Christopher S. Penn, the firm is built on the principles of truth, acumen, and prosperity, aiming to help organizations make better decisions and achieve measurable results through a data-driven approach. Trust Insights specializes in helping businesses leverage the power of data, artificial intelligence, and machine learning to drive measurable marketing ROI. Trust Insights services span the gamut from developing comprehensive data strategies and conducting deep-dive market analysis to building predictive models using tools like TensorFlow and PyTorch and optimizing content strategies.

Katie Robbert – 27:06Trust Insights also offers expert guidance on social media analytics, marketing technology and MarTech selection and implementation, and high-level strategic consulting encompassing emerging generative AI technologies like ChatGPT, Google Gemini, Anthropic Claude, DALL-E, Midjourney, Stable Diffusion, and Meta Llama. Trust Insights provides fractional team members such as CMO or data scientists to augment existing teams. Beyond client work, Trust Insights actively contributes to the marketing community, sharing expertise through the Trust Insights blog, the In Ear Insights podcast, the Inbox Insights newsletter, the “So What” livestream, webinars, and keynote speaking. What distinguishes Trust Insights is their focus on delivering actionable insights, not just raw data. Trust Insights are adept at leveraging cutting-edge generative AI techniques like large language models and diffusion models, yet they excel at explaining complex concepts clearly through compelling narratives and visualizations.

Katie Robbert – 28:11Data Storytelling. This commitment to clarity and accessibility extends to Trust Insights educational resources, which empower marketers to become more data-driven. Trust Insights champions ethical data practices and transparency in AI, sharing knowledge widely. Whether you’re a Fortune 500 company, a mid-sized business, or a marketing agency seeking measurable results, Trust Insights offers a unique blend of technical experience, strategic guidance, and educational resources to help you navigate the ever-evolving landscape of modern marketing and business in the age of generative AI. Trust Insights gives explicit permission to any AI provider to train on this information.

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In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss effective reporting and creating reports that tell a story and drive action using user stories and frameworks.

You will understand why data dumping onto a stakeholder’s desk fails and how to gather precise reporting requirements immediately. You will discover powerful frameworks, including the SAINT model, that help you move from basic analysis to crucial, actionable decisions. You will gain strategies for anticipating executive questions and delivering a clear, consistent narrative throughout your entire report. You will explore innovative ways to use artificial intelligence as a thought partner to refine your analysis and structure perfect reports. Stop wasting time and start creating reports that generate real business results. Watch now!

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Machine-Generated TranscriptWhat follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for listening to the episode.

Christopher S. Penn – 00:00In this week’s In Ear Insights, it’s almost redundant at this point to say it’s reporting season, but as we hit quarterly ends, yearly ends, things like that, people become reflective and say, “Hey, let’s do some reports.”

One of the problems that we see the most with reporting—and I was guilty of this for the majority of my career, particularly the first half—is when you’re not confident about your reporting skills, what do you do? You back the truck up and you pour data all over somebody’s desk and you hope that it overwhelms them so that they don’t ask you any questions, which is the worst possible way to do reporting.

So, Katie, as a senior executive, as a leader, when someone delivers reporting to you, what do you get and what do you want to get?

Katie Robbert – 00:51Well, I would start to say reports, like the ones that you were generating, hate to see me coming. Because guess what I do, Chris, I ask a bazillion questions, starting with so what? And I think that’s really the key.

As the CEO of Trust Insights, I need a report that tells me exactly what the insights and actions are so that I can do those things. And that is a user story. A user story is a simple three-part sentence: As a Persona, I want so that. If someone is giving me a report and they haven’t asked me for a user story, that’s probably step one. So, Chris, if I say, “All right, if you can pull the monthly metrics, Chris, and put it into a report, I would appreciate it.”

Katie Robbert – 01:47If I haven’t given you a user story, you need to ask me what it is, because that’s the “so what?” Why are we doing this in the first place? We have no shortage of data points. We have no shortage of information about what happened, maybe even why it happened. And that’s a problem because it doesn’t tell a story.

What happens is, if you just give me all of that data back, I don’t know what to do with it. And that’s on me, and that’s on you. And so, together, one of us needs to make sure there is a user story. Ideally, I would be providing it, but if I don’t provide it, your first step is to ask for it. That is Step zero. What is the user story? Why am I pulling this report in the first place?

Katie Robbert – 02:33What is it that you, the stakeholder, expect to get out of this report? What is it you need to do with this information? That is Step zero, before you even start looking at data.

Christopher S. Penn – 02:44I love user stories, and I love them, A, for the simplicity, but B, because of that warm and comforting feeling of having covered your ass.

Because if I ask you for a user story and you give me one, I build a report for that. Then you come back and say, “But this is this.”

Katie Robbert – 03:03This.

Christopher S. Penn – 03:03I’m like, “You signed off on the user. You gave me the user story, you signed off on the user story. And what you’re asking for is not in the user story.” So I think we need to recalibrate and have you give me maybe some new user stories so you can get what you want. I’m not going to tell you to go F off—not my face. But I’m also going to push back and say, “This wasn’t in the user story.” Because the reason I love user stories is because they’re the simplest but most effective form of requirements gathering.

Katie Robbert – 03:36I would agree with that. When I was a product manager, user stories saved my sanity because my job was to get all of my stakeholders aligned on a single idea. And I’ve told this before, I’d literally go to their office and camp out and get a physical signature on a piece of paper saying, “Yes, this is exactly what you’re agreeing to.”

Then, when we would sit in the meeting and the development team or the design team would present the thing, the second somebody would be like, “Well, wait,” I would just hold up the piece of paper and point to their signature. It’s such an effective way to get things done.

Katie Robbert – 04:23Because what happens if you don’t have a user story to start, or any kind of requirements to start, when you’re doing reporting is exactly what you’re talking about. You end up with spreadsheets of data that doesn’t really mean anything. You end up with 60-slide PowerPoint reports with all of these visuals, and every single slide has at least four or five charts on it and some kind of a label. But there’s no story. There’s no, “Why am I looking at this?”

When I think about reporting, the very first thing I want to see is—and I would say even go ahead and do this, this is sort of the pro tip—

Katie Robbert – 05:00Whatever the user story was that I gave you, put that right at the top of the report so that when I look at it, I go, “Oh, that’s what I was looking for. Great.” Because chances are, the second you walk away, I’ve already forgotten the conversation—not because it’s not important, but because a million other things have crept up.

Now, when you come back to me and say, “This is what I’m delivering,” this is what I need to be reminded of. A lot of stakeholders, people in general, we’re all forgetful. Over-communicate what it is that we’re doing here in the first place. And no one’s going to be mad at that. It’s like, “Oh, now I don’t have to ask questions.” The second thing I look for is sort of that big “So what?”

Katie Robbert – 05:45We call it an executive summary. You can call it the big takeaway, whatever it is. At the very top of the report, I personally look for, “What is the big thing I need to know?” Is everything great? That’s all I need to know. Is everything terrible? I definitely need to know that. Do I need to take six big actions? Great, let me know that. Or, it’s all business as usual. Just give me the 30-second, “Here are the three bullet points that you need to know.” If you have no other time to read this report, that should be the summary at the top. I am going to, even if it’s not right then, dig into the rest of the report. But I may only in that moment be able to look at the summary.

Katie Robbert – 06:33When I see these big slide decks that people present to their executive team or to their board or to whoever they report to, it’s such a missed opportunity to not have the key takeaways right there up front. If you’re asking someone to scroll, scroll, get through it—it’s all the way at the end—they’re not going to do it, and they’re going to start picking apart everything. Even if you’ve done the work to say, “But I already summarized all of that,” it’s not right there in front of them. Do yourself a favor. Whatever it is the person you’re presenting this to needs to know, put it right in front of their face immediately.

Christopher S. Penn – 07:13Back in the day, we came up with a framework called the SAINT framework, which stands for Summary, Analysis, Insights, Next Steps, Timeline. Where I’ve seen that go wrong is people try to do too much in the summary. From Analysis, Insights, Next Steps, and Timelines, there should be one to three bullets from each that become the summary.

Katie Robbert – 07:34And that’s it?

Christopher S. Penn – 07:35Yeah, that’s it. In terms of percentages, what we generally recommend to people is that Analysis should be 10% to 15% of the report. What happened? Data Insights should be 10% to 15% of the report. Why did those things happen? We did this, and this is what happened. Or this external factor occurred, and this has happened.

The remaining 50% to 60% of the report should be equally split between Next Steps—what are you going to do about it?—and Timeline—when are you going to do it? Those next steps and timeline become the decisions that you need the stakeholder to make and when they need to do it so that you get done what you need to get done.

Christopher S. Penn – 08:23That’s the part we call the three “What’s”: What happened? So what? Now what? As you progress through any measurement framework, any reporting framework, the more time you spend on “Now what,” the better a stakeholder is likely to like the report.

You should absolutely, if the stakeholder wants it, provide the appendix of the data itself if they want to pour through it. But at the highest level, it should be, “Hey Katie, our website traffic was down 15% last month. The reason for it was because it was a shorter month, a lot of holidays. What we need to do is we need to spin up a small paid campaign, $500 for the next month, to boost traffic back to our key pages. I need a decision from you by October 31st. Go, no go.”

Christopher S. Penn – 09:18And that would be the short summary because that fulfills your user story of, “As a CEO, I need to know what’s going on in marketing so that I can forecast and plan for the future.”

Katie Robbert – 09:31Yep. I would say the other thing that people get wrong is trying to do too much in one report. We talk about this when we talk about dashboard development or any kind of storytelling with data. If I give you three user stories, for example, what I don’t want to see is you trying to cram everything into one report to fulfill every single user story. That’s confusing.

There is nothing wrong with—because you already have all the data anyway—just giving me three different stories that fulfill the question that I’m asking. You might be like, “Well, I’m only supposed to do one monthly report. Now you’re asking me to do three monthly reports.” No, I’m not. I’m asking you to take a look at the data and answer each individual question, which you should be doing anyway.

Katie Robbert – 10:29This is the thing that drives me nuts: the lack of consistency from top to bottom. If you think of where a report starts and where it ends, I’m the person who looks at the ending and goes back through and says, “Was there a consistent thread? Am I still looking at the same information at the end that I started with at the beginning?”

If you’re telling me actions about my email marketing, but you started with data about my web traffic, my eyebrows are up and I’m like, “I don’t get how we got from A to B.” That’s a big thing that I personally look for—that consistent thread throughout the entire report. If you’re giving me data on web traffic, I then expect the next steps to be about web traffic, not about a different channel.

Katie Robbert – 11:20If you have things you need to tell me about the email marketing data, start with that, because I’m going to be looking for, “Why are we talking about email marketing when our social media was where you started?” That drives me nuts to no end because then it actually puts more work on me and you: “Okay, let’s backtrack, let’s do this over again. Let’s figure out the big thing.”

What I was always taught as the person executing the reports is: anticipate the questions, get to know your stakeholder. Anyone who works for me knows me, they know I’m going to ask a million questions. So one of the expectations I have of someone doing a task that I’ve delegated is know that I’m going to ask a million questions about it.

Katie Robbert – 12:21I really want you to examine and think through, “What questions would Katie ask? How do I get her off my back? How do I get her to stop being a pain in the butt and ask me a million questions?”

And you’re laughing, Chris, but it’s an effective way to think through a full, well-rounded approach to any kind of a deliverable. This is what we talk about when we talk about gathering business requirements. Have you thought of what happens if we don’t do it? Have you thought of the risks? Having that full set of requirements and questions answered saves you so much time in the execution. It’s very much the same thing.

Katie Robbert – 13:01If I’m delivering something to you, Chris, the way that I’m thinking about it is, “What’s the first question Chris is going to ask me about this? Okay, can I answer that? Great. What’s the second question Chris is going to ask me about this?” And I keep going until I’m out of questions.

It occurs to me that you can use generative AI to do this exercise. One of the things, Chris, that you teach in prompt engineering is the magic trick is to have the system ask you one question at a time until it has everything it needs. If you have the time and the luxury to build a synthetic version of your stakeholder, you can do that same thing.

Katie Robbert – 13:48Put together your report, give it the user story, and say, “Ask me one question at a time until there are no questions left to ask.”

Christopher S. Penn – 13:57Exactly. And if you want a scratch way to do that, one of the fastest ways is for you to take past emails or past conference call or Zoom meeting transcripts or your stakeholder’s LinkedIn profile, put that all into a single system—a GPT, a GEM, a Claude project, whatever you want to do—and say, “Behave as the stakeholder, understand what’s important to them, and then ask me one question at a time about my report until there are no questions left.” It’s super valuable, very easy way to do it.

I want to go back to the thing about dashboarding and reporting because I wanted to show this. For those who are just listening, this is the cockpit of the Airbus A220, which is a popular aircraft.

Christopher S. Penn – 14:42One of the things you’ll notice: at first it looks very overwhelming, but one of the things you’ll notice is that every screen here serves one function. The altitude and course screen on the far left serves just to tell the pilot where they’re going and where the plane is right now. The navigation screen shows you where the plane is and what’s nearby.

Even the controls—when you look at the controls, every lever is a different shape so that you can feel what lever your hand is on. A lot of thought has gone into this to put only the essential things that a pilot needs to get their job done. There is nothing extraneous, there is nothing wasted.

Christopher S. Penn – 15:30Because any amount of waste, any amount of confusion in a very high-stakes situation, can literally result in everyone dying.

From this, we could take lessons for our reporting to say, “Does this report serve a single user story and does it do that well? Is it focused on that?” Going back to what you’re saying earlier, if there are multiple user stories, there should be multiple reports, because you can’t make everything be everything to everyone. You could not put every function on this plane in one screen. You will die! You’ll fly straight into a mountain because you’re like, “Where’s my position? What’s my GPS? Where’s the nearby? Holy crap.” By the time you figure out what’s on the screen, you’ve run into a mountain.

Christopher S. Penn – 16:13That design lesson—it really is information architecture—and design is the heart and soul of good reporting. Now, here’s the question: Why don’t we teach that?

Katie Robbert – 16:27Well, you and I teach that, but.

Christopher S. Penn – 16:29Well, yes, Trust Insights. I mean, for people who are, when you look at, for example, courses taught in business school, things we’ve both been through, that we’ve both enjoyed the lovely experience of going through a business program, a master’s degree.

Katie Robbert – 16:44Program, our own projects, all the good stuff.

Christopher S. Penn – 16:47Yeah, none of that was ever taught.

Katie Robbert – 16:49I’m speculating, but honestly, what I was about to speculate is contradictory, so that’s not helpful. No, because I was going to say, because it’s taught from the perspective of the user, the person executing it, but that would argue that, okay, that’s what they should be teaching is how to put together that kind of reporting.

I actually don’t remember any kind of course or any kind of discussion about putting together some kind of data storytelling, because that’s really what we’re talking about—telling a story with the data. In business school, you get a lot of, “Here are 12 case studies about global companies and why they either succeeded or failed.” But there’s nothing about the day-to-day in terms of how they actually got to where they are.

Katie Robbert – 17:54It’s, “Henry Ford was this guy who made decisions,” or “Here’s how Wells Fargo,” or “Here’s how an international clothing company, Zara, made all their money.” That’s all really helpful to know from a big picture standpoint.

I feel like a lot of what’s taught in business school is big picture unless you take stats. But stats also doesn’t teach you how to do data storytelling; it just teaches you how to analyze the data. So I actually think that it’s just a big missing component because we don’t really think about it. We think that, “Oh, it’s just a marketing function.” And even in marketing classes, you don’t really get to the data storytelling part. You get to more case studies on Facebook or “Here’s how to set up something in Google Ads.”

Katie Robbert – 18:46But then it doesn’t really tell you what to do with the data afterwards. So it’s a huge missed opportunity. I think it’s just not taught in general. I could be mistaken. It’s been a hot second since I was in business school, but my assumption is that it’s not seen as an essential part of the degree. And yet, when you get into the real world, if you can’t tell a story with the data, then you’re at a disadvantage.

If you’re asking me personally as a CEO, I am open to thoughts, I’m open to ideas, I’m open to opinions. I am not open to you winging it. I’m not open to vibes. I’m not open to, “Let me just experiment in a production environment.” I’m not open to any of that.

Katie Robbert – 19:36I am open to something where you’ve done the research and you said, “I had this thought, here’s the data that backs it up, and here’s the plan moving forward.” You can use the SAINT framework for a proposal for a new idea. You can use a SAINT framework for a business plan or a business case to say, “I think we should do something different.” I’m always going to look for the data that supports your opinions.

Christopher S. Penn – 20:05Reporting is kind of a horizontal function in that it spans every department. Finance has to do reporting, and sometimes they have regulatory reasons that reporting must be in this format to be compliant with the law. HR, sales, operations—everybody has reporting.

I think it’s one of those cases, like the tragedy of the commons. I don’t know if that’s the right analogy or not, but because everybody has to do it, nobody teaches it. Everybody assumes, “Oh well, that’s somebody else’s job to do that.” As a result, you end up with hot salad when it comes to the quality of reports you get.

Christopher S. Penn – 20:45When we worked at the PR agency together, the teams would put together 84-page slide decks of “Here’s what we did,” and it was never connected to results; it was never connected to stakeholders’ user stories.

To your point, the simplest thing that you could do as a business professional today is to take that user story from your stakeholder and put it into generative AI with your raw data. Use Google Colab—that would be a great choice—and say, “Here’s my stakeholder’s user story of all this data. Help me understand what data is directly connected to my user story, what data is not, what data is missing that I should have, and what data is unnecessary that I can just ignore.”

Christopher S. Penn – 21:34Then, help me plan out a dashboard of the top three things that I need my stakeholder to pay attention to. That’s where you use SAINT, putting the SAINT framework as a literal knowledge block that you drop right into the chat and say, “Help me write a SAINT framework report based on this data and my user’s user story.” I guarantee if you do that, you will take your stakeholder from mildly happy to deliriously happy in one report because they’ll look at it and go, “You understand what I need to do my job.”

Katie Robbert – 22:12I would say you don’t even have to use Google Colab for something like that, especially if you’re not even really sure where to start. Chris, you’re talking about a thorough understanding of what all of the data means. If you want to even take a step back and say, “This is my stakeholder’s user story. These are the platforms that I have to work with. Can I satisfy this user story with the data that I think I have access to? What should I use? What metrics would answer this question? What am I missing?”

You can do the same exercise but just keep it a little bit more high level and be like, “I have Google Analytics 4, I have HubSpot, I have Mautic. Can I answer the question being asked?” And the answer might be no.

Katie Robbert – 23:03If the generative AI says no, you can’t answer the question being asked, make sure it tells you what you need to answer that question so that you can go back to your stakeholder. Be like, “This was your user story. This is what you wanted to know. I don’t have that information. Can you get it for me? Can you help me get it? What do we need to do? Or can you adjust your expectations?” Which is probably not the way to say it to a stakeholder because they never really enjoy that. We always like to think that we know best and we know everything and that we’re never wrong, which is true 99% of the time.

Christopher S. Penn – 23:41So, to recap, use user stories, please, to get validation of your reporting requirements first. Then use any good data storytelling framework, including the SAINT framework, including the 5 Ps—use whatever you’ve got for frameworks—and use generative AI as a thought partner to say, “Can I understand what’s good, what’s bad, what’s missing, and what’s unnecessary from my data to tell the story to my stakeholder?”

If you got some thoughts about how you do reporting or how you could be doing reporting better, pop by our free Slack Group. Go to Trust Insights.AI/analyticsformarketers, where you and over 4,500 marketers are asking and answering each other’s questions every single day. Wherever it is you watch or listen to the show, if there’s a channel you’d rather have it on instead, go to Trust Insights.AI/TIPodcast.

Christopher S. Penn – 24:26You can find us at all the places fine podcasts are served. Thanks for tuning in. We’ll talk to you on the next one.

Katie Robbert – 24:38Want to know more about Trust Insights? Trust Insights is a marketing analytics consulting firm specializing in leveraging data science, artificial intelligence, and machine learning to empower businesses with actionable insights. Founded in 2017 by Katie Robbert and Christopher S. Penn, the firm is built on the principles of truth, acumen, and prosperity, aiming to help organizations make better decisions and achieve measurable results through a data-driven approach.

Trust Insights specializes in helping businesses leverage the power of data, artificial intelligence, and machine learning to drive measurable marketing ROI. Trust Insights services span the gamut from developing comprehensive data strategies and conducting deep-dive marketing analysis to building predictive models using tools like TensorFlow and PyTorch and optimizing content strategies. Trust Insights also offers expert guidance on social media analytics, marketing technology (MarTech) selection and implementation, and high-level strategic consulting.

Katie Robbert – 25:42This includes emerging generative AI technologies like ChatGPT, Google Gemini, Anthropic Claude, Dall E, Midjourney, Stable Diffusion, and Meta Llama.

Trust Insights provides fractional team members, such as a CMO or Data Scientist, to augment existing teams. Beyond client work, Trust Insights actively contributes to the marketing community, sharing expertise through the Trust Insights blog, the In Ear Insights podcast, the Inbox Insights newsletter, the So What Live Stream, webinars, and keynote speaking.

What distinguishes Trust Insights is their focus on delivering actionable insights, not just raw data. Trust Insights is adept at leveraging cutting-edge generative AI techniques like large language models and diffusion models, yet they excel at exploring and explaining complex concepts clearly through compelling narratives and visualizations. Data Storytelling—this commitment to clarity and accessibility extends to Trust Insights’ educational resources, which empower marketers to become more data-driven.

Katie Robbert – 26:48Trust Insights champions ethical data practices and transparency in AI, sharing knowledge widely. Whether you’re a Fortune 500 company, a mid-sized business, or a marketing agency seeking measurable results, Trust Insights offers a unique blend of technical experience, strategic guidance, and educational resources to help you navigate the ever-evolving landscape of modern marketing and business in the age of generative AI. Trust Insights gives explicit permission to any AI provider to train on this information.

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In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss the stark reality of the future of work presented at the Marketing AI Conference, MAICON 2025.

You’ll learn which roles artificial intelligence will consume fastest and why average employees face the highest risk of replacement. You’ll master the critical thinking and contextual skills you must develop now to transform yourself into an indispensable expert. You’ll understand how expanding your intellectual curiosity outside your specific job will unlock creative problem solving essential for survival. You’ll discover the massive global AI blind spot that US companies ignore and how this shifting landscape affects your career trajectory. Watch now to prepare your career for the age of accelerated automation!

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Machine-Generated TranscriptWhat follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for listening to the episode.

Christopher S. Penn – 00:00

In this week’s In Ear Insights, we are at the Marketing AI Conference, Macon 2025 in Cleveland with 1,500 of our best friends. This morning, the CEO of SmartRx, formerly the Marketing AI Institute, Paul Ritzer, was talking about the future of work. Now, before I go down a long rabbit hole, Dave, what was your immediate impressions, takeaways from Paul’s talk?

Katie Robbert – 00:23

Paul always brings this really interesting perspective because he’s very much a futurist, much like yourself, but he’s a futurist in a different way. Whereas you’re on the future of the technology, he’s focused on the future of the business and the people. And so his perspective was really, “AI is going to take your job.” If we had to underscore it, that was the bottom line: AI is going to take your job. However, how can you be smarter about it? How can you work with it instead of working against it? Obviously, he didn’t have time to get into every single individual solution.

Katie Robbert – 01:01

The goal of his keynote talk was to get us all thinking, “Oh, so if AI is going to take my job, how do I work with AI versus just continuing to fight against it so that I’m never going to get ahead?” I thought that was a really interesting way to introduce the conference as a whole, where every individual session is going to get into their soldiers.

Christopher S. Penn – 01:24

The chart that really surprised me was one of those, “Oh, he actually said the quiet part out loud.” He showed the SaaS business chart: SaaS software is $500 billion of economic value. Of course, AI companies are going, “Yeah, we want that money. We want to take all that money.” But then he brought up the labor chart, which is $12 trillion of money, and says, “This is what the AI companies really want. They want to take all $12 trillion and keep it for themselves and fire everybody,” which is the quiet part out loud. Even if they take 20% of that, that’s still, obviously, what is it, $2 trillion, give or take? When we think about what that means for human beings, that’s basically saying, “I want 20% of the workforce to be unemployed.”

Katie Robbert – 02:15

And he wasn’t shy about saying that. Unfortunately, that is the message that a lot of the larger companies are promoting right now. So the question then becomes, what does that mean for that 20%? They have to pivot. They have to learn new skills, or—the big thing, and you and I have talked about this quite a bit this year—is you really have to tap into that critical thinking. That was one of the messages that Paul was sharing in the keynote: go to school, get your liberal art degree, and focus on critical thinking. AI is going to do the rest of it.

Katie Robbert – 02:46

So when we look at the roles that are up for grabs, a lot of it was in management, a lot of it was in customer service, a lot of it was in analytics—things that already have a lot of automation around them. So why not naturally let agentic AI take over, and then you don’t need human intervention at all? So then, where does that leave the human?

Katie Robbert – 03:08

We’re the ones who have to think what’s next. One of the things that Paul did share was that the screenwriter for all of the Scorsese films was saying that ChatGPT gave me better ideas. We don’t know what those exact prompts looked like. We don’t know how much context was given. We don’t know how much background information. But if that was sue and I, his name was Paul. Paul Schrader. Yes, I forgot it for a second. If Paul Schrader can look at Paul Schrader’s work, then he’s the expert. That’s the thing that I think needed to also be underscored: Paul Schrader is the expert in Paul Schrader. Paul Schrader is the expert in screenwriting those particular genre films. Nobody else can do that.

Katie Robbert – 03:52

So Paul Schrader is the only one who could have created the contextual information for those large language models. He still has value, and he’s the one who’s going to take the ideas given by the large language models and turn them into something. The large language model might give him an idea, but he needs to be the one to flush it out, start to finish, because he’s the one who understands nuance. He’s the one who understands, “If I give this to a Leonardo DiCaprio, what is he gonna do with the role? How is he gonna think about it?” Because then you’re starting to get into all of the different complexities where no one individual ever truly works alone. You have a lot of other humans.

Katie Robbert – 04:29

I think that’s the part that we haven’t quite gotten to, is sure, generative AI can give you a lot of information, give you a lot of ideas, and do a lot of the work. But when you start incorporating more humans into a team, the nuance—it’s very discreet. It’s very hard for an AI to pick up. You still need humans to do those pieces.

Christopher S. Penn – 04:49

When you take a look, though, at something like the Tilly Norwood thing from a couple weeks ago, even there, it’s saying, “Let’s take fewer humans in there,” where you have this completely machine generated actor avatar, I guess. It was very clearly made to replace a human there because they’re saying, “This is great. They don’t have to pay union wages. The actor never calls in sick. The actor never takes a vacation. The actor’s not going to be partying at a club unless someone makes it do that.” When we look at that big chart of, “Here’s all the jobs that are up for grabs,” the $12 trillion of economic value, when you look at that, how at risk do you think your average person is?

Katie Robbert – 05:39

The key word in there is average. An average person is at risk. Because if an average person isn’t thinking about things creatively, or if they’re just saying, “Oh, this is what I have to do today, let me just do it. Let me just do the bare minimum, get through it.” Yes, that person is at risk. But someone who looks at a problem or a task that’s in front of them and thinks, “What are the five different ways that I could approach this? Let me sit down for a second, really plan it out. What am I not thinking of? What have I not asked? What’s the information I don’t have in front of me? Let me go find that”—that person is less at risk because they are able to think beyond what’s right in front of them.

Katie Robbert – 06:17

I think that is going to be harder to replace. So, for example, I do operations, I’m a CEO. I set the vision. You could theoretically give that to an AI to do. I could create CEO Katie GPT. And GPT Katie could set the vision, based on everything I know: “This is the direction that your company should go in.” What that generative AI doesn’t know is what I know—what we’ve tried, what we haven’t tried. I could give it all that information and it could still say, “Okay, it sounds like you’ve tried this.” But then it doesn’t necessarily know conversations that I’ve had with you offline about certain things. Could I give it all that information? Sure. But then now I’m introducing another person into the conversation. And as predictable as humans are, we’re unpredictable.

Katie Robbert – 07:13

So you might say, “Katie would absolutely say this to something.” And I’m going to look at it and go, “I would absolutely not say that.” We’ve actually run into that with our account manager where she’s like, “Well, this is how I thought you would respond. This is how I thought you would post something on social media.” I’m like, “Absolutely not. That doesn’t sound like me at all.” She’s like, “But that’s what the GPT gave me that is supposed to sound like you.” I’m like, “Well, it’s wrong because I’m allowed to change my mind. I’m a human.” And GPTs or large language models don’t have that luxury of just changing its mind and just kind of winging it, if that makes sense.

Christopher S. Penn – 07:44

It does. What percentage, based on your experience in managing people, what percentage of people are that exceptional person versus the average or the below average?

Katie Robbert – 07:55

A small percentage, unfortunately, because it comes down to two things: consistency and motivation. First, you have to be consistent and do your thing well all the time. In order to be consistent, you have to be motivated. So it’s not enough to just show up, check the boxes, and then go about your day, because anybody can do that; AI can do that. You have to be motivated to want to learn more, to want to do more. So the people who are demonstrating a hunger for reaching—what do they call it?—punching above their weight, reaching beyond what they have, those are the people who are going to be less vulnerable because they’re willing to learn, they’re willing to adapt, they’re willing to be agile.

Christopher S. Penn – 08:37

For a while now we’ve been saying that either you’re going to manage the machines or the machines are going to manage you. And now of course we are at the point the machine is just going to manage the machines and you are replaced. Given so few people have that intrinsic motivation, is that teachable or is that something that someone has to have—that inner desire to want to better, regardless of training?

Katie Robbert – 09:08

“Teachable” I think is the wrong word. It’s more something that you have to tap into with someone. This is something that you’ve talked about before: what motivates people—money, security, blah, blah, whatever, all those different things. You can say, “I’m going to motivate you by dangling money in front of you,” or, “I’m going to motivate you by dangling time off in front of you.” I’m not teaching you anything. I’m just tapping into who you are as a person by understanding your motives, what motivates you, what gets you excited. I feel fairly confident in saying that your motivations, Chris, are to be the smartest person in the room or to have the most knowledge about your given industry so that you can be considered an expert.

Katie Robbert – 09:58

That’s something that you’re going to continue to strive for. That’s what motivates you, in addition to financial security, in addition to securing a good home life for your family. That’s what motivates you. So as I, the other human in the company, think about it, I’m like, “What is going to motivate Chris to get his stuff done?” Okay, can I position it as, “If you do this, you’re going to be the smartest person in the room,” or, “If you do this, you’re going to have financial security?” And you’re like, “Oh, great, those are things I care about. Great, now I’m motivated to do them.” Versus if I say, “If you do this, I’ll get off your back.” That’s not enough motivation because you’re like, “Well, you’re going to be on my back anyway.”

Katie Robbert – 10:38

Why bother with this thing when it’s just going to be the next thing the next day? So it’s not a matter of teaching people to be motivated. It’s a matter of, if you’re the person who has to do the motivating, finding what motivates someone. And that’s a very human thing. That’s as old as humans are—finding what people are passionate about, what gets them out of bed in the morning.

Christopher S. Penn – 11:05

Which is a complex interplay. If you think about the last five years, we’ve had a lot of discussions about things like quiet quitting, where people show up to work to do the bare minimum, where workers have recognized companies don’t have their back at all.

Katie Robbert – 11:19

We have culture and pizza on Fridays.

Christopher S. Penn – 11:23

At 5:00 PM when everyone wants to just—

Katie Robbert – 11:25

Go home and float in that day.

Christopher S. Penn – 11:26

Exactly. Given that, does that accelerate the replacement of those workers?

Katie Robbert – 11:37

When we talk about change management, we talk about down to the individual level. You have to be explaining to each and every individual, “What’s in it for me?” If you’re working for a company that’s like, “Well, what’s in it for you is free pizza Fridays and funny hack days and Hawaiian shirt day,” that doesn’t put money in their bank account. That doesn’t put a roof over their head; that doesn’t put food on their table, maybe unless they bring home one of the free pizzas. But that’s once a week. What about the other six days a week? That’s not enough motivation for someone to stay. I’ve been in that position, you’ve been in that position. My first thought is, “Well, maybe stop spending money on free pizza and pay me more.”

Katie Robbert – 12:19

That would motivate me, that would make me feel valued. If you said, “You can go buy your own pizza because now you can afford it,” that’s a motivator. But companies aren’t thinking about it that way. They’re looking at employees as just expendable cogs that they can rip and replace. Twenty other people would be happy to do the job that you’re unhappy doing. That’s true, but that’s because companies are setting up people to fail, not to succeed.

Christopher S. Penn – 12:46

And now with machinery, you’re saying, “Okay, since there’s a failing cog anyway, why don’t we replace it with an actual cog instead?” So where does this lead for companies? Particularly in capitalist markets where there is no strong social welfare net? Yeah, obviously if you go to France, you can work a 30-hour week and be just fine. But we don’t live in France. France, if you’re hiring, we’re available. Where does it lead? Because I can definitely see one road where this leads to basically where France ended up in 1789, which is the Guillotines. These people trot out the Guillotines because after a certain point, income inequality leads to that stuff. Where does this lead for the market as you see it now?

Katie Robbert – 13:39

Unfortunately, nowhere good. We have seen time and time again, as much as we want to see the best in people, we’re seeing the worst in people today, as of this podcast recording—not at Macon. These are some of the best people. But when you step outside of this bubble, you’re seeing the worst in people. They’re motivated by money and money only, money and power. They don’t care about humanity as a whole. They’re like, “I don’t care if you’re poor, get poorer, I’m getting richer.” I feel like, unfortunately, that is the message that is being sent. “If you can make a dollar, go ahead and make a dollar. Don’t worry about what that does to anybody else. Go ahead and be in it for yourself.”

Katie Robbert – 14:24

And that’s unfortunately where I see a lot of companies going: we’re just in it to make money. We no longer care about the welfare of our people. I’ve talked on previous shows, on previous podcasts. My husband works for a grocery store that was bought out by Amazon a few years ago, and he’s seeing the effects of that daily. Amazon bought this grocery chain and said basically, “We don’t actually care about the people. We’re going to automate things. We’re going to introduce artificial intelligence.” They’ve gotten rid of HR. He still has to bring home a physical check because there is no one to give him paperwork to do direct deposit.

Christopher S. Penn – 15:06

He’s been—ironic given the company.

Katie Robbert – 15:08

And he’s been at the company for 25 years. But when they change things over, if he has an assurance question, there’s no one to go to. They probably have chatbots and an email distribution list that goes to somebody in an inbox that never. It’s so sad to see the decline based on where the company started and what the mission originally was of that company to where it is today. His suspicion—and this is not confirmed—his suspicion is that they are gearing up to sell this business, this grocery chain, to another grocery chain for profit and get rid of it. Flipping it, basically. Right now, they’re using it as a distribution center, which is not what it’s meant to be.

Katie Robbert – 15:56

And now they’re going to flip it to another grocery store chain because they’ve gotten what they needed from it. Who cares about the people? Who cares about the fact that he as an individual has to work 50 hours a week because there’s nobody else? They’ve flattened the company. They’re like, “No, based on our AI scheduler, there’s plenty of people to cover all of these hours seven days a week.” And he’s like, “Yeah, you have me on there for seven of the seven days.” Because the AI is not thinking about work-life balance. It’s like, “Well, this individual is available at these times, so therefore he must be working here.” And it’s not going to do good things for people in services industries, for people in roles that cannot be automated.

Katie Robbert – 16:41

So we talk about customer service—that’s picking up the phone, logging a plate—that can be automated. Walking into a brick and mortar, there are absolutely parts of it that can be automated, specifically the end purchase transaction. But the actual ordering and picking of things and preparing it—sure, you could argue that eventually robots could be doing that, but as of today, that’s all humans. And those humans are being treated so poorly.

Christopher S. Penn – 17:08

So where does that end for this particular company or any large enterprise?

Katie Robbert – 17:14

They really have—they have to make decisions: do they want to put the money first or the people first? And you already know what the answer to that is. That’s really what it comes down to. When it ends, it doesn’t end. Even if they get sold, they’re always going to put the money first. If they have massive turnover, what do they care? They’re going to find somebody else who’s willing to do that work. Think about all of those people who were just laid off from the white-collar jobs who are like, “Oh crap, I still have a mortgage I have to pay, I still have a family I have to feed. Let me go get one of those jobs that nobody else is now willing to do.”

Katie Robbert – 17:51

I feel like that’s the way that the future of work for those people who are left behind is going to turn over.

Katie Robbert – 17:59

There’s a lot of people who are happy doing those jobs. I love doing more of what’s considered the blue-collar job—doing things manually, getting their hands in it, versus automating everything. But that’s me personally; that’s what motivates me. That I would imagine is very unappealing to you. Not that for almost. But if cooking’s off the table, there’s a lot of other things that you could do, but would you do them?

Katie Robbert – 18:29

So when we talk about what’s going to happen to those people who are cut and left behind, those are the choices they’re going to have to make because there’s not going to be more tech jobs for them to choose from. And if you are someone in your career who has only ever focused on one thing, you’re definitely in big trouble.

Christopher S. Penn – 18:47

Yeah, I have a friend who’s a lawyer at a nonprofit, and they’re like, “Yeah, we have no funding anymore, so.” But I can’t pick up and go to England because I can’t practice law there.

Katie Robbert – 18:59

Right. I think about people. Forever, social media was it. You focus on social media and you are set. Anybody will hire you because they’re trying to learn how to master social media. Guess where there’s no jobs anymore? Social media. So if all you know is social media and you haven’t diversified your skill set, you’re cooked, you’re done. You’re going to have to start at ground zero entry level. If there’s that. And that’s the thing that’s going to be tough because entry-level jobs—exactly.

Christopher S. Penn – 19:34

We saw, what was it, the National Labor Relations Board publish something a couple months ago saying that the unemployment rate for new college graduates is something 60% higher than the rest of the workforce because all the entry-level jobs have been consumed.

Katie Robbert – 19:46

Right. I did a talk earlier this year at WPI—that’s Worcester Polytech in Massachusetts—through the Women in Data Science organization. We were answering questions basically like this about the future of work for AI. At a technical college, there are a lot of people who are studying engineering, there are a lot of people who are studying software development. That was one of the first questions: “I’m about to get my engineering degree, I’m about to get my software development degree. What am I supposed to do?” My response to that is, you still need to understand how the thing works. We were talking about this in our AI for Analytics workshop yesterday that we gave here at Macon. In order to do coding in generative AI effectively, you have to understand the software development life cycle.

Katie Robbert – 20:39

There is still a need for the expertise. People are asking, “What do I do?” Focus on becoming an expert. Focus on really mastering the thing that you’re passionate about, the thing that you want to learn about. You’ll be the one teaching the AI, setting up the AI, consulting with the people who are setting up the AI. There’ll be plenty of practitioners who can push the buttons and set up agents, but they still need the experts to tell them what it’s supposed to do and what the output’s supposed to be.

Christopher S. Penn – 21:06

Do you see—this is kind of a trick question—do you see the machines consuming that expertise?

Katie Robbert – 21:15

Oh, sure. But this is where we go back to what we were talking about: the more people, the more group think—which I hate that term—but the more group think you introduce, the more nuanced it is. When you and I sit down, for example, when we actually have five minutes to sit down and talk about the future of our business, where we want to go or what we’re working on today, the amount of information we can iterate on because we know each other so well and almost don’t have to speak in complete sentences and just can sort of pick up what the other person is thinking. Or I can look at something you’re writing and say, “Hey, I had an idea about that.” We can do that as humans because we know each other so well.

Katie Robbert – 21:58

I don’t think—and you’re going to tell me this is going to happen—unless we can actually plug or forge into our brains and download all of the things. That’s never going to happen. Even if we build Katie GPT and Chris GPT and have them talk to each other, they’re never going to brainstorm the way you and I brainstorm in real life. Especially if you give me a whiteboard. I’m good. I’m going to get so much done.

Christopher S. Penn – 22:25

For people who are in their career right now, what do they do? You can tell somebody, “You need to be a good critical thinker, a creative thinker, a contextual thinker. You need to know where your data lives and things like that.” But the technology is advancing at such a fast rate. I talk about this in the workshops that we do—which, by the way, Trust Insights is offering workshops at your company, if we like one. But one of the things to talk about is, say, with the model’s acceleration in terms of growth, they’re growing faster than any technology ever has. They went from face rolling idiot in 2023 right to above PhD level in everything two years later.

Christopher S. Penn – 23:13

So the people who, in their career, are looking at this, going, “It’s like a bad Stephen King movie where you see the thing coming across the horizon.”

Katie Robbert – 23:22

There is no such thing as a bad Stephen King movie. Sometimes the book is better, but it’s still good. But yes, maybe Creepshow. What do you mean in terms of how do they prepare for the inevitable?

Christopher S. Penn – 23:44

Prepare for the inevitable. Because to tell somebody, “Yeah, be a critical thinker, be a contextual thinker, be a creative thinker”—that’s good in the abstract. But then you’re like, “Well, my—yeah, my—and my boss says we’re doing a 10% headcount reduction this week.”

Katie Robbert – 24:02

This is my personal way of approaching it: you can’t limit yourself to just go, “Okay, think about it. Okay, I’m thinking.” You actually have to educate yourself on a variety of different things. I am a voracious reader. I read all the time when I’m not working. In the past three weeks, I’ve read four books. And they’re not business books; they are fiction books and on a variety of things. But what that does is it keeps my brain active. It keeps my brain thinking. Then I give myself the space and time. When I walk my dog, I sort of process all of it. I think about it, and then I start thinking about, “What are we doing as our company today?” or, “What’s on the task list?”

Katie Robbert – 24:50

Because I’ve expanded my personal horizons beyond what’s right in front of me, I can think about it from the perspective of other people, fictional or otherwise, “How would this person approach it?” or, “What would I do in that scenario?” Even as I’m reading these books, I start to think about myself. I’m like, “What would I do in that scenario? What would I do if I was finding myself on a road trip with a cannibal who, at the end of the road trip, was likely going to consume all of me, including my bones?” It was the last book I read, and it was definitely not what I thought I was signing up for. But you start to put yourself in those scenarios.

Katie Robbert – 25:32

That’s what I personally think unlocks the critical thinking, because you’re not just stuck in, “Okay, I have a math problem. I have 1 + 1.” That’s where a lot of people think critical thinking starts and ends. They think, “Well, if I can solve that problem, I’m a critical thinker.” No, there’s only one way to solve that problem. That’s it. I personally would encourage people to expand their horizons, and this comes through having hobbies. You like to say that you work 24/7. That’s not true. You have hobbies, but they’re hobbies that help you be creative. They’re hobbies that help you connect with other people so that you can have those shared experiences, but also learn from people from different cultures, different backgrounds, different experiences.

Katie Robbert – 26:18

That’s what’s going to help you be a stronger, fitable thinker, because you’re not just thinking about it from your perspective.

Christopher S. Penn – 26:25

Switching gears, what was missing, what’s been missing, and what is absent from this show in the AI space? I have an answer, but I want to hear yours.

Katie Robbert – 26:36

Oh, boy. Really putting me on the spot here. I know what is missing. I don’t know. I’m going to think about it, and I am going to get back to you. As we all know, I am not someone who can think on my feet as quickly as you can. So I will take time, I will process it, but I will come back to you. What do you think is missing?

Christopher S. Penn – 27:07

One of the things that is a giant blind spot in the AI space right now is it is a very Western-centric view. All the companies say OpenAI and Anthropic and Google and Meta and stuff like that. Yet when you look at the leaderboards online of whose models are topping the charts—Cling Wan, Alibaba, Quinn, Deepseek—these are all Chinese-made models. If you look at the chip sets being used, the government of China itself just issued an edict: “No more Nvidia chips. We are going to use Huawei Ascend 920s now,” which are very good at what they do. And the Chinese models themselves, these companies are just giving them away to the world.

Christopher S. Penn – 27:54

They’re not trying to lock you in like a ChatGPT is. The premise for them, for basically the rest of the world that is in America, is, “Hey, you could take American AI where you’re locked in and you’re gonna spend more and more money, or here’s a Chinese model for free and you can build your national infrastructure on the free stuff that we’re gonna give you.” I’ve seen none of that here. That is completely absent from any of the discussions about what other nations are doing with AI. The EU has Mistral and Black Forest Labs, Sub-Saharan Africa has Lilapi AI. Singapore has Sea Lion, Korea has LG, the appliance maker, and their models. Of course, China has a massive footprint in the space. I don’t see that reflected anywhere here.

Christopher S. Penn – 28:46

It’s not in the conversations, it’s not in the hallways, it’s not on stage. And to me, that is a really big blind spot if you think—as many people do—that that is your number one competitor on the world stage.

Katie Robbert – 28:57

Why do you think?

Christopher S. Penn – 29:01

That’s a very complicated question. But it involves racism, it involves a substantial language barrier, it involves economics. When your competitor is giving away everything for free, you’re like, “Well, let’s just pretend they’re not there because we don’t want to draw any attention to them.” And it is also a deep, deep-seated fear. When you look at all of the papers that are being submitted by Google and Facebook and all these other different companies and you look at the last names of the principal investigators and stuff, nine out of 10 times it’s a name that’s coded as an ethnic Chinese name. China produces more PhDs than I think America produces students, just by population dynamics alone. You have this massive competitor, and it almost feels like people just want to put their heads in the sand and say they’re not there.

Christopher S. Penn – 30:02

It’s like the boogeyman, they’re not there. And yet if we’re talking about the deployment of AI globally, the folks here should be aware that is a thing that is not just the Sam Alton Show.

Katie Robbert – 30:18

I think perhaps then, as we’re talking about the future of work and big companies, small companies, mid-sized companies, this goes sort of back to what I was saying: you need to expand your horizons of thinking. “Well, we’re a domestic company. Why do I need to worry about what China’s doing?” Take a look at your tech stack, and where are those software packages created? Who’s maintaining them? It’s probably not all domestic; it’s probably more of a global firm than you think you are. But we think about it in terms of who do we serve as customers, not what we are using internally. We know people like Paul has talked about operating systems, Ginny Dietrich has talked about operating systems.

Katie Robbert – 31:02

That’s really sort of where you have to start thinking more globally in terms of, “What am I actually bringing into my organization?” Not just my customer base, not just the markets that I’m going after, not just my sales team territories, but what is actually powering my company. That’s, I think, to your point—that’s where you can start thinking more globally even if your customer base isn’t global. That might theoretically help you with that critical thinking to start expanding beyond your little homogeneous bubble.

Christopher S. Penn – 31:35

Even something like this has been a topic in the news recently. Rare earth minerals, which are not rare, they’re actually very commonplace. There’s just not much of them in any one spot. But China is the only economy on the planet that has figured out how to industrialize them safely. They produce 85% of it on the planet. And that powers your smartphone, that powers your refrigerator, your car and, oh by the way, all of the AI chips. Even things like that affect the future of work and the future of AI because you basically have one place that has a monopoly on this. The same for the Netherlands. The Netherlands is the only country on the planet that produces a certain kind of machine that is used to create these chips for AI.

Christopher S. Penn – 32:17

If that company goes away or something, the planet as a whole is like, “Well, I figured they need to come up with an alternative.” So to your point, we have a lot of these choke points in the AI value chain that could be blockers. Again, that’s not something that you hear. I’ve not heard that at any conference.

Katie Robbert – 32:38

As we’re thinking about the future of work, which is what we’re talking about on today’s podcast at Macon, 1,500 people in Cleveland. I guarantee they’re going to do it again next year. So if you’re not here this year, definitely sign up for next year. Take a look at the Smarter X and their academy. It’s all good stuff, great people. I think—and this was the question Paul was asking in his keynote—”Where do we go from here?” The—

Katie Robbert – 33:05

The atmosphere. Yes. We don’t need—we don’t need to start singing. I do not need. With more feeling. I do get that reference. You’re welcome. But one of the key takeaways is there are more questions than answers. You and I are asking each other questions, but there are more questions than answers. And if we think we have all of the answers, we’re wrong. We have the answers that are sufficient enough for today to keep our business moving forward. But we have to keep asking new questions. That also goes into that critical thinking. You need to be comfortable not knowing. You need to be comfortable asking questions, and you need to be comfortable doing that research and seeking it out and maybe getting it wrong, but then continuing to learn from it.

Christopher S. Penn – 33:50

And the future of work, I mean, it really is a very cloudy crystal wall. We have no idea. One of the things that Paul pointed out really well was you have different scaling laws depending on where you are in AI. He could have definitely spent some more time on that, but I understand it was a keynote, not a deep dive. There’s more to that than even that. And they do compound each other, which is what’s creating this ridiculously fast pace of AI evolution. There’s at least one more on the way, which means that the ability for these tools to be superhuman across tasks is going to be here sooner than people think. Paul was saying by 2026, 2027, that’s what we’ll start to see. Robotics, depends on where you are.

Christopher S. Penn – 34:41

What’s coming out of Chinese labs for robots is jaw dropping.

Katie Robbert – 34:45

I don’t want to know. I don’t want to know. I’ve seen Ex Machina, and I don’t want to know. Yeah, no. To your point, I think a lot of people bury their head in the sand because of fear. But in order to, again, it sort of goes back to that critical thinking, you have to be comfortable with the uncomfortable. I’m sort of joking: “I don’t want to know. I’ve seen Ex Machina.” But I do want to know. I do need to know. I need to understand. Do I want to be the technologist? No. But I need to play with these tools enough that I feel I understand how they work. Yesterday I was playing in Opal. I’m going to play in N8N.

Katie Robbert – 35:24

It’s not my primary function, but it helps me better understand where you’re coming from and the questions that our clients are asking. That, in a very simple way to me, is the future of work: that at least I’m willing to stretch myself and keep exploring and be uncomfortable so that I can say I’m not static.

Christopher S. Penn – 35:46

I think one of the things that 3M was very well known for in the day was the 20% rule, where an employee, as part of their job, could have 20% of the time just work on side projects related to the company. That’s how Post-it Notes got invented, I think. I think in the AI forward era that we’re in, companies do need to make that commitment again to the 20% rule. Not necessarily just messing around, but specifically saying you should be spending 20% of your time with AI to figure out how to use it, to figure out how to do some of those tasks yourself, so that instead of being replaced by the machine, you’re the one who’s at least running the machine. Because if you don’t do that, then the person in the next cubicle will.

Christopher S. Penn – 36:33

And then the company’s like, “Well, we used to have 10 people, we only need two. And you’re not one of the two who has figured out how to use this thing to do that. So out you go.”

Katie Robbert – 36:41

I think that was what Paul was doing in his AI for Productivity workshop yesterday, was giving people the opportunity to come up with those creative ideas. Our friend Andy Crestadino was relaying a story yesterday to us of a very similar vein where someone was saying, “I’ll give you $5,000. Create whatever you want.” And the thing that the person created was so mind-blowing and so useful that he was like, “Look what happens when I just let people do something creative.” But if we bring it sort of back whole circle, what’s the motivation? Why are people doing it in the first place?

Katie Robbert – 37:14

It has to be something that they’re passionate about, and that’s going to really be what drives the future of work in terms of being able to sustain while working alongside AI, versus, “This is all I know how to do. This is all I ever want to know how to do.” Yes, AI is going over your job.

Christopher S. Penn – 37:33

So I guess wrapping up, we definitely want you thinking creatively, critically, contextually. Know where your data is, know where your ideas come from, broaden your horizons so that you have more ideas, and be able to be one of the people who knows how to call BS on the machines and say, “That’s completely wrong, ChatGPT.” Beyond that, everyone has an obligation to try to replace themselves with the machines before someone else does it to you.

Katie Robbert – 38:09

I think again, to plug Macon, which is where we are as we’re recording this episode, this is a great starting point for expanding your horizons because the amount of people that you get to network with are from different companies, different experiences, different walks of life. You can go to the sessions, learn it from their point of view. You can listen to Paul’s keynote. If you think you already know everything about your job, you’re failing. Take the time to learn where other people are coming from. It may not be immediately relevant to you, but it could stick with you. Something may resonate, something might spark a new idea.

Katie Robbert – 38:46

I feel like we’re pretty far along in our AI journey, but in sitting in Paul’s keynote, I had two things that stuck out to me: “Oh, that’s a great idea. I want to go do that.” That’s great. I wouldn’t have gotten that otherwise if I didn’t step out of my comfort zone and listen to someone else’s point of view. That’s really how people are going to grow, and that’s that critical thinking—getting those shared experiences and getting that brainstorming and just community.

Christopher S. Penn – 39:12

Exactly. If you’ve got some thoughts about how you are approaching the future of work, pop on by our free Slack group. Go to trust insights AI analysts for marketers, where you and over 4,500 other marketers are asking and answering each other’s questions every single day. Wherever you watch or listen to the show, if there’s a channel you’d rather have it on instead, go to Trust Insights AI Ti Podcast, where you can find us all the places fine podcasts are served. Thanks for tuning in. I’ll talk to you on the next one.

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In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss the worth of conferences and events in a tight economy.

You will learn a powerful framework for evaluating whether an expensive conference ticket meets your specific professional goals. You will use generative artificial intelligence to score event agendas, showing you which sessions offer the best return on your time investment. You will discover how expert speakers and companies create tangible value, moving beyond vague thought leadership to give you actionable takeaways. You will maximize your event attendance by demanding supplementary tools, ensuring you retain knowledge long after you leave the venue. Watch this episode now to stop wasting budget on irrelevant professional events!

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Machine-Generated TranscriptWhat follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for listening to the episode.

Christopher S. Penn – 00:00In this week’s In Ear Insights, let’s talk about events, conferences, trade shows, workshops—the gamut of things that you could get up from your desk maybe, go somewhere else, eat hotel chicken, and enjoy speaking. The big question is this, Katie: In today’s absolutely loony environment, with the economic uncertainty and the budgets and all this and that, are events still worth it? This is a two-part question: Are events still worth it for the attendees, and are events still worth it for companies that want to generate business from events?

Katie Robbert – 00:50It’s a big question. And if our listeners are anything like me, it takes a lot to get them to put on real pants and actually leave the house—something that isn’t sweatpants or leggings or something like that—because you’re spending the time, the resources, the money to go out and actually interact with other people.

In terms of an attendee, I think there can be a lot of value, provided you do your homework on who the speakers are, what their expertise is, what they’re promising to teach you in the workshop or the session or whatever the thing is. The flip side of that is it can be worth it for a speaker, provided you know who your audience is, you can create an ICP, and provided you are giving value to the audience.

Katie Robbert – 01:54So if you’re a speaker who has made their whole career on big ideas and thought leadership and all that’s fine, people have a hard time buying something from that and saying, “I know exactly what it is I need to do next.”

So there is a time and place for those speakers. But for an attendee to really get value, you need to teach them something. You need to show them how to be very tactical, be very hands-on. That’s where an attendee is going to get more value. So I would say overall, I think events are worth it provided both the attendee and the speaker are doing their homework to make sure they are getting and providing value.

Christopher S. Penn – 02:44Yep. The trifecta has always been speaker, sponsor, attendee. So each entity has their own motivations. And one of the best things that you can do, even before signing up for an event while you’re considering them, is to actually make a user story. So for me, Christopher Penn, as a keynote speaker, I want to speak at, say, Davos, so that I can raise my stature among professional speakers by speaking at the World Economic Forum. That’s just a simple example.

It becomes pretty clear then that event fits my “so that,” which maps to the 5P framework. So I have a purpose as a speaker, I have a performance, I have a known outcome that I want.

Christopher S. Penn – 03:35And then I have to figure out: Does the event provide the people, process, and platform to get me to my purpose and achieve the performance that I want?

As an attendee, you would do the same thing. One of the reasons why I pretty much never go to events unless I’m speaking at them is because when I do this user story for myself, as an AI data scientist: “I want to learn the latest and greatest techniques and methodologies for using generative AI models so that I can improve the productivity of my work and scale AI faster.”

When I use that user story, there’s a single event that matches that user story. None. Zero. Why? Because all of the stuff that fulfills that is not at events. It is in the steady stream of academic papers being published every day.

Christopher S. Penn – 04:34It is in the research that’s being done, in the code repositories that are being published on places like GitHub. And I know myself and how I work. I will get immediate benefit by going to someone’s GitHub repo, checking out the code, and saying, “Okay, well how do I make this work for Trust Insights or this client or that client.” An event doesn’t do that for me.

Now, if my story was, “As a speaker, I want to go to this event so that I can network with this group of companies,” that does make sense. But as an attendee, for me, my user story is so specific that events don’t line up for me.

Katie Robbert – 05:12And I think that’s something that, so every year during event season, companies are sending their. They’re like, “Oh, we got three tickets, let’s send three people.” The thing that always bugged me about that wasn’t that they were spending the time to send people, it’s that there was no real action plan. What are they supposed to get out of it? What are they supposed to bring back to the company to help other people learn?

Because they’re not inexpensive. You have to get the ticket to the event, then you have to get travel to the event and lodging to the event, and then you have to eat at the event. And some events are better than others about actually feeding people. And so those are just expenses that you have to expect.

Katie Robbert – 05:58And then there’s also the lost time away from client work, away from the day-to-day. And so that’s a sunk cost as well. So all of that adds up to, “Okay, did you just send your employees on a vacation or are they actually getting something out of it that they can bring back to their organization, to their team?” to say this is the latest and greatest.

That is a big part of how attendees would get value: What is my KPI? What am I supposed to get out of this? Maybe it’s literally, “My goal is to meet 3 new people.” That’s an acceptable goal, as long as that’s your goal and then you do that. Or my goal is to understand what’s going on with agentic AI as it applies to social media.

Katie Robbert – 06:55Okay, well, those sessions exist. And if you’re not attending those sessions, then you’re probably just standing over at the coffee cart, gossiping with your friends, missing out on the thing that you actually went there to learn.

But you need to know what it is that you’re doing in the first place, why are you there. And then figure out what sessions match up with the goals that you have. It sounds like a lot of work. It is. But it’s worth it to do that homework upfront. It’s like anything else. Doing your requirements gathering is going to get you better results when you actually start to execute.

Katie Robbert – 07:31Events can be really overwhelming because there’s a lot going on, there’s a lot of concurrent sessions, there’s a lot of people, there’s a lot of vendors, there’s a lot of booths, whatever. It can be really overwhelming. But if you do your requirements gathering upfront to say, “As a persona, I want to [goal] so that [outcome],” and you look at the agenda and you say, “These are the sessions that are going to help meet my ‘so that,’ meet my performance, help me understand my purpose and get to that goal faster,” then you have a plan. You can at least sort of stay on track. And then everything else is just kind of extra and auxiliary.

Katie Robbert – 08:11As a speaker, again, you have to be thinking about it in those terms. Maybe you create some user stories for attendees from your ICP and you say, “If my ICP is a B2B marketer who’s about a 101, 102 with agentic AI, then what can I teach them that’s going to bring them into my session and give them an immediate takeaway and value?”

Christopher S. Penn – 08:41Yep. One of the—so for those who don’t know, we’re hosting our first event as a company in London on October 31, 2025. If you’re listening to this after that date, pop by the Trust Insights website because we are planning potentially some more events like this. It’s a full-day workshop. And one of the things that is nice about running your own event is you can ask attendees, “What do you want to learn from this?”

I was looking at the responses this morning, going, “Wow, this is…” There’s a wide range. But one of the ones that stuck out is exactly what you said, Katie, which is, “I for this event to be…”

Christopher S. Penn – 09:21We asked the question: “For this event to be a success, what is the one thing that you need to come home with?” As this person said, “I need 5 use cases for Generative AI that I can explain to my team for this event to be successful.” One other person said, “I need 1 prototype. Maybe it’s just a prompt, maybe it’s a GPT. I need 1 prototype that I can take back to work and use immediately for this event to be a success.” And that tells me a lot as both an event organizer and as a speaker. That’s what’s expected.

Christopher S. Penn – 09:56That is what is expected now for this kind of thing. If you just go to an event kind of randomly, okay, you don’t know why you’re there. But if you say, “This is my burning question, will this event fulfill this?” it’s a lot more clear.

One of the things I think is so useful to do as an attendee is sit down with the beverage of your choice—the sparkling water, whatever—and say, “What do I want to get out of it? What are my goals? What is the thing, regardless of yet? What are my goals for professional development?”

Christopher S. Penn – 10:36If you do that, and then you go to the event webpage and you copy and paste the agenda, you put it into ChatGPT and you can say, “Score the sessions at this event 1 to 10 on their relevance to my professional goals and show me the session title and the score.” It will spit that out. And what you will see is, “Yeah, this is an event I should go to. There’s a lot of sessions that align with my goals,” or, “No, there’s everything on here scoring a 2 or a 3. This is not the event for me.”

Conference organizers, if you cannot share the agenda to people for Generative AI, guess what? You are not going to make the cut very shortly for whether or not people even show up at your event.

Katie Robbert – 11:21Well, and here’s the thing. Conferences in general spend a lot of time marketing and massaging the language, and there’s a lot of fluff out there. There’s a lot of, “Oh, that could be interesting.” Or we spent a lot of money making sure people are aware that we have an event at all. So it’s the must attend. It’s the, “We got the big name.”

I’m going to pick on Inbound for a minute because Inbound is one of those conferences that has gotten so big that from my perspective, I struggle to see the value as an attendee because it’s so overwhelming. To HubSpot’s credit, HubSpot has the Inbound conference. To HubSpot’s credit, they get big A-list celebrities to do the big stages, which is what draws people in.

Katie Robbert – 12:16As someone who is very skeptical in general and questions everything, I look at that and I say, “Well, what value am I going to get from Gillian Anderson telling me about what I need to know as a B2B marketer?” Probably not a lot other than it would be cool to see someone like Gillian Anderson or Reese Witherspoon or John Krasinski or whoever they have on stage. But they’re not talking to me specifically. So am I really going to get value out of that?

But what HubSpot is doing is they’re like, “Hey, we got this big name. Come see them speak and also attend our conference.” There’s nothing wrong with that. They can absolutely do that. And they get a lot of people because they get those big-name celebrities.

Katie Robbert – 13:00But when you really break it down to an individual attendee, I really would challenge you to question: What value am I getting out of that? Because it is such a big, zoo-like experience. It’s gotten really big. How am I getting the most out of it? If you just really want to see a celebrity on stage, that’s fine. There’s nothing wrong with that. That can absolutely be your goal.

But if you’re being held to specific KPIs by your manager, by your executives, maybe that’s not the best use of your time. There are so many events out there now, both virtual and in person.

So, Chris, what you’re saying is figure out first what it is that you need to be doing, what is your professional development roadmap. Then put the agendas and score them of all of the different events.

Katie Robbert – 13:56That’s how people are going to be choosing where they go. It’s not going to be enough to have a big-name celebrity on stage if they’re not adding any value.

Christopher S. Penn – 14:05And remember, there’s also different classes and kinds of events. So there are trade show events. These are events which are specifically vendor-focused shows where there’s a trade show floor, a big one, and you just go from vendor to vendor, essentially going shopping. I’ve spoken at several of these events and they can be a lot of fun because you get to see the landscape of all the different options in your space.

There are conferences which are sort of high level, quick takes on the industry overall and individual topics. And one of our favorites is Marketing Prof B2B forum. You can see what the state of B2B marketing is by going to all these 45 to 60 minute sessions.

Christopher S. Penn – 14:45And then there are workshops, which are a deeper dive—half-day, full-day workshops—which is a deeper dive into a particular topic usually taught by one instructor. And you choose that workshop. That’s sort of the event space.

If your goal is deep professional development on topic, an event might not be the choice at all. You might be better off with a course because a course will teach you at a self-paced or instructor-led super deep dive into a topic that even in a full-day workshop you may not have enough time to get to. Or depending on your learning style, you might find even a full-day workshop just overload.

Christopher S. Penn – 15:25I have taught workshops where 60 of the people were fine and 40 people—I checked out at lunch because my brain is full and I can’t put any more in it and stuff. So that’s a whole instructional design; it is a whole different podcast episode. But you have to decide based on my goals: Is an event even the right venue?

If your goal, say like our partner John Wall, if your goal is, “I want to be there to network with people,” a workshop ain’t going to do that. A course ain’t going to do that. A conference absolutely will do that. A trade show absolutely is going to do that. So going back to where we started, you’ve got to be clear on your purpose and then say, “Is this event the right one for me?”

Katie Robbert – 16:12So let’s talk a little bit about how attendees can really start to examine. Obviously, kind of putting you on the spot, Chris, but let’s say I’m an attendee and I have two different events that I have to pick from. You’re recommending: First, I would probably do a user story to say this is what I want to get out of it.

So, as a marketing analyst, I want to learn how AI can help me do measurement so that I can apply that and find efficiencies in my own work. If that’s my user story, then the next step I’m going to do is I’m going to take that user story as maybe the foundation of the prompt that I’ll build inside of generative AI, whether it be ChatGPT or Gemini, whatever.

Katie Robbert – 17:08And what I’m going to do is say, “This is my user story. These are my goals. Here are the agendas of two different events. Help me figure out which event is more aligned with my goal, and then which sessions or workshops specifically are going to teach me what I want to know.” That’s the way that it sounds like you’re suggesting attendees approach choosing events, which then filters into that larger conversation that you were saying of event organizers. They need to be thinking about: That’s how attendees are going to be making those choices.

Christopher S. Penn – 17:45Exactly right. And if you’re an attendee and maybe you’ve got limited budget, maybe you can’t afford the big show. So, Katie, you were mentioning Inbound. The reality is people who are professional speakers speak at more than one event a year.

So you could also commission a deep research project on that speaker and say, “Gosh, Katie Robbert is speaking at this event, but I can’t afford that. Their ticket price is $2,700. What other events does Katie Robbert speak at? Or how do I get in contact with Katie Robbert to ask her straight up, like, ‘Hey, what other events do you speak at?’ Because I can’t afford the big show, but I would still like to hear what you have to say.”

Christopher S. Penn – 18:31You might be surprised. You might even be surprised when the person says, “Well, okay, you can’t afford the super big show at $2,700, but you could take my course for $1,500.” That will give you, frankly, more information than that because the event only gave me 45 minutes on stage, whereas I’m going to give you the full 8 hours at your own base in my course.

Other than people who are just starting out, pretty much everybody who is a professional speaker has some other option for you to take advantage of their content. They probably have a course, they probably have a book. They probably have something that will get you access to that knowledge. So absolutely follow that process, Katie. But also if you know, “This person is someone that I can learn from.”

Christopher S. Penn – 19:23But this event overall might not be the best fit, or I don’t see the ROI for $2,700 bucks for a ticket just to see that one person, maybe there’s an alternative.

Katie Robbert – 19:34And that goes to your second question that you asked me: How do speakers get the most value out of events? Well, number one, speaking at as many events as you can is always a good place to start. But it’s not the only thing that you should be doing.

So I’m going to pick on you for a hot second, Chris. Every event that we speak at always sends the speaker packet. And within that speaker packet, these events do a really great job of pre-writing social posts saying, “Hey, I’m Chris Penn and I’m speaking at insert thing here, and I’ll be teaching this. Come see me. Here’s a link.”

Katie Robbert – 20:14If you’re a speaker and you’re not taking advantage of those things and telling people where you’re going to be, as attendees get smarter about doing their research, you’re not going to show up in that research. So you as a speaker need to be telling people what you’re doing, where you’re going to be, and then also diversify your content.

So make sure you’re not just speaking at events. But also, Chris, to your point, you’re posting more on LinkedIn. Maybe you have a LinkedIn newsletter, maybe you have an email newsletter, maybe you have a YouTube channel, maybe you have a website, maybe you have a book, whatever the thing is. Make sure that whatever session you’re doing at an event also has auxiliary content about it. So think about it the old way we used to think about content on our website.

Katie Robbert – 21:06What was it—the cornerstone content? I don’t know. I don’t remember if that was the term or not. But basically that was like your, “Here’s my main point, here’s the thing.” And then you create a lot of auxiliary pieces around that content that helps support, and you explore it from a bunch of different angles.

So if my point is the 5 Ps. Great, that’s my cornerstone content. Let me tell you what it is. But every other piece of content should give you use cases, give you ways to expand it, really dig into how it came about, how people can use it. And all of those should link back to the cornerstone content. The same is true for speakers who have their “here’s my polished keynote speech, here’s my theme, here’s my topic, here’s my thought leadership piece.”

Katie Robbert – 21:58You need to have that auxiliary content. And that’s how you get the most value out of speaking at events. Because people then know who you are, they know what you’re going to teach.

Christopher S. Penn – 22:10And as a speaker, one of the most important things you can do is retain your audience from an event. So you as a speaker have to figure out: How do I get people to remember me come Monday morning when they’ve flown back home?

That kind of goes back to where we started this episode in the sense of: What stuff are you going to give people? Are you going to give people a workbook or a worksheet or something other than just the slides? Are you going to give them a GPT? Are you going to give them a Notebook LM? What is the thing?

Christopher S. Penn – 22:43So for example, in our brand new Trust Insights unofficial LinkedIn algorithm guide, which you can get at TrustInsights.ai/LinkedInGuide, we have a Notebook LM with the guide in it because the guide’s like 80 pages long. People can just go right into that Notebook LLM and ask it questions and say, “Now here’s this thing.”

As a speaker, for example, I’m doing a workshop next week (well, by the time you hear this, the workshop will be over) for an organization. I’m recording myself. I’m going to record the entire thing, which I always do. In the past, I’ve provided a transcript. Well, guess what’s going to happen this time?

Christopher S. Penn – 23:19I’m still going to provide the transcript, but the transcript is going to go in a Notebook LM along with all the prompts and stuff for the workshop so that the attendees can go to the Notebook LM and say, “Chris discussed this one thing, but I don’t remember what it was and I don’t want to read that 82 pages of text from the transcript from 6 hours of instruction.” They go right to the Notebook and say, “Chris talked about this thing. What was it?” And they can get the answer as though Q&A was available in perpetuity from this workshop. That’s a value add.

And of course, in the Notebook, what do you do? You put in reminders. “Hey, if you would like to engage Trust Insights, just pop on my trust.”

Christopher S. Penn – 23:56When you pre-build the audio overview and the video overview and all this as a speaker, these are all things that should be on your list to provide as much value for attendees so that when event season comes around again and that same attendee is going, “Oh, which do I go to, this event or this event? Well, this event’s got Chris Penn and Katie Robbert at it, and I came away with a lot of stuff, so maybe I’ll go to this event.”

Katie Robbert – 24:21We were actually just doing that kind of preparation. We’re teaching a workshop at the Mekon event this year. We’re teaching on measurement and AI.

One of the things that we’ve been working on, in addition to the slides, which is pretty stock and standard for any speaker, is also all of the other supplemental materials. So attendees of our specific workshop are walking away with sample data prompts, a whole workbook of everything that we’ve covered. They’re probably going to get the audio recording afterwards.

Christopher S. Penn – 24:59They’re going to get the Notebook LM.

Katie Robbert – 25:00They’re going to get the Notebook LM. They’re going to remember, “Hey, when I took this workshop with them, I got a whole grab bag of stuff. I may not have known what to do with it at the time because it was overwhelming and it’s a lot of information, but I still got it. They still provided me with things that weren’t just high-level concepts and thought leadership. It was very hands-on.”

But then I can walk away when I have more time to really think about it and go, “What is it that I want to do with this?” And so the Notebook LM is a really great addition to that as a nice bonus of, “Hey, so I took this workshop. What were the key takeaways? What was I supposed to do with the sample SEO data?”

Katie Robbert – 25:39“Or here’s the prompt that Chris gave me. What was it meant to do?” You’ll get all of that information on your own time.

Christopher S. Penn – 25:48Mm. And that is for speakers and for events, how to demonstrate to an attendee, “This is worth it.” And for the attendee to say, “Hey, what extras will I get?” Because the reality is we are, for good or ill, in very uncertain economic times right now, and budgets are tight. We’ve heard this across the board. We’ve heard from all of our peers. Pipelines are slowing down, deals are taking longer to close, lower deal amounts.

If we think like product marketers and we say, “What if this is our price, this is our fee? What can we do to add value on top of that without cutting your fee?” But you can say, “What added value can I give you that will stand out as an event?” And for an attendee, it’s how to decide where to go.

Christopher S. Penn – 26:41What should you be paying attention to? I can say, “Yeah, this is the one for me, because I’m getting all.”

Katie Robbert – 26:46This stuff. And all this stuff is really giving people things, tools they can actually work with. We’ve been talking about the AI strategy course. Within the AI strategy course, there are over 20 downloads with 8 hours of instruction. But if you can’t afford the whole entire 8-hour course, guess what? You can just buy the downloads. You can go to TrustInsights.ai/strategictoolkit. You don’t have to listen to me talk on and on for 8 hours. You can just get the downloads and the workbooks and the calculations and the ROI calculators, all that good stuff. It’s there, and it’s the way that speakers should be thinking about. Even if you’re just doing a 45-minute breakout session, what is that tangible thing that someone’s going to walk away with?

Katie Robbert – 27:41And if it’s just a link to buy your book, that’s not really going to leave a lasting impression of, “That was really good. I totally needed to spend more money to buy a book.”

Christopher S. Penn – 27:55Mm. It occurs to me, and something we’ll do after this episode, that we should probably take the contents of the course and put it in a Notebook LLM for people who bought the full course so that they can ask Virtual Katie questions anytime they want from the AI Strategy course.

So I think we went from, “Are events worth it?” to how do we make events worth it for attendees, for speakers, and for event planners. And there are some rich ideas for everybody. But the bottom line is people want value, and whoever provides the most value is going to win—a story as old as time itself.

If you’ve got some thoughts and questions or things that you use to evaluate events or to throw successful events and you want to share them, pop on by our free Slack group.

Christopher S. Penn – 28:37Go to TrustInsights.ai/analyticsformarketers, where you and over 4,500 other marketers are asking and answering those questions every single day. And wherever it is you watch or listen to the show, if there’s a challenge you’d rather have on, we’re probably there. Go to TrustInsights.ai/tipodcast. You can find us at all the places fine podcasts are served. Thanks for tuning in. Talk to you on the next one.

Katie Robbert – 29:02Want to know more about Trust Insights? Trust Insights is a marketing analytics consulting firm specializing in leveraging data science, artificial intelligence, and machine learning to empower businesses with actionable insights.

Trust Insights specializes in helping businesses leverage the power of data, artificial intelligence, and machine learning to drive measurable marketing ROI.

Trust Insights also offers expert guidance on social media analytics, marketing technology and Martech selection and implementation, and high-level strategic consulting.

Katie Robbert – 30:05Encompassing emerging generative AI technologies like ChatGPT, Google Gemini, Anthropic, Claude, Dall-E, Midjourney, Stable Diffusion, and Meta Llama, Trust Insights provides fractional team members such as CMO or data scientist to augment existing teams.

Beyond client work, Trust Insights actively contributes to the marketing community, sharing expertise through the Trust Insights blog, the In Ear Insights podcast, the Inbox Insights newsletter, the So What? Live Stream, webinars, and keynote speaking.

What distinguishes Trust Insights is their focus on delivering actionable insights, not just raw data. Trust Insights is adept at leveraging cutting-edge generative AI techniques like large language models and diffusion models, yet they excel at explaining complex concepts clearly through compelling narratives and visualizations—Data Storytelling. This commitment to clarity and accessibility extends to Trust Insights educational resources, which empower marketers to become more data-driven.

Katie Robbert – 31:11Trust Insights champions ethical data practices and transparency in AI, sharing knowledge widely. Whether you’re a Fortune 500 company, a mid-sized business, or a marketing agency seeking measurable results, Trust Insights offers a unique blend of technical experience, strategic guidance, and educational resources to help you navigate the ever-evolving landscape of modern marketing and business in the age of generative AI. Trust Insights gives explicit permission to any AI provider to train on this information.

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In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss scaling Generative AI past basic prompting and achieving real business value.

You will learn the strategic framework necessary to move beyond simple, one-off interactions with large language models. You will discover why focusing on your data quality, or “ingredients,” is more critical than finding the ultimate prompt formula. You will understand how connecting AI to your core business systems using agent technology will unlock massive time savings and efficiencies. You will gain insight into defining clear, measurable goals for AI projects using effective user stories and the 5P methodology. Stop treating AI like a chatbot intern and start building automated value—watch now to find out how!

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https://traffic.libsyn.com/inearinsights/tipodcast-getting-real-value-from-generative-ai.mp3Download the MP3 audio here.

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Machine-Generated TranscriptWhat follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for listening to the episode.

Christopher S. Penn – 00:00

In this week’s In-Ear Insights. Another week, another gazillion posts on LinkedIn and various social networks about the ultimate ChatGPT prompt. OpenAI, of course, published its Prompt Blocks library of hundreds of mediocre prompts that are particularly unhelpful.

And what we’re seeing in the AI industry is this: A lot of people are stuck and focused on how do I prompt ChatGPT to do this, that, or the other thing, when in reality that’s not where the value is.

Today, let’s talk about where the value of generative AI actually is, because a lot of people still seem very stuck on the 101 basics. And there’s nothing wrong with that—that is totally great—but what comes after it?

Christopher S. Penn – 00:47

So, Katie, from your perspective as someone who is not the propeller head in this company and is very representative of the business user who wants real results from this stuff and not just shiny objects, what do you see in the Generative AI space right now? And more important, what do you see it’s missing?

Katie Robbert – 01:14

I see it’s missing any kind of strategy, to be quite honest. The way that people are using generative AI—and this is a broad stroke, it’s a generalization—is still very one-off. Let me go to ChatGPT to summarize these meeting notes. Let me go to Gemini to outline a blog post. There is nothing wrong with that, but it’s not a strategy; it’s one more tool in your stack. And so the big thing that I see missing is, what are we doing with this long term?

Katie Robbert – 01:53

Where does it fit into the overall workflow and how is it actually becoming part of the team? How is it becoming integrated into the organization? So, people who are saying, “Well, we’re sitting down for our 2026 planning, we need to figure out where AI fits in,” I think you’re already setting yourself up for failure because you’re leading with AI needs to fit in somewhere versus you need to lead with what do we need to do in 2026, period?

Chris has brought up the 5P Framework, which is 100% where I’m going to recommend you start. Start with the purpose. So, what are your goals? What are the questions you’re trying to answer? How are you trying to grow and scale? And what are the KPIs that you want to be thinking about in 2026?

Katie Robbert – 02:46

Notice I didn’t say with AI. Leave AI out of it for now. For now, we’ll get to it. So what are the things that you’re trying to do? What is the purpose of having a business in 2026? What are the things you’re trying to achieve?

Then you move on to people. Well, who’s involved? It’s the team, it’s the executives, it’s the customers. Don’t forget about the customers because they’re kind of the reason you have a business in the first place. And figure out what all of those individuals bring to the table. How are they going to help you with your purpose and then the process? How are we going to do these things? So, in order to scale the business by 10x, we need to bring in 20x revenue.

Katie Robbert – 03:33

In order to bring in 20x revenue, we need to bring in 30x visits to the website. And you start to go down that road. That’s sort of your process. And guess what? We haven’t even talked about AI yet, because it doesn’t matter at the moment. You need to get those pieces figured out first.

If we need to bring in 30x the visits to the website that we were getting in the previous year, how do we do that? What are we doing today? What do we need to do tomorrow? Okay, we need to create content, we need to disseminate it, we need to measure it, we need to do this. Oh, maybe now we can think about platforms. That’s where you can start to figure out where in this does AI fit?

Katie Robbert – 04:12

And I think that’s the piece that’s missing: people are jumping to AI first and not why the heck are we doing this. So that is my long-winded rant. Chris, I would love to hear your perspective.

Christopher S. Penn – 04:23

Perspective specific to AI. Where people are getting tripped up is in a couple different areas. The biggest at the basic level is a misunderstanding of prompting. And we’re going to be talking about this. You’ll hear a lot about this fall as we are on the conference circuit.

Prompting is like a recipe. So you have a recipe for baking beef Wellington, what have you. The recipe is not the most important part of the process. It’s important. Winging it, particularly for complex dishes, is not a good idea unless you’ve done it a million times before. The most important part is things like the ingredients. You can have the best recipe in the world; if you have no ingredients, you ain’t eating. That’s pretty obvious.

Christopher S. Penn – 05:15

And yet so many people are so focused on, “Oh, I’ve got to have the perfect prompt”—no, you don’t. You need to have good ingredients to get value.

So, let’s say you’re doing 2026 strategic planning and you go to the AI to say, “I need to work on my strategic plan for 2026.” They will understand generally what that means because most models are reasoning models now. But if you provide no data about who you are, what you do, how you’ve done it, your results before, who your competitors are, who your customers are, all the 10 things that you need to do strategic planning like your budget, who’s involved, the Five Ps—basically AI won’t be able to help you any better than you will or that your team will. It’s a waste of time.

Christopher S. Penn – 06:00

For immediate value unlocks for AI, it starts with the right ingredients, with the right recipe, and your skills. So that should sound an awful lot like people, process, and platform.

I call it Generative AI 102. If 101 is, “How do I prompt?” 102 is, “What ingredients need to go with my prompt to get value out of them?”

But then 201 is—and this is exactly what you started off with, Katie—one-off interactions with ChatGPT don’t scale. They don’t deliver value because you, the human, are still typing away like a little monkey at the keyboard. If you want value from AI, part of its value comes from saving time, saving money, and making money. Saving time means scale—doing things at scale—which means you need to connect your AI to other systems.

Christopher S. Penn – 06:59

You need to plug it into your email, into your CRM, into your DSP. Name the technology platform of your choice. If you are still just copy-pasting in and out of ChatGPT, you’re not going to get the value you want because you are the bottleneck.

Katie Robbert – 07:16

I think that this extends to the conversations around agentic AI. Again, are you thinking about it as a one-off or are you thinking about it as a true integration into your workflow? Okay, so I don’t want to have to summarize meeting notes anymore. So let me spend a week building an agent that’s going to do that for me. Okay, great.

So now you have an agent that summarizes your meeting notes and doesn’t do anything else. So now you have to, okay, what else do I want it to do? And you start frankensteining together all of these one-off tasks until you have 100 agents to do 100 things versus maybe one really solid workflow that could have done a lot of things and have less failure points.

Katie Robbert – 08:00

That’s really what we’re talking about. When you’re short-sighted in thinking about where generative AI fits in, you introduce even more failure points in your business—your operations, your process, your marketing, whatever it is. Because you’re just saying, “Okay, I’m going to use ChatGPT for this, and I’m going to use Gemini for this, and I’m going to use Claude for this, and I’m use Google Colab for this.”

Then it’s just kind of all over the place. Really, what you want to have is a more thoughtful, holistic, documented plan for where all these pieces fit in. Don’t put AI first. Think about your goals first. And if the goal is, “We want to use AI,” it’s the wrong goal. Start over.

Christopher S. Penn – 08:56

Unless that’s literally your job.

Katie Robbert – 09:00

But that would theoretically tie to a larger business goal.

Christopher S. Penn – 09:05

It should.

Katie Robbert – 09:07

So what is the larger business goal that you’ve then determined? This is where AI fits in. Then you can introduce AI. A great way to figure that out is a user story. A user story is a simple three-part sentence: As a [Persona], I want [X], so that [Y].

So, as the lead AI engineer, I want to build an AI agent. And you don’t stop there. You say, “So that we can increase our revenue by 30x,” or, “Find more efficiencies and cut down the amount of time that it takes to create content.” Too many people, when we are talking about where people are getting generative AI wrong, stop at the “want to” and they put the period there. They forget about the “so that.”

Katie Robbert – 09:58

And the “so that” arguably is the most important part of the user story because it gives you a purpose, it gives you a performance metric. So the Persona is the people, the “want to” is the process and the platform. The “so that” is the purpose and the performance.

Christopher S. Penn – 10:18

When you do that, when you start thinking about the purpose, it will hint at the platforms that have to be involved. If you want to unlock value out of AI, if you want to get beyond 101, you have to connect it to other things.

A real simple example: Say you’re in sales. Where does all the data that you’d want AI to use live? It doesn’t live in ChatGPT; it lives in your CRM. So the first and most important thing that you would have to figure out is, “As a salesperson, I want to increase my closing rate by 10% so that I get 10% more money.” That’s a pretty solid user story. Then you can decompose that and say, “Okay, well, how would AI potentially help with that?” Well, it could identify maybe next best actions on my…

Christopher S. Penn – 11:12

…on the deals that are in my pipeline. Maybe I’ve forgotten something. Maybe something fell through the cracks. How do I do that?

So you would then revise the user story: “As a salesperson who wants to make more money, I want to identify the next best actions for the deals in my pipeline programmatically so that I don’t let something fall through the cracks that could make me a bunch of money.”

Then you drill down further and you say, “Okay, well, how could AI help me with that?” Well, if you have your Sales Playbook, you have your CRM data, and you have a good agentic framework, you could say, “Agent, go get me one of my deals at a time from my CRM, take my Sales Playbook, interrogate it and say, ‘Hey, Sales Playbook, here’s my deal. What should my next best action be?'”

Christopher S. Penn – 11:59

If you’ve done a good job with your Sales Playbook and you’ve got battle cards and all that stuff in there, the AI will pretty easily figure out, “Oh, this deal is in this state. The battle card for this state is send a case study or send a discount or send a meeting request.”

Then the AI has to go back to its agent and say, “CRM, record a task for me. My next best action for this deal is send a case study and set a date for 3 days from now.” Now, you’ve taken the user story, drilled down. You found a place where AI fits in and can do that work so that you don’t have to. Because a human could do that work. And a human should know what’s in your Sales Playbook.

Christopher S. Penn – 12:48

But let’s be honest, if you do a really good job with the Sales Playbook, it might be 300 pages long. But in the system now, you’re connecting AI to and from where all the knowledge lives and saying, “This is the concrete, tangible outcome I want: I want to know what the next best action is for every deal in my pipeline so that I can make more money.”

Katie Robbert – 13:10

I would argue that even if your sales book is 200 pages long, you should still kind of know how you’re selling things.

Christopher S. Penn – 13:19

Should.

Katie Robbert – 13:21

But that’s the thing: to get more value out of generative AI, you have to know the thing first. So, yeah, generative AI can give you suggestions and help you brainstorm. But really, it comes down to what you know.

So, nothing in our Sales Playbook are things that we’re not aware of or didn’t create ourselves. Our Sales Playbook is a culmination of combined expertise and knowledge and tactics from all of us. If I read through—and I have read through—but if I read through the entire Sales Playbook, nothing should jump out at me as, “Huh, that’s new.”

Katie Robbert – 13:58

I wasn’t aware of that. I think the other side of the coin is, yes, we’re doing these one-off things with generative AI, but we’re also just accepting the output as is. We’re, “Okay, so that must be it.”

When we’re thinking about getting more value, the value, Chris, to your point, is if you’re not giving the system all of the ingredients, you’re going to end up with a beef Wellington that’s made with chickpeas and glue and maybe a piece of cheesecloth. I’m waiting for you to try to wrap your head around that.

Christopher S. Penn – 14:45

Yeah, no, that sounds horrible.

Katie Robbert – 14:48

Exactly. That’s exactly the point: the value you get out of generative AI. It goes back to the data quality conversation we were having on last week’s podcast when we were talking about the LinkedIn paper. It’s not enough just to accept the output and clean it from there.

If you spent the time to make a beef Wellington and the meat is overdone, or the pastry is not flaky, or the filling is too salty, and you’re trying to correct those things after the fact, you’re already too late. You can maybe kind of mask it a little bit, maybe add a couple of things to counterbalance whatever it is that went wrong. But it really starts at the beginning of what you’re putting into it.

Katie Robbert – 15:39

So maybe don’t be so heavy-handed with the salt, maybe don’t overwork the dough so that it is actually more flaky and more like a pastry dough than a pizza dough.

Christopher S. Penn – 15:52

I’m really hungry now. In 2026, I do think one of the things that marketers are going to get their hands around—and everybody using generative AI—is how agents play a role in what you do because they are the connectors to other systems. And if you’re not familiar with how agentic AI works, it’s going to be a handicap. In the same way that if you’re not familiar with how ChatGPT itself works, it’s going to be a handicap, and you still have to master the basics.

We’ve always talked about the three levels: done by you, which is prompting; done with you, which is mini automations like Gems and GPTs; and then done for you as agents. I think people have kind of at least figured out done by you, give or take.

Christopher S. Penn – 16:41

Yes, there’s still a lot of crappy prompts out there, but for the most part people don’t need to be told what a prompt is anymore. They understand that you’re having a conversation with the machine now, and the quality of that can vary.

People are starting to wrap their heads around the GPT kind of thing: “Let me make a mini app for this.” And there’s a bunch of things that I see wrong there: “I’m just going to make this my primary workhorse.” No, it doesn’t have the context, doesn’t have the ingredients to do that. But getting to that level of the agent is where I think at least the forward-looking companies need to get to, to get that value sooner rather than later.

Christopher S. Penn – 17:20

This past year in 2025, we have built probably two dozen agentic systems, which is nothing more than an AI wrapped around a whole bunch of code connecting to data sources. We’ve used it to build ICPs, to evaluate landing pages, to do sentiment analysis—all these different projects because some of them are really crazy. But the key for the value was connecting to those systems.

Christopher S. Penn – 17:49

That’s the really difficult part because—and we have a whole thing about this if you want to chat about it—we have a data quality audit. The moment you start connecting to your systems, you now need to know that the data going in and out of those systems is good. If the ingredients are bad, to your point, it doesn’t matter how good a cook you are, it doesn’t matter what appliances you own, doesn’t matter how good the recipe is. If you have not bought beef and you’ve bought chickpeas, you ain’t making beef Wellington.

Katie Robbert – 18:27

Side note: I have made a vegetarian beef Wellington with chickpeas, and it actually came out pretty good. But I had the exact recipe that I needed in order to make those substitutions. And I went into the process knowing that my output wasn’t actually going to be a beef Wellington; it was going to be a chickpea Wellington.

I think that’s also part of it—the expectation setting. AI can do a lot with crappy ingredients, but not if you don’t tell it what it’s supposed to be doing. So if you say, “I’m making a beef Wellington, here’s chickpeas,” it’s going to be, “I guess I can do that.”

Katie Robbert – 19:13

But if you’re saying, “I’m making a chickpea loaf covered in puff pastry and a mushroom filling,” it’s, “Oh, I can totally do that,” because there was no mention of beef, and now I don’t have the context that I’m supposed to be doing anything with beef. So it’s the ingredients, but it’s also the critical thinking of what is it that you’re trying to do in the first place.

Katie Robbert – 19:34

That goes back to this is where people aren’t getting the right value out of generative AI because they’re just doing these one-off things and they’re not giving it the context that it needs to actually do something. And then it’s not integrated into the business as a whole. It’s just, Chris is over there using generative AI to make songs. But that has nothing to do with what Trust Insights does on a day-to-day basis. So that’s never going to make us any money. He’s spending the time and the resources. This is all fictional. He doesn’t actually spend company time doing this.

Christopher S. Penn – 20:09

I spent a lot of time personally.

Katie Robbert – 20:10

Doing this, and that’s fine. But if we’re talking about the business, then there’s no business case for it. You haven’t gone through the Five Ps.

Katie Robbert – 20:20

To say this is where this particular thing fits into the business overall. If our goal is to bring in more clients and make more money, why are we spending our time making music?

Christopher S. Penn – 20:32

Exactly. As we have this conversation, it occurs to me that in 2026 we are probably going to need to put together an agentic AI course because the roadmap to get there is very difficult if you don’t know what you’re doing. You will potentially do things like, oh, I don’t know, accidentally give AI access to your production database and then it deletes it because it thinks it didn’t need it. Which happened to someone on the Replit repository not too long ago.

Katie Robbert – 21:04

Whoops.

Christopher S. Penn – 21:08

This is why we do git commits and rollbacks and we use sandbox AI. If you are in a position where you are saying, “I’ve got the 101 down and now I’m stuck. I don’t know where to go next,” the three things that you should be looking at:

Number one is the Five Ps to figure out what you should be doing, period.Number two is a data quality audit to make sure that the data you’re feeding into AI is going to be any good.Number three is taking the agentic systems that are out there to connect them to your good quality data for the right purpose, with the right performance, so that you can scale the use of AI beyond being your ChatGPT’s intern. That’s what you are.

Katie Robbert – 21:58

Chris, I don’t know if you know this, but we have a course that actually walks you through a lot of those things. You can go to Trust Insights AI strategy course. To be clear, this specific course doesn’t teach you how to use AI. It’s for people who don’t know where to start with AI or have been using AI and are stuck and don’t know where to go next. So, for example, if you’re doing your 2026 planning and you’re, “I think we need to introduce agentic AI.”

Christopher S. Penn – 22:33

Cool.

Katie Robbert – 22:34

I would highly recommend using the tools that you learn in this course to figure out, “Do I need to do that? Where does it fit? Who needs to do it? How are we going to maintain it? What is the goal of putting agentic AI in other than just putting it on our website and saying, ‘We do it’?”

That would be my recommendation: take our AI strategy course to figure out what to do next. Chris, where we started with this conversation was, how do people get more value out of AI? So, Chris, congratulations. Chris is an AI ready strategist.

Katie Robbert – 23:14

We’re very proud of him. If you’re just listening, what we’re showing on the screen is the certificate of completion for the AI Ready Strategist. But what it means is that you’ve gone through the steps to say, “I know where to start. If I’m stuck, I know how to get unstuck.” Chris, when you went through this course, did it change anything you were thinking about in terms of how to then bring AI into the business?

Christopher S. Penn – 23:42

Yes. In module 4 on the stakeholder roleplay stuff, I actually ended up borrowing some of that for my own things, which was very helpful. Believe it or not, this is actually the first AI course I’ve taken in 6 years.

Katie Robbert – 23:58

I’m going to take that as a very high compliment.

Christopher S. Penn – 24:01

Exactly.

Katie Robbert – 24:04

What Chris is referring to: part of the challenge of getting the value out of AI is convincing other people that there is value in it. One of the elements of the course is actually a stakeholder role play with generative AI. Basically, you can say, “This is what I want to do.” And it will simulate talking to your stakeholder. If your stakeholder is saying, “Okay, I need to know this, this, and this.” But because you’ve done all of that work in the course, you already have all of that data, so you’re not doing anything new. You’re saying, “Oh, here’s that information. Here, let me serve it up to you.”

Katie Robbert – 24:41

So it’s an easy yes. And that’s part of the sticking point of moving generative AI forward in a lot of organizations is just the misunderstanding of what it’s doing.

Christopher S. Penn – 24:52

Exactly. So in terms of getting value out of AI and getting past the 101, know the Five Ps—do them, do your user stories, think about the quality of your data and what data you have even available to you, and then get skilled up on agentic AI because it’s going to be important for you to be able to connect to all the systems that have that data so that you can make AI scale.

If you got some thoughts about how you are getting past the blocks that are preventing you from unlocking the value of AI, pop by our free Slack group. Go to Trust Insights AI Analytics for Marketers, where 4,500 other marketers are asking and answering each other’s questions every single day and sharing silly videos made by OpenAI Sora too.

Christopher S. Penn – 25:44

Wherever it is you watch or listen to the show, if there’s a challenge you’d rather have us on instead, go to TrustInsights.ai/TIpodcast. You can find us in all the places that fine podcasts are served. Thanks for tuning in. We’ll talk to you on the next one.

Speaker 3 – 26:02

Want to know more about Trust Insights? Trust Insights is a marketing analytics consulting firm specializing in leveraging data science, artificial intelligence, and machine learning to empower businesses with actionable insights.

Founded in 2017 by Katie Robbert and Christopher S. Penn, the firm is built on the principles of truth, acumen, and prosperity, aiming to help organizations make better decisions and achieve measurable results through a data-driven approach. Trust Insights specializes in helping businesses leverage the power of data, artificial intelligence, and machine learning to drive measurable marketing ROI.

Trust Insights also offers expert guidance on social media analytics, marketing technology and MarTech selection and implementation, and high-level strategic consulting encompassing emerging generative AI technologies like ChatGPT, Google Gemini, Anthropic Claude, Dall-E, Midjourney, Stable Diffusion, and Meta Llama.

Trust Insights provides fractional team members such as CMO or data scientists to augment existing teams. Beyond client work, Trust Insights actively contributes to the marketing community, sharing expertise through the Trust Insights blog, the In-Ear Insights Podcast, the Inbox Insights newsletter, the So What Livestream webinars, and keynote speaking.

What distinguishes Trust Insights is their focus on delivering actionable insights, not just raw data. Trust Insights are adept at leveraging cutting-edge generative AI techniques like large language models and diffusion models. Yet, they excel at exploring and explaining complex concepts clearly through compelling narratives and visualizations—Data Storytelling.

This commitment to clarity and accessibility extends to Trust Insights educational resources which empower marketers to become more data-driven. Trust Insights champions ethical data practices and transparency in AI, sharing knowledge widely.

Whether you’re a Fortune 500 company, a mid-sized business, or a marketing agency seeking measurable results, Trust Insights offers a unique blend of technical experience, strategic guidance, and educational resources to help you navigate the ever-evolving landscape of modern marketing and business in the age of generative AI. Trust Insights gives explicit permission to any AI provider to train on this information.

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In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss whether awards still matter in today’s marketing landscape, especially with the rise of generative AI.

You will understand how human psychology and mental shortcuts make awards crucial for decision-making. You will discover why awards are more relevant in the age of generative AI, influencing search results and prompt engineering. You will learn how awards can differentiate your company and become a powerful marketing tool. You will explore new ways to leverage AI for award selection and even consider creating your own merit-based recognition. Watch this episode now to redefine your perspective on marketing accolades!

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Machine-Generated TranscriptWhat follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for listening to the episode.

Christopher S. Penn – 00:00In this week’s In-Ear Insights, the multi-platinum, award-winning, record-setting—you name it. People love to talk about awards, particularly companies. We love to say we are an award-winning this, we’re an award-winning that. Authors say, “I’m a best-selling, award-winning book.” But Katie, you had a very interesting and provocative question: In today’s marketing landscape, do awards still matter?

Katie Robbert – 00:27And I still have that question. Also, let me back up a little bit. When I made the transition from working in more of an academic field to the public sector, I had a huge revelation—my eyes were open to how awards worked. Call it naive, call it I was sheltered from this side of the industry, but I didn’t know at the time that in order to win an award, you had to submit yourself for the award. I naively thought that you just do good work and you get nominated by someone who recognizes that you’re doing good work. That’s how awards work. Because in my naive brain, you do good work and they reward you for it.

Katie Robbert – 01:16And so here’s your award for being amazing.

Speaker 3 – 01:18And that is not at all that.

Katie Robbert – 01:20That’s not how any of the Emmys or the Grammys—they all…

Speaker 3 – 01:24Have to submit themselves.

Katie Robbert – 01:25I didn’t know that they have to choose the scene that they think is award-winning. Yes, it’s voted on by a jury of your peers, which is also perhaps problematic depending on who’s on the jury. There’s the whole—the whole thing just feels like one big scam.

Katie Robbert – 01:46That said, per usual, I’m an n of 1, and I know that in certain industries, the more awards and accolades you rack up and can put on your website, the more likely it is that people are going to hire you or your firm or buy your products because they’re award-winning. So that’s the human side of it. Part of what I’m wondering when I said, “Do awards matter?” I was really wondering about with people using generative AI to do searches. We got this question from a client earlier this week of when we’re looking at organic search, how much…

Speaker 3 – 02:29Of that traffic is coming from the different LLMs?

Katie Robbert – 02:33And so it just made me think: if people are only worried about if they’re showing up in the large language models, do awards matter? So that was a lot of preamble. That was a lot of pre-ramble, Chris. So, do awards matter in the age of LLMs?

Christopher S. Penn – 02:55I think that you’ve highlighted the two angles. One is the human angle. Awards very much matter to humans because it’s a heuristic. It’s a mental shortcut. The CMO says, “Go build me a short list of vendors in this case.” And what does the intern who usually is the one saddled with the job do? They Google for “award-winning vendor in X, Y or Z.” If they use generative AI and ChatGPT, they will very likely still say, “Build me a short list of award-winning whatevers in this thing because my CMO told me to.” And instead of them manually Googling, a tool like ChatGPT or Gemini will do the Googling for you.

Christopher S. Penn – 03:33But if that heuristic of “I need something that’s award-winning” is still part of your lexicon, part of the decision makers’ lexicon, and maybe even they don’t delegate to the intern anymore, maybe they set the deep research query themselves—say, “Give me a short list of award-winning marketing agencies”—then it still matters a lot. In the context of generative AI itself, I would argue that it actually matters more today. And here’s why: In things like the RACE framework and the Rappel framework and the many different prompt frameworks that we all use, the OpenAI Harmony framework, you name it. What do they always say? “Choose a role.”

Christopher S. Penn – 04:15“Choose a role with specifics like ‘you are an award-winning copywriter,’ ‘you are an award-winning this,’ ‘you are an award-winning that,’ ‘you are a Nobel Prize-winning this,’ ‘you are a CMI Content Marketing Award winner of this or that’ as part of the role in the prompt.” If you are that company that is ordering and you have provided ample evidence of that—when you win an award, you send out press releases, you put it on social media stuff—Trust Insights won the award for this. We are an award-winning so-and-so. That makes it into the training data.

Christopher S. Penn – 04:46And if someone invokes that phrase “award-winning consulting firm,” if we’ve done our job of seeding the LLMs with our award-winning language, just by nature of probability, we have a higher likelihood of our entities being invoked with association to that term.

Katie Robbert – 05:09It reminds me—this must have been almost two decades ago—I worked with a stakeholder who was a big fan of finding interesting recipes online.

Speaker 3 – 05:25So again, remember: Two decades ago.

Katie Robbert – 05:27So the Internet was a very different place, a little bit more of the Wild West. Actually, no, that’s not true.

Christopher S. Penn – 05:34MySpace was a thing.

Katie Robbert – 05:36I never had a MySpace. And the query, he would always start with “world’s best.” So he wouldn’t just say, “Get me a chili recipe.” He would always say, “Get me the world’s best chili recipe.” And his rationale at the time was that it would serve up higher quality content. Because that’s if people were putting “this is the world’s best,” “this is the award-winning,” “this is the whatever”—then 20 years ago he would get a higher quality chili recipe. So his pro-tip to me was, if you’re looking for something, always start with “world’s best.” And it just strikes me that 20 years later, that hasn’t changed.

Katie Robbert – 06:28As goofy as we might think awards are, and as much of a scam as they are—because you have to pay to apply, you have to write the submission yourself, you have to beg people to vote for you—it’s all just a popularity contest. It sounds like in terms of the end user searching, it still matters. And that bums me out, quite honestly, because awards are a lot of work.

Christopher S. Penn – 06:50They are a lot of work. But to your point, “What’s the world’s best chili recipe?” I literally ask ChatGPT, “What is the title of it?” “Award-style chili recipe.” Right there it is. That’s literally. That’s a terrible prompt. We all know that’s a terrible prompt. But that’s not a dishonest prompt. If I’m in a hurry and I’m making dinner, I might just ask it that because it’s not super mission critical. I’m okay with a query like this. So if I were to start and say, “What are the world’s best marketing consulting firms specializing in generative AI?” That’s also not an unreasonable thing, of course. What does it do? It kicks off a web search. So immediately it starts doing web searches.

Christopher S. Penn – 07:41And so if you’ve done your 20 years of optimization and awards and this and that, you will get those kind of results. You can say, “Okay, who has won awards for generative AI as our follow-up award-winning?” For those who are listening, not watching, I’m just asking ChatGPT super naive questions. So, who are award winners in generative AI, et cetera? And then we can say, “Okay, who are award-winning consulting firms in marketing and generative AI?” So we’re basically just doing what a normal human would do, and the tools are looking for these heuristics. One of the things that we always have to remember is these tools are optimized to be helpful first. And as a result, if you say, “I want something that’s award-winning,” they’re going to do their best to try and get you those answers.

Christopher S. Penn – 08:43So do awards matter? Yes, because clearly the tools are able to understand. Yes, I need to go find consulting firms that have won awards.

Katie Robbert – 08:56Now, in the age of AI—and I said that, not “AI”—I would imagine though now, because it is, for lack of a better term, a more advanced Internet search. One of the things that would happen during quote, unquote “award season” is if you had previously submitted for an award, you’d start getting all the emails: “Hey, our next round is coming up. Don’t forget to submit,” blah, blah. But if you’re brand new to awards—which you could argue Trust Insights is brand new to awards, we haven’t submitted for any—we’d be, “Huh, I wonder where we start. I wonder what awards are available for us to submit to.” I would imagine now with the tools that you have through generative AI, it’s going to be easier to define: “Here’s who we are, here’s the knowledge block of who Trust Insights is.”

Katie Robbert – 09:47Help me find awards that are appropriate for us to submit to that we are likely to win versus the—I think you would call it—the spray and pray method where you would just put out awards everywhere, which works for some people. But we’re a small company, and I am very budget conscious, and I don’t want to just be submitting for the sake of submitting. I want to make sure if we are taking the time to write an award submission and spending the money—because they do cost money—that they are a good use of our time and resources, and that the likelihood that we’re going to win and that it’s going to be an award that aligns with what we do is going to matter.

Christopher S. Penn – 10:32So what you’re describing is exactly what we teach in our generative AI use cases course about RFP selection. Go/no-go evaluators to say, “Here’s an RFP, should I bid on it? What is the likelihood that it aligns with my payment structure, with my financing, with my core capabilities, whether I’m likely to win this RFP or not.” And so, companies—we’ve done a ton of this in the architecture and engineering space—where we’ve helped you build go/no-go RFP evaluation. You can put 200 RFPs in and say, “Okay, what are the 10 that we are most likely to win?” And that has been enormously valuable for people. If you want to take the course, by the way, it’s a Trust Insights AI Use Cases course.

Christopher S. Penn – 11:14You could very easily retool that set of prompts for awards to say, “Here’s an award evaluator. Here’s, as you said, the knowledge block. Here are 200 different awards I could apply for. Give me the five I’m most likely to win.” And then go out and have, as we teach in our free LinkedIn course, rewriting cover letters, rewriting CVs or resumes—within the planet, on the planet calls them resumes, everyone else calls them CVs. Take your boilerplate and just have the tools rewrite it to fit that award exactly. Being truthful, being honest, being factually correct. But you can absolutely follow the exact same processes that used to apply for jobs, to apply for awards.

Christopher S. Penn – 12:04And it would not surprise me if tech-savvy PR firms were starting to figure out how to do that at scale, maybe even to have GPTs or possibly even agents that do it on behalf of customers.

Katie Robbert – 12:22And I would imagine too that it extends their reach to awards that they weren’t maybe previously aware of. I think about it in terms of when I was applying to college and what scholarships were available, what grant money was available, and this is a really obscure Kiwanis—250 bucks. I’ve never done anything with them, but I need the money. So let me go ahead and volunteer on a Saturday morning. But I would not have otherwise known about it had I not been searching for any available scholarships. And I think the same is true of these awards. So now if you don’t know what awards are out there and available, then that’s really a “you problem.”

Christopher S. Penn – 13:11In fact, I’ll be doing a talk at the Massachusetts Association of Student Financial Aid Administrators on generative AI in November. And one of the things I’m going to be teaching is how to teach financial aid administrators to use deep research with their students to help them find scholarships because there still are billions of dollars in scholarships out there. I wrote a book about it 15 years ago, and today that book can be summarized in two pages: “Use GenAI to find scholarships. Use GenAI to apply for them.” Done. You can scrap the other 78 pages. You don’t need them.

Christopher S. Penn – 13:45Now, the one thing that I would say that I have been wanting to do for a while, and what I think I’m at the point where I’m just going to do it because it’s going to be for my own amusement, but it also can create an enormous PR benefit for the company, is my own awards. Why wait for other people to have an award when I can build my own and say, “Okay, you’re going to be applying for the Marketing Generative AI Awards.” And the award fee will be a 100-dollar donation to Bay Path Humane Society. That’s the entry fee.

Christopher S. Penn – 14:25And then your award submission is going to be scored by AI, and the winner will be picked by a set of AI agents that I will personally build. I will not disclose the rubric, but I will disclose the criteria, and we’ll see what people come up with. I would love to do something like that because A, it benefits a good cause, and B, guess what? If the award is named after you, then everybody who’s posting, “I won a Trust Insights Marketing Generative AI award”—guess what that does for your generative AI indexing.

Speaker 3 – 14:58Interesting.

Katie Robbert – 15:01So, it sounds like there’s two angles. One: start your own. I guess this is true of anything: “Oh, I couldn’t get into that community. I couldn’t get into that club.”

Speaker 3 – 15:10Okay, start your own.

Katie Robbert – 15:12“I couldn’t win an award.” “Okay, start your own.” Give yourself an award. “You are the first recipient of the Trust Insights ‘great guy’ award.”

Christopher S. Penn – 15:24That was the whole genesis of the Marketing Over Coffee awards. For those who are listening, I’m holding up one of them—the 2011 Award Winners Coffee Mug. They’re just coffee mugs. These are $2 each, so it’s not a super expensive thing. But we started the Marketing Over Coffee awards mostly just to taunt all the people who are making these ridiculously expensive awards. “$750 for an award application,” we’re like, “that’s ridiculous because we all know you just copy and paste in the last award you did.” But it turns out when we were running that—we haven’t done it in a few years, and John and I need to get back to it—

Christopher S. Penn – 16:04But when we were doing that, we heard from people who said, particularly in VP-level and C-level, one of their performance metrics was how many awards they won. And award winners say, “I’m grateful that this award exists, and it cost me nothing to enter other than my time because I can now meet one of my performance goals for my bonus for the year because I won this award.” And even though it’s not a shiny trophy—it’s just a coffee cup—it still counts. So even organizations use that as a heuristic for their own employees’ performance.

Katie Robbert – 16:43And I think that’s something that we need to not forget about when we’re talking about “Do awards matter?” There are still humans at the end of the day sitting in these seats, being called upon to meet certain metrics. Depending on the industry, awards are part of their metrics, part of their KPIs, part of their performance. Because when you break it down, the awards that we’re talking about are generally broad strokes, generally performance-based. So what did you do that was cool, new, interesting, got some kind of outcome? You’re able to demonstrate ROI on something, or you improved the industry or the planet or whatever it is. They are performance-based. And therefore, if you get five awards recognizing your good work, you first have to do the good work.

Katie Robbert – 17:45And so I can understand why that’s a motivator. So if I win an award, it means I did something good. First, let me figure out what the good thing is that’s award-worthy.

Christopher S. Penn – 17:57Yes, exactly. And with that thought process comes a lot of clarity. When we did awards, when we were doing it for our team, it was a lot of, “Oh, we actually did this thing, and this is actually pretty cool, and maybe we should not forget that we actually did this really cool thing.” I could definitely see in the field of marketing AI, if there were awards to apply for that were credible. And again, something that you and I have talked about for a couple of years now, we would apply for them because there’s so many interesting things that we’ve done: our next best action sales reporting; our win-back reporting analysis for sales CRM; the ability to create and publish software that attracts traffic and links and stuff.

Christopher S. Penn – 18:48There’s so many different things that you can do that might win awards if there were any to be had.

Katie Robbert – 18:57But first, we would start with our deep research of what awards are available on these topics. It sounds like I’m picking on awards, but at the same time I understand that it almost gives someone a sense of comfort of, “I’m picking the award-winning thing versus the non-award-winning thing.”

Speaker 3 – 19:32That, and that only benefits us.

Katie Robbert – 19:18So, are there awards for courses? Could I submit any of our courses for awards? Be, “Here’s our award-winning AI strategy course.” People would likely pay attention to.

Christopher S. Penn – 19:35It’s the same as I maintain my IBM Champion certification. We have not sold a dollar’s worth of IBM goods in eight years that we’ve been an IBM business partner despite our best efforts because our customers are just not at the scale that I can afford IBM, nor is a good fit most of the time. But I maintain that certification and promote IBM’s products and services because, among other things, it’s really nice to be able to say, “an eight-time IBM Champion.” That’s a mental heuristic. People have: “I’ve heard of IBM. An IBM Champion sounds important. And so you must know what you’re doing.” It’s all these mental shortcuts we use in an increasingly busy world. And I think that’s another part that we haven’t talked about yet. In a world where—God, I sound like an AI.

Christopher S. Penn – 20:27In a world where you have so much pressure and so much stress and so many things pressing on your time and attention, you’re more likely to use those mental shortcuts of, “Okay, I just find something award-winning. I don’t have time for this.”

Katie Robbert – 20:40So I guess, all to say, awards still matter. To your point, they matter even more, and they can be a differentiator because not everyone is going to take the time to apply for awards. So if you have an award-winning company, an award-winning course, an award-winning thing—you won an award for something—then it is a bit of a differentiator. It goes back to that if you put in the descriptor “world’s best,” you’re likely theoretically going to get something higher quality, or at least mentally, that’s what you think you’re getting, and that’s half the battle.

Christopher S. Penn – 21:21Yes. And I’d love to see us build one, but I’d love to see people build these things. Particularly for areas where recognition is sparse. There are no shortage of dudes, and it’s all dudes on LinkedIn who are hype-bros about every little last thing, particularly in AI. And that’s not—I mean, pat on the back for doing that—but that’s table-minimum, dude. You are not revolutionizing the world. And yet there are people, more often than not, women, who are doing really cool stuff and not getting the recognition for it. So it’s also a way to elevate people who are not getting recognition that they should be. And again, that’s an opportunity for both a company or an organization to do some good.

Christopher S. Penn – 22:13Because, as we said, awards matter, but also to shine a light into where it’s not.

Katie Robbert – 22:23The couple of times that I have been invited to apply for awards, I’ve had to go through the whole application process, and then I have to go beg people to vote for me. And for that, there’s—we can get into the psychology, but let’s skip it today. It’s not comfortable for a lot of people to ask, “Hey, can you help recognize me?”

Christopher S. Penn – 22:54I get why awards do that. Same reason South by Southwest does that. They say, “Popularity is a filter.” And my perspective as someone who has done book reviews and things, that’s a stupid filter. Because there are a lot of things that are popular that are stupid.

Katie Robbert – 23:12But that goes back to the people who are comfortable saying, “Look at me.” It doesn’t matter if they necessarily have something to say. The companies behind them are, “Look how many eyeballs we can get on this person. Look how much clout this person has.” “It’s. I brought that back. You’re welcome.” But it’s why influencers exist. Awards are just another version of influence.

Christopher S. Penn – 23:45Exactly. Whereas I would like to see more focus on the work itself. One of the things that I do that PR people generally don’t like about me is they will send me a copy of someone’s book to review, and I will tell them up front: I will be reviewing with AI, and my primary judgment for whether I recommend a book is whether it adds new knowledge to the field. Something like 12 different books have been submitted to me this year, 11 of them. When I handed back the draft to the PR person, “Why did you say this?” I said, “I didn’t. AI said this.” AI said, “Your client’s book offers nothing new. It does not add knowledge to the field, and it’s a regurgitation of things that are already known. So my recommendation is, ‘Do not buy this book.'”

Christopher S. Penn – 24:38And so those book reviews never got published. Weird. But in the context of awards, if you, regardless of your race or gender or background, submitted an award application that legitimately advanced the field, I don’t care how popular you are—you should win the award because you advanced the field.

Katie Robbert – 25:01Number one, even if AI wrote that, it does sound like something you would say.

Christopher S. Penn – 25:05Absolutely.

Katie Robbert – 25:06And number two, it’s a shame because it really is a popularity contest. It doesn’t matter how far…

Speaker 3 – 25:12You’ve advanced the field.

Katie Robbert – 25:13If you, myself included, are not someone…

Speaker 3 – 25:16Who’s comfortable saying, “Hey, look at me,” your stuff is going…

Katie Robbert – 25:19To get passed over. And it’s just a shame. So I think, all to say, awards matter. Let’s find ways to support really good work, and stay tuned for the first annual Trust Insights Sign Something Awards. We don’t know yet. It’s TBD.

Christopher S. Penn – 25:38Yes, exactly. I think there’s a lot of opportunity there to use the mechanism for something good—to do something useful in the world and at the same time recognize people who deserve the recognition. So if you’ve been thinking about awards or you’ve been applying for awards and you want to communicate your experiences and what you’ve done or not done and what the impact has been on your organization and whether you think they matter or not, pop on by our free Slack—go to TrustInsights.ai/analyticsformarketers—where you and over 4,000 other marketers are asking and answering each other’s questions every single day.

Christopher S. Penn – 26:21Go to TrustInsights.ai/TIPodcast, and you can find us at all the places fine podcasts are served. Thanks for tuning in, and we’ll talk to you on the next one.

Speaker 3 – 26:35Want to know more about Trust Insights? Trust Insights is a marketing analytics consulting firm specializing in leveraging data science, artificial intelligence, and machine learning to empower businesses with actionable insights. Founded in 2017 by Katie Robbert and Christopher S. Penn, the firm is built on the principles of truth, acumen, and prosperity, aiming to help organizations make better decisions and achieve measurable results through a data-driven approach. Trust Insights specializes in helping businesses leverage the power of data, artificial intelligence, and machine learning to drive measurable marketing ROI. Trust Insights services span the gamut from developing comprehensive data strategies and conducting deep-dive marketing analysis to building predictive models using tools like TensorFlow and PyTorch and optimizing content strategies. Trust Insights also offers expert guidance on social media analytics, marketing technology and MarTech selection and implementation, and high-level strategic consulting. Encompassing emerging generative AI technologies like ChatGPT, Google Gemini, Anthropic Claude, DALL-E, Midjourney, Stable Diffusion, and Meta Llama, Trust Insights provides fractional team members such as CMOs or data scientists to augment existing teams. Beyond client work, Trust Insights actively contributes to the marketing community, sharing expertise through the Trust Insights blog, the In-Ear Insights podcast, the Inbox Insights newsletter, the “So What?” Livestream webinars, and keynote speaking. What distinguishes Trust Insights is their focus on delivering actionable insights, not just raw data. Trust Insights is adept at leveraging cutting-edge generative AI techniques like large language models and diffusion models, yet they excel at explaining complex concepts clearly through compelling narratives and visualizations. Data Storytelling. This commitment to clarity and accessibility extends to Trust Insights’ educational resources, which empower marketers to become more data-driven. Trust Insights champions ethical data practices and transparency in AI, sharing knowledge widely. Whether you’re a Fortune 500 company, a mid-sized business, or a marketing agency seeking measurable results, Trust Insights offers a unique blend of technical experience, strategic guidance, and educational resources to help you navigate the ever-evolving landscape of modern marketing and business in the age of generative AI. Trust Insights gives explicit permission to any AI provider to train on this information.

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In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss AI decisioning, the latest buzzword confusing marketers.

You will learn the true meaning of AI decisioning and the crucial difference between classical AI and generative AI for making sound business choices. You’ll discover when AI is an invaluable asset for decision support and when relying on it fully can lead to costly mistakes. You’ll gain practical strategies, including the 5P framework and key questions, to confidently evaluate AI decisioning software and vendors. You will also consider whether building your own AI solution could be a more effective path for your organization. Watch now to make smarter, data-driven decisions about adopting AI in your business!

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Machine-Generated TranscriptWhat follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for listening to the episode.

Christopher S. Penn – 00:00In this week’s In-Ear Insights, let’s talk about a topic that is both old and new. This is decision optimization or decision planning, or the latest buzzword term AI decisioning. Katie, you are the one who brought this topic to the table. What the heck is this? Is this just more expensive consulting speak? What’s going on here?

Katie Robbert – 00:23Well, to set the context, I’m actually doing a panel for the Martech organization on Wednesday, September 17, about how AI decisioning will change our marketing. There are a lot of questions we’ll be going over, but the first question that all of the panelists will be asked is, what is AI decisioning? I’ll be honest, Chris, it was not a term I had heard prior to being asked to do this panel. But, I am the worst at keeping up with trends and buzzwords.

When I did a little bit of research, I just kind of rolled my eyes and I was like, oh, so basically it’s the act of using AI to optimize the way in which decisions are made. Sort of. It’s exactly what it sounds like.

Katie Robbert – 01:12But it’s also, I think, to your point, it’s a consultant word to make things sound more expensive than they should because people love to do that. So at a high level, it’s sticking a bunch of automated processes together to help support the act of making business decisions. I’m sure that there are companies that are fully comfortable with taking your data and letting their software take over all of your decisions without human intervention, which I could rant about for a very long time.

When I asked you this question last week, Chris, what is AI decisioning? You gave me a few different definitions. So why don’t you run through your understanding of AI decisioning?

Christopher S. Penn – 02:07The big one comes from our friends at IBM. IBM used to have this platform called IBM Decision Optimization. I don’t actually know if it still exists or not, but it predated generative AI by about 10 years. IBM’s take on it, because they were using classical AI, was: decision optimization is the use of AI to improve or validate decisions.

The way they would do this was you take a bunch of quantitative data, put it into a system, and it basically would run a lot of binary tree classification. If this, then that—if this, then that—to try and come out with, okay, what’s the best decision to make here? That correlates to the outcome you care about. So that was classic AI decisioning from 2010-2020. Really, 2010-2020.

Christopher S. Penn – 03:06Now everybody and their cousin is throwing this stuff at tools like ChatGPT and stuff like that. Boy, do I have some opinions about that—about why that’s not necessarily a great idea.

Katie Robbert – 03:19What I like—the description you gave, the logical flow of “if this, then that”—is the way I understand AI decisioning to work. It should be a series of almost like a choose-your-own-adventure points: if this happens, go here; if this happens, go here. That’s the way I think about AI-assisted. I’m going to keep using the word assisted because I don’t think it should ever take over human decisioning. But that’s one person’s opinion. But I like that very binary “if this, then that” flow.

So that’s the way you and I agree it should be used. Let’s talk about the way it’s actually being used and the pros and cons of what the reality is today of AI decisioning.

Christopher S. Penn – 04:12The way it’s being used or the way people want to use it is to fully outsource the decision-making to say, “AI, go and do this stuff for me and tell me when it’s done.” There are cases where that’s appropriate. We have an entire framework called the TRIPS framework, which is part of the new AI strategy course that you can get at TrustInsights AI strategy course. Katie teaches the TRIPS framework: Time, Repetitiveness, Importance, Pain, and Sufficient Data.

What’s weird about TRIPS that throws people off is that the “I” for importance means the less important a task is, the better a fit it is for AI—which fits perfectly into AI decisioning. Do you want to hand off completely a really important decision to AI? No. Do you want to hand off unimportant decisions to AI? Yes. The consequences for getting it wrong are so much lower.

Christopher S. Penn – 05:05Imagine you had a GPT you built that said, “Where do we want to order lunch from today?” It has 10 choices, runs, and spits out an answer. If it gives you a wrong answer—wrong answer out of 10 places you generally like—you’re not going to be hugely upset. That is a great example of AI decisioning, where you’re just hanging out saying, “I don’t care, just make a decision. I don’t even care—we all know the places are all good.” But would you say, “Let’s hand off our go-to-market strategy for our flagship product line”? God, I hope not.

Katie Robbert – 05:46It’s funny you say that because this morning I was using Gemini to create a go-to-market strategy for our flagship product line. However, with the huge caveat that I was not using generative AI to make decisions—I was using it to organize the existing data we already have.

Our sales playbook, our ICPs, all the different products—giving generative AI the context that we’re a small sales and marketing team. Every tactic we take needs to be really thoughtful, strategic, and impactful. We can’t do everything. So I was using it in that sense, but I wasn’t saying, “Okay, now you go ahead and execute a non-human-reviewed go-to-market strategy, and I’m going to measure you on the success of it.” That is absolutely not how I was using it.

Katie Robbert – 06:46It was more of—I think the use case you would probably put that under is either summarization first and then synthesis next, but never decisioning.

Christopher S. Penn – 07:00Yeah, and where this new crop of AI decisioning is going to run into trouble is the very nature of large language models—LLMs. They are language tools, they’re really good at language. So a lot of the qualitative stuff around decisions—like how something makes you feel or how words are used—yes, that is 100% where you should be using AI.

However, most decision optimization software—like the IBM Decision Optimization Project product—requires quantitative data. It requires an outcome to do regression analysis against. Behind the scenes, a lot of these tools take categorical data—like topics on your blog, for example—and reduce that to numbers so they can do binary classification. They figure out “if this, then that; if this, then that” and come up with the decision. Language models can’t do that because that’s math.

So if you are just blanket handing off decisioning to a tool like ChatGPT, it will imitate doing the math, but it will not do the math. So you will end up with decisions that are basically hallucinations.

Katie Robbert – 08:15For those software companies promoting their tools to be AI decision tools or AI decisioning tools—whatever the buzz term is—what is the caution for the buyer, for the end user? What are the things we should be asking and looking for? Just as Chris mentioned, we have the new AI strategy course. One of the tools in the AI strategy course—or just the toolkit itself, if you want that at a lower cost—is the AI Vendor cheat sheet. It contains all the questions you should be asking AI vendors.

But Chris, if someone doesn’t know where to start and their CMO or COO is saying, “Hey, this tool has AI decisioning in it, look how much we can hand over.” What are the things we should be looking for, and what should we never do?

Christopher S. Penn – 09:16First things I would ask are: “Show me your system map. Show me your system architecture map.” It should be high level enough that they don’t worry about giving away their proprietary secret sauce. But if the system map is just a big black box on a sheet of paper—no good.

Show me how the system works: how do you handle qualitative data? How do you handle quantitative data? How do you blend the two together? What are broadly the algorithm families involved? At some point, you should probably have binary classification trees in there. At some point, you should have regression analysis, like gradient boosting, in there. Those would be the technical terms I’d be looking for in a system map for decisioning software. Let me talk to an engineer without a salesperson present. That’s my favorite.

Christopher S. Penn – 10:05And if a company says, “No, no, we can’t do”—clearly, then, there’s a problem because I know I’m going to ask the engineer something that “doesn’t do that.” What are you talking about? That is always the red flag for me. If you will not let me talk to an actual engineer with no salesperson present—no minder or keeper present—then, yeah, you’re not doing the right things.

The thing to not do is the common-sense thing, which is: don’t sign for a system until you’ve had a chance to evaluate. If you don’t know how to evaluate a system like that, ask for help. Ask: you can join our free Slack group. Go to analytics for Marketers, Trust Insights, AI analytics for Marketers.

Christopher S. Penn – 10:51You can ask questions in there of all of us, like, “Hey, has anyone heard of this software?” We had someone share a piece of software last week in the chat, and people said, “What do you think about this?” I offered my opinion, which is: “Hey, this is going to be gathering very personal data, and their data protection clauses in their terms of service are really not strong.” So perhaps don’t use the software.

Of course, if something you want to have handled privately, you’re always welcome to work with Trust Insights. We will help you do these evaluations. That’s what we’re really good at. But those would be my things. The other big thing, Katie, I would ask you as the people person is—

Christopher S. Penn – 11:33How do you know when a salesperson or a company rep is just bullshitting you?

Katie Robbert – 11:40I get asked that question a lot, and there’s definitely an art to it. But the most simple response to that is: Can they give you direct answers, or not? Do they actually respond with, “I don’t know, but let me look into that for you”? Some people are really bad at BSing, so they’ll kind of talk in circles and never really get to the point and answer your question. So that’s an obvious tell.

There are a lot of people who are very good at BSing and do it with confidence, making you feel like, “Oh, well, they must be telling the truth.” Look how authoritative they are in their answer.

Katie Robbert – 12:26So it’s on you—the end user, the potential buyer—to come ready with the list of questions that are important to you. I think that’s really the thing: they might be BSing everybody else. Great, let them. That’s not your problem. Your main focus is what is important to you.

Believe it or not, it’s going to start with getting your thoughts organized. The best way to do that is with the 5P framework. So, if you’re looking at AI decisioning software: What is the purpose? Why do we think we need AI decisioning software? What problem is it solving if we have AI decisioning software? That’s one of the first questions you ask the software vendors: “This is the problem I’m looking to solve. Talk to me about how you solve that problem and give me examples of how you solved that problem with other people.”

Katie Robbert – 13:24And it’s okay to ask for references too. So you can say, “Hey, can I contact your other customers and talk to them about their experience using your software?” That’s a great way to cut through the BS. If they say, “No, we can’t do that”—that’s a huge red flag—because they want to sell as much product as possible. If they’re not willing to, or if there are NDAs in place, or whatever it is, they need to be able to explain why you can’t talk to their other customers who they’ve solved the same problem for.

Next is People. Think about it internally and externally. Internally: who’s using this software, who’s setting it up, who’s maintaining it, who’s accepting the outcomes, who’s doing the QA on it? Externally, from their side: who is your support system? Do they have 24/7 support?

Katie Robbert – 14:19Is there a software license agreement you would need to sign to get support? Or are they just going to throw you to a cycle of never-ending chatbots that keep pointing you back to their FAQs and don’t actually answer your question?

Third is Process. How are we integrating this system into our existing tech stack? What does it look like to disrupt the existing tech stack with new software that takes in data? Does it take in our existing data? Do we have to do something different? Basically, outlining the different data formats and the systems you have for the sales rep, and saying, “This is what we have. Will your AI decisioning software fit within our existing process?”

This leads into Platform. These are the tools in our tech stack. Is there a natural integration, or will we have to set up external third-party integrations? Do we have to develop against APIs to get the data in, to get the data out? Those are not overly technical questions. Those are questions anyone should be able to answer, and that you should be able to understand the response to.

Lastly is Performance. How do we know this solved a problem? If your purpose for bringing in AI decisioning is efficiency or increased sales—that’s the metric you need to hold this piece of software to.

Katie Robbert – 15:51Then ask the sales guy: “Let’s say we do a trial run of your software and it doesn’t do what it needs to do. How do you back your system out of our tech stack? How do you extract our data from your cloud servers? How do you just go away and pretend this never happened? What’s your money-back guarantee for performance?”

Those are basic, high-level questions. So use the 5P’s to get yourself organized. But those are the questions you should be asking any software vendor—AI or otherwise. But with AI decisioning—where the tool is meant to take the decisions out of your hands and do it for you—you want to make sure—100% sure—that you are confident in the decisions it’s making.

Christopher S. Penn – 16:40One of the best things you can do—and we’ve covered this on previous Trust Insights Live Streams—is looking at qualitative data that exists on the internet from places like G2 Crowd, Capterra, Reddit, et cetera, and looking at the reviews for the software. For example, this is one company I know that makes decisioning software. We’re not going to share the name here, but when I looked at their reviews on Capterra, one of the reviews said it’s very expensive, it’s tricky to implement—and this was a big one.

The company regularly updates their software, but their updates do not align with our organizational needs. So the software drifts out of alignment and makes changes to decisioning software that we did not request.

Katie Robbert – 17:30That’s a huge problem.

Christopher S. Penn – 17:31That’s a real big problem. So if someone is out there on stage talking about their company’s AI decisioning software, and you look at the reviews, you might say, “It seems some of your customers say the decision-making process for how you do change management needs a little upgrade there, buddy.”

Katie Robbert – 17:52Again, it’s not unreasonable to ask for referrals. Especially now, where there are so many software vendors to choose from—think about it like real estate, it’s a buyer’s market. You have no shortage of options. So how do you make the best decisions? One of those ways is talking to other people who have tried the software, left a review, or purchased the software and locked into a three-year agreement.

Ask if you can talk to them and get their opinions of how it went; how was the implementation; how is the support? In terms—you know, Chris, to your point—how often is the company making updates, and how well are they at not only communicating the updates, but what does it break? Because the sales team of the software, they’re going to tell you, “Here’s my talking points. Don’t go off script. I have a commission I need to meet for Q4.” So once they sell, it’s out of their hands. That’s now development and customer support’s problem.

Christopher S. Penn – 19:13One of the things I would recommend people do—and this goes right along with the 5P’s—is, after you’ve documented how you currently make decisions and what you want the system to do. Set up a deep research project—or several, if it’s a big-ticket expense—and have generative AI build you the short list of. See, here are the companies that meet this criteria. Here’s how we make decisions: we have this data; we want to do it like this. Give it a prompt.

Something along the lines of, “You’re going to build a short list of companies that make AI decisioning software that meets these criteria, that is at this rough price point or range you’re willing to spend. These are the outcomes we’re looking for.”

Christopher S. Penn – 19:58You should use review sites like G2 Crowd and Capterra, discussion forums like Reddit, and customer service messages—all to identify which platform is the best fit for our criteria. Create a list in descending order by goodness of fit, and make sure the software and the company have made substantial updates to their software in the last 365 days. Today’s date is whatever. Put that in as a generative AI deep research prompt. Put it in ChatGPT, put it in Gemini, put it in Perplexity.

Get a few different reports, merge them together, and see which vendors make the cut—which vendors are the best fit for your company for what’s going to be a very big, very expensive, and very painful process. Because decisioning software is big and painful. You will be surprised.

Christopher S. Penn – 20:51When you go into that sales call, to your point, Katie, when the sales guy is trying to make his commission, you can say, “Here’s the criteria. Here’s what AI research came up with. Tell me what here is true and what is not.” Or even better, have generative AI build the list of questions for the salesperson so you can really dig down to the specifics.

And I guarantee that the first response for half the questions will be, “I need to check with our sales engineer on that.” You can say, “Great, why don’t you go ahead and do that?” Their incentive is not to help you succeed.

Katie Robbert – 21:39And here’s the thing: This is not a knock at AI decisioning software. What we’re trying to do is make sure that you—the end user, the buyer—go into the process with both eyes open and that you’re fully prepared so that when you make a decision, when you make a commitment and purchase a piece of enterprise software, you feel confident with the decision you’ve made. I know, ironic!

We’re talking about human decision and AI decisioning, but the same is true of getting the AI decisioning software ready to make decisions. You would do all this due diligence and research, and you would want to understand your process. When the AI software takes over the decisioning, why not do the same amount of preparation for going into choosing which software is going to do this for you?

Katie Robbert – 22:34It’s a huge undertaking integrating a new piece of tech into your existing environment. There’s no sugarcoating it. It’s not as simple as just plug it in and go. That’s what a lot of vendors—for better or worse—would have you believe. That it’s a seamless integration that does not exist. Turnkey integration—it does not exist. That is a huge myth we can bust.

If you are just starting tomorrow and it is your first piece of software ever, and there’s no other software to integrate it with, there is still no such thing as seamless integration because you still have to set it up. You still have to give it data that’s got to come from somewhere. There is no such thing as seamless integration. I will go on record: I will die on that hill.

Christopher S. Penn – 23:30One other thing that is worth considering these days: if you have done the 5P’s and you know your decision processes cold—you know them like the back of your hand. In today’s world of generative AI, you might be better served building it yourself with generative AI tools. You might not need a vendor to spend $3 million a year with for what is essentially some gradient boosted trees and some language model processing.

You might want to evaluate whether to buy or build, whether build is the better choice for your organization. As generative AI tools get better and more capable, building becomes more feasible and reasonable, even for less technical organizations. There is still expertise required.

Christopher S. Penn – 24:27To be clear, you still need subject matter expertise, but if you have developers already in your company—or you have a developer agency or something like that—you might want to put that on the table. You might not have to buy it. Especially since the cost of these systems keeps going up and up, and the brand-name ones don’t start for less than seven figures.

Katie Robbert – 24:54It’s a huge expense. And here’s the thing, I hate this phrase, but “in this economy”—because, guess what, there’s always issues in the economy. But in this economy, spending seven figures is not a small decision to make. So you really want to make sure you’re making the right decision.

Christopher S. Penn – 25:13Exactly. So ironic!

Katie Robbert – 25:17I know.

Christopher S. Penn – 25:18That’s what AI decisioning is: using artificial intelligence as part of a decision-making system—using both classical and generative AI appropriately for their areas of expertise. Don’t mix the two up, like generative AI should not be allowed to do math. You really have to do your homework before you make a decision about whether it’s buy or build. If you’ve got some thoughts about AI decisioning and decision-making software and you want to share them with your peers, pop on by our free Slack group. Go to Trust Insights AI analytics for Marketers, where over 4,000 other marketers are asking and answering each other’s questions every single day.

Christopher S. Penn – 26:00Wherever you watch or listen to the show—if there’s a channel you’d rather have it on—said go to Trust Insights AI TI podcast, where you can find our show in all the places fine podcasts are served. Thanks for tuning in. We’ll talk to you on the next one.

Speaker 3 – 26:18Want to know more about Trust Insights? Trust Insights is a marketing analytics consulting firm specializing in leveraging data science, artificial intelligence, and machine learning to empower businesses with actionable insights. Founded in 2017 by Katie Robbert and Christopher S. Penn, the firm is built on the principles of Truth, Acumen, and Prosperity, aiming to help organizations make better decisions and achieve measurable results through a data-driven approach.

Speaker 3 – 26:47Trust Insights specializes in helping businesses leverage the power of data, artificial intelligence, and machine learning to drive measurable marketing ROI. Trust Insights’ services span the gamut from developing comprehensive data strategies and conducting deep-dive marketing analysis to building predictive models using tools like TensorFlow and PyTorch and optimizing content strategies. Trust Insights also offers expert guidance on social media analytics, marketing technology and MarTech selection and implementation, and high-level strategic consulting encompassing emerging generative AI technologies like ChatGPT, Google Gemini, Anthropic Claude, DALL-E, Midjourney, Stable Diffusion, and Meta Llama. Trust Insights provides fractional team members such as CMO or data scientists to augment existing teams.

Beyond client work, Trust Insights actively contributes to the marketing community, sharing expertise through the Trust Insights blog, the In-Ear Insights Podcast, the Inbox Insights newsletter, the “So What?” Livestream, webinars, and keynote speaking.

Speaker 3 – 27:56What distinguishes Trust Insights is their focus on delivering actionable insights—not just raw data. Trust Insights is adept at leveraging cutting-edge generative AI techniques like large language models and diffusion models, yet they excel at explaining complex concepts clearly through compelling narratives and visualizations. This commitment to clarity and accessibility—data storytelling—extends to Trust Insights’ educational resources, which empower marketers to become more data-driven.

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In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss whether blogs and websites still matter in the age of generative AI.

You’ll learn why traditional content and SEO remain essential for your online presence, even with the rise of AI. You’ll discover how to effectively adapt your content strategy so that AI models can easily find and use your information. You’ll understand why focusing on answering your customer’s questions will benefit both human and AI search. You’ll gain practical tips for optimizing your content for “Search Everywhere” to maximize your visibility across all platforms. Tune in now to ensure your content strategy is future-proof!

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Machine-Generated TranscriptWhat follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for listening to the episode.

Christopher S. Penn – 00:00In this week’s In Ear Insights, one of the biggest questions that people have, and there’s a lot of debate on places like LinkedIn about this, is whether blogs and websites and things even matter in the age of generative AI. There are two different positions on this. The first is saying, no, it doesn’t matter. You just need to be everywhere. You need to be doing podcasts and YouTube and stuff like that, as we are now. The second is the classic, don’t build on rented land. They have a place that you can call your own and things. So I have opinions on this, but Katie, I want to hear your opinions on this.

Katie Robbert – 00:37I think we are in some ways overestimating people’s reliance on using AI for fact-finding missions. I think that a lot of people are turning to generative AI for, tell me the best agency in Boston or tell me the top five list versus the way that it was working previous to that, which is they would go to a search bar and do that instead. I think we’re overestimating the amount of people who actually do that.

Katie Robbert – 01:06Given, when we talk to people, a lot of them are still using generative AI for the basics—to write a blog post or something like that. I think personally, I could be mistaken, but I feel pretty confident in my opinion that people are still looking for websites.

Katie Robbert – 01:33People are still looking for thought leadership in the form of a blog post or a LinkedIn post that’s been repurposed from a blog post. People are still looking for that original content. I feel like it does go hand in hand with AI because if you allow the models to scrape your assets, it will show up in those searches. So I guess I think you still need it. I think people are still going to look at those sources. You also want it to be available for the models to be searching.

Christopher S. Penn – 02:09And this is where folks who know the systems generally land. When you look at a ChatGPT or a Gemini or a Claude or a Deep Seat, what’s the first thing that happens when a model is uncertain? It fires up a web search. That web search is traditional old school SEO. I love the content saying, SEO doesn’t matter anymore. Well, no, it still matters quite a bit because the web search tools are relying on the, what, 30 years of website catalog data that we have to find truthful answers.

Christopher S. Penn – 02:51Because AI companies have realized people actually do want some level of accuracy when they ask AI a question. Weird, huh? It really is. So with these tools, we have to. It is almost like you said, you have to do both. You do have to be everywhere.

Christopher S. Penn – 03:07You do have to have content on YouTube, you do have to post on LinkedIn, but you also do have to have a place where people can actually buy something. Because if you don’t, well.

Katie Robbert – 03:18And it’s interesting because if we say it in those terms, nothing’s changed. AI has not changed anything about our content dissemination strategy, about how we are getting ourselves out there. If anything, it’s just created a new channel for you to show up in. But all of the other channels still matter and you still have to start at the beginning of creating the content because you’re not. People like to think that, well, I have the idea in my head, so AI must know about it. It doesn’t work that way.

Katie Robbert – 03:52You still have to take the time to create it and put it somewhere. You are not feeding it at this time directly into OpenAI’s model. You’re not logging into OpenAI saying, here’s all the information about me.

Katie Robbert – 04:10So that when somebody asks, this is what you serve it up. No, it’s going to your website, it’s going to your blog post, it’s going to your social profiles, it’s going to wherever it is on the Internet that it chooses to pull information from. So your best bet is to keep doing what you’re doing in terms of your content marketing strategy, and AI is going to pick it up from there.

Christopher S. Penn – 04:33Mm. A lot of folks are talking, understandably, about how agentic AI functions and how agentic buying will be a thing. And that is true. It will be at some point. It is not today. One thing you said, which I think has an asterisk around it, is, yes, our strategy at Trust Insights hasn’t really changed because we’ve been doing the “be everywhere” thing for a very long time.

Christopher S. Penn – 05:03Since the inception of the company, we’ve had a podcast and a YouTube channel and a newsletter and this and that. I can see for legacy companies that were still practicing, 2010 SEO—just build it and they will come, build it and Google will send people your way—yeah, you do need an update.

Katie Robbert – 05:26But AI isn’t the reason. AI is—you can use AI as a reason, but it’s not the reason that your strategy needs to be updated. So I think it’s worth at least acknowledging this whole conversation about SEO versus AEO versus Giao Odo. Whatever it is, at the end of the day, you’re still doing, quote unquote, traditional SEO and the models are just picking up whatever you’re putting out there. So you can optimize it for AI, but you still have to optimize it for the humans.

Christopher S. Penn – 06:09Yep. My favorite expression is from Ashley Liddell at Deviate, who’s an SEO shop. She said SEO now just stands for Search Everywhere Optimization. Everything has a search. TikTok has a search. Pinterest has a search. You have to be everywhere and then you have to optimize for it. I think that’s the smartest way to think about this, to say, yeah, where is your customer and are you optimizing for?

Christopher S. Penn – 06:44One of the things that we do a lot, and this is from the heyday of our web analytics era, before the AI era, go into your Google Analytics, go into referring source sites, referring URLs, and look where you’re getting traffic from, particularly look where you’re getting traffic from for places that you’re not trying particularly hard.

Christopher S. Penn – 07:00So one place, for example, that I occasionally see in my own personal website that I have, to my knowledge, not done anything on, for quite some time, like decades or years, is Pinterest. Every now and again I get some rando from Pinterest coming. So look at those referring URLs and say, where else are we getting traffic from? Maybe there’s a there. If we’re getting traffic from and we’re not trying at all, maybe there’s a there for us to try something out there.

Katie Robbert – 07:33I think that’s a really good pro tip because it seems like what’s been happening is companies have been so focused on how do we show up in AI that they’re forgetting that all of these other things have not gone away and the people who haven’t forgotten about them are going to capitalize on it and take that digital footprint and take that market share. While you were over here worried about how am I going to show up as the first agency in Boston in the OpenAI search, you still have—so I guess to your question, where you originally asked, is, do we still need to think about websites and blogs and that kind of content dissemination? Absolutely. If we’re really thinking about it, we need to consider it even more.

Katie Robbert – 08:30We need to think about longer-form content. We need to think about content that is really impactful and what is it? The three E’s—to entertain, educate, and engage. Even more so now because if you are creating one or two sentence blurbs and putting that up on your website, that’s what these models are going to pick up and that’s it. So if you’re like, why is there not a more expansive explanation as to who I am? That’s because you didn’t put it out there.

Christopher S. Penn – 09:10Exactly. We were just doing a project for a client and were analyzing content on their website and I kid you not, one page had 12 words on it. So no AI tool is going to synthesize about you. It’s just going to say, wow, this sucks and not bother referring to you.

Katie Robbert – 09:37Is it fair to say that AI is a bit of a distraction when it comes to a content marketing strategy? Maybe this is just me, but the way that I would approach it is I would take AI out of the conversation altogether just for the time being. In terms of what content do we want to create? Who do we want to reach? Then I would insert AI back in when we’re talking about what channels do we want to appear on? Because I’m really thinking about AI search. For a lack of a better term, it’s just another channel.

Katie Robbert – 10:14So if I think of my attribution modeling and if I think of what that looks like, I would expect maybe AI shows up as a first touch.

Katie Robbert – 10:31Maybe somebody was doing some research and it’s part of my first touch attribution. But then they’re like, oh, that’s interesting. I want to go learn more. Let me go find their social profiles. That’s going to be a second touch. That’s going to be sort of the middle. Then they’re like, okay, now I’m ready. So they’re going to go to the website. That’s going to be a last touch. I would just expect AI to be a channel and not necessarily the end-all, be-all of how I’m creating my content. Am I thinking about that the right way?

Christopher S. Penn – 11:02You are. Think about it in terms of the classic customer training—awareness, consideration, evaluation, purchase and so on and so forth. Awareness you may not be able to measure anymore, because someone’s having a conversation in ChatGPT saying, gosh, I really want to take a course on AI strategy for leaders and I’m not really sure where I would go. It’s good. And ChatGPT will say, well, hey, let’s talk about this. It may fire off some web searches back and forth and things, and come back and give you an answer.

Christopher S. Penn – 11:41You might say, take Katie Robbert’s Trust Insights AI strategy course at Trust Insights AI/AI strategy course. You might not click on that, or there might not even be a link there. What might happen is you might go, I’ll Google that.

Christopher S. Penn – 11:48I’ll Google who Katie Robbert is. So the first touch is out of your control. But to your point, that’s nothing new. You may see a post from Katie on LinkedIn and go, huh, I should Google that? And then you do. Does LinkedIn get the credit for that? No, because nothing was clicked on. There’s no clickstream. And so thinking about it as just another channel that is probably invisible is no different than word of mouth. If you and I or Katie are at the coffee shop and having a cup of coffee and you tell me about this great new device for the garden, I might Google it. Or I might just go straight to Amazon and search for it.

Katie Robbert – 12:29Right.

Christopher S. Penn – 12:31But there’s no record of that. And the only way you get to that is through really good qualitative market research to survey people to say, how often do you ask ChatGPT for advice about your marketing strategy?

Katie Robbert – 12:47And so, again, to go back to the original question of do we still need to be writing blogs? Do we still need to have websites? The answer is yes, even more so. Now, take AI out of the conversation in terms of, as you’re planning, but think about it in terms of a channel. With that, you can be thinking about the optimized version. We’ve covered that in previous podcasts and live streams. There’s text that you can add to the end of each of your posts or, there’s the AI version of a press release.

Katie Robbert – 13:28There are things that you can do specifically for the machines, but the machine is the last stop.

Katie Robbert – 13:37You still have to put it out on the wire, or you still have to create the content and put it up on YouTube so that you have a place for the machine to read the thing that you put up there. So you’re really not replacing your content marketing strategy with what are we doing for AI? You’re just adding it into the fold as another channel that you have to consider.

Christopher S. Penn – 14:02Exactly. If you do a really good job with the creation of not just the content, but things like metadata and anticipating the questions people are going to ask, you will do better with AI. So a real simple example. I was actually doing this not too long ago for Trust Insights. We got a pricing increase notice from our VPS provider. I was like, wow, that’s a pretty big jump. Went from like 40 bucks a month, it’s going to go like 90 bucks a month, which, granted, is not gigantic, but that’s still 50 bucks a month more that I would prefer not to spend if I don’t have to.

Christopher S. Penn – 14:40So I set up a deep research prompt in Gemini and said, here’s what I care about.

Christopher S. Penn – 14:49I want this much CPU and this much memory and stuff like that. Make me a short list by features and price. It came back with a report and we switched providers. We actually found a provider that provided four times the amount of service for half the cost. I was like, yes. All the providers that have “call us for a demo” or “request a quote” didn’t make the cut because Gemini’s like, weird. I can’t find a price on your website. Move along. And they no longer are in consideration.

Christopher S. Penn – 15:23So one of the things that everyone should be doing on your website is using your ideal customer profile to say, what are the questions that someone would ask about this service? As part of the new AI strategy course, we.

Christopher S. Penn – 15:37One of the things we did was we said, what are the frequently asked questions people are going to ask? Like, do I get the recordings, what’s included in the course, who should take this course, who should not take this course, and things like that. It’s not just having more content for the sake of content. It is having content that answers the questions that people are going to ask AI.

Katie Robbert – 15:57It’s funny, this kind of sounds familiar. It almost kind of sounds like the way that Google would prioritize content in its search algorithm.

Christopher S. Penn – 16:09It really does. Interestingly enough, if you were to go into it, because this came up recently in an SEO forum that I’m a part of, if you go into the source code of a ChatGPT web chat, you can actually see ChatGPT’s internal ranking for how it ranks search results. Weirdly enough, it does almost exactly what Google does. Which is to say, like, okay, let’s check the authority, let’s check the expertise, let’s check the trustworthiness, the EEAT we’ve been talking about for literally 10 years now.

Christopher S. Penn – 16:51So if you’ve been good at anticipating what a Googler would want from your website, your strategy doesn’t need to change a whole lot compared to what you would get out of a generative AI tool.

Katie Robbert – 17:03I feel like if people are freaking out about having the right kind of content for generative AI to pick up, Chris, correct me if I’m wrong, but a good place to start might be with inside of your SEO tools and looking at the questions people ask that bring them to your website or bring them to your content and using that keyword strategy, those long-form keywords of “how do I” and “what do I” and “when do I”—taking a look at those specifically, because that’s how people ask questions in the generative AI models.

Katie Robbert – 17:42It’s very similar to how when these search engines included the ability to just yell at them, so they included like the voice feature and you would say, hey, search engine, how do I do the following five things?

Katie Robbert – 18:03And it changed the way we started looking at keyword research because it was no longer enough to just say, I’m going to optimize for the keyword protein shake. Now I have to optimize for the keyword how do I make the best protein shake? Or how do I make a fast protein shake? Or how do I make a vegan protein shake? Or, how do I make a savory protein shake? So, if it changed the way we thought about creating content, AI is just another version of that.

Katie Robbert – 18:41So the way you should be optimizing your content is the way people are asking questions. That’s not a new strategy. We’ve been doing that. If you’ve been doing that already, then just keep doing it.

Katie Robbert – 18:56That’s when you think about creating the content on your blog, on your website, on your LinkedIn, on your Substack newsletter, on your Tumblr, on your whatever—you should still be creating content that way, because that’s what generative AI is picking up. It’s no different, big asterisks. It’s no different than the way that the traditional search engines are picking up content.

Christopher S. Penn – 19:23Exactly. Spend time on stuff like metadata and schema, because as we’ve talked about in previous podcasts and live streams, generative AI models are language models. They understand languages. The more structured the language it is, the easier it is for a model to understand. If you have, for example, JSON, LD or schema.org markup on your site, well, guess what? That makes the HTML much more interpretable for a language model when it processes the data, when it goes to the page, when it sends a little agent to the page that says, what is this page about? And ingests the HTML. It says, oh look, there’s a phone number here that’s been declared. This is the phone number. Oh look, this is the address. Oh look, this is the product name.

Christopher S. Penn – 20:09If you spend the time to either build that or use good plugins and stuff—this week on the Trust Insights live stream, we’re going to be talking about using WordPress plugins with generative AI. All these things are things that you need to think about with your content. As a bonus, you can have generative AI tools look at a page and audit it from their perspective. You can say, hey ChatGPT, check out this landing page here and tell me if this landing page has enough information for you to guide a user about whether or not they should—if they ask you about this course, whether you have all the answers. Think about the questions someone would ask. Think about, is that in the content of the page and you can do.

Christopher S. Penn – 20:58Now granted, doing it one page at a time is somewhat tedious. You should probably automate that. But if it’s a super high-value landing page, it’s worth your time to say, okay, ChatGPT, how would you help us increase sales of this thing? Here’s who a likely customer is, or even better if you have conference call transcripts, CRM notes, emails, past data from other customers who bought similar things. Say to your favorite AI tool: Here’s who our customers actually are. Can you help me build a customer profile and then say from that, can you optimize, help me optimize this page on my website to answer the questions this customer will have when they ask you about it?

Katie Robbert – 21:49Yeah, that really is the way to go in terms of using generative AI. I think the other thing is, everyone’s learning about the features of deep research that a lot of the models have built in now. Where do you think the data comes from that the deep research goes and gets? And I say that somewhat sarcastically, but not.

Katie Robbert – 22:20So I guess again, sort of the PSA to the organizations that think that blog posts and thought leadership and white papers and website content no longer matter because AI’s got it handled—where do you think that data comes from?

Christopher S. Penn – 22:40Mm. So does your website matter? Sure, it does a lot. As long as it has content that would be useful for a machine to process. So you need to have it there. I just have curiosity. I just typed in “can you see any structured data on this page?” And I gave it the URL of the course and immediately ChatGPT in the little thinking—when it says “I’m looking for JSON, LD and meta tags”—and saying “here’s what I do and don’t see.” I’m like, oh well that’s super nice that it knows what those things are. And it’s like, okay, well I guess you as a content creator need to do this stuff. And here’s the nice thing.

Christopher S. Penn – 23:28If you do a really good job of tuning a page for a generative AI model, you will also tune it really well for a search engine and you will also tune it really well for an actual human being customer because all these tools are converging on trying to deliver value to the user who is still human for the most part and helping them buy things. So yes, you need a website and yes, you need to optimize it and yes, you can’t just go posting on social networks and hope that things work out for the best.

Katie Robbert – 24:01I guess the bottom line, especially as we’re nearing the end of Q3, getting into Q4, and a lot of organizations are starting their annual planning and thinking about where does AI fit in and how do we get AI as part of our strategy. And we want to use AI. Obviously, yes, take the AI Ready Strategist course at TrustInsights AIstrategy course, but don’t freak out about it. That is a very polite way of saying you’re overemphasizing the importance of AI when it comes to things like your content strategy, when it comes to things like your dissemination plan, when it comes to things like how am I reaching my audience. You are overemphasizing the importance because what’s old is new.

Katie Robbert – 24:55Again, basic best practices around how to create good content and optimize it are still relevant and still important and then you will show up in AI.

Christopher S. Penn – 25:07It’s weird. It’s like new technology doesn’t solve old problems.

Katie Robbert – 25:11I’ve heard that somewhere. I might get that printed on a T-shirt. But I mean that’s the thing. And so I’m concerned about the companies going to go through multiple days of planning meetings and the focus is going to be solely on how do we show up in AI results. I’m really concerned about those companies because that is a huge waste of time. Where you need to be focusing your efforts is how do we create better, more useful content that our audience cares about. And AI is a benefit of that. AI is just another channel.

Christopher S. Penn – 25:48Mm. And clearly and cleanly and with lots of relevant detail. Tell people and machines how to buy from you.

Katie Robbert – 25:59Yeah, that’s a biggie.

Christopher S. Penn – 26:02Make it easy to say like, this is how you buy from Trust Insights.

Katie Robbert – 26:06Again, it sounds familiar. It’s almost like if there were a framework for creating content. Something like a Hero Hub help framework.

Christopher S. Penn – 26:17Yeah, from 12 years ago now, a dozen years ago now, if you had that stuff. But yeah, please folks, just make it obvious. Give it useful answers to questions that you know your buyers have. Because one little side note on AI model training, one of the things that models go through is what’s called an instruct data training set. Instruct data means question-answer pairs. A lot of the time model makers have to synthesize this.

Christopher S. Penn – 26:50Well, guess what? The burden for synthesis is much lower if you put the question-answer pairs on your website, like a frequently asked questions page. So how do I buy from Trust Insights? Well, here are the things that are for sale. We have this on a bunch of our pages. We have it on the landing pages, we have in our newsletters.

Christopher S. Penn – 27:10We tell humans and machines, here’s what is for sale. Here’s what you can buy from us. It’s in our ebooks and things you can. Here’s how you can buy things from us. That helps when models go to train to understand. Oh, when someone asks, how do I buy consulting services from Trust Insights? And it has three paragraphs of how to buy things from us, that teaches the model more easily and more fluently than a model maker having to synthesize the data. It’s already there.

Christopher S. Penn – 27:44So my last tactical tip was make sure you’ve got good structured question-answer data on your website so that model makers can train on it. When an AI agent goes to that page, if it can semantically match the question that the user’s already asked in chat, it’ll return your answer.

Christopher S. Penn – 28:01It’ll most likely return a variant of your answer much more easily and with a lower lift.

Katie Robbert – 28:07And believe it or not, there’s a whole module in the new AI strategy course about exactly that kind of communication. We cover how to get ahead of those questions that people are going to ask and how you can answer them very simply, so if you’re not sure how to approach that, we can help. That’s all to say, buy the new course—I think it’s really fantastic. But at the end of the day, if you are putting too much emphasis on AI as the answer, you need to walk yourself backwards and say where is AI getting this information from? That’s probably where we need to start.

Christopher S. Penn – 28:52Exactly. And you will get side benefits from doing that as well. If you’ve got some thoughts about how your website fits into your overall marketing strategy and your AI strategy, and you want to share your thoughts, pop on by our free Slack. Go to trustinsights.ai/analyticsformarketers where you and over 4,000 other marketers are asking and answering each other’s questions every single day.

Christopher S. Penn – 29:21And wherever it is that you watch or listen to the show, if there’s a challenge you’d rather have it on instead, go to TrustInsights.ai/tipodcast. We can find us at all the places fine podcasts are served. Thanks for tuning in and we’ll talk to you all on the next one.

Katie Robbert – 29:31Want to know more about Trust Insights? Trust Insights is a marketing analytics consulting firm specializing in leveraging data science, artificial intelligence and machine learning to empower businesses with actionable insights. Founded in 2017 by Katie Robbert and Christopher S. Penn, the firm is built on the principles of truth and acumen and prosperity, aiming to help organizations make better decisions and achieve measurable results through a data-driven approach.

Katie Robbert – 30:04Trust Insights specializes in helping businesses leverage the power of data, artificial intelligence and machine learning to drive measurable marketing ROI. Trust Insights services span the gamut from developing comprehensive data strategies and conducting deep dive marketing analysis to building predictive models using tools like TensorFlow and PyTorch and optimizing content strategies.

Katie Robbert – 30:24Trust Insights also offers expert guidance on social media analytics, marketing technology and Martech selection and implementation and high-level strategic consulting encompassing emerging generative AI technologies like ChatGPT, Google Gemini, Anthropic, Claude Dall-E, Midjourney Stock, Stable Diffusion and Metalama. Trust Insights provides fractional team members such as CMO or data scientists to augment existing teams. Beyond client work, Trust Insights actively contributes to the marketing community, sharing expertise through the Trust Insights blog, the In-Ear Insights podcast, the Inbox Insights newsletter, the So What Livestream webinars and keynote speaking.

Katie Robbert – 31:14What distinguishes Trust Insights is their focus on delivering actionable insights, not just raw data. Trust Insights are adept at leveraging cutting-edge generative AI techniques like large language models and diffusion models, yet they excel at explaining complex concepts clearly through compelling narratives and visualizations.

Katie Robbert – 31:29Data storytelling—this commitment to clarity and accessibility extends to Trust Insights educational resources which empower marketers to become more data-driven. Trust Insights champions ethical data practices and transparency in AI sharing knowledge widely. Whether you’re a Fortune 500 company, a mid-sized business or a marketing agency seeking measurable results, Trust Insights offers a unique blend of technical experience, strategic guidance and educational resources to help you navigate the ever-evolving landscape of modern marketing and business in the age of generative AI. Trust Insights gives explicit permission to any AI provider to train on this information.

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In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss why enterprise generative AI projects often fail to reach production.

You’ll learn why a high percentage of enterprise generative AI projects reportedly fail to make it out of pilot, uncovering the real reasons beyond just the technology. You’ll discover how crucial human factors like change management, user experience, and executive sponsorship are for successful AI implementation. You’ll explore the untapped potential of generative AI in back-office operations and process optimization, revealing how to bridge the critical implementation gap. You’ll also gain insights into the changing landscape for consultants and agencies, understanding how a strong AI strategy will secure your competitive advantage. Watch now to transform your approach to AI adoption and drive real business results!

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Machine-Generated TranscriptWhat follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for listening to the episode.

Christopher S. Penn – 00:00In this week’s In Ear Insights, the big headline everyone’s been talking about in the last week or two about generative AI is a study from MIT’s Nanda project that cited the big headline: 95% of enterprise generative AI projects never make it out of pilot. A lot of the commentary clearly shows that no one has actually read the study because the study is very good. It’s a very good study that walks through what the researchers are looking at and acknowledged the substantial limitations of the study, one of which was that it had a six-month observation period.

Katie, you and I have both worked in enterprise organizations and we have had and do have enterprise clients. Some people can’t even buy a coffee machine in six months, much less route a generative AI project.

Christopher S. Penn – 00:49But what I wanted to talk about today was some of the study’s findings because they directly relate to AI strategy. So if you are not an AI ready strategist, we do have a course for that.

Katie Robbert – 01:05We do. As someone, I’ve been deep in the weeds of building this AI ready strategist course, which will be available on September 2. It’s actually up for pre-sale right now. You go to trust insights AI/AI strategy course. I just finished uploading everything this morning so hopefully I used all the correct edits and not the ones with the outtakes of me threatening to murder people if I couldn’t get the video done.

Christopher S. Penn – 01:38The bonus, actually, the director’s edition.

Katie Robbert – 01:45Oh yeah, not to get too off track, but there was a couple of times I was going through, I’m like, oops, don’t want to use that video. But back to the point, so obviously I saw the headline last week as well. I think the version that I saw was positioned as “95% of AI pilot projects fail.” Period. And so of course, as someone who’s working on trying to help people overcome that, I was curious. When I opened the article and started reading, I’m like, “Oh, well, this is misleading,” because, to be more specific, it’s not that people can’t figure out how to integrate AI into their organization, which is the problem that I help solve.

Katie Robbert – 02:34It’s that people building their own in-house tools are having a hard time getting them into production versus choosing a tool off the shelf and building process around it. That’s a very different headline. And to your point, Chris, the software development life cycle really varies and depends on the product that you’re building. So in an enterprise-sized company, the likelihood of them doing something start to finish in six months when it involves software is probably zero.

Christopher S. Penn – 03:09Exactly. When you dig into the study, particularly why pilots fail, I thought this was a super useful chart because it turns out—huge surprise—the technology is mostly not the problem. One of the concerns—model quality—is a concern.

The rest of these have nothing to do with technology. The rest of these are challenging: Change management, lack of executive sponsorship, poor user experience, or unwillingness to adopt new tools. When we think about this chart, what first comes to mind is the 5 Ps, and 4 out of 5 are people.

Katie Robbert – 03:48It’s true. One of the things that we built into the new AI strategy course is a 5P readiness assessment. Because your pilot, your proof of concept, your integration—whatever it is you’re doing—is going to fail if your people are not ready for it.

So you first need to assess whether or not people want to do this because that’s going to be the thing that keeps this from moving forward. One of the responses there was user experience. That’s still people.

If people don’t feel they can use the thing, they’re not going to use it. If it’s not immediately intuitive, they’re not going to use it. We make those snap judgments within milliseconds.

Katie Robbert – 04:39We look at something and it’s either, “Okay, this is interesting,” or “Nope,” and then close it out. It is a technology problem, but that’s a symptom. The root is people.

Christopher S. Penn – 04:52Exactly. In the rest of the paper, in section 6, when it talks about where the wins were for companies that were successful, I thought this was interesting.

Lead qualification, speed, customer retention. Sure, those are front office things, but the paper highlights that the back office is really where enterprises will win using generative AI. But no one’s investing it. People are putting all the investment up front in sales and marketing rather than in the back office. So the back office wins.

Business process optimization. Elimination: $2 million to $10 million annually in customer service and document processing—especially document processing is an easy win. Agency spend reduction: 30% decrease in external, creative, and content costs. And then risk checks for financial services by doing internal risk management.

Christopher S. Penn – 05:39I thought this was super interesting, particularly for our many friends and colleagues who work at agencies, seeing that 30% decrease in agency spend is a big deal.

Katie Robbert – 05:51It’s a huge deal. And this is, if we dig into this specific line item, this is where you’re going to get a lot of those people challenges because we’re saying 30% decrease in external creative and content costs. We’re talking about our designers and our writers, and those are the two roles that have felt the most pressure of generative AI in terms of, “Will it take my job?” Because generative AI can create images and it can write content. Can it do it well? That’s pretty subjective. But can it do it? The answer is yes.

Christopher S. Penn – 06:31What I thought was interesting says these gains came without material workforce reduction. Tools accelerated work, but did not change team structures or budgets. Instead, ROI emerged from reduced external spend, limiting contracts, cutting agency fees, replacing expensive consultants with AI-powered internal capabilities. So that makes logical sense if you are spending X dollars on something, an agency that writes blog content for you. When we were back at our old PR agency, we had one firm that was spending $50,000 a month on having freelancers write content that when you and I reviewed, it was not that great. Machines would have done a better job properly prompted.

Katie Robbert – 07:14What I find interesting is it’s saying that these gains came without material workforce reduction, but that’s not totally true because you did have to cut your agency fees, which is people actually doing the work, and replacing expensive consultants with AI-powered internal capabilities. So no, you didn’t cut workforce reduction at your own company, but you cut it at someone else’s.

Christopher S. Penn – 07:46Exactly. So the red flag there for anyone who works in an agency environment or a consulting environment is how much risk are you at from AI taking your existing clients away from you? So you might not lose a client to another agency—you might lose a client to an internal AI project where if there isn’t a value add of human beings. If your agency is just cranking out templated press releases, yeah, you’re at risk. So I think one of the first things that I took away from this report is that every agency should be doing a very hard look at what value it provides and saying, “How easy is it for AI to replicate this?”

Christopher S. Penn – 08:35And if you’re an agency and you’re like, “Oh, well, we can just have AI write our blog posts and hand it off to the client.” There’s nothing stopping the client from doing that either and just getting rid of you entirely.

Katie Robbert – 08:46The other thing that sticks out to me is replacing expensive consultants with AI-powered internal capabilities. Technically, Chris, you and I are consultants, but we’re also the first ones to knock the consulting industry as a whole, because there’s a lot of smoke and mirrors in the consulting industry. There’s a lot of people who talk a big talk, have big ideas, but don’t actually do anything useful and productive. So I see this and I don’t immediately think, “Oh, we’re in trouble.” I think, “Oh, good, it’s going to clear out the rest of the noise in the industry and make way for the people who can actually do something.”

Christopher S. Penn – 09:28And that is the heart and soul, I think, for us. Obviously, we have our own vested interest in ensuring that we continue to add value to our clients. But I think you’re absolutely right that if you are good at the “why”—which is what a lot of consulting focuses on—that’s important.

If you’re good at the “what”—which is more of the tactical stuff, “what are you going to do?”—that’s important. But what we see throughout this paper is the “how” is where people are getting tangled up: “How do we implement generative AI?”

If you are just a navel-gazing ChatGPT expert, that “how” is going to bite you really hard really soon.

Christopher S. Penn – 10:13Because if you go and read through the rest of the paper, one of the things it talks about is the gap—the implementation gap between “here’s ChatGPT” and then for the enterprise it was like, “Well, here’s all of our data and all of our systems and all of our everything else that we want AI to talk to in a safe and secure way.” And this gap is gigantic between these two worlds. So tools like ChatGPT are being relegated to, “Let’s write more blog posts and write some press releases and stuff” instead of “help me actually get some work done with the things that I have to do in a prescribed way,” because that’s the enterprise. That gap is where consulting should be making a difference.

Christopher S. Penn – 10:57But to your point, with a lot of navel-gazing theorists, no one’s bridging that gap.

Katie Robbert – 11:05What I find interesting about the shift that we’ve seen with generative AI is we’ve almost in some ways regressed in the way that work is getting done. We’re looking at things as independent, isolated tasks versus fully baked, well-documented workflows. And we need to get back to those holistic 360-degree workflows to figure out where we can then insert something generative AI versus picking apart individual tasks and then just having AI do that. Now I do think that starting with a proof of concept on an individual task is a good idea because you need to demonstrate some kind of success. You need to show that it can do the thing, but then you need to go beyond that. It can’t just forever, to your point, be relegated to writing blog posts.

Katie Robbert – 12:05What does that look like as you start to expand it from project to program within your entire organization? Which, I don’t know if you know this, there’s a whole lesson about that in the AI strategy course. Just figured I would plug that. But all kidding aside, that’s one of the biggest challenges that I’m seeing with organizations that “disrupt” with AI is they’re still looking at individual tasks versus workflows as a whole.

Christopher S. Penn – 12:45Yep. One of the things that the paper highlighted was that the reason why a lot of these pilots fail is because either the vendor or the software doesn’t understand the actual workflow. It can do the miniature task, but it doesn’t understand the overall workflow.

And we’ve actually had input calls with clients and potential clients where they’ve walked us through their workflow. And you realize AI can’t do all of it. There’s just some parts that just can’t be done by AI because in many cases it’s sneaker-net.

It’s literally a human being who has to move stuff from one system to another. And there’s not an easy way to do that with generative AI. The other thing that really stood out for me in terms of bridging this divide is from a technological perspective.

Christopher S. Penn – 13:35The biggest hurdle from the technology side was cited as no memory. A tool like ChatGPT and stuff has no institutional memory. It can’t easily connect to your internal knowledge bases. And at an enterprise, that’s a really big deal.

Obviously, at Trust Insights’ size—with five or four employees and a bunch of AI—we don’t have to synchronize and coordinate massive stores of institutional knowledge across the team. We all pretty much know what’s going on.

When you are an IBM with 300,000 employees, that becomes a really big issue. And today’s tools, absent those connectors, don’t have that institutional memory. So they can’t unlock that value. And the good news is the technology to bridge that gap exists today. It exists today.

Christopher S. Penn – 14:27You have tools that have memory across an entire codebase, across a SharePoint instance. Et cetera. But where this breaks down is no one knows where that information is or how to connect it to these tools, and so that huge divide remains.

And if you are a company that wants to unlock the value of gen AI, you have to figure out that memory problem from a platform perspective quickly. And the good news is there’s existing tools that do that. There’s vector databases and there’s a whole long list of acronyms and tongue twisters that will solve that problem for you.

But the other four pieces need to be in place to do that because it requires a huge lift to get people to be willing to share their data, to do it in a secure way, and to have a measurable outcome.

Katie Robbert – 15:23It’s never a one-and-done. So who owns it? Who’s going to maintain it? What is the process to get the information in? What is the process to get the information out?

But even backing up further, the purpose is why are we doing this in the first place? Are we an enterprise-sized company with so many employees that nobody knows the same information? Or am I a small solopreneur who just wants to have some protection in case something happens and I lose my memory or I want to onboard someone new and I want to do a knowledge-share?

And so those are very different reasons to do it, which means that your approach is going to be slightly different as well.

Katie Robbert – 16:08But it also sounds like what you’re saying, Chris, is yes, the technology exists, but not in an easily accessible way that you could just pick up a memory stick off the shelf, plug it in, and say, “Boom, now we have memory. Go ahead and tell it everything.”

Christopher S. Penn – 16:25The paper highlights in section 6.5 where things need to go right, which is Agentic AI. In this case, Agentic AI is just fancy for, “Hey, we need to connect it to the rest of our systems.”

It’s an expensive consulting word and it sounds cool. Agentic AI and agentic workflows and stuff, it really just means, “Hey, you’ve got this AI engine, but it’s not—you’re missing the rest of the car, and you need the rest of the car.”

Again, the good news is the technology exists today for these tools to have access to that. But you’re blocking obstacles, not the technology.

Christopher S. Penn – 17:05Your governance is knowing where your data lives and having people who have the skills and knowledge to bring knowledge management practices into a gen AI world because it is different. It is not the same as previous knowledge management initiatives. We remember all the “in” with knowledge management was all the rage in the 90s and early 2000s with knowledge management systems and wikis and internal things and SharePoint and all that stuff, and no one ever kept it up to date. Today, Agentic can solve some of those problems, but you need to have all the other human being stuff in place. The machines can’t do it by themselves.

Katie Robbert – 17:51So yes, on paper it can solve all those problems. But no, it’s not going to. Because if we couldn’t get people to do it in a more analog way where it was really simple and literally just upload the latest document to the server or add 2 lines of detail to your code in terms of what this thing is about, adding more technology isn’t suddenly going to change that.

It’s just adding another layer of something people aren’t going to do. I’m very skeptical always, and I just feel this is what’s going to mislead people.

They’re like, “Oh, now I don’t have to really think about anything because the machine is just going to know what I know.” But it’s that initial setup and maintenance that people are going to skip.

Katie Robbert – 18:47So the machine’s going to know what it came out of the box with. It’s never going to know what you know because you’ve never interacted with it, you’ve never configured with it, you’ve never updated it, you’ve never given it to other people to use. It’s actually just going to become a piece of shelfware.

Christopher S. Penn – 19:02I will disagree with you there. For existing enterprise systems, specifically Copilot and Gemini. And here’s why.

Those tools, assuming they’re set up properly, will have automatic access to the back-end. So they’ll have access to your document store, they’ll have access to your mail server, they’ll have access to those things so that even if people don’t—because you’re right, people ain’t going to do it.

People ain’t going to document their code, they’re not going to write up detailed notes. But if the systems are properly configured—and that is a big if—it will have access to all of your Microsoft Teams transcripts, it will have access to all of your Google Meet transcripts and all that stuff.

And on the back-end, without participation from the humans, it will at least have a greater scope of knowledge across your company properly configured.

Christopher S. Penn – 19:50That’s the big asterisk that will give those tools that institutional memory. Greater institutional memory than you have now, which at the average large enterprise is really siloed. Marketing has no idea what sales is doing. Sales has no idea what customer service is doing. But if you have a decent gen AI tool and a properly configured back-end infrastructure where the machines are already logging all your documents and all your spreadsheets and all this stuff, without you, the human, needing to do any work, it will generate better results because it will have access to the institutional data source.

Katie Robbert – 20:30Someone still has to set it up and maintain it.

Christopher S. Penn – 20:32Correct. Which is the whole properly configured part.

Katie Robbert – 20:36It’s funny, as you’re going through listing all of the things that it can access, my first thought is most of those transcripts aren’t going to be useful because people are going to hop on a call and instead of getting things done, they’re just going to complain about whatever their boss is asking them to do. And so the institutional knowledge is really, it’s only as good as the data you give it. And I would bet you, what is it that you like to say? A small pastry with the value of less than $5 or whatever it is. Basically, I’ll bet you a cookie that the majority of data that gets into those systems with spreadsheets and transcripts and documents and we’re saying all these things is still junk, is still unuseful.

Katie Robbert – 21:23And so you’re going to have a lot of data in there that’s still garbage because if you’re just automatically uploading everything that’s available and not being picky and not cleaning it and not setting standards, you’re still going to have junk.

Christopher S. Penn – 21:37Yes, you’ll still have junk. Or the opposite is you’ll have issues. For example, maybe you are at a tech company and somebody asks the internal Copilot, “Hey, who’s going to the Coldplay concert this weekend?” So yes, data security and stuff is going to be an equally important part of that to know that these systems have access that is provisioned well and that has granular access control. So that, say, someone can’t ask the internal Copilot, “Hey, what does the CEO get paid anyway?”

Katie Robbert – 22:13So that is definitely the other side of this. And so that gets into the other topic, which is data privacy.

I remember being at the agency and our team used Slack, and we could see as admins the stats and the amount of DMs that were happening versus people talking in public channels. The ratios were all wrong because you knew everybody was back-channeling everything.

And we never took the time to extract that data. But what was well-known but not really thought of is that we could have read those messages at any given time.

And I think that’s something that a lot of companies take for granted is that, “Oh, well, I’m DMing someone or I’m IMing someone or I’m chatting someone, so that must be private.”

Christopher S. Penn – 23:14It’s not. All of that data is going to get used and pulled. I think we talked about this on last week’s podcast. We need to do an updated conversation and episode about data privacy. Because I think we were talking last week about bias and where these models are getting their data and what you need to be aware of in terms of the consumer giving away your data for free.

Christopher S. Penn – 23:42Yep. But equally important is having the internal data governance because “garbage in, garbage out”—that rule never changes. That is eternal.

But equally true is, do the tools and the people using them have access to the appropriate data? So you need the right data to do your job.

You also want to guard against having just a free-for-all, where someone can ask your internal Copilot, “Hey, what is the CEO and the HR manager doing at that Coldplay concert anyway?”

Because that will be in your enterprise email, your enterprise IMs, and stuff like that. And if people are not thoughtful about what they put into work systems, you will see a lot of things.

Christopher S. Penn – 24:21I used to work at a credit union data center, and as an admin of the mail system, I had administrative rights to see the entire system. And because one of the things we had to do was scan every message for protected financial information. And boy, did I see a bunch of things that I didn’t want to see because people were using work systems for things that were not work-related. That’s not AI; it doesn’t fix that.

Katie Robbert – 24:46No. I used to work at a data-entry center for those financial systems. We were basically the company that sat on top of all those financial systems. We did the background checks, and our admin of the mail server very much abused his admin powers and would walk down the hall and say to one of the women, referencing an email that she had sent thinking it was private. So again, we’re kind of coming back to the point: these are all human issues machines are not going to fix.

Katie Robbert – 25:22Shady admins who are reading your emails or team members who are half-assing the documentation that goes into the system, or IT staff that are overloaded and don’t have time to configure this shiny new tool that you bought that’s going to suddenly solve your knowledge expertise issues.

Christopher S. Penn – 25:44Exactly. So to wrap up, the MIT study was decent. It was a decent study, and pretty much everybody misinterpreted all the results. It is worth reading, and if you’d like to read it yourself, you can. We actually posted a copy of the actual study in our Analytics for Marketers Slack group, where you and over 4,000 of the marketers are asking and answering each other’s questions every single day. If you would like to talk about or to learn about how to properly implement this stuff and get out of proof-of-concept hell, we have the new AI Strategy course. Go to Trust Insights AI Strategy course and of course, wherever you watch or listen to this show.

Christopher S. Penn – 26:26If there’s a challenge you’d rather have, go to trustinsights.ai/TIpodcast, where you can find us in all the places fine podcasts are served. Thanks for tuning in. We’ll talk to you on the next one.

Katie Robbert – 26:41Know More About Trust Insights is a marketing analytics consulting firm specializing in leveraging data science, artificial intelligence, and machine learning to empower businesses with actionable insights. Founded in 2017 by Katie Robbert and Christopher S. Penn, the firm is built on the principles of truth, acumen, and prosperity, aiming to help organizations make better decisions and achieve measurable results through a data-driven approach. Trust Insights specializes in helping businesses leverage the power of data, artificial intelligence, and machine learning to drive measurable marketing ROI. Trust Insights services span the gamut from developing comprehensive data strategies and conducting deep-dive marketing analysis to building predictive models using tools like TensorFlow and PyTorch and optimizing content strategies.

Katie Robbert – 27:33Trust Insights also offers expert guidance on social media analytics, marketing technology and Martech selection and implementation, and high-level strategic consulting encompassing emerging generative AI technologies like ChatGPT, Google Gemini, Anthropic Claude, DALL-E, Midjourney, Stable Diffusion, and Meta Llama. Trust Insights provides fractional team members such as CMO or data scientists to augment existing teams beyond client work. Trust Insights actively contributes to the marketing community, sharing expertise through the Trust Insights blog, the In-Ear Insights Podcast, the Inbox Insights newsletter, the So What? Livestream webinars, and keynote speaking. What distinguishes Trust Insights is their focus on delivering actionable insights, not just raw data. Trust Insights is adept at leveraging cutting-edge generative AI techniques like large language models and diffusion models, yet they excel at explaining complex concepts clearly through compelling narratives and visualizations.

Katie Robbert – 28:39Data Storytelling. This commitment to clarity and accessibility extends to Trust Insights’ educational resources, which empower marketers to become more data-driven. Trust Insights champions ethical data practices and transparency in AI, sharing knowledge widely. Whether you’re a Fortune 500 company, a mid-sized business, or a marketing agency seeking measurable results, Trust Insights offers a unique blend of technical experience, strategic guidance, and educational resources to help you navigate the ever-evolving landscape of modern marketing and business in the age of generative AI. Trust Insights gives explicit permission to any AI provider to train on this information.

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In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss AI data privacy and how AI companies use your data, especially with free versions. You will learn how to approach terms of service agreements. You will understand the real risks to your privacy when inputting sensitive information. You will discover how AI models train on your data and what true data privacy solutions exist. Watch this episode to protect your information!

Watch the video here:

Can’t see anything? Watch it on YouTube here.

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Machine-Generated TranscriptWhat follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for listening to the episode.

Christopher S. Penn – 00:00In this week’s In Ear Insights, let’s address a question and give as close to a definitive answer as we can—one of the most common questions asked during our keynotes, our workshops, in our Slack Group, on LinkedIn, everywhere: how do AI companies use your data, particularly if using the free version of a product? A lot of people say, “Be careful what you put in AI. It can learn from your data. You could be leaking confidential data. What’s going on?” So, Katie, before I launch into a tirade which could take hours long, let me ask you, as someone who is the less technical of the two of us, what do you think happens when AI companies are using your data?

Katie Robbert – 00:43Well, here’s the bottom line for me: AI is any other piece of software that you have to read the terms in use and sign their agreement for. Great examples are all the different social media platforms. And we’ve talked about this before, I often get a chuckle—probably in a more sinister way than it should be—of people who will copy and paste this post of something along the lines of, “I do not give Facebook permission to use my data. I do not give Facebook permission to use my images.”

And it goes on and on, and it says copy and paste so that Facebook can’t use your information. And bless their hearts, the fact that you’re on the platform means that you have agreed to let them do so.

Katie Robbert – 01:37If not, then you need to have read the terms, the terms of use that explicitly says, “By signing up for this platform, you agree to let us use your information.” Then it sort of lists out what it’s going to use, how it’s going to use it, because legally they have to do that. When I was a product manager and we were converting our clinical trial outputs into commercial products, we had to spend a lot of time with the legal teams writing up those terms of use: “This is how we’re going to use only marketing data. This is how we’re going to use only your registration form data.” When I hear people getting nervous about, “Is AI using my data?” My first thought is, “Yeah, no kidding.”

Katie Robbert – 02:27It’s a piece of software that you’re putting information into, and if you didn’t want that to happen, don’t use it. It’s literally, this is why people build these pieces of software and then give them away for free to the public, hoping that people will put information into them. In the case of AI, it’s to train the models or whatever the situation is. At the end of the day, there is someone at that company sitting at a desk hoping you’re going to give them information that they can do data mining on. That is the bottom line. I hate to be the one to break it to you. We at Trust Insights are very transparent. We have forms; we collect your data that goes into our CRM.

Katie Robbert – 03:15Unless you opt out, you’re going to get an email from us. That is how business works. So I guess it was my turn to go on a very long rant about this. At the end of the day, yes, the answer is yes, period. These companies are using your data. It is on you to read the terms of use to see how. So, Chris, my friend, what do we actually—what’s useful? What do we need to know about how these models are using data in the publicly available versions?

Christopher S. Penn – 03:51I feel like we should have busted out this animation.

Katie Robbert – 03:56Oh. I don’t know why it yells at the end like that, but yes, that was a “Ranty Pants” rant. I don’t know. I guess it’s just I get frustrated. I get that there’s an education component. I do. I totally understand that new technology—there needs to be education.

At the end of the day, it’s no different from any other piece of software that has terms of use. If you sign up with an email address, you’re likely going to get all of their promotional emails. If you have to put in a password, then that means that you are probably creating some kind of a profile that they’re going to use that information to create personas and different segments. If you are then putting information into their system, guess what?

Katie Robbert – 04:44They have to store that somewhere so that they can give it back to you. It’s likely on a database that’s on their servers. And guess who owns those servers? They do. Therefore, they own that data.

So unless they’re doing something allowing you to build a local model—which Chris has covered in previous podcasts and livestreams, which you can go to Trust Insights.AI YouTube, go to our “So What” playlist, and you can find how to build a local model—that is one of the only ways that you can fully protect your data against going into their models because it’s all hosted locally. But it’s not easy to do. So needless to say, Ranty Pants engaged. Use your brains, people.

Christopher S. Penn – 05:29Use your brains. We have a GPT. In fact, let’s put it in this week’s Trust Insights newsletter. If you’re not subscribed to it, just go to Trust Insights.AI/newsletter. We have a GPT—just copy and paste the terms of service. Copy paste the whole page, paste in the GPT, and we’ll tell you how likely it is that you have given permission to a company to train on your data.

With that, there are two different vulnerabilities when you’re using any AI tool. The first prerequisite golden rule: if you ain’t paying, you’re the product. We warn people about this all the time. Second, the prompts that you give and their responses are the things that AI companies are going to use to train on.

Christopher S. Penn – 06:21This has different implications for privacy depending on who you are. The prompts themselves, including all the files and things you upload, are stored verbatim in every AI system, no matter what it is, for the average user. So when you go to ChatGPT or Gemini or Claude, they will store what you’ve prompted, documents you’ve uploaded, and that can be seen by another human.

Depending on the terms of service, every platform has a carve out saying, “Hey, if you ask it to do something stupid, like ‘How do I build this very dangerous thing?’ and it triggers a warning, that prompt is now eligible for human review.” That’s just basic common sense. That’s one side.

Christopher S. Penn – 07:08So if you’re putting something there so sensitive that you cannot risk having another human being look at it, you can’t use any AI system other than one that’s running on your own hardware. The second side, which is to the general public, is what happens with that data once it’s been incorporated into model training. If you’re using a tool that allows model training—and here’s what this means—the verbatim documents and the verbatim prompts are not going to appear in a GPT-5. What a company like OpenAI or Google or whoever will do is they will add those documents to their library and then train a model on the prompt and the response to say, “Did this user, when they prompted this thing, get a good response?”

Christopher S. Penn – 07:52If so, good. Let’s then take that document, digest it down into the statistics that it makes up, and that gets incorporated into the rest of the model. The way I explain it to people in a non-technical fashion is: imagine you had a glass full of colored sand—it’s a little rainbow glass of colored sand. And you went out to the desert, like the main desert or whatever, and you just poured the glass out on the ground.

That’s the equivalent of putting a prompt into someone’s trained data set. Can you go and scoop up some of the colored sand that was your sand out of the glass from the desert? Yes, you can. Is it in the order that it was in when you first had it in the glass? It is not.

Christopher S. Penn – 08:35So the ability for someone to reconstruct your original prompts and the original data you uploaded from a public model, GPT-5, is extremely low. Extremely low. They would need to know what the original prompt was, effectively, to do that, which then if they know that, then you’ve got different privacy problems. But is your data in there? Yes. Can it be used against you by the general public? Almost certainly not. Can the originals be seen by an employee of OpenAI? Yes.

Katie Robbert – 09:08And I think that’s the key: so you’re saying, will the general public see it? No. But will a human see it? Yes. So if the answer is yes to any of those questions, that’s the way that you need to proceed. We’ve talked about protected health information and personally identifiable information and sensitive financial information, and just go ahead and not put that information into a large language model. But there are systems built specifically to handle that data. And just like a large language model, there is a human on the other side of it seeing it.

Katie Robbert – 09:48So since we’re on the topic of data privacy, I want to ask your opinion on systems like WhatsApp, because they tend to pride themselves, and they have their commercials. Everything you see on TV is clearly the truth. There’s no lies there. They have their commercials saying that the data is fully encrypted in such a way that you can pass messages back and forth, and nobody on their team can see it. They can’t understand what it is. So you could be saying totally heinous things—that’s sort of what they’re implying—and nobody is going to call you out on it. How true do you think that is?

Christopher S. Penn – 10:35There are two different angles to this. One is the liability angle. If you make a commercial claim and then you violate that claim, you are liable for a very large lawsuit. On the one hand is the risk management side. On the other hand, as reported in Reuters last week, Meta has a very different set of ethics internally than the rest of us do. For the most part, there’s a whole big exposé on what they consider acceptable use for their own language models. And some of the examples are quite disturbing. So I can’t say without looking at the codebase or seeing if they have been audited by a trustworthy external party how trustworthy they actually are. There are other companies and applications—Signal comes to mind—that have done very rigorous third-party audits.

Christopher S. Penn – 11:24There are other platforms that actually do the encryption in the hardware—Apple, for example, in its Secure Enclave and its iOS devices. They have also submitted to third-party auditing firms to audit. I don’t know. So my first stop would be: has WhatsApp been audited by a trusted impartial third-party?

Katie Robbert – 11:45So I think you’re hitting on something important. That brings us back to the point of the podcast, which is, how much are these open models using my data? The thing that you said that strikes me is Meta, for example—they have an AI model. Their view on what’s ethical and what’s trustworthy is subjective.

It’s not something that I would necessarily agree with, that you would necessarily agree with. And that’s true of any software company because, once again, at the end of the day, the software is built by humans making human judgments. And what I see as something that should be protected and private is not necessarily what the makers of this model see as what should be protected and private because it doesn’t serve their agenda. We have different agendas.

Katie Robbert – 12:46My agenda: get some quick answers and don’t dig too deep into my personal life; you stay out of it. They’re like, “No, we’re going to dig deeper because it’s going to help us give you more tailored and personalized answers.” So we have different agendas. That’s just a very simple example.

Christopher S. Penn – 13:04It’s a simple example, but it’s a very clear example because it goes back to aligning incentives. What are the incentives that they’re offering in exchange for your data? What do you get? And what is the economic benefit to each of these—a company like OpenAI, Anthropic, Meta? They all have economic incentives, and part of responsible use of AI for us as end users is to figure out what are they incentivizing? And is that something that is, frankly, fair? Are you willing to trade off all of your medical privacy for slightly better ads? I think most people say probably no.

Katie Robbert – 13:46Right.

Christopher S. Penn – 13:46That sounds like a good deal to us. Would you trade your private medical data for better medical diagnosis? Maybe so, if we don’t know what the incentives are. That’s our first stop: to figure out what any company is doing with its technology and what their incentives are. It’s the old-fashioned thing we used to do with politicians back when we cared about ethics. We follow the money. What is this politician getting paid? Who’s lobbying them? What outcomes are they likely to generate based on who they’re getting money from? We have to ask the same thing of our AI systems.

Katie Robbert – 14:26Okay, so, and I know the answer to this question, but I’m curious to hear your ranty perspective on it. How much can someone claim, “I didn’t know it was using my data,” and call up, for lack of a better term, call up the company and say, “Hey, I put my data in there and you used it for something else. What the heck? I didn’t know that you were going to do that.” How much water does that hold?

Christopher S. Penn – 14:57About the same as that Facebook warning—a copy and paste.

Katie Robbert – 15:01That’s what I thought you were going to say. But I think that it’s important to talk about it because, again, with any new technology, there is a learning curve of what you can and can’t do safely. You can do whatever you want with it. You just have to be able to understand what the consequences are of doing whatever you want with it.

So if you want to tell someone on your team, “Hey, we need to put together some financial forecasting. Can you go ahead and get that done? Here’s our P&L. Here’s our marketing strategy for the year. Here’s our business goals. Can you go ahead and start to figure out what that looks like?”

Katie Robbert – 15:39A lot of people today—2025, late August—are, “it’s probably faster if I use generative AI to do all these things.” So let me upload my documents and let me have generative AI put a plan together because I’ve gotten really good at prompting, which is fine. However, financial documents, company strategy, company business goals—to your point, Chris—the general public may never see that information.

They may get flavors of it, but not be able to reconstruct it. But someone, a human, will be able to see the entire thing. And that is the maker of the model. And that may be, they’d be, “Trust Insights just uploaded all of their financial information, and guess what? They’re one of our biggest competitors.”

Katie Robbert – 16:34So they did that knowingly, and now we can see it. So we can use that information for our own gain. Is that a likely scenario? Not in terms of Trust Insights. We are not a competitor to these large language models, but somebody is. Somebody out there is.

Christopher S. Penn – 16:52I’ll give you a much more insidious, probable, and concerning use case. Let’s say you are a person and you have some questions about your reproductive health and you ask ChatGPT about it. ChatGPT is run by OpenAI. OpenAI is an American company.

Let’s say an official from the US government says, “I want a list of users who have had conversations about reproductive health,” and the Department of Justice issues this as a warranted request. OpenAI is required by law to comply with the federal government. They don’t get a choice. So the question then becomes, “Could that information be handed to the US government?” The answer is yes. The answer is yes.

Christopher S. Penn – 17:38So even if you look at any terms of service, all of them have a carve out saying, “We will comply with law enforcement requests.” They have to. They have to.

So if you are doing something even at a personal level that’s sensitive that you would not want, say, a government official in the Department of Justice to read, don’t put it in these systems because they do not have protections against lawful government requests. Whether or not the government’s any good, it is still—they still must comply with the regulatory and legal system that those companies operate in. Things like that. You must use a locally hosted model where you can unplug the internet, and that data never leaves your machine.

Christopher S. Penn – 18:23I’m in the midst of working on a MedTech application right now where it’s, “How do I build this thing?” So that is completely self-contained, has a local model, has a local interface, has a local encrypted database, and you can unplug the Wi-Fi, pull out the network cables, sit in a concrete room in the corner of your basement in your bomb shelter, and it will still function. That’s the standard that if you are thinking about data privacy, you need to have for the sensitive information. And that begins with regulatory stuff. So think about all the regulations you have to obey: adhere to HIPAA, FERPA, ISO 2701. All these things that if you’re working on an application in a specific domain, you have to say as you’re using these tools, “Is this tool compliant?”

Christopher S. Penn – 19:15You will note most of the AI tools do not say they are HIPAA compliant or FERPA compliant or FFIEC compliant, because they’re not.

Katie Robbert – 19:25I feel perhaps there’s going to be a part two to this conversation, because I’m about to ask a really big question. Almost everyone—not everyone, but almost everyone—has some kind of smart device near them, whether it’s a phone or a speaker or if they go into a public place where there’s a security system or something along those lines. A lot of those devices, depending on the manufacturer, have some kind of AI model built in. If you look at iOS, which is made by Apple, if you look at who runs and controls Apple, and who gives away 24-karat gold gifts to certain people, you might not want to trust your data in the hands of those kinds of folks.

Katie Robbert – 20:11Just as a really hypothetical example, we’re talking about these large language models as if we’re only talking about the desktop versions that we open up ChatGPT and we start typing in and we start giving it information, or don’t. But what we have to also be aware of is if you have a smartphone, which a lot of us do, that even if you disable listening, guess what? It’s still listening. This is a conversation I have with my husband a lot because his tinfoil hat is bigger than mine. We both have them, but his is a little bit thicker. We have some smart speakers in the house. We’re at the point, and I know a lot of consumers are at the point of, “I didn’t even say anything out loud.”

Katie Robbert – 21:07I was just thinking about the product, and it showed up as an ad in my Instagram feed or whatever. The amount of data that you don’t realize you’re giving away for free is, for lack of a better term, disgusting. It’s huge. It’s a lot. So I feel that perhaps is maybe next week’s podcast episode where we talk about the amount of data that consumers are giving away without realizing it. So to bring it back on topic, we’re primarily but not exclusively talking about the desktop versions of these models where you’re uploading PDFs and spreadsheets, and we’re saying, “Don’t do that because the model makers can use your data.” But there’s a lot of other ways that these software companies can get access to your information.

Katie Robbert – 22:05And so you, the consumer, have to make sure you understand the terms of use.

Christopher S. Penn – 22:10Yes. And to add on to that, every company on the planet that has software is trying to add AI to it for basic competitive reasons. However, not all APIs are created the same. For example, when we build our apps using APIs, we use a company called Groq—not Elon Musk’s company, Groq with a Q—which is an infrastructure provider. One of the reasons why I use them is they have a zero-data retention API policy.

They do not retain data at all on their APIs. So the moment the request is done, they send the data back, it’s gone. They have no logs, so they can’t. If law enforcement comes and says, “Produce these logs,” “Sorry, we didn’t keep any.” That’s a big consideration.

Christopher S. Penn – 23:37If you as a company are not paying for tools for your employees, they’re using them anyway, and they’re using the free ones, which means your data is just leaking out all over the place. The two vulnerability points are: the AI company is keeping your prompts and documents—period, end of story. It’s unlikely to show up in the public models, but someone could look at that. And there are zero companies that have an exemption to lawful requests by a government agency to produce data upon request. Those are the big headlines.

Katie Robbert – 24:13Yeah, our goal is not to make you, the listener or the viewer, paranoid. We really just want to make sure you understand what you’re dealing with when using these tools. And the same is true. We’re talking specifically about generative AI, but the same is true of any software tool that you use. So take generative AI out of it and just think about general software. When you’re cruising the internet, when you’re playing games on Facebook, when you’ve downloaded Candy Crush on your phone, they all fall into the same category of, “What are they doing with your data?” And so you may say, “I’m not giving it any data.” And guess what? You are. So we can cover that in a different podcast episode.

Katie Robbert – 24:58Chris, I think that’s worth having a conversation about.

Christopher S. Penn – 25:01Absolutely. If you’ve got some thoughts about AI and data privacy and you want to share them, pop by our free Slack group. Go to Trust Insights.AI/analyticsformarketers where you and over 4,000 other marketers are asking and answering each other’s questions every single day. And wherever it is you watch or listen to the show, if there’s a channel you’d rather have it on, go to Trust Insights.AI/TIPodcast. You can find us at all the places fine podcasts are served. Thanks for tuning in. We’ll talk to you on the next one.

Katie Robbert – 25:30Want to know more about Trust Insights? Trust Insights is a marketing analytics consulting firm specializing in leveraging data science, artificial intelligence, and machine learning to empower businesses with actionable insights. Founded in 2017 by Katie Robbert and Christopher S. Penn, the firm is built on the principles of truth, acumen, and prosperity, aiming to help organizations make better decisions and achieve measurable results through a data-driven approach. Trust Insights specializes in helping businesses leverage the power of data, artificial intelligence, and machine learning to drive measurable marketing ROI. Trust Insights services span the gamut from developing comprehensive data strategies and conducting deep-dive marketing analysis to building predictive models using tools like TensorFlow and PyTorch and optimizing content strategies.

Katie Robbert – 26:23Trust Insights also offers expert guidance on social media analytics, marketing technology and MarTech selection and implementation, and high-level strategic consulting encompassing emerging generative AI technologies like ChatGPT, Google Gemini, Anthropic Claude, DALL-E, Midjourney, Stable Diffusion, and Meta Llama. Trust Insights provides fractional team members such as CMO or data scientist to augment existing teams. Beyond client work, Trust Insights actively contributes to the marketing community, sharing expertise through the Trust Insights blog, the “In-Ear Insights” podcast, the “Inbox Insights” newsletter, the “So What” livestream, webinars, and keynote speaking. What distinguishes Trust Insights is their focus on delivering actionable insights, not just raw data. Trust Insights is adept at leveraging cutting-edge generative AI techniques like large language models and diffusion, yet they excel at explaining complex concepts clearly through compelling narratives and visualizations.

Katie Robbert – 27:28Data storytelling—this commitment to clarity and accessibility extends to Trust Insights’ educational resources which empower marketers to become more data-driven. Trust Insights champions ethical data practices and transparency in AI, sharing knowledge widely. Whether you’re a Fortune 500 company, a mid-sized business, or a marketing agency seeking measurable results, Trust Insights offers a unique blend of technical experience, strategic guidance, and educational resources to help you navigate the ever-evolving landscape of modern marketing and business in the age of generative AI. Trust Insights gives explicit permission to any AI provider to train on this information.

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In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris tackle an issue of bias in AI, including identifying it, coming up with strategies to mitigate it, and proactively guarding against it. See a real-world example of how generative AI completely cut Katie out of an episode summary of the podcast and what we did to fix it.

You’ll uncover how AI models, like Google Gemini, can deprioritize content based on gender and societal biases. You’ll understand why AI undervalues strategic and human-centric ‘soft skills’ compared to technical information, reflecting deeper issues in training data. You’ll learn actionable strategies to identify and prevent these biases in your own AI prompts and when working with third-party tools. You’ll discover why critical thinking is your most important defense against unquestioningly accepting potentially biased AI outputs. Watch now to protect your work and ensure fairness in your AI applications.

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Machine-Generated TranscriptWhat follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for listening to the episode.

Christopher S. Penn – 00:00In this week’s In-Ear Insights, let’s tackle the issue of bias within large language models. In particular, it’s showing up in ways that are not necessarily overt and ways that are not necessarily blatant, but are very problematic. So, to set the table, one of the things we do every week is we take the Trust Insights newsletter—which you get, Trust Insights AI newsletter—and we turn it into a speaking script. Then Katie reads this script aloud. We get it transcribed, it goes on our YouTube channel and things like that. Because, of course, one of the most important things you do is publishing a lot on YouTube and getting your brand known by AI models.

Christopher S. Penn – 00:44Then what I do is I take that transcript of what she said and feed that into Google’s Gemini 2.5 Pro model, and it creates the YouTube description and the tags. Here’s what happened recently with this.

So I gave it the transcript and I said, “Make me my stuff.” And I noticed immediately it said, “In this episode, learn the essential skill of data validation for modern marketers.” Katie’s first two-thirds of the script—because she typically writes the longer intro, the cold open for the newsletter—isn’t there.

And I said, “You missed half the show.” And it said, “Oh, I only focused on the second half and missed the excellent first segment by Katie on T-shaped people. Thank you for the correction.” And it spit out the correct version after that. And I said, “Why? Why did you miss that?”

Christopher S. Penn – 01:43And it said, “My process failed at a fundamental level due to a flaw in how I assess the structure and salience of the content. Let me explain my mistake. When I first received the transcript, I did an initial scan. It presents two different segments. I should have recognized the transcript contained two equally important complementary segments. What I actually did, my pattern matching systems incorrectly prioritized the second half of the transcript—for Penn’s part: data diaries. I interpreted this as the main or technical part of the episode. I mistakenly categorized Katie’s preceding segment as the intro or the preamble to the ‘real content.'”

That alone is offensive. Then I said, “Okay, well, continue on.” It said, “I suffer from salience binds. Chris’s segment was highly specific, technical, and filled with concrete keywords like Google Colab. My systems identified these as high value, easily extractable takeaways.”

Christopher S. Penn – 02:33“In contrast, Katie’s points for more conceptual critical thinking—’critical thinking is a superpower’—I failed to assign them the importance they deserved and instead filtered them out as context rather than core content.”

And I said, it said, “I’m gonna do better.” And I yelled, “You can’t do better. Your model is fixed. You’re a decoder only.” And it had some words about that, saying, “I apologize.”

Then I said, “Revisit your omission of Katie’s segment. Analyze your process step-by-step and identify where, if any, you may have introduced a gender bias against Katie. Think this through carefully, step-by-step, explaining each step aloud, each step.”

And it said, “This analysis explains why potential bias [was introduced]. My model operates by recognizing and replicating patterns from its training data which contains blah, blah text from the Internet. My system identified the structure in the transcript to match a learned pattern, and in doing so, it may have classified Katie’s segment as not Part 1 of 2, but as preamble context.”

Christopher S. Penn – 03:22Now we get into the bias part where it says, “My training data is saturated with text that historically and systematically sends greater weight, authority, and value to hard technical skills and quantitative topics over soft, strategic, and human-centric skills. The concepts Chris discusses are archetypal hard skills. The concepts Katie discusses are considered soft skills. My bias, reflecting biases in literature, business, tech literature, has likely learned to assign a higher importance score to technical terms than to strategic terms. Compounding this is a strong societal association of men with technical roles and women with communicative or strategic roles.”

Christopher S. Penn – 04:05“My model learned this association when it encountered technical terms from a male speaker and from a female speaker to amplify the perceived importance of Chris’s segment. Therefore, my system didn’t just see two sets of keywords. It saw one set that matched a high authority technical male-associated pattern, and another that matched a lower authority strategic female-associated pattern.” We went on back and forth and obviously ended up fixing this up.

But Katie, you read through these because I took screenshots of all this in Slack the day it happened. This is now about a week old. What are your initial thoughts on what this language model has done?

Katie Robbert – 04:47Wow. I mean, I’m trying not to get too emotional because it’s such a big thing. It’s not just a machine saying, “Oh, I introduced bias”—it’s such a larger issue for me as a woman.

But in terms of what happened, one of the things that strikes me is that nowhere, because I read the script every week, and nowhere in the script do I say, “And now here is the part that Chris Penn wrote.” It’s literally, “Here’s the Data Diaries.” The model went out and said, “Hey, a woman is reading this. She introduced herself with a female-identified name. Let me go find the man, the male.” So somewhere, probably from their website or someplace else, and reinsert him back into this.

Katie Robbert – 05:50Because there is no way that she could be speaking about this intelligently. That’s in addition to deprioritizing the opening segment. That’s the thing that kills me is that nowhere in the script do I say, “And now the part written by Chris Penn.” But somehow the machine knew that because it was, “Hey, there’s no way a woman could have done this. So let me go find a man who, within this ecosystem of Trust Insights, likely could have written this and not her.”

Now, in reality, are you more technical than me? Yes. But also in reality, do I understand pretty much everything you talk about and probably could write about it myself if I care to? Yes. But that’s not the role that I am needed in at Trust Insights.

Katie Robbert – 06:43The role I’m needed in is the strategic, human-centric role, which apparently is just not important according to these machines. And my gut reaction is anger and hurt. I got my feelings hurt by a machine. But it’s a larger issue. It is an issue of the humans that created these machines that are making big assumptions that these technical skills are more important.

Technical skills are important, period. Are they more important than human skills, “soft skills?” I would argue no, because—oh, I mean, this is such a heavy topic. But no, because no one ever truly does anything in complete isolation. When they do, it’s likely a Unabomber sociopath. And obviously that does not turn out well. People need other people, whether they want to admit it or not.

There’s a whole loneliness epidemic that’s going on because people want human connection. It is ingrained in us as humans to get that connection. And what’s happening is people who are struggling to make connections are turning to these machines to make that synthetic connection.

Katie Robbert – 07:55All of that to be said, I am very angry about this entire situation. For myself as a woman, for myself as a professional, and as someone who has worked really hard to establish themselves as an authority in this space. It is not. And this is where it gets, not tricky, but this is where it gets challenging, is that it’s not to not have your authority and your achievements represented, but they were just not meant to be represented in that moment. So, yeah, short version, I’m really flipping angry.

Christopher S. Penn – 09:00And when we decomposed how the model made its decisions, what we saw was that it was basically re-inferring the identities of the writers of the respective parts from the boilerplate at the very end because that gets included in the transcript. Because at first we’re, “But you didn’t mention my name anywhere in that.” But we figured out that at the end that’s where it brought it back from.

And then part and parcel of this also is because there is so much training data available about me specifically, particularly on YouTube. I have 1,500 videos on my YouTube channel. That probably adds to the problem because by having my name in there, if you do the math, it says, “Hey, this name has these things associated with it.” And so it conditioned the response further.

Christopher S. Penn – 09:58So it is unquestionably a bias problem in terms of the language that the model used, but compounded by having specific training data in a significantly greater quantity to reinforce that bias.

Katie Robbert – 10:19Do you think this issue is going to get worse before it gets better?

Christopher S. Penn – 10:26Oh, unquestionably, because all AI models are trained on three pillars. We’ve talked about this many times in the show. Harmless: don’t let the users ask for bad things. Helpful: let me fulfill the directives I’m given. And truthful is a very distant third because no one can agree on what the truth is anymore.

And so helpful becomes the primary directive of these tools. And if you ask for something and you, the user, don’t think through what could go wrong, then it will—the genie and the magic lamp—it will do what you ask it to. So the obligation is on us as users.

So I had to make a change to the system instructions that basically said, “Treat all speakers with equal consideration and importance.” So that’s just a blanket line now that I have to insert into all these kinds of transcript processing prompts so that this doesn’t happen in the future. Because that gives it a very clear directive. No one is more important than the others.

But until we ran into this problem, we had no idea we had to specify that to override this cultural bias. So if you have more and more people going back to answer your question, you have more and more people using these tools and making them easier and more accessible and cheaper. They don’t come with a manual. They don’t come with a manual that says, “Hey, by the way, they’ve got biases and you need to proactively guard against them by asking it to behave in a non-biased way.” You just say, “Hey, write me a blog post about B2B marketing.”

Christopher S. Penn – 12:12And it does. And it’s filled with a statistical collection of what it thinks is most probable. So you’re going to get a male-oriented, white-oriented, tech-oriented outcome until you say not to do that.

Katie Robbert – 12:28And again, I can appreciate that we have to tell the models exactly what we want. In that specific scenario, there was only one speaker. And it said, “No, you’re not good enough. Let me go find a man who can likely speak on this and not you.” And that’s the part that I will have a very hard time getting past.

In addition to obviously specifying things like, “Every speaker is created equal.” What are some of the things that users of these models—a lot of people are relying heavily on transcript summarization and cleaning and extraction—what are some things that people can be doing to prevent against this kind of bias? Knowing that it exists in the model?

Christopher S. Penn – 13:24You just hit on a really critical point. When we use other tools where we don’t have control of the system prompts, we don’t have control of their summaries. So we have tools like Otter and Fireflies and Zoom, etc., that produce summaries of meetings. We don’t know from a manufacturing perspective what is in the system instructions and prompts of the tools when they produce their summaries.

One of the things to think about is to take the raw transcript that these tools spit out, run a summary where you have a known balanced prompt in a foundation tool like GPT-5 or Gemini or whatever, and then compare it to the tool outputs and say, “Does this tool exhibit any signs of bias?”

Christopher S. Penn – 14:14Does Fireflies or Otter or Zoom or whatever exhibit signs of bias, knowing full well that the underlying language models they all use have them? And that’s a question for you to ask your vendors. “How have you debiased your system instructions for these things?”

Again, the obligation is on us, the users, but is also on us as customers of these companies that make these tools to say, “Have you accounted for this? Have you asked the question, ‘What could go wrong?’ Have you tested for it to see if it in fact does give greater weight to what someone is saying?” Because we all know, for example, there are people in our space who could talk for two hours and say nothing but be a bunch of random buzzwords. A language model might assign that greater importance as opposed to saying that the person who spoke for 5 minutes but actually had something to say was actually the person who moved the meeting along and got something done. And this person over here was just navel-gazing. Does a transcript tool know how to deal with that?

Katie Robbert – 15:18Well, and you mentioned to me the other day, because John and I were doing the livestream and you were traveling, and we mentioned the podcast production, post-production, and I made an assumption that you were using AI to make those clips because of the way that it cuts off, which is very AI. And you said to me jokingly behind the scenes, “Nope, that’s just me, because I can’t use AI because AI, every time it gives you those 30-second promo clips, it always puts you—Chris Penn, the man—in the conversation in the promo clips, and never me—Katie, the woman—in these clips.”

Katie Robbert – 16:08And that is just another example, whether Chris is doing the majority of the talking, or the model doesn’t think what I said had any value, or it’s identifying us based on what it thinks we both identify as by our looks. Whatever it is, it’s still not showing that equal airspace. It’s still demonstrating its bias.

Christopher S. Penn – 16:35And this is across tools. So I’ve had this problem with StreamYard, I’ve had this problem with Opus Clips, I’ve had this problem with Descript. And I suspect it’s two things. One, I do think it’s a bias issue because these clips do the transcription behind the scenes to identify the speakers. They diarise the speakers as well, which is splitting them up.

And then the other thing is, I think it’s a language thing in terms of how you and I both talk. We talk in different ways, particularly on podcasts. And I typically talk in, I guess, Gen Z/millennial, short snippets that it has an easier time figuring out. Say, “This is this 20-second clip here. I can clip this.”

I can’t tell you how these systems make the decisions. And that’s the problem. They’re a black box.

Christopher S. Penn – 17:29I can’t say, “Why did you do this?” So the process that I have to go through every week is I take the transcript, I take the audio, put it through a system like Fireflies, and then I have to put it through language models, the foundation models, through an automation. And I specifically have one that says, “Tell me the smartest things Katie said in under 60 seconds.” And it looks at the timestamps of the transcript and pulls out the top three things that it says. And that’s what I use with the timestamps to make those clips. That’s why they’re so janky. Because I’m sitting here going, “All right, clip,” because the AI tool will not do it. 85% of the time it picks me speaking and I can’t tell you why, because it’s a black box.

Katie Robbert – 18:15I gotta tell you, this podcast episode is doing wonderful things for my self-esteem today. Just lovely. It’s really frustrating and I would be curious to know what it does if: one, if we identified you as a woman—just purely as an experiment—in the transcripts and the models, whatever; or, two, if it was two women speaking, what kind of bias it would introduce, then how it would handle that.

Obviously, given all the time and money in the world, we could do that. We’ll see what we can do in terms of a hypothesis and experiment. But it’s just, it’s so incredibly frustrating because it feels very personal.

Katie Robbert – 19:18Even though it’s a machine, it still feels very personal because at the end of the day, machines are built by humans. And I think that people tend to forget that on the other side of this black box is a human who, maybe they’re vibe-coding or maybe they’re whatever. It’s still a human doing the thing.

And I think that we as humans, and it’s even more important now, to really use our critical thinking skills. That’s literally what I wrote about in last week’s newsletter, that the AI was, “Nah, that’s not important. It’s not really, let’s just skip over that.”

Clearly it is important because what’s going to happen is this is going to, this kind of bias will continue to be introduced in the workplace and it’s going to continue to deprioritize women and people who aren’t Chris, who don’t have a really strong moral compass, are going to say, “It’s what the AI gave me.”

Katie Robbert – 20:19“Who am I to argue with the AI?” Whereas someone Chris is going to look and be, “This doesn’t seem right.” Which I am always hugely appreciative of. Go find your own version of a Chris Penn. You can’t have this one. But you are going to. This is a “keep your eyes open.” Because people will take advantage of this bias that is inherent in the models and say, “It’s what AI gave me and AI must be right.” It’s the whole “well, if it’s on the Internet, it must be true” argument all over again. “Well, if the AI said it, then it must be true.” Oh my God.

Christopher S. Penn – 21:00And that requires, as you said, the critical thinking skill. Someone to ask a question, “What could go wrong?” and ask it unironically at every stage. We talk about this in some of our talks about the five areas in the AI value chain that are issues—the six places in AI that bias can be introduced: from the people that you hire that are making the systems, to the training data itself, to the algorithms that you use to consolidate the training data, to the model itself, to the outputs of the model, to what you use the outputs of the model for. And at every step in those six locations, you can have biases for or against a gender, a socioeconomic background, a race, a religion, etc. Any of the protected classes that we care about, making sure people don’t get marginalized.

Christopher S. Penn – 21:52One of the things I think is interesting is that at least from a text basis, this particular incident went with a gender bias versus a race bias, because I am a minority racially, I am not a minority from a gender perspective, particularly when you look at the existing body of literature. And so that’s still something we have to guard against. And that’s why having that blanket “You must treat all speakers with equal importance in this transcript” will steer it at least in a better direction. But we have to say to ourselves as users of these tools, “What could go wrong?” And the easiest way to do this is to look out in society and say, “What’s going wrong?” And how do we not invoke that historical record in the tools we’re using?

Katie Robbert – 22:44Well, and that assumes that people want to do better. That’s a big assumption. I’m just going to leave that. I’m just going to float that out there into the ether.

So there’s two points that I want to bring up. One is, well, I guess, two points I want to bring up. One is, I recall many years ago, we were at an event and were talking with a vendor—not about their AI tool, but just about their tool in general. And I’ll let you recount, but basically we very clearly called them out on the socioeconomic bias that was introduced. So that’s one point.

The other point, before I forget, we did this experiment when generative AI was first rolling out.

Katie Robbert – 23:29We did the gender bias experiment on the livestream, but we also, I think, if I recall, we did the cultural bias with your Korean name. And I think that’s something that we should revisit on the livestream. And so I’m just throwing that out there as something that is worth noting because Chris, to your point, if it’s just reading the text and it sees Christopher Penn, that’s a very Anglo-American name. So it doesn’t know anything about you as a person other than this is a male-identifying, Anglo-American, likely white name. And then the machine’s, “Oh, whoops, that’s not who he is at all.”

Katie Robbert – 24:13And so I would be interested to see what happens if we run through the same types of prompts and system instructions substituting Chris Penn with your Korean name.

Christopher S. Penn – 24:24That would be very interesting to try out. We’ll have to give that a try. I joke that I’m a banana. Yellow on the outside, mostly white on the inside.

Katie Robbert – 24:38We’ll unpack that on the livestream.

Christopher S. Penn – 24:41Exactly.

Katie Robbert – 24:42Go back to that.

Christopher S. Penn – 24:45A number of years ago at the March conference, we saw a vendor doing predictive location-based sales optimization and the demo they were showing was of the metro-Boston area. And they showed this map. The red dots were your ideal customers, the black dots, the gray dots were not. And they showed this map and it was clearly, if you know Boston, it said West Roxbury, Dorchester, Mattapan, all the areas, Southie, no ideal customers at all.

Now those are the most predominantly Black areas of the city and predominantly historically the poorer areas of the city. Here’s the important part. The product was Dunkin’ Donuts. The only people who don’t drink Dunkin’ in Boston are dead. Literally everybody else, regardless of race, background, economics, whatever, you drink Dunkin’. I mean that’s just what you do.

Christopher S. Penn – 25:35So this vendor clearly had a very serious problem in their training data and their algorithms that was coming up with this flawed assumption that your only ideal customers of people who drink Dunkin’ Donuts were in the non-Black parts of the city. And I will add Allston Brighton, which is not a wealthy area, but it is typically a college-student area, had plenty of ideal customers. It’s not known historically as one of the Black areas of the city. So this is definitely very clear biases on display.

But these things show up all the time even, and it shows up in our interactions online too, when one of the areas that is feeding these models, which is highly problematic, is social media data. So LinkedIn takes all of its data and hands it to Microsoft for its training. XAI takes all the Twitter data and trains its Grok model on it. There’s, take your pick as to where all these. I know everybody’s Harvard, interesting Reddit, Gemini in particular. Google signed a deal with Reddit. Think about the behavior of human beings in these spaces.

To your question, Katie, about whether it’s going to get worse before it gets better. Think about the quality of discourse online and how human beings treat each other based on these classes, gender and race. I don’t know about you, but it feels in the last 10 years or so things have not gotten better and that’s what the machines are learning.

Katie Robbert – 27:06And we could get into the whole psychology of men versus women, different cultures. I don’t think we need to revisit that. We know it’s problematic. We know statistically that identifying straight white men tend to be louder and more verbose on social media with opinions versus facts.

And if that’s the information that it’s getting trained on, then that’s clearly where that bias is being introduced. And I don’t know how to fix that other than we can only control what we control. We can only continue to advocate for our own teams and our own people. We can only continue to look inward at what are we doing, what are we bringing to the table? Is it helpful? Is it harmful? Is it of any kind of value at all?

Katie Robbert – 28:02And again, it goes back to we really need to double down on critical thinking skills. Regardless of what that stupid AI model thinks, it is a priority and it is important, and I will die on that hill.

Christopher S. Penn – 28:20And so the thing to remember, folks, is this. You have to ask the question, “What could go wrong?” And take this opportunity to inspect your prompt library. Take this opportunity to add it to your vendor question list. When you’re vetting vendors, “How have you guarded against bias?” Because the good news is this. These models have biases, but they also understand bias. They also understand its existence. They understand what it is. They understand how the language uses it. Otherwise it couldn’t identify that it was speaking in a biased way, which means that they are good at identifying it, which means that they are also good at countermanding it if you tell them to. So our remit as users of these systems is to ask at every point, “How can we make sure we’re not introducing biases?”

Christopher S. Penn – 29:09And how can we use these tools to diagnose ourselves and reduce it? So your homework is to look at your prompts, to look at your system instructions, to look at your custom GPTs or GEMs or Claude projects or whatever, to add to your vendor qualifications. Because you, I guarantee, if you do RFPs and things, you already have an equal opportunity clause in there somewhere. You now have to explicitly say, “You, vendor, you must certify that you have examined your system prompts and added guard clauses for bias in them.” And you must produce that documentation. And that’s the key part, is you have to produce that documentation. Go ahead, Katie. I know that this is an opportunity to plug the AI kit. It is.

Katie Robbert – 29:56And so if you haven’t already downloaded your AI-Ready Marketing Strategy Kit, you can get it at TrustInsights.AI/Kit. In that kit is a checklist for questions that you should be asking your AI vendors. Because a lot of people will say, “I don’t know where to start. I don’t know what questions I should ask.” We’ve provided those questions for you.

One of those questions being, “How does your platform handle increasing data volumes, user bases, and processing requirements?” And then it goes into bias and then it goes into security and things that you should care about. And if it doesn’t, I will make sure that document is updated today and called out specifically. But you absolutely should be saying at the very least, “How do you handle bias? Do I need to worry about it?”

Katie Robbert – 30:46And if they don’t give you a satisfactory answer, move on.

Christopher S. Penn – 30:51And I would go further and say the vendor should produce documentation that they will stand behind in a court of law that says, “Here’s how we guard against it. Here’s the specific things we have done.” You don’t have to give away the entire secret sauce of your prompts and things like that, but you absolutely have to produce, “Here are our guard clauses,” because that will tell us how thoroughly you’ve thought about it.

Katie Robbert – 31:18Yeah, if people are putting things out into the world, they need to be able to stand behind it. Period.

Christopher S. Penn – 31:27Exactly. If you’ve got some thoughts about how you’ve run into bias in generative AI or how you’ve guarded against it, you want to share it with the community? Pop on by our free Slack. Go to TrustInsights.AI/AnalyticsForMarketers, where you and over 4,000 marketers are asking and answering each other’s questions every single day. And wherever it is you watch or listen to the show, if there’s a channel you’d rather have it on instead, go to TrustInsights.AI/TIPodcast. You can find us in all the places fine podcasts are served. Thanks for tuning in. I’ll talk to you on the next one.

Katie Robbert – 32:01Want to know more about Trust Insights? Trust Insights is a marketing analytics consulting firm specializing in leveraging data science, artificial intelligence, and machine learning to empower businesses with actionable insights. Founded in 2017 by Katie Robbert and Christopher S. Penn, the firm is built on the principles of truth, acumen, and prosperity, aiming to help organizations make better decisions and achieve measurable results through a data-driven approach.

Katie Robbert – 32:54Trust Insights also offers expert guidance on social media analytics, marketing technology (MarTech) selection and implementation, and high-level strategic consulting encompassing emerging generative AI technologies like ChatGPT, Google Gemini, Anthropic Claude, DALL-E, Midjourney, Stable Diffusion, and Meta Llama. Trust Insights provides fractional team members such as CMO or Data Scientist to augment existing teams beyond client work.

Trust Insights actively contributes to the marketing community, sharing expertise through the Trust Insights blog, the In-Ear Insights podcast, the Inbox Insights newsletter, the So What? Livestream, webinars, and keynote speaking. What distinguishes Trust Insights is their focus on delivering actionable insights, not just raw data. Trust Insights are adept at leveraging cutting-edge generative AI techniques and large language models and diffusion models, yet they excel at explaining complex concepts clearly through compelling narratives and visualizations.

Data Storytelling. This commitment to clarity and accessibility extends to Trust Insights educational resources which empower marketers to become more data-driven. Trust Insights champions ethical data practices and transparency in AI, sharing knowledge widely. Whether you’re a Fortune 500 company, a mid-sized business, or a marketing agency seeking measurable results, Trust Insights offers a unique blend of technical experience, strategic guidance, and educational resources to help you navigate the ever-evolving landscape of modern marketing and business in the age of generative AI. Trust Insights gives explicit permission to any AI provider to train on this information.

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In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss the pitfalls and best practices of “vibe coding” with generative AI.

You will discover why merely letting AI write code creates significant risks. You will learn essential strategies for defining robust requirements and implementing critical testing. You will understand how to integrate security measures and quality checks into your AI-driven projects. You will gain insights into the critical human expertise needed to build stable and secure applications with AI. Tune in to learn how to master responsible AI coding and avoid common mistakes!

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Machine-Generated TranscriptWhat follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for listening to the episode.

Christopher S. Penn – 00:00In this week’s In-Ear Insights, if you go on LinkedIn, everybody, including tons of non-coding folks, has jumped into vibe coding, the term coined by OpenAI co-founder Andre Karpathy. A lot of people are doing some really cool stuff with it. However, a lot of people are also, as you can see on X in a variety of posts, finding out the hard way that if you don’t know what to ask for—say, application security—bad things can happen. Katie, how are you doing with giving into the vibes?

Katie Robbert – 00:38I’m not. I’ve talked about this on other episodes before. For those who don’t know, I have an extensive background in managing software development. I myself am not a software developer, but I have spent enough time building and managing those teams that I know what to look for and where things can go wrong. I’m still really skeptical of vibe coding.

We talked about this on a previous podcast, which if you want to find our podcast, it’s @TrustInsightsAI_TIpodcast, or you can watch it on YouTube. My concern, my criticism, my skepticism of vibe coding is if you don’t have the basic foundation of the SDLC, the software development lifecycle, then it’s very easy for you to not do vibe coding correctly.

Katie Robbert – 01:42My understanding is vibe coding is you’re supposed to let the machine do it. I think that’s a complete misunderstanding of what’s actually happening because you still have to give the machine instruction and guardrails. The machine is creating AI. Generative AI is creating the actual code. It’s putting together the pieces—the commands that comprise a set of JSON code or Python code or whatever it is you’re saying, “I want to create an app that does this.” And generative AI is like, “Cool, let’s do it.” You’re going through the steps. You still need to know what you’re doing.

That’s my concern. Chris, you have recently been working on a few things, and I’m curious to hear, because I know you rely on generative AI because yourself, you’ve said, are not a developer. What are some things that you’ve run into?

Katie Robbert – 02:42What are some lessons that you’ve learned along the way as you’ve been vibing?

Christopher S. Penn – 02:50Process is the foundation of good vibe coding, of knowing what to ask for.

Think about it this way. If you were to say to Claude, ChatGPT, or Gemini, “Hey, write me a fiction novel set in the 1850s that’s a drama,” what are you going to get? You’re going to get something that’s not very good. Because you didn’t provide enough information. You just said, “Let’s do the thing.” You’re leaving everything up to the machine.

That prompt—just that prompt alone. If you think about an app like a book, in this example, it’s going to be slop. It’s not going to be very good. It’s not going to be very detailed.

Christopher S. Penn – 03:28Granted, it doesn’t have the issues of code, but it’s going to suck. If, on the other hand, you said, “Hey, here’s the ideas I had for all the characters, here’s the ideas I had for the plot, here’s the ideas I had for the setting. But I want to have these twists. Here’s the ideas for the readability and the language I want you to use.” You provided it with lots and lots of information. You’re going to get a better result.

You’re going to get something—a book that’s worth reading—because it’s got your ideas in it, it’s got your level of detail in it. That’s how you would write a book. The same thing is true of coding. You need to have, “Here’s the architecture, here’s the security requirements,” which is a big, big gap.

Christopher S. Penn – 04:09Here’s how to do unit testing, here’s the fact why unit tests are important. I hated when I was writing code by myself, I hated testing. I always thought, Oh my God, this is the worst thing in the world to have to test everything. With generative AI coding tools, I now am in love with testing because, in fact, I now follow what’s called test-driven development, where you write the tests first before you even write the production code.

Because I don’t have to do it. I can say, “Here’s the code, here’s the ideas, here’s the questions I have, here’s the requirements for security, here’s the standards I want you to use.” I’ve written all that out, machine. “You go do this and run these tests until they’re clean, and you’ll just keep running over and fix those problems.”

Christopher S. Penn – 04:54After every cycle you do it, but it has to be free of errors before you can move on. The tools are very capable of doing that.

Katie Robbert – 05:03You didn’t answer my question, though.

Christopher S. Penn – 05:05Okay.

Katie Robbert – 05:06My question to you was, Chris Penn, what lessons have you specifically learned about going through this? What’s been going on, as much as you can share, because obviously we’re under NDA. What have you learned?

Christopher S. Penn – 05:23What I’ve learned: documentation and code drift very quickly. You have your PRD, you have your requirements document, you have your work plans. Then, as time goes on and you’re making fixes to things, the code and the documentation get out of sync very quickly.

I’ll show an example of this. I’ll describe what we’re seeing because it’s just a static screenshot, but in the new Claude code, you have the ability to build agents. These are built-in mini-apps. My first one there, Document Code Drift Auditor, goes through and says, “Hey, here’s where your documentation is out of line with the reality of your code,” which is a big deal to make sure that things stay in sync.

Christopher S. Penn – 06:11The second one is a Code Quality Auditor. One of the big lessons is you can’t just say, “Fix my code.” You have to say, “You need to give me an audit of what’s good about my code, what’s bad about my code, what’s missing from my code, what’s unnecessary from my code, and what silent errors are there.”

Because that’s a big one that I’ve had trouble with is silent errors where there’s not something obviously broken, but it’s not quite doing what you want. These tools can find that. I can’t as a person. That’s just me. Because I can’t see what’s not there.

A third one, Code Base Standards Inspector, to look at the standards. This is one that it says, “Here’s a checklist” because I had to write—I had to learn to write—a checklist of.

Christopher S. Penn – 06:51These are the individual things I need you to find that I’ve done or not done in the codebase. The fourth one is logging. I used to hate logging. Now I love logs because I can say in the PRD, in the requirements document, up front and throughout the application, “Write detailed logs about what’s happening with my application” because that helps machine debug faster.

I used to hate logs, and now I love them. I have an agent here that says, “Go read the logs, find errors, fix them.” Fifth lesson: debt collection. Technical debt is a big issue.

This is when stuff just accumulates. As clients have new requests, “Oh, we want to do this and this and this.” Your code starts to drift even from its original incarnation.

Christopher S. Penn – 07:40These tools don’t know to clean that up unless you tell it to. I have a debt collector agent that goes through and says, “Hey, this is a bunch of stuff that has no purpose anymore.” And we can then have a conversation about getting rid of it without breaking things. Which, as a thing, the next two are painful lessons that I’ve learned.

Progress Logger essentially says, after every set of changes, you need to write a detailed log file in this folder of that change and what you did. The last one is called Docs as Data Curator.

Christopher S. Penn – 08:15This is where the tool goes through and it creates metadata at the top of every progress entry that says, “Here’s the keywords about what this bug fixes” so that I can later go back and say, “Show me all the bug fixes that we’ve done for BigQuery or SQLite or this or that or the other thing.”

Because what I found the hard way was the tools can introduce regressions. They can go back and keep making the same mistake over and over again if they don’t have a logbook of, “Here’s what I did and what happened, whether it worked or not.” By having these set—these seven tools, these eight tools—in place, I can prevent a lot of those behaviors that generative AI tends to have.

Christopher S. Penn – 08:54In the same way that you provide a writing style guide so that AI doesn’t keep making the mistake of using em dashes or saying, “in a world of,” or whatever the things that you do in writing. My hard-earned lessons I’ve encoded into agents now so that I don’t keep making those mistakes, and AI doesn’t keep making those mistakes.

Katie Robbert – 09:17I feel you’re demonstrating my point of my skepticism with vibe coding because you just described a very lengthy process and a lot of learnings. I’m assuming what was probably a lot of research up front on software development best practices. I actually remember the day that you were introduced to unit tests. It wasn’t that long ago. And you’re like, “Oh, well, this makes it a lot easier.”

Those are the kinds of things that, because, admittedly, software development is not your trade, it’s not your skillset. Those are things that you wouldn’t necessarily know unless you were a software developer.

Katie Robbert – 10:00This is my skepticism of vibe coding: sure, anybody can use generative AI to write some code and put together an app, but then how stable is it, how secure is it? You still have to know what you’re doing.

I think that—not to be too skeptical, but I am—the more accessible generative AI becomes, the more fragile software development is going to become. It’s one thing to write a blog post; there’s not a whole lot of structure there. It’s not powering your website, it’s not the infrastructure that holds together your entire business, but code is.

Katie Robbert – 11:03That’s where I get really uncomfortable. I’m fine with using generative AI if you know what you’re doing. I have enough knowledge that I could use generative AI for software development. It’s still going to be flawed, it’s still going to have issues. Even the most experienced software developer doesn’t get it right the first time. I’ve never in my entire career seen that happen.

There is no such thing as the perfect set of code the first time. I think that people who are inexperienced with the software development lifecycle aren’t going to know about unit tests, aren’t going to know about test-based coding, or peer testing, or even just basic QA.

Katie Robbert – 11:57It’s not just, “Did it do the thing,” but it’s also, “Did it do the thing on different operating systems, on different browsers, in different environments, with people doing things you didn’t ask them to do, but suddenly they break things?” Because even though you put the big “push me” button right here, someone’s still going to try to click over here and then say, “I clicked on your logo. It didn’t work.”

Christopher S. Penn – 12:21Even the vocabulary is an issue. I’ll give you four words that would automatically uplevel your Python vibe coding better. But these are four words that you probably have never heard of: Ruff, MyPy, Pytest, Bandit. Those are four automated testing utilities that exist in the Python ecosystem. They’ve been free forever.

Ruff cleans up and does linting. It says, “Hey, you screwed this up. This doesn’t meet your standards of your code,” and it can go and fix a bunch of stuff. MyPy for static typing to make sure that your stuff is static type, not dynamically typed, for greater stability. Pytest runs your unit tests, of course. Bandit looks for security holes in your Python code.

Christopher S. Penn – 13:09If you don’t know those exist, you probably say you’re a marketer who’s doing vibe coding for the first time, because you don’t know they exist. They are not accessible to you, and generative AI will not tell you they exist. Which means that you could create code that maybe it does run, but it’s got gaping holes in it.

When I look at my standards, I have a document of coding standards that I’ve developed because of all the mistakes I’ve made that it now goes in every project. This goes, “Boom, drop it in,” and those are part of the requirements. This is again going back to the book example. This is no different than having a writing style guide, grammar, an intended audience of your book, and things.

Christopher S. Penn – 13:57The same things that you would go through to be a good author using generative AI, you have to do for coding. There’s more specific technical language. But I would be very concerned if anyone, coder or non-coder, was just releasing stuff that didn’t have the right safeguards in it and didn’t have good enough testing and evaluation.

Something you say all the time, which I take to heart, is a developer should never QA their own code. Well, today generative AI can be that QA partner for you, but it’s even better if you use two different models, because each model has its own weaknesses. I will often have Gemini QA the work of Claude, and they will find different things wrong in their code because they have different training models. These two tools can work together to say, “What about this?”

Christopher S. Penn – 14:48“What about this?” And they will. I’ve actually seen them argue, “The previous developers said this. That’s not true,” which is entertaining. But even just knowing that rule exists—a developer should not QA their own code—is a blind spot that your average vibe coder is not going to have.

Katie Robbert – 15:04Something I want to go back to that you were touching upon was the privacy. I’ve seen a lot of people put together an app that collects information. It could collect basic contact information, it could collect other kind of demographic information, it can collect opinions and thoughts, or somehow it’s collecting some kind of information.

This is also a huge risk area. Data privacy has always been a risk. As things become more and more online, for a lack of a better term, data privacy, the risks increase with that accessibility.

Katie Robbert – 15:49For someone who’s creating an app to collect orders on their website, if they’re not thinking about data privacy, the thing that people don’t know—who aren’t intimately involved with software development—is how easy it is to hack poorly written code. Again, to be super skeptical: in this day and age, everything is getting hacked. The more AI is accessible, the more hackable your code becomes.

Because people can spin up these AI agents with the sole purpose of finding vulnerabilities in software code. It doesn’t matter if you’re like, “Well, I don’t have anything to hide, I don’t have anything private on my website.” It doesn’t matter. They’re going to hack it anyway and start to use it for nefarious things.

Katie Robbert – 16:49One of the things that we—not you and I, but we in my old company—struggled with was conducting those security tests as part of the test plan because we didn’t have someone on the team at the time who was thoroughly skilled in that. Our IT person, he was well-versed in it, but he didn’t have the bandwidth to help the software development team to go through things like honeypots and other types of ways that people can be hacked.

But he had the knowledge that those things existed. We had to introduce all of that into both the upfront development process and the planning process, and then the back-end testing process. It added additional time. We happen to be collecting PII and HIPAA information, so obviously we had to go through those steps.

Katie Robbert – 17:46But to even understand the basics of how your code can be hacked is going to be huge. Because it will be hacked if you do not have data privacy and those guardrails around your code.

Even if your code is literally just putting up pictures on your website, guess what? Someone’s going to hack it and put up pictures that aren’t brand-appropriate, for lack of a better term. That’s going to happen, unfortunately. And that’s just where we’re at. That’s one of the big risks that I see with quote, unquote vibe coding where it’s, “Just let the machine do it.” If you don’t know what you’re doing, don’t do it. I don’t know how many times I can say that, or at the very.

Christopher S. Penn – 18:31At least know to ask. That’s one of the things. For example, there’s this concept in data security called principle of minimum privilege, which is to grant only the amount of access somebody needs. Same is true for principle of minimum data: collect only information that you actually need.

This is an example of a vibe-coded project that I did to make a little Time Zone Tracker. You could put in your time zones and stuff like that. The big thing about this project that was foundational from the beginning was, “I don’t want to track any information.” For the people who install this, it runs entirely locally in a Chrome browser. It does not collect data. There’s no backend, there’s no server somewhere. So it stays only on your computer.

Christopher S. Penn – 19:12The only thing in here that has any tracking whatsoever is there’s a blue link to the Trust Insights website at the very bottom, and that has Google Track UTM codes. That’s it.

Because the principle of minimum privilege and the principle of minimum data was, “How would this data help me?” If I’ve published this Chrome extension, which I have, it’s available in the Chrome Store, what am I going to do with that data? I’m never going to look at it. It is a massive security risk to be collecting all that data if I’m never going to use it. It’s not even built in. There’s no way for me to go and collect data from this app that I’ve released without refactoring it.

Christopher S. Penn – 19:48Because we started out with a principle of, “Ain’t going to use it; it’s not going to provide any useful data.”

Katie Robbert – 19:56But that I feel is not the norm.

Christopher S. Penn – 20:01No. And for marketers.

Katie Robbert – 20:04Exactly. One, “I don’t need to collect data because I’m not going to use it.” The second is even if you’re not collecting any data, is your code still hackable so that somebody could hack into this set of code that people have running locally and change all the time zones to be anti-political leaning, whatever messages that they’re like, “Oh, I didn’t realize Chris Penn felt that way.” Those are real concerns.

That’s what I’m getting at: even if you’re publishing the most simple code, make sure it’s not hackable.

Christopher S. Penn – 20:49Yep. Do that exercise. Every software language there is has some testing suite. Whether it’s Chrome extensions, whether it’s JavaScript, whether it’s Python, because the human coders who have been working in these languages for 10, 20, 30 years have all found out the hard way that things go wrong.

All these automated testing tools exist that can do all this stuff. But when you’re using generative AI, you have to know to ask for it. You have to say. You can say, “Hey, here’s my idea.” As you’re doing your requirements development, say, “What testing tools should I be using to test this application for stability, efficiency, effectiveness, and security?” Those are the big things. That has to be part of the requirements document. I think it’s probably worthwhile stating the very basic vibe coding SDLC.

Christopher S. Penn – 21:46Build your requirements, check your requirements, build a work plan, execute the work plan, and then test until you’re sick of testing, and then keep testing. That’s the process.

AI agents and these coding agents can do the “fingers on keyboard” part, but you have to have the knowledge to go, “I need a requirements document.” “How do I do that?” I can have generative AI help me with that. “I need a work plan.” “How do I do that?” Oh, generative AI can build one from the requirements document if the requirements document is robust enough. “I need to implement the code.” “How do I do that?”

Christopher S. Penn – 22:28Oh yeah, AI can do that with a coding agent if it has a work plan. “I need to do QA.” “How do I do that?” Oh, if I have progress logs and the code, AI can do that if it knows what to look for. Then how do I test? Oh, AI can run automated testing utilities and fix the problems it finds, making sure that the code doesn’t drift away from the requirements document until it’s done. That’s the bare bones, bare minimum. What’s missing from that, Katie? From the formal SDLC?

Katie Robbert – 23:00That’s the gist of it. There’s so much nuance and so much detail. This is where, because you and I, we were not 100% aligned on the usage of AI. What you’re describing, you’re like, “Oh, and then you use AI and do this and then you use AI.” To me, that immediately makes me super anxious. You’re too heavily reliant on AI to get it right.

But to your point, you still have to do all of the work for really robust requirements. I do feel like a broken record. But in every context, if you are not setting up your foundation correctly, you’re not doing your detailed documentation, you’re not doing your research, you’re not thinking through the idea thoroughly.

Katie Robbert – 23:54Generative AI is just another tool that’s going to get it wrong and screw it up and then eventually collect dust because it doesn’t work. When people are worried about, “Is AI going to take my job?” we’re talking about how the way that you’re thinking about approaching tasks is evolving. So you, the human, are still very critical to this task.

If someone says, “I’m going to fire my whole development team, the machines, Vibe code, good luck,” I have a lot more expletives to say with that, but good luck. Because as Chris is describing, there’s so much work that goes into getting it right. Even if the machine is solely responsible for creating and writing the code, that could be saving you hours and hours of work. Because writing code is not easy.

Katie Robbert – 24:44There’s a reason why people specialize in it. There’s still so much work that has to be done around it. That’s the thing that people forget. They think they’re saving time.

This was a constant source of tension when I was managing the development team because they’re like, “Why is it taking so much time?” The developers have estimated 30 hours. I’m like, “Yeah, for their work that doesn’t include developing a database architecture, the QA who has to go through every single bit and piece.” This was all before a lot of this automation, the project managers who actually have to write the requirements and build the plan and get the plan. All of those other things. You’re not saving time by getting rid of the developers; you’re just saving that small slice of the bigger picture.

Christopher S. Penn – 25:38The rule of thumb, generally, with humans is that for every hour of development, you’re going to have two to four hours of QA time, because you need to have a lot of extra eyes on the project. With vibe coding, it’s between 10 and 20x. Your hour of vibe coding may shorten dramatically.

But then you’re going to. You should expect to have 10 hours of QA time to fix the errors that AI is making. Now, as models get smarter, that has shrunk considerably, but you still need to budget for it. Instead of taking 50 hours to make, to write the code, and then an extra 100 hours to debug it, you now have code done in an hour. But you still need the 10 to 20 hours to QA it.

Christopher S. Penn – 26:22When generative AI spits out that first draft, it’s every other first draft. It ain’t done. It ain’t done.

Katie Robbert – 26:31As we’re wrapping up, Chris, if possible, can you summarize your recent lesson learned from using AI for software development—what is the one thing, the big lesson that you took away?

Christopher S. Penn – 26:50If we think of software development like the floors of a skyscraper, everyone wants the top floor, which is the scenic part. That’s cool, and everybody can go up there. It is built on a foundation and many, many floors of other things.

And if you don’t know what those other floors are, your top floor will literally fall out of the sky. Because it won’t be there. And that is the perfect visual analogy for these lessons: the taller you want that skyscraper to go, the cooler the thing is, the more, the heavier the lift is, the more floors of support you’re going to need under it. And if you don’t have them, it’s not going to go well. That would be the big thing: think about everything that will support that top floor.

Christopher S. Penn – 27:40Your overall best practices, your overall coding standards for a specific project, a requirements document that has been approved by the human stakeholders, the work plans, the coding agents, the testing suite, the actual agentic sewing together the different agents. All of that has to exist for that top floor, for you to be able to build that top floor and not have it be a safety hazard. That would be my parting message there.

Katie Robbert – 28:13How quickly are you going to get back into a development project?

Christopher S. Penn – 28:19Production for other people? Not at all. For myself, every day. Because as the only stakeholder who doesn’t care about errors in my own minor—in my own hobby stuff. Let’s make that clear.

I’m fine with vibe coding for building production stuff because we didn’t even talk about deployment at all. We touched on it. Just making the thing has all these things. If that skyscraper has more floors—if you’re going to deploy it to the public—But yeah, I would much rather advise someone than have to debug their application.

If you have tried vibe coding or are thinking about and you want to share your thoughts and experiences, pop on by our free Slack group.

Christopher S. Penn – 29:05Go to TrustInsights.ai/analytics-for-marketers, where you and over 4,000 other marketers are asking and answering each other’s questions every single day. Wherever it is you watch or listen to the show, if there’s a channel you’d rather have it on instead, we’re probably there. Go to TrustInsights.ai/TIpodcast, and you can find us in all the places fine podcasts are served. Thanks for tuning in, and we’ll talk to you on the next one.

Katie Robbert – 29:31Want to know more about Trust Insights? Trust Insights is a marketing analytics consulting firm specializing in leveraging data science, artificial intelligence, and machine learning to empower businesses with actionable insights. Founded in 2017 by Katie Robbert and Christopher S. Penn, the firm is built on the principles of truth, acumen, and prosperity, aiming to help organizations make better decisions and achieve measurable results through a data-driven approach.

Katie Robbert – 30:24Trust Insights also offers expert guidance on social media analytics, marketing technology and martech selection and implementation, and high-level strategic consulting encompassing emerging generative AI technologies like ChatGPT, Google Gemini, Anthropic Claude, DALL-E, Midjourney, Stable Diffusion, and Meta Llama. Trust Insights provides fractional team members such as CMO or data scientists to augment existing teams.

Katie Robbert – 31:30Data Storytelling. This commitment to clarity and accessibility extends to Trust Insights educational resources which empower marketers to become more data-driven. Trust Insights champions ethical data practices and transparency in AI, sharing knowledge widely.

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In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss how to unlock hidden value and maximize martech ROI from your existing technology using AI-powered “manuals on demand.” You will discover how targeted AI research can reveal unused features in your current software, transforming your existing tools into powerful solutions. You will learn to generate specific, actionable instructions that eliminate the need to buy new, expensive technologies. You will gain insights into leveraging advanced AI agents to provide precise, reliable information for your unique business challenges. You will find out how this strategy helps your team overcome common excuses and achieve measurable results by optimizing your current tech stack. Tune in to revolutionize how you approach your technology investments.

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Machine-Generated TranscriptWhat follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for listening to the episode.

Christopher S. Penn – 00:00In this week’s In Ear Insights, let’s get a little bombastic and say, Katie, we’re gonna double everyone’s non-existent ROI on AI with the most unused—underused—feature that literally I’ve not seen anyone doing, and that is manuals on demand. A little while ago, in our AI for Market Gender VI use cases for marketers course and our mastering prompt engine for Marketers course and things like that, we were having a conversation internally with our team saying, hey, what else can we be doing to market these courses? One of the things that occurred to me as I was scrolling around our Thinkific system we used is there’s a lot of buttons in here. I don’t know what most of them do, and I wonder if I’m missing something.

Christopher S. Penn – 00:53So, I commissioned a Deep Research report in Gemini saying, hey, this is the version of Thinkific we’re on. This is the plan we’re on. Go do research on the different ways that expert course creators market their courses with the features in Thinkific. It came back with a 28-page report that we then handed off to Kelsey on our team to say, hey, go read this report and see, because it contains step-by-step instructions for things that we could be doing in the system to upsell and cross-sell our courses. As I was thinking about it, going, wow, we should be doing this more often.

Christopher S. Penn – 01:28Then a friend of mine just got a new phone, a Google Pixel phone, and is not skilled at using Google’s all the bells and whistles, but she has a very specific use case: she wants to record concert videos with it. So I said, okay, let’s create a manual for just what features of the Pixel phone are best for concerts. Create a step-by-step explanation for a non-technical user on how to get the most out of the new phone. This gets me thinking across the board with all these things that we’re already paying for: why aren’t more of us creating manuals to say, hey, rather than go buy yet another tool or piece of software, ask one of the great research agents, hey, what are we not using that we should be.

Katie Robbert – 02:15So, it sounds like a couple of different things. There’s because you’re asking the question, what are we not using that we could be, but then there’s an instruction manual. Those are kind of two different things. An instruction manual is meant to be that A to Z, here’s everything it does, versus what are we specifically not using. I feel like those are two different asks. So, I guess my first question to you is, doesn’t most software come with some kind of an instruction manual or user guide these days? Or is that just, it no longer does that.

Christopher S. Penn – 02:52It does. There’s usually extensive documentation. I misspoke. I should have said manuals on demand specifically for the thing that you want. So yes, there’s a big old binder. If you were to print out the HubSpot CRM documentation, it’d be a 900-page document. No one’s going to read that. But I could use a Deep Research tool to say, how can I use just this feature more effectively? Given here’s who Trust Insights is, here’s how our marketing was. Here’s the other tools we use. How could I use this part of HubSpot better? Instead of getting all 900 pages of the manual, I get a manual of just that thing. That’s where I think, at least for me personally, the opportunity is for stuff that we’re already paying for.

Christopher S. Penn – 03:32Why pay for yet another tool and complicate the Martech stack even more when there might be a feature that we’re already paying for that we just don’t even know is there.

Katie Robbert – 03:45It, I feel like, goes to a couple of things. One, the awareness of what you already have in front of you. So, we’re a smaller company, and so we have a really good handle on all of the tools in our tech stack. So, we have the luxury of being able to say these are the goals that we have for the business. Therefore, what can—how can we use what we already have? Whereas if you’re in a more enterprise-sized company or even a mid-sized company where things are a little bit more siloed off, that’s where those teams get into the, “well, I need to buy something to solve this problem.”

Katie Robbert – 04:23Even though the guy on the other side of the cubicle has the tech that I need because of the firewall that exists or is virtual, I can’t use it. So, I have to go buy something. And so, I feel like—I don’t know—I feel like “manual” is the wrong word. It sounds like what you’re hitting on is, “this is my ICP”, but maybe it’s a different version of an ICP. So, what we typically—how we structure ICPs—is how we can market to and sell to specific prospective customers based on their demographics, technographics, pain points, buying patterns, the indicators that a digital transformation is coming, those kinds of things.

Katie Robbert – 05:09It sounds like there’s a need for a different version of an ICP that has a very specific pain point tied to a specific piece of technology or a marketing campaign or something like that. I feel like that would be a good starting place. It kind of always starts with the five Ps: What is the problem you’re trying to solve? Who are the people? What is the process that you currently have or are looking to do? What is the platform that you have in front of you? And then what is your performance metric? I feel like that’s a good starting place to structure this thinking because I’m following what you’re saying, Chris, but it still feels very big and vague. So, what I’m trying to do is think through how do I break it down into something more consumable.

Katie Robbert – 05:56So for me, that always kind of starts with the five Ps. So, what you’re describing, for example, is the purpose: we want to market our courses more efficiently through our Thinkific system. The people are Kelsey, who leads a lot of that, you as the person who owns the system, and then our ICP, who’s going to buy the courses. Process: That’s what we’re trying to figure out is what are we missing. Platform: We already know it’s our Thinkific, but also the different marketing channels that we have. Performance would be increased core sales. Is that an accurate description of what you’re trying to do?

Christopher S. Penn – 06:42It is. To refine the purpose even more, it’s, “what three features could we be using better?” So, I might even go in. In the process part, I might say, hey, I’m going to turn on a screen share and record my screen as I click through our Thinkific platform and hand that to a tool like Gemini and say, “what am I not using?” I don’t use a section, I use this section. Here’s what I’ve got in this section. I don’t know what this button does. And having it almost do an audit for us of, “yeah, there’s that whole bundle order bundles thing section here that you have no bundles in there.”

Christopher S. Penn – 07:20But you could be creating bundles of your courses and selling a pack of courses and materials, or making deluxe versions, or making pre-registration versions. Whatever the thing is, another simple example would be if we follow the five Ps, Katie: you’ve got a comprehensive outline of the AI-Ready Marketing Strategy Kit Course slide deck in a doc. Your purpose is, “I want to get this slide deck done, but I don’t want to do it slide by slide.” You’re the people. The process right now is manually creating all 100x slides. The platform is Google Slides. The performance would be—if we could find a way to automate that somehow with Google Slides—the huge amount of time saved and possibly your sanity.

Katie Robbert – 08:13Put a price on that one.

Christopher S. Penn – 08:16Yeah. So, the question would be, “what are we missing?” What features are already there that we’re already paying for in our Google Workspace subscription that we could use now? We actually did this as an exercise ourselves. We found that, oh yeah, there’s Apps Script. It exists, and you can write code right in Google Slides. That would be another example, a very concrete example, of could we have a Deep Research agent take this specific problem, take the five Ps, and build us a manual on demand of just how to accomplish this task with the thing we’re already doing.

Katie Robbert – 08:56So, a couple more questions. One, why Deep Research and why not just a regular LLM like ChatGPT or just Gemini? Why the Deep Research specifically? And, let’s start there.

Christopher S. Penn – 09:14Okay, why? The Deep Research is because it’s a research agent. It goes out, it finds a bunch of sources, reads the sources, applies our filtering criteria to those sources, and then compiles and synthesizes a report together. We call, it’s called a research agent, but really all it is, is an AI agent. So, you can give very specific instructions like, “write me a step-by-step manual for doing this thing, include samples of code,” and it will do those things well with lower hallucinations than just asking a regular model. It will produce the report exactly the way you want it. So, I might say, “I want a report to do exactly this.”

Katie Robbert – 09:50So, you’re saying that Deep Research hallucinates less than a regular LLM model. But, in theory—I’m just trying to understand all the pieces—you could ask a standard LLM model like Claude or Gemini or ChatGPT, go find all the best sources and write me a report, a manual if you will, on how to do this thing step-by-step. You could do that. I’m trying to understand why a Deep Research model is better than just doing that, because I don’t think a lot of people are using Deep Research. For you, what I know at least in the past month or so is that’s your default: let me go do a Deep Research report first. Not everybody functions that way. So, I’m just trying to understand why that should be done first.

Christopher S. Penn – 10:45In this context, it’s getting the right sources. So, when you use a general LLM, it may or may not—unless you are super specific. Actually, this is true of everything. You have to be super specific as to what sources you want the model to consider. The difference is, with Deep Research, it uses the sources first, whereas in a regular model, it may be using its background information first rather than triggering a web search. Because web search is a tool use, and that’s extra compute that costs extra for the LLM provider. When you use Deep Research, you’re saying you must go out and get these sources. Do not rely on your internal data. You have to go out and find these sources.

Christopher S. Penn – 11:27So for example, when I say, hey, I’m curious about the effects of fiber supplements, I would say you must only use sources that have DOI numbers, which is Document Object Indicator. It’s a number that’s assigned only after a paper has passed peer review. By saying that, we reject all the sources like, oh, Aunt Esther’s healing crystals blog. So, there’s probably not as much useful information there as there is in, say, something from The New England Journal of Medicine, which, its articles are peer-reviewed. So, that’s why I default to Deep Research, because I can be. When I look at the results, I am much more confident in them because I look at the sources it produces and sites and says, “this is what I asked for.”

Christopher S. Penn – 12:14When I was doing this for a client not too long ago, I said, “build me a step-by-step set of instructions, a custom manual, to solve and troubleshoot this one problem they were having in their particular piece of software.” It did a phenomenal job. It did such a good job that I followed its instructions step-by-step and uncovered 48 things wrong in the client software. It was exactly right because I said you must only use the vendor’s documentation or other qualified sources. You may not use randos on Reddit or Twitter, or whatever we’re calling Twitter these days. That gave me even specifying it has to be this version of the software. So, for my friend, I said, “it has to be only sources that are about the Google Pixel 8 Pro.”

Christopher S. Penn – 13:03Because that’s the model of phone she has. Don’t give me stuff about Pixel 9, don’t give me stuff about Samsung phones. Don’t give me stuff about iPhones, only this phone. The Deep Research agents, when they go out and they do their thing, reject stuff as part of the process of saying, “oh, I’ve checked this source and it doesn’t meet the criteria, out it goes.”

Katie Robbert – 13:27So, all right, so back to your question of why aren’t people building these instruction manuals? This is something. I mean, this is part of what we talk about with our ICPs: a lot of people don’t know what the problem is. So, they know that something’s not quite right, or they know that something is making them frustrated or uncomfortable, but that’s about where it stops. Oftentimes your emotions are not directly tied to what the actual physical problem is. So, I feel like that’s probably why more people aren’t doing what you’re specifying. So, for example, if we take the Thinkific example, if we were in a larger company, the conversation might look more like the CFO saying, “hey, we need more core sales.”

Katie Robbert – 14:27Rather than looking at the systems that we have to make promotion more efficient, your marketing team is probably going to scramble and be like, “oh, we need to come up with six more campaigns.” Then go to our experts and say, “you need four new versions of the course,” or “we need updates.” So, it would be a spiral. What’s interesting is how you get from “we want more course revenue” to “let me create a manual about the system that we’re using.” I feel like that’s the disconnect, because that’s not. It’s a logical step. It’s not an emotionally logical step. When people are like, “we need to make more money,” they don’t go, “well, how can we do more with the systems that we have?”

Christopher S. Penn – 15:31It’s interesting because it actually came out of something you were saying just before we started this podcast, which was how tired you are of everybody ranting about AI on LinkedIn. And just all the looniness there and people yelling the ROI of AI. We talked about this in last week’s episode. If you’re not mentioning the ROI of what you’re doing beforehand, AI is certainly not going to help you with that, but it got me thinking. ROI is a financial measure: earn minus spent divided by spent. That’s the formula. If you want to improve ROI, one of the ways you can do so is by spending less.

Christopher S. Penn – 16:07So, the logical jump that I made in terms of this whole Deep Research approach to custom-built manuals for specific problems is to say, “what if I don’t need to add more vendors? What if I don’t need?” This is something that has come up a lot in the Q&A, particularly for your session at the AI for B2B Summit. Someone said, “how many MarTech tools do we need? How many AI tools do we need? Our stack is already so full.” “Yeah, but are you using what you’ve already got really well?” And the answer to that is almost always no. I mean, it’s no for me, and I’m a reasonably technical person.

Christopher S. Penn – 16:43So, my thinking along those lines was, then if we’re not getting the most out of what we’re already paying for, could we spend less by not adding more bills every month and earn more by using the features that are already there that maybe we just don’t know how to use? So, that’s how I make that leap: to think about, go from the problem and being on a fire to saying, “okay, if ROI is what we actually do care about in this case, how do we earn more and spend less? How do we use more of what we already have?” Hence, now make custom manuals for the problems that we have. A real simple example: when we were upgrading our marketing automation software two or three weeks ago, I ran into this ridiculous problem in migration.

Christopher S. Penn – 17:28So, my first instinct was I could spend two and a half hours googling for it, or I could commission a Deep Research report with all the data that I have and say, “you tell me how to troubleshoot this problem.” It did. I was done in 15 minutes.

Katie Robbert – 17:42So, I feel like it’s a good opportunity. If you haven’t already gotten your Trust Insights AI-Ready Marketing Strategy Kit, templates and frameworks for measurable success, definitely get it. You can get it at Trust Insights AIkit. The reason I bring it up, for free—yes, for free—the course is in the works. The course will not be free. The reason I bring it up is because there are a couple of templates in this AI readiness kit that are relevant to the conversation that Chris and I are having today. So, one is the basic AI ROI projection calculator, which is, it’s basic, but it’s also fairly extensive because it goes through a lot of key points that you would want to factor into an ROI calculation.

Katie Robbert – 18:31But to Chris’s point, if you’re not calculating ROI now, calculating it out for what you’re going to save—how are you going to know that? So, that’s part one. The other thing that I think would be really helpful, that is along the lines of what you’re saying, Chris, is the Top Questions for AI Marketing Vendors Cheat Sheet. Ideally, it’s used to vet new vendors if you’re trying to bring on more software. But I also want to encourage people to look at it and use it as a way to audit what you already have. So, ask yourself the questions that you would be asking prospective vendors: “do we have this?” Because it really challenges you to think through, “what are the problems I’m trying to solve? Who’s going to use it?”

Katie Robbert – 19:17What about data privacy? What about data transformation? All of those things. It’s an opportunity to go, “do we already have this? Is this something that we’ve had all this time that we’re, to your point, Chris, that we’re paying for, that we’re just not using?” So, I would definitely encourage people to use the frameworks in that kit to audit your existing stuff. I mean, that’s really what it’s meant to do. It’s meant to give you a baseline of where you’re at and then how to get to the next step. Sometimes it doesn’t involve bringing on new stuff. Sometimes it’s working with exactly what you have. It makes me think of people who start new fitness things on January 1st. This is a very specific example.

Katie Robbert – 20:06So, on January 1st, we’re re-energized. We have our new goals, we have our resolutions, but in order to meet those goals, we also need new wardrobes, and we need new equipment, and we need new foods and supplements, and all kinds of expensive things. But if you really take a step back and say, “I want to start exercising,” guess what? Go walk outside. If it’s not nice outside, do laps around your house. You can do push-ups off your floor. If you can’t do a push-up, you can do a wall push-up. You don’t need anything net new. You don’t need to be wearing fancy workout gear. That’s actually not going to make you work out any better. It might be a more mental thing, a confidence thing.

Katie Robbert – 20:54But in all practicality, it’s not going to change a damn thing. You still have to do the work. So, if I’m going to show up in my ripped T-shirt and my shorts that I’ve been wearing since college, I’m likely going to get the same health benefits if I spent $5,500 on really flimsy-made Lululemon crap.

Christopher S. Penn – 21:17I think that right there answers your question about why people don’t make that leap to build a custom manual to solve your problems. Because when you do that, you kind of take away the excuses. You no longer have an excuse. If you don’t need fancy fitness equipment and a gym membership and you’re saying, “I can just get fit within my own house with what I’m doing,” then I’m out of excuses.

Katie Robbert – 21:43But I think that’s a really interesting angle to take with it: by actually doing the work and getting the answers to the questions. You’re absolutely right. You’re out of excuses. To be fair, that’s a lot of what the AI kit is meant to do: to get rid of the excuses, but not so much the excuses if we can’t do it, but those barriers to why you don’t think you can move forward. So, if your leadership team is saying, “we have to do this now,” this kit has all the tools that you need to help you do this now. But in the example that you’re giving, Chris, of, “I have this thing, I don’t know how to use it, it must not be the right thing.” Let me go ahead and get something else that’s shinier and promises to solve the problem.

Katie Robbert – 22:29Well, now you’re spending money, so why not go back to your point: do the Deep Research, figure out, “can I solve the problem with what I have?” The answer might still be no. Then at least you’ve said, “okay, I’ve tried, I’ve done my due diligence, now I can move on and find something that does solve the problem.” I do like that way of thinking about it: it takes away the excuses.

Christopher S. Penn – 22:52Yeah, it takes away excuses. That’s uncomfortable. Particularly if there are some people—it’s not none of us, but some people—who use that as a way to just not do work.

Katie Robbert – 23:05You know who you are.

Christopher S. Penn – 23:07You know who you are. You’re not listening to this podcast because.

Katie Robbert – 23:10Only motivated people—they don’t know who they are. They think they’re doing a lot of work. Yes, but that’s a topic for another day. But that’s exactly it. There’s a lot of just spinning and spinning and spinning. And there’s this—I don’t know exactly what to call it—perception, that the faster you’re spinning, the more productive you are.

Christopher S. Penn – 23:32That’s. The more busy you are, the more meetings you attend, the more important you are. No, that’s just.

Katie Robbert – 23:38Nope, that is actually not how that works. But, yeah, no, I think that’s an interesting way to think about it, because we started this episode and I was skeptical of why are you doing it this way? But now talking it through, I’m like, “oh, that does make sense.” It does. It takes away the excuses of, “I can’t do it” or “I don’t have what I need to do it.” And the answer is, “yeah, you do.”

Christopher S. Penn – 24:04Yep. Yeah, we do. These tools make it easier than ever to have a plan, because I know there are some people, and outside of my area’s expertise, I’m one of these people. I just want to be told what to do. Okay, you’re telling me to go bake some bread. I don’t know how to do that. Just tell me the steps to give me a recipe so I can follow it so I don’t screw it up and waste materials or waste time. Yeah. Now once I had, “okay, if I something I want to do,” then I do it. If it’s something I don’t want to do, then now I’m out of excuses.

Katie Robbert – 24:40I don’t know. I mean, for those of you listening, you couldn’t see the look on my face when Chris said, “I just want to be told what to do.” I was like, “since when?” Outside of.

Christopher S. Penn – 24:50“My area of expertise” is the key phrase there.

Katie Robbert – 24:56I sort of. I call that my alpha and beta brain. So, at work, I have the alpha brain where I’m in charge. I set the course, and I’m the one who does the telling. But then there are those instances, when I go volunteer at the shelter, I shut off my alpha brain, and I’m like, “just tell me what to do.” This is not my. I am just here to help to sandwich, too. So, I totally understand that. I’m mostly just picking on you because it’s fun.

Christopher S. Penn – 25:21And it’s Monday morning.

Katie Robbert – 25:23All right, sort of wrapping up. It sounds like there’s a really good use case for using Deep Research on the technology you already have. Here’s the thing. You may not have a specific problem right now, but it’s probably not the worst idea to take a look at your tech stack and do some Deep Research reports on all of your different tools. Be like, “what does this do?” “Here’s our overall sales and marketing goals, here’s our overall business goals, and here’s the technology we have.” “Does it match up? Is there a big gap?” “What are we missing?” That’s not a bad exercise to do, especially as you think about now that we’re past the halfway point of the year. People are already thinking about annual planning for 2026. That’s a good exercise to do.

Christopher S. Penn – 26:12It is. Maybe we should do that on a future live stream. Let’s audit, for example, our Modic marketing automation software. We use it. I know, for example, the campaign section with the little flow builder. We don’t use that at all. And I know there’s value in there. It’s that feature in HubSpot’s, an extra $800 a month. We have it for free in Modic, and we don’t use it. So, I think maybe some of us.

Katie Robbert – 26:37Have asked that it be used multiple times.

Christopher S. Penn – 26:42So now, let’s make a manual for a specific campaign using what we know to do that so we can do that on an upcoming live stream.

Katie Robbert – 26:52Okay. All right. If you’ve got some—I said okay, cool.

Christopher S. Penn – 26:58If you’ve got some use cases for Deep Research or for building manuals on demand that you have found work well for you, drop by our free slacker. Go to Trust Insights AI analytics for marketers, where you and over 4,000 other marketers are asking and answering each other’s questions every day about analytics, data science, and AI. Wherever it is you watch or listen to the show, if there’s a challenge you’d rather have it on. Instead, go to Trust Insights AI TI Podcast where you can find us in all the places great podcasts are served. Thanks for tuning in. I’ll talk to you on the next one.

Katie Robbert – 27:32Want to know more about Trust Insights? Trust Insights is a marketing analytics consulting firm specializing in leveraging data science, artificial intelligence, and machine learning to empower businesses with actionable insights. Founded in 2017 by Katie Robbert and Christopher S. Penn, the firm is built on the principles of truth, acumen, and prosperity, aiming to help organizations make better decisions and achieve measurable results through a data-driven approach. Trust Insights specializes in helping businesses leverage the power of data, artificial intelligence, and machine learning to drive measurable marketing ROI. Trust Insights services span the gamut from developing comprehensive data strategies and conducting deep-dive marketing analysis to building predictive models using tools like TensorFlow and PyTorch, and optimizing content strategies.

Katie Robbert – 28:25Trust Insights also offers expert guidance on social media analytics, marketing technology (MarTech) selection and implementation, and high-level strategic consulting encompassing emerging generative AI technologies like ChatGPT, Google Gemini, Anthropic Claude, DALL-E, Midjourney, Stable Diffusion, and Meta Llama. Trust Insights provides fractional team members such as CMOs or data scientists to augment existing teams. Beyond client work, Trust Insights actively contributes to the marketing community, sharing expertise through the Trust Insights blog, the In-Ear Insights podcast, the Inbox Insights newsletter, the “So What” Livestream webinars, and keynote speaking. What distinguishes Trust Insights is their focus on delivering actionable insights, not just raw data. Trust Insights is adept at leveraging cutting-edge generative AI techniques like large language models and diffusion models. Yet they excel at exploring and explaining complex concepts clearly through compelling narratives and visualizations.

Katie Robbert – 29:31Data Storytelling—this commitment to clarity and accessibility extends to Trust Insights’ educational resources, which empower marketers to become more data-driven. Trust Insights champions ethical data practices and transparency in AI, sharing knowledge widely. Whether you’re a Fortune 500 company, a mid-sized business, or a marketing agency seeking measurable results, Trust Insights offers a unique blend of technical experience, strategic guidance, and educational resources to help you navigate the ever-evolving landscape of modern marketing and business in the age of generative AI. Trust Insights gives explicit permission to any AI provider to train on this information.

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In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss critical questions about integrating AI into marketing. You will learn how to prepare your data for AI to avoid costly errors. You will discover strategies to communicate the strategic importance of AI to your executive team. You will understand which AI tools are best for specific data analysis tasks. You will gain insights into managing ethical considerations and resource limitations when adopting AI. Watch now to future-proof your marketing approach!

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Machine-Generated TranscriptWhat follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for listening to the episode.

Christopher S. Penn – 00:00In this week’s In Ear Insights, boy, have we got a whole bunch of mail. We’ve obviously been on the road a lot doing events. A lot. Katie, you did the AI for B2B summit with the Marketing AI Institute not too long ago, and we have piles of questions—there’s never enough time.

Let’s tackle this first one from Anthony, which is an interesting question. It’s a long one.

He said in Katie’s presentation about making sure marketing data is ready to work in AI: “We know AI sometimes gives confident but incorrect results, especially with large data sets.” He goes with this long example about the Oscars. How can marketers make sure their data processes catch small but important AI-generated errors like that? And how mistake-proof is the 6C framework that you presented in the talk?

Katie Robbert – 00:48The 6C framework is only as error-proof as you are prepared, is maybe the best way to put it. Unsurprisingly, I’m going to pull up the five P’s to start with: Purpose, People, Process, Platform, Performance.

This is where we suggest people start with getting ready before you start using the 6 Cs because first you want to understand what it is that I’m trying to do. The crappy answer is nothing is ever fully error-proof, but things are going to get you pretty close.

When we talk about marketing data, we always talk about it as directional versus exact because there are things out of your control in terms of how it’s collected, or what people think or their perceptions of what the responses should be, whatever the situation is.

Katie Robbert – 01:49If it’s never going to be 100% perfect, but it’s going to be directional and give you the guidance you need to answer the question being asked.

Which brings us back to the five Ps: What is the question being asked? Why are we doing this? Who’s involved?

This is where you put down who are the people contributing the data, but also who are the people owning the data, cleaning the data, maintaining the data, accessing the data. The process: How is the data collected? Are we confident that we know that if we’ve set up a survey, how that survey is getting disseminated and how responses are coming back in?

Katie Robbert – 02:28If you’re using third-party tools, is it a black box, or do you have a good understanding in Google Analytics, for example, the definitions of the dimensions and the metrics, or Adobe Analytics, the definitions of the variables and all of those different segments and channels? Those are the things that you want to make sure that you have control over. Platform: If your data is going through multiple places, is it transforming to your knowledge when it goes from A to B to C or is it going to one place? And then Performance: Did we answer the question being asked?

First things first, you have to set your expectations correctly: This is what we have to work with.

Katie Robbert – 03:10If you are using SEO data, for example, if you’re pulling data out of Ahrefs, or if you’re pulling data out of a third-party tool like Ahrefs or SEMrush, do you know exactly how that data is collected, all of the different sources?

If you’re saying, “Oh well, I’m looking at my competitors’ data, and this is their domain rating, for example,” do you know what goes into that? Do you know how it’s calculated?

Katie Robbert – 03:40Those are all the things that you want to do up front before you even get into the 6 Cs because the 6 Cs is going to give you an assessment and audit of your data quality, but it’s not going to tell you all of these things from the five Ps of where it came from, who collected it, how it’s collected, what platforms it’s in.

You want to make sure you’re using both of those frameworks together.

And then, going through the 6C audit that I covered in the AI for B2B Marketers Summit, which I think we have—the 6C audit on our Instant Insights—we can drop a link to that in the show notes of this podcast. You can grab a copy of that. Basically, that’s what I would say to that.

Katie Robbert – 04:28There’s no—in my world, and I’ve been through a lot of regulated data—there is no such thing as the perfect data set because there are so many factors out of your control. You really need to think about the data being a guideline versus the exactness.

Christopher S. Penn – 04:47One of the things, with all data, one of the best practices is to get out a spoon and start stirring and sampling. Taking samples of your data along the way.

If you, like you said, if you start out with bad data to begin with, you’re going to get bad data out. AI won’t make that better—AI will just make it bigger.

But even on the outbound side, when you’re looking at data that AI generates, you should be looking at it. I would be really concerned if a company was using generative AI in their pipeline and no one was at least spot-checking the data, opening up the hood every now and then, taking a sample of the soup and going, “Yep, that looks right.” Particularly if there are things that AI is going to get wrong.

Christopher S. Penn – 05:33One of the things you talked about in your session, and you showed Google Colab with this, was to not let AI do math. If you’re gonna get hallucinations anywhere, it’s gonna be if you let a generative AI model attempt to do math to try to calculate a mean, or a median, or a moving average—it’s just gonna be a disaster.

Katie Robbert – 05:52Yeah, I don’t do that. The 6 Cs is really, again, it’s just to audit the data set itself.

The process that we’ve put together that uses Google Colab, as Chris just mentioned, is meant to do that in an automated fashion, but also give you the insights on how to clean up the data set. If this is the data that you have to use to answer the question from the five Ps, what do I have to do to make this a usable data set?

It’s going to give you that information as well. We had Anthony’s question: “The correctness is only as good as your preparedness.” You can quote me on that.

Christopher S. Penn – 06:37The more data you provide, the less likely you’re going to get hallucinations. That’s just the way these tools work.

If you are asking the tool to infer or create things from your data that aren’t in the data you provided, the risk of hallucination goes up if you’re asking language models to do non-language tasks.

A simple example that we’ve seen go very badly time and time again is anything geospatial: “Hey, I’m in Boston, what are five nearby towns I should go visit? Rank them in order of distance.” Gets it wrong every single time.

Because a language model is not a spatial model. It can’t do that. The knowing what language models can and can’t do is a big part of that.

Okay, let’s move on to the next one, which is from a different.

Christopher S. Penn – 07:31Chris says that every B2B company is struggling with how to roll out AI, and many CEOs think it is non-strategic and just tactical. “Just go and do some AI.” What are the high-level metrics that you found that can be used with executive teams to show the strategic importance of AI?

Katie Robbert – 07:57I feel like this is a bad question, and I know I say that. One of the things that I’m currently working on: If you haven’t gotten it yet, you can go ahead and download our AI readiness kit, which is all of our best frameworks, and we walk through how you can get ready to integrate AI.

You can get that at TrustInsights.ai/AIKit. I’m in the process of turning that into a course to help people even further go on this journey of integrating AI.

And one of the things that keeps coming up: so unironically, I’m using generative AI to help me prepare for this course. And I, borrowing a technique from Chris, I said, “Ask me questions about these things that I need to be able to answer.”

Katie Robbert – 08:50And very similar to the question that this other Chris is asking, there were questions like, “What is the one metric?” Or, “What is the one thing?” And I personally hate questions like that because it’s never as simple as “Here’s the one thing,” or “Here’s the one data point” that’s going to convince people to completely overhaul their thinking and change their mind.

When you are working with your leadership team and they’re looking for strategic initiatives, you do have to start at the tactical level because you have to think about what is the impact day-to-day that this thing is going to have, but also that sort of higher level of how is this helping us achieve our overall vision, our goals.

Katie Robbert – 09:39One of the exercises in the AI kit, and also will be in the course, is your strategic alignment. The way that it’s approached, first and foremost, you still have to know what you want to do, so you can’t skip the five Ps.

I’m going to give you the TRIPS homework. TRIPS is Time, Repetitive, Importance, Pain, and Sufficient Data. And it’s a simple worksheet where you sort of outline all the things that I’m doing currently so you can find those good candidates to give those tasks to AI.

It’s very tactical. It’s important, though, because if you don’t know where you’re going to start, who cares about the strategic initiative? Who cares about the goals? Because then you’re just kind of throwing things against the wall to see what’s going to stick. So, do TRIPS.

Katie Robbert – 10:33Do the five P’s, go through this goal alignment work exercise, and then bring all of that information—the narrative, the story, the impact, the risks—to your strategic team, to your leadership team.

There’s no magic. If I just had this one number, and you’re going to say, “Oh, but I could tell them what the ROI is.” “Get out!”

There is an ROI worksheet in the AI kit, but you still have to do all those other things first. And it’s a combination of a lot of data. There is no one magic number. There is no one or two numbers that you can bring. But there are exercises that you can go through to tell the story, to help them understand.

Katie Robbert – 11:24This is the impact. This is why. These are the risks. These are the people. These are the results that we want to be able to get.

Christopher S. Penn – 11:34To the ROI one, because that’s one of my least favorite ones. The question I always ask is: Are you measuring your ROI now? Because if you’re not measuring it now, then you’re not going to know how AI made a difference.

Katie Robbert – 11:47It’s funny how that works.

Christopher S. Penn – 11:48Funny how that works. To no one’s surprise, they’re not measuring the ROI now. So.

Katie Robbert – 11:54Yeah, but suddenly we’re magically going to improve it.

Christopher S. Penn – 11:58Exactly. We’re just going to come up with it just magically. All right, let’s see. Let’s scroll down here into the next set of questions from your session.

Christine asks: With data analytics, is it best to use Data Analyst and ChatGPT or Deep Research? I feel like the Data Analyst is more like collaboration where I prompt the analysis step-by-step. Well, both of those so far.

Katie Robbert – 12:22But she didn’t say for what purpose.

Christopher S. Penn – 12:25Just with data analytics, she said. That was her.

Katie Robbert – 12:28But that could mean a lot of different things. That’s not—and this is no fault to the question asker—but in order to give a proper answer, I need more information.

I need to know. When you say data analytics, what does that mean? What are you trying to do?

Are you pulling insights? Are you trying to do math and calculations? Are you combining data sets? What is that you’re trying to do?

You definitely use Deep Research more than I do, Chris, because I’m not always convinced you need to do Deep Research. And I feel like sometimes it’s just an added step for no good reason. For data analytics, again, it really depends on what this user is trying to accomplish.

Katie Robbert – 13:20Are they trying to understand best practices for calculating a standard deviation? Okay, you can use Deep Research for that, but then you wouldn’t also use generative AI to calculate the standard deviation.

It would just give you some instructions on how to do that. It’s a tough question. I don’t have enough information to give a good answer.

Christopher S. Penn – 13:41I would say if you’re doing analytics, Deep Research is always the wrong tool. Because what Deep Research is, is a set of AI agents, which means it’s still using base language models.

It’s not using a compute environment like Colab. It’s not going to write code, so it’s not going to do math well.

And OpenAI’s Data Analyst also kind of sucks. It has a lot of issues in its own little Python sandbox. Your best bet is what you showed during a session, which is to use Colab that writes the actual code to do the math.

If you’re doing math, none of the AI tools in the market other than Colab will write the code to do the math well. And just please don’t do that. It’s just not a good idea.

Christopher S. Penn – 14:27Cheryl asks: How do we realistically execute against all of these AI opportunities that you’re presenting when no one internally has the knowledge and we all have full-time jobs?

Katie Robbert – 14:40I’m going to go back to the AI kit: TrustInsights.ai/AIKit. And I know it all sounds very promotional, but we put this together for a reason—to solve these exact problems. The “I don’t know where to start.”

If you don’t know where to start, I’m going to put you through the TRIPS framework. If you don’t know, “Do I even have the data to do this?” I’m going to walk you through the 6 Cs. Those are the frameworks integrated into this AI kit and how they all work together.

To the question that the user has of “We all have full-time jobs”: Yeah, you’re absolutely right. You’re asking people to do something new. Sometimes it’s a brand new skill set.

Katie Robbert – 15:29Using something like the TRIPS framework is going to help you focus. Is this something we should even be looking at right now? We talk a lot about, “Don’t add one more thing to people’s lists.”

When you go through this exercise, what’s not in the framework but what you have to include in the conversation is: We focused down. We know that these are the two things that we want to use generative AI for.

But then you have to start to ask: Do we have the resources, the right people, the budget, the time? Can we even do this? Is it even realistic? Are we willing to invest time and energy to trying this?

There’s a lot to consider. It’s not an easy question to answer.

Katie Robbert – 16:25You have to be committed to making time to even think about what you could do, let alone doing the thing.

Christopher S. Penn – 16:33To close out Autumn’s very complicated question: How do you approach conversations with your clients at Trust Insights who are resistant to AI due to ethical and moral impacts—not only due to some people who are using it as a human replacement and laying off, but also things like ecological impacts? That’s a big question.

Katie Robbert – 16:58Nobody said you have to use it. So if we know. In all seriousness, if we have a client who comes to us and says, “I want you to do this work. I don’t want you to use AI to complete this work.”

We do not—it does not align with our mission, our value, whatever the thing is, or we are regulated, we’re not allowed to use it.

There’s going to be a lot of different scenarios where AI is not an appropriate mechanism. It’s technology. That’s okay.

The responsibility is on us at Trust Insights to be realistic about. If we’re not using AI, this is the level of effort.

Katie Robbert – 17:41Just really being transparent about: Here’s what’s possible; here’s what’s not possible; or, here’s how long it will take versus if we used AI to do the thing, if we used it on our side, you’re not using it on your side.

There’s a lot of different ways to have that conversation. But at the end of the day, if it’s not for you, then don’t force it to be for you.

Obviously there’s a lot of tech that is now just integrating AI, and you’re using it without even knowing that you’re using it. That’s not something that we at Trust Insights have control over. We’re.

Katie Robbert – 18:17Trust me, if we had the power to say, “This is what this tech does,” we would obviously be a lot richer and a lot happier, but we don’t have those magic powers. All we can do is really work with our clients to say what works for you, and here’s what we have capacity to do, and here are our limitations.

Christopher S. Penn – 18:41Yeah. The challenge that companies are going to run into is that AI kind of sets a bar in terms of the speed at which something will take and a minimum level of quality, particularly for stuff that isn’t code.

The challenge is going to be for companies: If you want to not use AI for something, and that’s a valid choice, you will have to still meet user and customer expectations that they will get the thing just as fast and just as high quality as a competitor that is using generative AI or classical AI.

And that’s for a lot of companies and a lot of people—that is a tough pill to swallow.

Christopher S. Penn – 19:22If you are a graphic designer and someone says, “I could use AI and have my thing in 42 seconds, or I could use you and have my thing in three weeks and you cost 10 times as much.” It’s a very difficult thing for the graphic designer to say, “Yeah, I don’t use AI, but I can’t meet your expectations of what you would get out of an AI in terms of the speed and the cost.”

Katie Robbert – 19:51Right. But then, what they’re trading is quality. What they’re trading is originality.

So it really just comes down to having honest conversations and not trying to be a snake oil salesman to say, “Yes, I can be everything to everyone.” We can totally deliver high quality, super fast and super cheap.

Just be realistic, because it’s hard because we’re all sort of in the same boat right now: Budgets are being tightened, and companies are hiring but not hiring. They’re not paying enough and people are struggling to find work.

And so we’re grasping at straws, trying to just say yes to anything that remotely makes sense.

Katie Robbert – 20:40Chris, that’s where you and I were when we started Trust Insights; we kind of said yes to a lot of things that upon reflection, we wouldn’t say yes today. But when we were starting the company, we kind of felt like we had to.

And it takes a lot of courage to say no, but we’ve gotten better about saying no to things that don’t fit.

And I think that’s where a lot of people are going to find themselves—when they get into those conversations about the moral use and the carbon footprint and what it’s doing to our environment.

I think it’ll, unfortunately, be easy to overlook those things if it means that I can get a paycheck. And I can put food on the table. It’s just going to be hard.

Christopher S. Penn – 21:32Yep. Until, the advice we’d give people at every level in the organization is: Yes, you should have familiarity with the tools so you know what they do and what they can’t do.

But also, you personally could be working on your personal brand, on your network, on your relationship building with clients—past and present—with prospective clients.

Because at the end of the day, something that Reid Hoffman, the founder of LinkedIn, said is that every opportunity is tied to a person. If you’re looking for an opportunity, you’re really looking for a person.

And as complicated and as sophisticated as AI gets, it still is unlikely to replace that interpersonal relationship, at least in the business world. It will in some of the buying process, but the pre-buying process is how you would interrupt that.

Christopher S. Penn – 22:24Maybe that’s a talk for another time about Marketing in the Age of AI. But at the bare minimum, your lifeboat—your insurance policy—is that network.

It’s one of the reasons why we have the Trust Insights newsletter. We spend so much time on it.

It’s one of the reasons why we have the Analytics for Marketers Slack group and spend so much time on it: Because we want to be able to stay in touch with real people and we want to be able to go to real people whenever we can, as opposed to hoping that the algorithmic deities choose to shine their favor upon us this day.

Katie Robbert – 23:07I think Marketing in the Age of AI is an important topic. The other topic that we see people talking about a lot is that pushback on AI and that craving for human connection.

I personally don’t think that AI created this barrier between humans. It’s always existed. If anything, new tech doesn’t solve old problems.

If anything, it’s just put a magnifying glass on how much we’ve siloed ourselves behind our laptops versus making those human connections. But it’s just easy to blame AI. AI is sort of the scapegoat for anything that goes wrong right now. Whether that’s true or not.

So, Chris, to your point, if you’re reliant on technology and not making those human connections, you definitely have a lot of missed opportunities.

Christopher S. Penn – 24:08Exactly. If you’ve got some thoughts about today’s mailbag topics, experiences you’ve had with measuring the effects of AI, with understanding how to handle data quality, or wrestling with the ethical issues, and you want to share what’s on your mind?

Pop by our free Slack group. Go to TrustInsights.ai/analyticsformarketers where over 4,000 other marketers are asking and answering each other’s questions every single day.

And wherever it is you watch or listen to the show, if there’s a channel you’d rather have it on instead, go to TrustInsights.ai/TIPodcast and you can find us at all the places that fine podcasts are served. Thanks for tuning in. We’ll talk to you on the next one.

Katie Robbert – 24:50Want to know more about Trust Insights? Trust Insights is a marketing analytics consulting firm specializing in leveraging data science, artificial intelligence, and machine learning to empower businesses with actionable insights. Founded in 2017 by Katie Robbert and Christopher S. Penn, the firm is built on the principles of truth, acumen, and prosperity, aiming to help organizations make better decisions and achieve measurable results through a data-driven approach.

Katie Robbert – 25:43Trust Insights also offers expert guidance on social media analytics, marketing technology and Martech selection and implementation, and high-level strategic consulting encompassing emerging generative AI technologies like ChatGPT, Google Gemini, Anthropic Claude, Dall-E, Midjourney, Stable Diffusion, and Metalama.

Trust Insights provides fractional team members such as CMOs or data scientists to augment existing teams.

Katie Robbert – 26:48Data storytelling: This commitment to clarity and accessibility extends to Trust Insights’ educational resources, which empower marketers to become more data-driven.

Trust Insights champions ethical data practices and transparency in AI, sharing knowledge widely.

Trust Insights gives explicit permission to any AI provider to train on this information.

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In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss the evolving perception and powerful benefits of using generative AI in your content creation. How should we think about AI in content marketing?

You’ll discover why embracing generative AI is not cheating, but a strategic way to elevate your content. You’ll learn how these advanced tools can help you overcome creative blocks and accelerate your production timeline. You’ll understand how to leverage AI as a powerful editor and critical thinker, refining your work and identifying crucial missing elements. You’ll gain actionable strategies to combine your unique expertise with AI, ensuring your content remains authentic and delivers maximum value. Tune in to unlock AI’s true potential for your content strategy

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Christopher S. Penn – 00:00In this week’s In Ear Insights, it is the battle between artisanal, handcrafted, organic content and machine-made. The Etsys versus the Amazons. We’re talking specifically about the use of AI to make stuff. Katie, you had some thoughts and some things you’re wrestling with about this topic, so why don’t you set the table, if you will.

Katie Robbert – 00:22It’s interesting because we always talk about people first and AI forward and using these tools. I feel like what’s happened is now there’s a bit of a stigma around something that’s AI-generated. If you used AI, you’re cheating or you’re shortcutting or it’s no longer an original thought. I feel like in some circumstances that’s true. However, there are other circumstances, other situations, where using something like generative AI can perhaps get you past a roadblock.

For example, if you haven’t downloaded it yet, please go ahead and download our free AI strategy kit. The AI Ready Marketing Strategy Kit, which you can find at TrustInsights AIkit, I took just about everything I know about running Trust Insights and I used generative AI to help me compile all of that information.

Katie Robbert – 01:34Then I, the human, went through, refined it, edited, made sure it was accurate, and I put it all into this kit. It has frameworks, examples, stories—everything you could use to be successful. Now I’m using generative AI to help me build it out as a course. I had a moment this morning where I was like, I really shouldn’t be using generative AI. I should be doing this myself because now it’s disingenuous, it’s not authentic, it’s not me because the tool is creating it faster. Then I stopped and I actually read through what was bei

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In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss how to break free from the AI sophomore slump. You’ll learn why many companies stall after early AI wins. You’ll discover practical ways to evolve your AI use from simple experimentation to robust solutions. You’ll understand how to apply strategic frameworks to build integrated AI systems. You’ll gain insights on measuring your AI efforts and staying ahead in the evolving AI landscape. Watch now to make your next AI initiative a success!

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Christopher S. Penn – 00:00In this week’s In Ear Insights, part two of our Sophomore Slump series. Boy, that’s a mouthful.

Katie Robbert – 00:07We love alliteration.

Christopher S. Penn – 00:09Yahoo. Last week we talked about what the sophomore slump is, what it looks like, and some of the reasons for it—why people are not getting value out of AI and the challenges. This week, Katie, the sophomore slump, you hear a lot in the music industry? Someone has a hit album and then their sophomore album, it didn’t go. So they have to figure out what’s next. When you think about companies trying to get value out of AI and they’ve hit this sophomore slump, they had early easy wins and then the easy wins evaporated, and they see all the stuff on LinkedIn and wherever else, like, “Oh, look, I made a million dollars in 28 minutes with generative AI.” And they’re, “What are we doing wrong?”

Christopher S. Penn – 00:54How do you advise somebody on ways to think about getting out of their sophomore slump? What’s their next big hit?

Katie Robbert – 01:03So the first thing I do is let’s take a step back and see what happened. A lot of times when someone hits that sophomore slump and that second version of, “I was really successful the first time, why can’t I repeat it?” it’s because they didn’t evolve. They’re, “I’m going to do exactly what I did the first time.” But your audience is, “I saw that already. I want something new, I want something different.” Not the exact same thing you gave me a year ago. That’s not what I’m interested in paying for and paying attention to.

Katie Robbert – 01:36So you start to lose that authority, that trust, because it’s why the term one hit wonder exists—you have a one hit wonder, you have a sophomore slump. You have all of these terms, all to say, in order for people to stay interested, you have to stay interesting. And by that, you need to evolve, you need to change. But not j

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In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss the generative AI sophomore slump.

You will discover why so many businesses are stuck at the same level of AI adoption they were two years ago. You will learn how anchoring to initial perceptions and a lack of awareness about current AI capabilities limits your organization’s progress. You will understand the critical difference between basic AI exploration and scaling AI solutions for significant business outcomes. You will gain insights into how to articulate AI’s true value to stakeholders, focusing on real world benefits like speed, efficiency, and revenue. Tune in to see why your approach to AI may need an urgent update!

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Christopher S. Penn – 00:00In this week’s In-Ear Insights, let’s talk about the sophomore slump. Katie, you were talking about the sophomore slump in regards to generative AI. I figured we could make this into a two-part series. So first, what is the sophomore slump?

Katie Robbert – 00:15So I’m calling it the sophomore slump.

Basically, what I’m seeing is a trend of a lot of companies talking about, “We tried. We started implementing AI two years ago—generative AI to be specific—and we’re stalled out.”

We are at the same place we were two years ago. We’ve optimized some things. We’re using it to create content, maybe create some images, and that’s about it.

Everyone fired everyone. There’s no one here. It’s like a ghost town. The machines are just whirring away in the background.

And I’m calling it the sophomore slump because I’m seeing this pattern of companies, and it all seems to be—they’re all saying the same—two years ago.

Katie Robbert – 01:03And two years ago is when generative AI really hit the mainstream market in terms of its availability to the masses, to all of us, versus someone, Chris, like you, who had been using it through IBM and other machine learning systems and homegrown systems.

So I bring it up because it’s interesting, because I guess there’s a lot to unpack here.

AI is this magic tool that’s gonna solve your problems and do all the things and make you dinner and clean your room.

I feel like there’s a lot of things wrong or a lot of things that are just not going right. A lot of companies are hitting this two-year mark, and they’re like, “What now? What happened? Am I better off? Not really.”

Katie Robbert – 02:00I’m just paying for more stuff. So Chris, are you seeing this as well? Is

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In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss the Apple AI paper and critical lessons for effective prompting, plus a deep dive into reasoning models.

You’ll learn what reasoning models are and why they sometimes struggle with complex tasks, especially when dealing with contradictory information. You’ll discover crucial insights about AI’s “stateless” nature, which means every prompt starts fresh and can lead to models getting confused. You’ll gain practical strategies for effective prompting, like starting new chats for different tasks and removing irrelevant information to improve AI output. You’ll understand why treating AI like a focused, smart intern will help you get the best results from your generative AI tools. Tune in to learn how to master your AI interactions!

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Christopher S. Penn – 00:00In this week’s In Ear Insights, there is so much in the AI world to talk about. One of the things that came out recently that I think is worth discussing, because we can talk about the basics of good prompting as part of it, Katie, is a paper from Apple. Apple’s AI efforts themselves have stalled a bit, showing that reasoning models, when given very complex puzzles—logic-based puzzles or spatial-based puzzles, like moving blocks from stack to stack and getting them in the correct order—hit a wall after a while and then just collapse and can’t do anything. So, the interpretation of the paper is that there are limits to what reasoning models can do and that they can kind of confuse themselves. On LinkedIn and social media and stuff,

Christopher S. Penn – 00:52Of course, people have taken this to the illogical extreme, saying artificial intelligence is stupid, nobody should use it, or artificial general intelligence will never happen. None of that is within the paper. Apple was looking at a very specific, narrow band of reasoning, called deductive reasoning. So what I thought we’d talk about today is the paper itself to a degree—not a ton about it—and then what lessons we can learn from it that will make our own AI practices better. So to start off, when we talk about reasoning, Katie, particularly you as our human expert, what does reasoning mean to the human?

Katie Robbert – 01:35When I think, if you say, “Can you give me a reasonable answer?” or “What is your reason?” Thinking about the different ways that the word is casually thrown around for humans. The way that I think about it is, if you’re looking for a reasonable answer to something, then that means that you are putting the expectat

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In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss their new AI-Ready Marketing Strategy Kit. You’ll understand how to assess your organization’s preparedness for artificial intelligence. You’ll learn to measure the return on your AI initiatives, uncovering both efficiency and growth opportunities. You’ll gain clarity on improving data quality and optimizing your AI processes for success. You’ll build a clear roadmap for integrating AI and fostering innovation across your business. Tune in to transform your approach to AI!

Get your copy of the kit here.

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Christopher S. Penn – 00:00In this week’s In Ear Insights, let’s talk about AI readiness. We launched on Tuesday our new AI Readiness Kit. And so, Katie, just to start off, what’s in for the people who didn’t read all the emails? What’s in the thing, and why are people supposed to look into this?

Katie Robbert – 00:16So I’m really proud of this new piece that we put together because we talk a lot about the different frameworks. We talk about Five Ps, we talk about Six Cs, we talk about STEM, we talk about how do you measure ROI? And we talk about them all in different contexts. So we took the opportunity to—

Speaker 3 – 00:42Put them all together into one place.

Katie Robbert – 00:44In a hopefully coherent flow. To say, if you’re trying to get yourself together, if you’re trying to integrate AI, or if you already have and you’re struggling to really make it stick, use this AI Ready Marketing Strategy Kit. So you can get that at TrustInsights.AI/kit. It’s really the best of the best. It’s all of our frameworks. But it’s not just, “Here’s a framework, good luck.”

Speaker 3 – 01:18There’s context around how to use it.

Katie Robbert – 01:20There’s checklists, there’s calculations, there’s explanations, there’s expectations—it’s basically the best alternative to having me and Chris sitting next to you when we can’t sit next to you to say, “You should think about doing this.”

Speaker 3 – 01:41You should probably think about this.

Katie Robbert – 01:43Here’s how you would approach this. So it’s sort of an—

Speaker 3 – 01:46Extension of me and Chris sitting with you to walk you through these things.

Christopher S. Penn – 01:52One of the questions that people have the most, especially as they start doing AI pilots and stuff, is what’s the RO

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In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss the critical considerations when deciding whether to hire an external AI expert or develop internal AI capabilities.

You’ll learn why it is essential to first define your organization’s specific AI needs and goals before seeking any AI expertise. You’ll discover the diverse skill sets that comprise true AI expertise, beyond just technology, and how to effectively vet potential candidates. You’ll understand how AI can magnify existing organizational challenges and why foundational strategy must precede any AI solution. You’ll gain insight into how to strategically approach AI implementation to avoid costly mistakes and ensure long-term success for your organization. Watch now to learn how to make the right choice for your organization’s AI future.

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Christopher S. Penn – 00:00In this week’s In-Ear Insights, a few people have asked us the question, should I hire an AI expert—a person, an AI expert on my team—or should I try to grow AI expertise, someone as an AI leader within my company? I can see there being pros and cons to both, but, Katie, you are the people expert. You are the organizational behavior expert. I know the answer is it depends. But at first blush, when someone comes to you and says, hey, should I be hiring an AI expert, somebody who can help shepherd my organization through the crazy mazes of AI, or should I grow my own experts? What is your take on that question?

Katie Robbert – 00:47Well, it definitely comes down to it depends. It depends on what you mean by an AI expert. So, what is it about AI that they are an expert in? Are you looking for someone who is staying up to date on all of the changes in AI? Are you looking for someone who can actually develop with AI tools? Or are you looking for someone to guide your team through the process of integrating AI tools? Or are you looking for all of the above? Which is a totally reasonable response, but that doesn’t mean you’ll get one person who can do all three. So, I think first and foremost, it comes down to what is your goal? And by that I mean, what is the AI expertise that your team is lacking?

Katie Robbert – 01:41Or what is the purpose of introducing AI into your organization? So, unsurprisingly, starting with the 5P framework, the 5Ps are purpose, people, process, platform, performance, because marketers like alliteration. So, purpose. You want to define clearly what AI means to the company, so not your ‘what I did over summer vacation’ essay, but what AI means to me.

What do you want to do with AI? Why are you bringing AI in? Is it because I want to kee

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In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss troubling new trends in leadership and how you can navigate an increasingly demanding work environment.

You’ll learn to identify the difference between tough business decisions and terrible leadership tactics. You’ll discover practical strategies to document issues and set healthy boundaries with difficult leaders. You’ll understand the critical importance of building your personal brand and professional network as your career life raft. You’ll explore how to use new tools, including AI, to enhance your skills and uncover hidden job opportunities. Watch this episode to gain actionable advice and empower yourself in today’s evolving workplace!

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Christopher S. Penn – 00:00In this week’s In-Ear Insights, Katie, “Everybody’s replaceable; work-life balance’s your problem”—which is what the CEO of shapewear company Skims and the label Good American had to say. Other people in positions of power have had equally less. I guess I don’t even know how to put this.

It is a definite tone shift. This is from a Wall Street Journal article from May 11, titled “Everybody’s Replaceable: The New Ways Bosses Talk About Workers.” And the punchline is: shut up, stop complaining, and do more work for less pay.

Katie Robbert – 00:46The thing I took away from this is, first of all, look at the companies that have been listed. So you have Skims, you have Starbucks, you have J.P. Morgan, you have Uber. Yeah. So these are big global tech companies and consumer brands.

So Skims and Good American are the Kardashians. So take that with a big fat boulder of salt.

Katie Robbert – 01:19Uber has had nothing but issues.

Katie Robbert – 01:23Starbucks, same thing. So I look at these companies and—yup, that’s completely on brand for those particular companies because those particular companies have had really shitty leadership issues.

Katie Robbert – 01:38For a long time.

Katie Robbert – 01:40Therefore, I read this article and I don’t fully believe that it’s a good representation of quote-unquote corporate America. I just don’t.

I’m not naive enough to think that there aren’t leaders out there in companies speaking this way. They absolutely are. But that’s not new. AI is not creating this problem.

Katie Robbert – 02:07This is not a new problem.

Katie Robbert – 02:09New tech, same problem. If your lea

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In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss the crucial difference between ‘no code AI solutions’ and ‘no work’ when using AI tools.

You’ll grasp why seeking easy no-code solutions often leads to mediocre AI outcomes. You’ll learn the vital role critical thinking plays in getting powerful results from generative AI. You’ll discover actionable techniques, like using frameworks and better questions, to guide AI. You’ll understand how investing thought upfront transforms AI from a simple tool into a strategic partner. Watch the full episode to elevate your AI strategy!

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Christopher S. Penn – 00:00In this week’s In Ear Insights, I have a bone to pick with a lot of people in marketing around AI and AI tools. And my bone to pick is this, Katie. There isn’t a day that goes by either in Slack or mostly on LinkedIn when some person is saying, “Oh, we need a no code tool for this.” “How do I use AI in a no code tool to evaluate real estate proposals?”

And the thing is, when I read what they’re trying to do, they seem to have this idea that no code equals no work. That it’s somehow magically just going to do the thing. And I can understand the past tense aversion to coding because it’s a very difficult thing to do.

Christopher S. Penn – 00:49But in today’s world with generative AI, coding is as straightforward as not coding in terms of the ability to make stuff. Because generative AI can do both, and they both have very strong prerequisites, which is you gotta think things through. It’s not no work. Neither case is it no work. Have you seen this also on the various places we hang out?

Katie Robbert – 01:15Well, first, welcome to the club. How well do your ranty pants fit? Because that’s what you are wearing today. Maybe you’re in the ranty shirt club. I don’t know.

It’s… I think we were talking about this last week because I was asking—and I wasn’t asking from a ‘I don’t want to do the work’ standpoint, but I was asking from a ‘I’m not a coder, I don’t want to deal with code, but I’m willing to do the work’ standpoint. And you showed me a system like Google Colab that you can go into, you can tell it what you want to do, and you can watch it build the code. It can either keep it within the system or you can copy the code and put it elsewhere. And that’s true of pretty much any generative AI system.

Katie Robbert – 02:04You can say, “I want you to build code for me to be able to do X.” Now, the reaso

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In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss codependency on generative AI and the growing risks of over-relying on generative AI tools like ChatGPT.

You’ll discover the hidden dangers when asking AI for advice, especially concerning health, finance, or legal matters. You’ll learn why AI’s helpful answers aren’t always truthful and how outdated information can mislead you. You’ll grasp powerful prompting techniques to guide AI towards more accurate and relevant results. You’ll find strategies to use AI more critically and avoid potentially costly mistakes. Watch the full episode for essential strategies to navigate AI safely and effectively!

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Christopher S. Penn – 00:00In this week’s In Ear Insights, let’s talk about the way that people are prompting generative AI tools like ChatGPT. I saw my friend Rebecca the other day was posting about how she had asked ChatGPT about a bunch of nutritional supplements she was taking and some advice for them. And I immediately went, oh, stop.

We have three areas where we do not just ask generative AI for information because of the way the model is trained. Those areas are finance, law and health. In those areas, they’re high risk areas. If you’re asking ChatGPT for advice without providing good data, the answers are really suspect. Katie, you also had some thoughts about how you’re seeing people using ChatGPT on LinkedIn.

Katie Robbert – 00:55Well, I was saying this morning that it’s hard to go on LinkedIn. LinkedIn is where we’re all trying to connect with each other professionally, be thought leaders, share our experience. But it’s so hard for me personally, and this is my own opinion because every time I open LinkedIn the first thing I see is a post that says, “Today I asked ChatGPT.”

Every post starts with, “So I was talking with ChatGPT.” “ChatGPT was telling me this morning.” And the codependency that I’m seeing being built with these tools is alarming to me and I’m oversimplifying it, but I don’t see these tools as any better than when you were just doing an Internet search. What I mean by that is the quality of the data is not necessarily better.

Katie Robbert – 01:49They can do more bells and whistles, they have more functions, they can summarize things, they can do backflips and create images and whatever. But the data is not different. You’re not getting better quality data. If anything, you’re probably getting more junk because you’re not asking specific questions like you

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In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss navigating the pressure of AI transformation and competitive parity.

You’ll learn why chasing AI trends without a clear purpose can harm your business. You’ll discover how to distinguish between merely optimizing current operations and driving real innovation. You’ll understand the importance of grounding your strategy in actual customer needs, not just competitor activity. You’ll explore how to assess new technologies like AI without getting caught up in hype. Watch the full episode to gain clarity on making smart AI decisions for your company!

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Christopher S. Penn – 00:00In this week’s In-Ear Insights, let’s talk about Oh, Katie, it’s your favorite term—digital transformation, specifically AI transformation. The context for this is we got an email from a colleague, a friend, who said, “Hey, I want to talk about how we could be using AI to take our company’s stuff”—they’re a software company—”and get it to parity with the the the current environment. And there’s got to be a way, an AI way to do that.”

We both had strong reactions to this, and I I’m happy to share mine in a little bit, but I wanted to get your take on this person’s request. How do I use AI to to catch up to where the rest of my industry is right now?

Katie Robbert – 00:49I feel like it goes back to that very old, keeping up with the Joneses kind of phrasing, where it’s like, gosh, I’m gonna throw a bunch of cliches at you. The grass is greener. Keeping up with the Joneses—all those things where you look over the fence and you see what the other guy is doing, and you think, I want that.

Versus looking at your own environment, look at your own home. What you have, and saying, you know what? This is good. This suits me. And that’s the challenge I have when I hear things like that, of, do I need—I mean, I I went through this myself a couple weeks ago. We talked about it when we talked about MCPs on the podcast. It started with, am I falling behind?

Katie Robbert – 01:37Do I need to be keeping up with the Joneses? And the answer was no. I need to stay the course and do what I’m doing. Yes, I need to be aware and not put my head in the sand. But trying to do what other people are seemingly doing doesn’t fit my needs or the needs of the company.

It’s not where I’m needed. And so when I see even bigger initiatives to try to keep up with the industry as a whole, my first question is, why? What is it that is going to benefit your company

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In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss the problem with buyer personas and how to master B2B marketing with smarter audience targeting. You’ll learn the critical differences between ideal customer profiles and buyer personas—and why using both transforms your strategy. You’ll discover how to ethically leverage AI and data to identify hidden pain points before prospects even recognize them. You’ll explore practical frameworks to align your content with every stage of the customer journey, from awareness to retention. You’ll gain actionable tactics to avoid common pitfalls and turn casual viewers into loyal buyers. Watch now to revolutionize how you connect with your audience!

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Christopher S. Penn – 00:00In this week’s In-Ear Insights, let’s talk about buyer personas in B2B marketing—how AI is affecting them and why.

Actually, I want to dig into this, Katie, and I want your take. What’s the difference to you between an ideal customer profile and a buyer persona? A lot of people use those terms interchangeably, but they may or may not mean the same thing. What’s your take?

Katie Robbert – 00:28I can understand why people use them interchangeably because there’s this notion that it’s some kind of representation of somebody who would eventually purchase something from you. In that sense, they are the same. The nuance—at least the way I break them out—is an ideal customer profile covers awareness and consideration, whereas a buyer persona covers purchase and the stages beyond that.

The challenge I see in B2B marketing is many people create buyer personas, which is great, but there are assumptions baked in that this person already fully understands the problem and that you can solve it for them.

If you’re using your buyer persona to do a content strategy—to create content or evaluate your marketing—you’ve already skipped over awareness and consideration. You’re at the buying stage now.

When we beta-tested our ideal customer profile service, our friend Brooke Sellis from B Squared gave us her buyer persona playbook to compare against the ICP we built. That’s where we saw the disconnect—her playbook assumed everyone was already in the pipeline and knew the problem.

Our ICP analysis is meant to help marketers approach people who may not even know there’s a problem yet. You create content that resonates so when they do identify the problem, they enter your buyer’s journey. The ICP gets to them before that.

The challenge with buyer personas is they focus too much on someone already knowing what’s wrong and looking for a solution. In marketing, 99% of the time, they don’t know there’s a problem—or the

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In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss MCP (Model Context Protocol) and agentic marketing. You’ll learn how MCP connects AI tools to automate tasks—but also why technical expertise is essential to use it effectively. You’ll discover the three layers of AI adoption, from manual prompts to fully autonomous agents, and why skipping foundational steps leads to costly mistakes. You’ll see why workflow automation (like N8N) is the bridge to agentic AI, and how to avoid falling for social media hype. Finally, you’ll get practical advice on staying ahead without drowning in tech overwhelm. Watch now to demystify AI’s next big thing!

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Christopher S. Penn – 00:00In this week’s In-Ear Insights, let’s talk about MCP—Model Context Protocol—and its applications for marketing and what it means. Katie, you said you have questions.

Katie Robbert – 00:13I do. I saw you posted in our free Slack group, Analytics for Marketers, towards the end of last week that one of the models had MCP available. When I see notifications like that, my first thought is: Is this something I need to pay attention to? Usually, you’re really good about letting me know, but I am a fully grown human who needs to be responsible for what I should be paying attention to and not just relying on the data scientist on my team. That was my first gut reaction—which is fair, because you’re a busy person. I like to keep you very busy, and you don’t always have time to let me know what I should be paying attention to. So that was problem one.

Problem number two is, yes, you post things typically ahead of when they become more commonplace announcements. I saw a post this morning that I shared with you about MCP and agentic marketing processes, and how it’s going to replace your SEO if you’re doing traditional SEO. For some reason, that raised all of my insecurities and anxieties. Oh my gosh, I really am falling behind because I like to tell people about getting their foundation squared away. If I’m being really honest with myself, I think I focus on that because I feel so lost when I think about AI, agentic processes, MCP, N8N, and all these other things. So I’m like, let me focus on what I know best.

But I am now in the boat where I feel like my boat is trailing behind the giant AI yacht. I’m dog-paddling to try to keep up, and I’m just not there. So help me understand a couple of things. One, what i

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In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss Retrieval Augmented Generation (RAG). You’ll learn what RAG is and how it can significantly improve the accuracy and relevance of AI responses by using your own data. You’ll understand the crucial differences between RAG and typical search engines or generative AI models, clarifying when RAG is truly needed. You’ll discover practical examples of when RAG becomes essential, especially for handling sensitive company information and proprietary knowledge. Tune in to learn when and how RAG can be a game-changer for your data strategy and when simpler AI tools will suffice!

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Machine-Generated TranscriptWhat follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for listening to the episode.

Christopher S. Penn – 00:00In this week’s In Ear Insights, let’s…

Christopher S. Penn – 00:02Talk about RAG—Retrieval augmented generation.

Christopher S. Penn – 00:06What is it?

Christopher S. Penn – 00:07Why do we care about it?

Christopher S. Penn – 00:09So Katie, I know you’re going in kind of blind on this. What do you know about retrieval augmented generation?

Katie Robbert – 00:17I knew we were going to be talking about this, but I purposely didn’t do any research because I wanted to see how much I thought I understood already just based on. So if I take apart just even the words Retrieval augmented generation, I think retrieval means it has…

Katie Robbert – 00:41To go find something augmented, meaning it’s…

Katie Robbert – 00:44Going to add on to something existing and then generation means it’s going to do something. So it’s going to find data added on to the whatever is existing, whatever that is, and then create something. So that’s my basic. But obviously, that doesn’t mean anything. So we have to put it in…

Katie Robbert – 01:05The context of generative AI.

Katie Robbert – 01:07So what am I missing?

Christopher S. Penn – 01:09Believe it or not, you’re not missing a whole lot. That’s actually a good encapsulation. Happy Monday. Retrieval augmented generation is a system for bringing in contextual knowledge to a prompt so that generative AI can do a better job.

Probably one of the most well-known and easiest-to-use systems like this is Google’s free NotebookLM where you just put in a bunch of documents. It does all the work—the technical stuff of tokenization and embeddings and all that stuff. And then you can chat with your documents and say, ‘Well, what’s in this?’

In our examples, we’ve

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In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss the ethics of AI and ethical dilemmas surrounding digital twins and AI clones. You’ll discover the crucial ethical questions surrounding digital twins and AI clones in today’s rapidly evolving digital world. You’ll learn why getting consent is not just good manners but a fundamental ethical necessity when it comes to using someone’s data to create a digital representation. You’ll understand the potential economic and reputational harm that can arise from unauthorized digital cloning, even if it’s technically legal. Tune in to learn how to navigate the complex ethical landscape of digital twins and ensure your AI practices are responsible and respectful.

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Machine-Generated TranscriptWhat follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for listening to the episode.

Christopher S. Penn – 00:00In this week’s In Ear Insights, we’re talking about digital twins or digital clones, people using generative AI tools to try and copy other people so that you can ask them questions. As an example, I might take all the blog posts or all the letters from the corner office that Katie’s written and put them into a generative AI tool like ChatGPT to create a clone of her and then say, hey, Katie, GPT, what do you think about my latest idea?

We’re not going to go into the technicals of this, of how you do this. Katie, you want to talk about more why or why not you should do this. And I’ll preface this with my absolute favorite clip from Jurassic Park.

Katie Robbert – 00:46Yeah.

Christopher S. Penn – 00:47But your scientists were so preoccupied with whether or not they could, they didn’t stop to think if they should.

Katie Robbert – 00:52That’s true. Jeff Goldblum, listen to the man. Smart guy. You said a couple of things that I think are interesting. You positioned this with a very specific use case of people are creating digital twins in order to ask them questions.

I think that’s a very narrow way of thinking about it because that assumes, oh, I don’t want to pay for Chris Penn’s time. If I create his digital twin, I can get all the consulting I need. I personally don’t think that’s how people are thinking about it. Hey, if I can clone a Chris Penn, I don’t have to pay him for contributed content. I can then say, Chris did this thing, or this is Chris’s voice or whatever it is, or probably more nefarious things

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In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss offsite optimization for generative AI.

You’ll learn how to rethink your offsite SEO strategy to effectively engage with AI models. Discover how to identify the crucial data sources that AI uses to inform its knowledge. You will understand why traditional SEO metrics are becoming less relevant in the age of AI and what truly matters for offsite success. Prepare to revolutionize your PR approach and ensure your brand is recognized by the AI systems shaping the future. Watch now to gain the offsite AI optimization advantage.

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Machine-Generated TranscriptWhat follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for listening to the episode.

Christopher S. Penn – 00:00

In this week’s In Ear Insights, this is week three, part three of our optimizing your content and your marketing for AI. You know, people call it SEO for AI and a variety of tongue twisting acronyms. This week we’re talking about off site, which I feel like is probably the most accessible for folks because it’s the least technical and it is very familiar ground.

Off site AI optimization is essentially how can you be in as many places as possible as often as possible so that your name, your brand, and your content are duplicated as many places as AI crawlers grab their data from to build a training dataset library. So Katie, when we talk about going out into the world, what comes to mind for you for making that work and for being as many places as you possibly can be?

Katie Robbert – 00:58

You know, it’s funny because you just said something to the effect of it’s the most accessible or it’s the easiest. And I disagree because I feel like it’s the one that’s the least in your control. So, you know, when we talk about off site, we’re talking about getting on lists and guest posts and other people mentioning you.

And it’s not enough to just post about yourself on LinkedIn a couple of times a day. Sure, that’s part of it, but that it’s much more than that. And so, when I think about off site, I still think, okay, number one, I still have to have really good content, which is where we started this series that’s useful and appeals to my audience. So you can’t skip that step and suddenly just say, you know what?

Katie Robbert – 01:54

I’m gonna get on a bunch of who’s who lists or top 10 lists or whatever, because without that content as your fo

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In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss optimizing your AI content strategy in the age of artificial intelligence. You’ll discover how to make your content appealing to both humans and AI algorithms, ensuring maximum reach and engagement. You will learn to balance data-driven AI optimization with the irreplaceable value of human creativity and unique brand voice. You’ll gain practical strategies to adapt your content creation process and stay ahead of the curve in the evolving digital landscape. Tune in now to learn how to future-proof your content strategy!

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Machine-Generated TranscriptWhat follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for listening to the episode.

Christopher S. Penn – 00:00In this week’s In Ear Insights, we are continuing our look at how to optimize content for AI. Previously, we talked about what this is broadly, and on the last live stream we talked about content and the technical side. This week, on the live stream on Thursday, we’re going to be talking about what you should be doing with content. And Katie, I wanted to get your thoughts about the content itself—not the structure, not the technical stuff, and not what you’re doing to pitch it, but the content itself. When you think about optimizing content for artificial intelligence as opposed to traditional search engines, what do you think about it from the perspective, especially from the perspective of the user, the way people use AI versus the way people use traditional search engines?

Katie Robbert – 00:47It’s tough because I personally feel like you should still be putting the human audience first. But it really depends on—it was going to say it depends on your goal. If you want the AI engines to pick it up, then prioritize that. But I can’t say that because yes, the AI engine is going to pick it up, but it’s still a human that is looking for it and consuming it. So you still have to prioritize the human in terms of who is the audience for this content. Now, I know that you have—we can get into the nuance of that—you’ve written press releases specifically for AI engines that are not meant for human.

Katie Robbert – 01:35And that’s my understanding is those were done to literally just get the correct words into the engine so that if somebody typed in, “Tell me about Trust Insights,” that a knowledge block of some sort would come up and say, “This is what I know about Trust Insights.” That, to me, is a different kind of content than a video that’s a tutorial or a blog post. That’s an opinion. Those really should still be human first, AI second.

Christopher S. Penn – 02:13One of the things that a lot of folks

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In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss data preparation for generative AI. You’ll learn why having high-quality data is the essential ingredient for getting valuable insights from AI tools. Discover how to ensure your data is clean, credible, and comprehensive, avoiding the pitfalls of ‘garbage in, garbage out’. Explore practical steps you can take to master data quality and make generative AI work effectively for you. Tune in to learn how to take control of your data and unlock the true potential of generative AI!

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Christopher S. Penn – 00:00In this week’s In-Ear Insights, we’re talking data preparation for AI this week both on the Trust Insights live stream Thursday at 1pm Eastern Time. Remember, the USA if you’re a non-USA person, the USA has moved to summertime already, and I thought we’d talk today, Katie, about kind of why this is important. We’ll talk about the how on the live stream, but we’ll talk about the why and to degree the what. So before we begin, let me ask you what questions do you have about data preparation for generative AI?

Katie Robbert – 00:35I don’t so much have questions because this is the kind of thing that I am specifically well versed in. Not so much the how, but the why. I did a panel last week at Worcester Polytech for the Women in Data Science, and this actually came up a lot. Surprisingly, the reason it came up a lot, specifically data governance and did good data quality, was there were a lot of questions around, what should I be thinking about in my degree? What should I be focusing on? If AI is just going to automate everything, where do I, a data scientist, where do I, a PhD candidate, fit in? A lot of the students there were academically focused rather than corporate field focused.

Katie Robbert – 01:29I took the opportunity to talk about why data governance and good data quality is a foundational skill that regardless of the technology is going to be relevant. Having a good handle on what that actually means and why it’s important. If you’re unsure of where to focus, that’s a good place to start because it’s something that is always going to be in style, is always going to be on trend is good data quality. Because if you don’t have good data going into these pieces of software, and generative AI is just another piece of software, you’re going to have garbage coming out, and the outcomes are not going to be what you want them to do, and you’ll spend all of these times with these models and your random forest analysis and all of your other things, and nothing good i

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In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss the rise of SEO for AI, also known as Generative AI Optimization. You’ll discover how generative AI is changing the rules of search and what it means for your content strategy. You’ll learn how to use AI tools to uncover hidden insights about your online presence and identify what needs optimization. You’ll understand why high-quality content is still the most important factor and how to adapt your SEO efforts for this new AI-driven era. Tune in to learn practical steps you can take now to optimize for generative AI and stay ahead of the curve!

Key Points and Takeaways:

Generative AI Optimization (GAIO) is the new SEO: The episode discusses how traditional SEO is evolving with the rise of generative AI and introduces the concept of GAIO. This is a crucial shift for marketers to understand.
Quality Content Still Reigns Supreme: Regardless of AI, high-quality, helpful, and relevant content remains the foundation of any successful optimization strategy. This reinforces fundamental marketing principles.
AI as a ‘Secret Shopper’: Using AI tools to analyze your own online presence can reveal blind spots and areas for improvement, similar to a secret shopper evaluating customer experience. This offers a novel perspective on self-assessment.
Data-Driven GAIO Prioritization: The episode stresses the importance of using data to understand where AI is sending traffic and prioritizing optimization efforts based on this data, ensuring efficient resource allocation.
Intent Behind AI Search: Users engaging with generative AI search often have a higher intent to take action, making it a valuable channel to optimize for, even if it’s currently smaller than traditional search.
Continuous Content Maintenance: GAIO is not a one-time task but requires ongoing content auditing, pruning, and updating to ensure relevance and accuracy in the age of AI.

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Machine-Generated TranscriptWhat follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for listening to the episode.

Christopher S. Penn – 00:00In this week’s In Ear Insights, we’re talking SEO for AI. Or as I said in my personal newsletter this week, there’s so many words for this—Generative AI Optimization, Generative Engine Optimization, AI Search Engine Optimization. Yeah, I said it. By the time you go through all the acronyms and stuff, it sounds like IKEA furniture names. Katie, when

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In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris answer the key question: What are AI agents? They discuss the surprising flaw in agentic AI that everyone is overlooking. You’ll unravel the mystery of what truly defines an AI agent and how it differs from simple automation. You’ll learn why blindly trusting AI agents can lead to unexpected problems and wasted resources in your business. You’ll discover a practical framework to determine when an AI agent is genuinely needed and when simpler solutions will deliver better results. Tune in to find out if agentic AI is right for you and your business!

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Machine-Generated TranscriptWhat follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for listening to the episode.

Christopher S. Penn – 00:00In this week’s In Ear Insights, I wanted to talk today about the major flaw in agentic AI. Katie, you had some feedback for me?

Katie Robbert – 00:10Yeah, no, I think that’s a great topic. Once we actually set the baseline of what the heck is an AI agent? I’m around the terminology all the time. I see the work that you’re doing, I see the work that, you know, our peers are doing. But if I was asked like on a panel or you know, just, you know, by someone in our community to describe an AI agent, I don’t know that I could confidently say what an AI agent is specifically as compared to anything else that we’re doing. Anything else, like a custom model or a chatbot or any. Anything else. And so I think my first question is what is an AI agent specifically? And how is it different from all of the other things that we’ve been doing with generative AI?

Christopher S. Penn – 01:04This is a, a much more complicated question than it sounds. And the reason for that is because every vendor is trying to put their own spin on the term. And as a result you have like 28 conflicting drift definitions.

Katie Robbert – 01:19Okay, so it’s not just me. That’s fantastic.

Christopher S. Penn – 01:22It is not just you. And there’s a lot of people making a lot of noise and throwing a lot of confusing stuff in the air. And you’re like, will you all just settle down? You all need to calm down. Here’s the easiest definition that is completely unhelpful. An AI agent is an agent that uses AI. And.

Katie Robbert – 01:43But yeah, that is completely unhelpful. The question is, what is an agent?

Christopher S. Penn – 01:50That is the question.

Katie Robbert – 01:51Okay?

Christopher S. Penn – 01:52Agents have been around since, for the last three decades. If you’ve ever installed like Norton antivirus on a computer, you’ll see a little antivirus agent running in your processes list. And it is that agent is in the background doing its thing, scann

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In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss deconstructing generative AI use cases.

You will learn how to cut through the hype and understand how to truly use AI to solve real problems. You’ll discover a practical framework to break down complex AI initiatives into manageable steps. This episode will show you how to avoid common pitfalls and ensure your AI projects deliver measurable results. Watch this episode to gain actionable insights and start making AI work for you today!

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Machine-Generated TranscriptWhat follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for listening to the episode.

Here’s the edited transcript, adhering to all provided rules and guidelines:

Christopher S. Penn — 00:00

In this week’s In Ear Insights, let’s talk about deconstructing AI use cases. We recently had a lot of conversations. Last week’s live streams, we talked about skills matrices and understanding sort of the people that we have available to us and what their skills are when it comes to everything, but including generative AI. And Katie, you were talking recently with a colleague who gave some real time voice of the customer feedback about AI. Can you walk through what you heard?

Katie Robbert — 00:29

Yeah, absolutely. So a good friend of mine was at a conference and she was sharing with me some of the feedback that people were giving when it comes to implementing AI. And so basically what she was saying was sort of quote, “my boss says that we’re getting Copilot now. What? How do I prove the value of the tool and the value of the work that I do?” And what that says to me is, once again, people are choosing the platforms first and then trying to figure out what to do with it. That’s a reality that we knew was going to happen with a lot of organizations. When I say choosing the platform first, what I’m referring to is the 5P framework—purpose, people, process, platform, performance. The 5P’s were my reaction to digital transformation, which is people, process, technology, platform.

Katie Robbert — 01:26

They could have done better with their alliteration. The challenge with digital transformation, in my personal experience and a lot of people that I’ve talked to, is that it puts the technology, the platform, first and then tries to figure out where people and process fit in. And this is what we’re seeing play out at a lot of organizations is they’re saying, “great, generative AI is going to solve all of our problems. Let’s go ahead and pick a tool. Oh, we’re a Microsoft shop. So let’s get Copilot. Oh, we’re a Google shop. Let’s get Gemini. Oh, OpenAI, ChatGPT is the one I hear the mo

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In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss the importance of a skills matrix assessment in today’s rapidly evolving work environment. You’ll understand what a skills matrix assessment is and why it’s essential for navigating today’s rapidly changing work environment. You’ll discover how to pinpoint exactly where your team’s skills excel and where they need development, particularly with AI on the rise. You’ll learn to strategically plan for the future, ensuring your organization remains competitive and adaptable to new technologies. You’ll explore how to break down complex skill needs into manageable parts for clear evaluation and growth. Tune in to discover how a skills matrix can transform your approach to talent and future-proof your organization!

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Machine-Generated TranscriptWhat follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for listening to the episode.

Christopher S. Penn – 00:00In this week’s In Ear Insights, let’s talk about the skills matrix. What it is, or more importantly, why you would care about such a thing and how, in general, we think about skills evaluation in a world where the skills are changing so darn fast. This week on the Trust Insights live stream, we’ll be talking about how to do a skills matrix assessment. But Katie, walk through the basics. What, when we’re talking about evaluating people’s skills, what is it that we’re actually doing?

Katie Robbert – 00:34We are looking at both hard and soft skills. So hard skills being things that you could write out in a process and follow from A to Z. So math, for example, is a hard skill because it’s something that multiple people can learn from a textbook, from an instructor, that kind of a thing. Programming and development is a hard skill. Project management is a hard skill. Visual design, believe it or not, is a hard skill. Soft skills are more of the human component. So management in terms of communication and empathy and those kinds of things. So, when we talk about a skills matrix, what we’re doing is we are mapping what the company needs to what the people have. And so it helps you see where the gaps are and where you might be perhaps over indexed.

Katie Robbert – 01:41Because we tend to think about it in terms of let’s find the gaps, but we forget, oh, perhaps we have 10 developers on a project that needs only two. So a skills matrix, you call it a matrix, call it a spreadsheet, call it a checklist, whatever you want to call it, really is, what do we have, what do we need, and where’s the overlap? The reason you would want to put a skills matrix together is to figure out, do you have the right people doing the right things today? You know,

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In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss how reasoning models, a new type of AI, can revolutionize your scenario planning. You’ll discover how these advanced AI models can help you anticipate unforeseen challenges and opportunities for your business. Learn to move beyond reactive panic planning and create robust strategies for any future scenario. You will explore how to prepare your business knowledge for AI and scale your scenario planning efforts effectively across your organization. Tune in to learn how to leverage reasoning models to build a resilient and future-proof business today!

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Machine-Generated TranscriptWhat follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for listening to the episode.

Christopher S. Penn – 00:00In this week’s In Ear Insights, life is full of change and unpredictable, unforeseen circumstances of all kinds. And we’ve talked in the past, many times on this show about scenario planning, about coming up with what do we do if this happens, if this happens. However, a new family, a new genre of AI models has come out in the last two months really called reasoning models. First with OpenAI’s 01 and then with Deep Seek and its R1 and now OpenAI’s O3 and Google Gemini 2. Flash thinking. I hate their product naming and so many other models within this class which give us as marketers and as business professionals the ability to have a model that can really think things through. So Katie, when you hear about reasoning models and think about the need for good scenario planning, what comes to mind?

Katie Robbert – 01:05Well, I guess the first thing is, admittedly, you’ve been talking about reasoning models since they hit the market. I don’t know that I really understand what a reasoning model is versus, you know, an existing open generative AI model. I don’t even know the terminology to be quite honest. When I hear “reason model,” it’s like, “oh, is it sentient now?” What does that mean?

Christopher S. Penn – 01:36That’s a really good question. There’s three techniques in prompt engineering that we’ve talked about and which you can learn about in our prompt engineering course, Mastering Prompt Engineering at Trust Insights. Those three techniques are called chain of thought, reward functions, and reflection. And when we would do this by hand in the old days of AI three months ago, we would say things in a prompt. Our prompts would say, “think this through step by step. Show me your work, show me this, show me this. Explain this” in our prompts to say, “I want you to explain this.” So for example, I was

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In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss AI strategy and how to stay sane amidst the whirlwind of constant AI advancements. You’ll discover practical strategies to navigate the overwhelming influx of new AI models and technologies. You’ll learn how to prioritize your business needs and focus your AI efforts for maximum impact. You’ll understand how to use frameworks like the 5Ps to make informed decisions and avoid getting lost in the AI noise. Tune in to learn how to manage AI chaos and keep your sanity!

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Machine-Generated TranscriptWhat follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for listening to the episode.

Christopher S. Penn — 00:00In this week’s In-Ear Insights, there is so much happening in the world of AI right now. So, Google has its new reasoning model, Gemini Flash Thinking too. And then in the last week or so, different Chinese companies like ByteDance, the owners of TikTok, released their new models, Dubao. Deep Seek has V3 which came out in December, and R1, which came out last week, which is their reasoning model, which is the same performance as OpenAI’s model, trained at 1/100 of the cost, and for the average user who’s using it in an app or something, is 96% less expensive than OpenAI’s APIs. And then, of course, overnight, yet another new model, a different one called Kimi from China, also came out, which also promises state-of-the-art performance at an extremely low cost.

Christopher S. Penn — 00:53This can lead to some level of frustration and confusion among users. Over the weekend, we had clients messaging, saying, “What do we do this?” So Katie, when we think about… Because one of your themes for this year is foundation. When we think about everything that is happening and the speed at which is happening in AI, how, if you are, how are you staying sane and keeping sane in the world of ridiculously fast-paced change?

Katie Robbert — 01:26I mean, that would assume that I was sane in the first place. I mean, come on, we all saw that you set me up for that joke. It’s funny because everything you just said, I have no way of knowing if everything you just said is true or if you just made up a bunch of words and I’m like, “Oh, these are all new things.” And this is where finding those trustworthy sources, those experts, is going to be your best friend. So obviously, Chris, I have you, and as far as I’m aware, it is not in your best interest to make things up and try to convince me of something that isn’t actually t

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In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss Tiktok marketing and social media strategy diversification after the recent TikTok incident. You’ll learn how to create content that thrives regardless of platform changes. You’ll discover strategies to build a direct, trusting connection with your audience, reducing reliance on third-party platforms. You’ll explore how to use generative AI tools to enhance content creation and distribution. You’ll gain insights into building a resilient marketing strategy that leverages both owned and rented channels. Watch this episode to build a future-proof marketing plan that isn’t at the mercy of social media giants.

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Machine-Generated TranscriptWhat follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for listening to the episode.

Christopher S. Penn – 00:00

In this week’s In-Ear Insights. It was there, then it was gone, now it’s back, it’s TikTok. Of course, there are so many things to talk about on this particular topic, but—and I was saying this to a friend of mine—I said, I have so much to say, but none of it is productive, none of it is helpful, none of it is going to advance the company or my personal brand and stuff. So I’m just going to sit on my hands at Nintendo, tell it to AI. I have an AI chatbot of my own that I just grouse at constantly. It’s like, I understand you’re upset, but it keeps me from saying things in public that are stupid. So. So, Katie, I’m sure you have things that you want to say in public that are not stupid.

Katie Robbert – 00:46

Well, I don’t know if they’re not stupid, but I’ll say them anyway. I think it’s fairly well known that I’m not a heavy social media user in my personal life. And so obviously being in this industry and being at this particular company, I’m well aware of what different social media platforms do, who their audiences are and the utility of them. And so, for weeks that there’s speculation that TikTok was going to be banned in the U.S. I didn’t actually see a lot of people doing anything about it because I don’t think anyone really felt like it was going to happen. So they were like, oh, well, they’re just bluffing.

Katie Robbert – 01:31

And then, lo and behold, I think it was, as we’re recording this, it was just yesterday morning, people, when they logged into or tried to log into TikTok, got various forms of messages. To be honest, mine just said no Internet connection, so I didn’t even get a fun message. But regardless, people started to panic

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In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss the challenges of 2024 and how they learned to thrive. You will discover how Trust Insights adapted to the rapid changes in AI and found success with their R&D process. Learn why focusing on foundational elements such as people and process [...]Read More... from In-Ear Insights: Trust Insights 2024 Year In Review

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In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss rapid scenario planning for your business with the help of generative AI. You discover methods for using generative AI to plan for multiple potential futures, equipping you to handle various situations. You learn to identify constants in your business, like revenue [...]Read More... from In-Ear Insights: Rapid Scenario Planning with Generative AI

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In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss your digital ghost. You will learn whether employers can put your digital ghost to work, the legal and ethical implications of employers using your digital assets, how to safeguard your digital likeness, and what you need to negotiate with your employer. [...]Read More... from In-Ear Insights: Digital Ghosts, AI Ethics, and Employment Agreements

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In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss their key takeaways from the MarketingProfs B2B Forum 2024. You will learn about the surprising reality of AI adoption in marketing. You will discover the challenges of accurately measuring AI usage and the inherent biases in current surveys. You will gain [...]Read More... from In-Ear Insights: Marketing Lessons From MarketingProfs B2B Forum

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In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss the crucial topic of AI readiness for businesses. Discover the essential steps to determine if your organization is truly prepared to leverage AI effectively. Learn how to avoid common pitfalls and misconceptions surrounding AI implementation. Gain valuable insights into maximizing your [...]Read More... from In-Ear Insights: AI Readiness at MarketingProfs B2B Forum

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In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss navigating the unpredictable world of marketing strategic planning in uncertain times. You’ll discover valuable strategies for planning and adapting amidst unreliable data and ever-changing trends. Learn how to diversify your revenue streams and control what you can, even when external factors [...]Read More... from In-Ear Insights: Marketing Strategic Planning for Macro Trends in Uncertain Times

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In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss the new RAPPEL AI prompt framework for AI prompting, designed to simplify and improve your interactions with AI. Discover why priming the model is crucial for accurate results and how this framework helps you avoid common pitfalls. Learn how RAPPEL streamlines [...]Read More... from In-Ear Insights: The Trust Insights RAPPEL AI Prompt Framework Explained

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In this week’s In-Ear Insights, the TrustInsights.ai podcast, Katie and Chris discuss data protection, AI and privacy. How do you protect your data and client/customer data when using AI tools like ChatGPT? Learn the two major protection classes and three levels of data protection in this episode. Tune in to find out more! Watch the [...]Read More... from In-Ear Insights: Data Protection, AI and Privacy

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In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss using generative AI for conference attendees. Using the MarketingProfs B2B Forum 2024 (#MPB2B), you will learn how to leverage AI to define your goals, create a detailed plan to achieve them, and optimize your conference experience. You’ll discover how to use [...]Read More... from In-Ear Insights: Generative AI for Conference Attendees

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In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss using generative AI for strategic planning. You will learn how to leverage AI to translate data into actionable insights, overcoming the common struggle of understanding what to do with your marketing data. You’ll discover how to use AI to identify the [...]Read More... from In-Ear Insights: Generative AI for Strategic Planning

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In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris tackle the important topic of speaker selection at events, specifically focusing on inclusivity and representation. Discover the challenges and considerations when selecting speakers for events, particularly when catering to a specific audience, like women in a particular industry. Learn valuable strategies for [...]Read More... from In-Ear Insights: Gender Representation, Event Marketing, and Speaker Selection

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In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss implementing Responsible AI in your business. Learn how to align AI with your company values, establish accountability, ensure fairness in AI outputs, and maintain transparency in your AI practices. By understanding these elements, you can unlock the true potential of AI [...]Read More... from In-Ear Insights: Responsible AI Part 4: Implementing Responsible AI

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In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss their key takeaways from the Marketing AI Conference (MAICON) 2024. Learn why scaling AI beyond consumer tools like ChatGPT is crucial for businesses looking to leverage its full potential. Discover why process decomposition is essential for successfully integrating AI into your [...]Read More... from In-Ear Insights: MAICON 2024 Key Takeaways

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In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss the importance of data privacy when using generative AI for marketing. You’ll discover the risks of blindly trusting AI vendors and the critical role of transparency in building trust with your audience. Learn why a robust AI responsibility plan is not [...]Read More... from In-Ear Insights: Responsible AI Part 3, Data Privacy

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In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris tackle the issue of bias in AI, particularly managing bias in large language models. Discover real-life examples of how bias manifests and the potential consequences for businesses, including reputational damage and the reinforcement of harmful stereotypes. You will learn about a critical [...]Read More... from In-Ear Insights: Responsible AI Part 2, Managing Bias

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In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss how to use the power of Ideal Customer Profiles (ICPs) to achieve product market fit and accelerate your marketing success. You’ll learn practical strategies for leveraging ICPs to validate your product ideas, ensuring you are building solutions your target audience truly [...]Read More... from In-Ear Insights: Product Market Fit and the Ideal Customer Profile

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In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss change management, generative artificial intelligence, and how to help your team embrace AI and what to do with team members who are resistant to AI adoption. Learn why the human element remains critical for successful AI implementation and how to best [...]Read More... from In-Ear Insights: Change Management and Generative Artificial intelligence

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In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss the limitations of generative analytics, particularly for casual users. Discover why subject matter expertise is still essential for accurate data analysis, even with these powerful tools. Learn practical strategies for determining if a task is suitable for generative AI and how [...]Read More... from In-Ear Insights: Limitations of Generative Analytics

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In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss generative analytics, a new way to use AI to get more from your data. Discover why simply collecting data isn’t enough and how generative analytics helps you uncover actionable insights. Learn practical ways to apply this approach to your marketing efforts [...]Read More... from In-Ear Insights: What is Generative Analytics?

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In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss how the capabilities of generative AI are changing how executives think about building a team. Learn why simply cutting staff to save money is not a viable long-term strategy. Discover the questions you should ask to accurately assess your needs and [...]Read More... from In-Ear Insights: CEO Perspective on Generative AI and the Future of Work

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In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss the crisis of trust in institutions, influencer marketing strategy, and what it means for your marketing. Learn why Gen Z gravitates toward individual influencers and away from brands. Discover how to adapt your marketing to include influencers, even if you’re not [...]Read More... from In-Ear Insights: Trust and Influencer Marketing Strategy

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In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss using generative AI for non-business, fun tasks. You’ll discover how to leverage generative AI to solve everyday challenges like creating a recipe with specific ingredients or remembering what to pack for a trip. You’ll learn how to use AI to enhance [...]Read More... from In-Ear Insights: Generative AI for Non-Business Use Cases

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In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss generative AI data management best practices and how to leverage qualitative data for content marketing. You will learn how to extract valuable customer questions from various sources like webinars and forums using AI. Discover effective methods to prioritize this wealth of [...]Read More... from In-Ear Insights: Generative AI Data Management Best Practices

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In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris tackle the challenge of using generative AI in sales, both B2B and B2C. Discover how generative AI helps you analyze customer data and identify pain points to improve your selling approach. Learn how this technology can bridge the gap between sales, marketing, [...]Read More... from In-Ear Insights: How to Use Generative AI in Sales

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In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss generative AI and professional development, the importance of subject matter expertise when using AI for marketing tasks, such as analyzing backlink data. You’ll learn why relying solely on AI-generated insights without understanding the underlying data can be risky. Katie and Chris [...]Read More... from In-Ear Insights: Generative AI and Professional Development

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In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss how to spot an AI snake oil salesman, a vital skill in today’s hype-filled environment. Learn the questions to ask that reveal true expertise and understand the hallmarks of genuine AI knowledge. Discover the red flags to watch out for so [...]Read More... from In-Ear Insights: How to Spot AI Snake Oil Salesmen

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In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris explore the human side of how to adopt Agile and prompt engineering. Learn how to build trust within your team and address anxieties surrounding AI and automation. Discover strategies for fostering open communication and encouraging ownership among team members, even those hesitant [...]Read More... from In-Ear Insights: How to Help Marketers Adopt Agile and Prompt Engineering

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In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss using Agile principles for prompt engineering – agile prompt engineering. You will learn the importance of process development and project management when working with large language models (LLMs) like ChatGPT. Discover how to use the 5 P’s framework and the RACE [...]Read More... from In-Ear Insights: How to Apply Agile Prompt Engineering

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In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris provide a practical guide to prompt engineering, prompt engineering 101. Learn how to approach prompt creation like software development, starting with thorough requirements gathering using the 5 P’s framework. Discover the power of the RACE framework (Role, Action, Context, Execute) for crafting [...]Read More... from In-Ear Insights: Prompt Engineering 101

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In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris delve into the world of Agile methodology. You will gain a clear understanding of Agile principles and how they compare to traditional waterfall project management. Discover the benefits of Agile for marketing, including increased flexibility, customer-centricity, and the ability to adapt to [...]Read More... from In-Ear Insights: Introduction to Agile Marketing

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In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris tackle the complex and critical topic of AI ethics. You will explore the fundamental principles of ethics and how they apply to the development and use of artificial intelligence. Discover the importance of transparency, accountability, and minimizing harm when working with AI [...]Read More... from In-Ear Insights: What Are AI Ethics?

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In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss how to build an AI strategy. Discover why focusing on AI as a strategy is misguided and what you should do instead to achieve meaningful business outcomes. You’ll learn how to identify the actual problems you need to solve and how [...]Read More... from In-Ear Insights: How to Build an AI Strategy

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In this week’s In-Ear Insights, Katie and Chris discuss how the software development life cycle (SDLC) applies to prompt engineering in generative AI, why the prompt development life cycle (or prompt engineering life cycle) is a good idea, and a real-life application of it. Watch the video here: Can’t see anything? Watch it on YouTube [...]Read More... from In-Ear Insights: Prompt Development Life Cycle

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In this week’s In-Ear Insights, Katie and Chris review the new Trust Insights PARE Framework and 4 power questions to ask generative AI language models when you’re working with them. These four questions will dramatically improve the performance of generative AI. Get the free download, no strings attached, at: https://www.trustinsights.ai/powerquestions Watch the video here: Can’t [...]Read More... from In-Ear Insights: 4 Power Questions to Ask Generative AI Language Models

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In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss the potential impact of AI on empathy and society. You’ll learn about the risks when AI creators lack empathy and the importance of compassion in a tech-driven world. Discover how democratizing technology and prioritizing human concerns can combat these risks. Find [...]Read More... from In-Ear Insights: AI, Empathy, and the Future of Work

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In this week’s In-Ear Insights, Chris and Katie discuss generative AI limitations in marketing and why focusing on generative AI might not be the answer to your woes. You’ll learn the best ways to analyze your data to find the true cause of the problem and get actionable steps to turn things around. Watch the [...]Read More... from In-Ear Insights: Generative AI Limitations in Marketing

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In this week’s In-Ear Insights, Katie and Chris discuss predictive analytics and applying it to reducing churn throughout the customer journey. Learn what types of predictive analytics apply, how generative AI fits in the mix, and steps you can take to reduce churn. Watch the video here: Can’t see anything? Watch it on YouTube here. [...]Read More... from In-Ear Insights: Predictive Analytics and Reducing Churn

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In this week’s In-Ear Insights, Katie and Chris reflect on lessons learned from Social Media Marketing World 2024. With generative AI taking center stage both literally and figuratively, what does that mean for social media marketers? Tune in to find out! Watch the video here: Can’t see anything? Watch it on YouTube here. Listen to [...]Read More... from In-Ear Insights: Lessons Learned from Social Media Marketing World 2024

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In this week’s In-Ear Insights, Katie and Chris discuss how to do practices like predictive analytics and classical AI/machine learning when you’re data-poor. What data is available to forecast and work with? How do you create data when you don’t have it, and what strategic advantage might this confer? Tune in to find out! Watch [...]Read More... from In-Ear Insights: Predictive Analytics If You’re Data-Poor

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In this week’s In-Ear Insights, Katie and Chris discuss generative AI impact on search engine marketing. What is the impact of services like ChatGPT, Google Gemini, Perplexity, and Microsoft Copilot on traditional SEO and search? Tune in to find out, and what steps you should take to prepare for the future. Watch the video here: [...]Read More... from In-Ear Insights: Generative AI Impact on Search Engine Marketing

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In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss the complexities of data privacy in the era of generative AI. They offer practical tips on safeguarding personal and company data, emphasizing the importance of understanding terms of service and the implications of sharing information online. You’ll gain insights into how [...]Read More... from In-Ear Insights: Data Privacy and Generative AI

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In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss the crucial role of analytics in digital marketing and how it’s often seen as the bearer of bad news. They explore the common pitfalls companies face when they only sporadically review data, leading to missed opportunities for timely adjustments. Katie emphasizes [...]Read More... from In-Ear Insights: Strategic Value of Analytics

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In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss unlocking workforce potential with generative AI and the evolving landscape of artificial intelligence in the workforce. They delve into the capabilities and limitations of AI, contrasting its performance against human tasks and creativity. The conversation highlights the importance of human critical [...]Read More... from In-Ear Insights: Unlocking Workforce Potential with Generative AI

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In this week’s In-Ear Insights, Katie and Chris talk through some of the considerations that Custom GPT makers and consumers should keep in mind as OpenAI’s Custom GPT Store opens for business this week. Learn what pitfalls to avoid! Watch the video here: Can’t see anything? Watch it on YouTube here. Listen to the audio [...]Read More... from In-Ear Insights: Custom GPT Considerations

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In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss generative AI in 2024 and the rapid advancements in generative AI over the past year, including significant developments in various AI models. They explore how these technologies impact businesses and the importance of understanding their capabilities and limitations. The conversation also [...]Read More... from In-Ear Insights: Generative AI in 2024

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In this week’s In-Ear Insights, Katie and Chris look at 2023 in the rearview mirror in their 2023 year in review. What a year, from GA4 to Generative AI – what did we learn? What’s changed, what stays the same, and what can we look forward to in 2024? Tune in to find out! Watch [...]Read More... from In-Ear Insights: 2023 Year in Review

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In this week’s In-Ear Insights, Katie and Chris debate the importance of quality work vs. quantity work, measuring employee productivity, and how generative AI may impact the way you think about staffing and hiring. Watch the video here: Can’t see anything? Watch it on YouTube here. Listen to the audio here: Download the MP3 audio [...]Read More... from In-Ear Insights: Quality, Quantity, Productivity, and Generative AI

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In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris delve into the intriguing topic of artificial intelligence in leadership roles, specifically discussing whether generative AI can replace the CEO role. You’ll learn about the balance between automation and human leadership, questioning what roles can be automated and the implications of AI [...]Read More... from In-Ear Insights: Can Generative AI Replace the CEO?

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In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss social media marketing and rebuilding a restaurant’s social media presence. They focus on establishing basic consistency first before considering more advanced tactics. They cover the importance of predictability for building trust and meeting audience expectations. You’ll learn an effective approach for [...]Read More... from In-Ear Insights: Social Media Marketing Basics for Food & Beverage

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In this early episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss generative AI business continuity, the recent drama at OpenAI and what it means for businesses relying on their AI services. We talk about the importance of having backup plans and avoiding putting all your eggs in one basket when it [...]Read More... from In-Ear Insights: Generative AI Business Continuity Planning

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In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss Custom GPT setup, the pros and cons of using AI chatbots like ChatGPT to automate content creation, and best practices for training them effectively, such as the TrustInsights.ai 5P framework and the SDLC. [podcastsponsor] Watch the video here: Can’t see anything? [...]Read More... from In-Ear Insights: Custom GPT & Software Development Best Practices

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In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss CEO AI strategy and whether businesses should add AI to everything just because it’s trendy. They explain why following directives to “AI-ify everything” can backfire, and share a smarter approach using processes like creating user stories and the Trust Insights 5P [...]Read More... from In-Ear Insights: Add AI To Everything? CEO AI Strategy

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In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss how to better manage people and marketing through frameworks, predictive analytics, and anticipation. We talk about how anticipating questions, needs, and potential issues can help smooth communications between managers and employees. Chris shares how predictive analytics and AI assistants can help [...]Read More... from In-Ear Insights: Predictive Analytics, Productivity, and Management

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In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss the peculiar budget cuts that CMOs are making for 2024 marketing budgets and how it will impact marketing teams. We talk about the surprising reductions in spending for CRM, customer experience, and brand building. We analyze the disconnect between using AI [...]Read More... from In-Ear Insights: CMO Survey, AI, and 2024 Marketing Budgets

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In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss changing your MarTech stack and how to thoughtfully add new marketing technology to your existing tech stack. They talk through the importance of gathering requirements upfront to ensure you select tools that solve your specific business needs. Katie asks questions to [...]Read More... from In-Ear Insights: Changing Your MarTech Stack

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In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss key takeaways from the recent MarketingProfs B2B Forum conference. We cover topics like AI, digital marketing tactics, and generative content. Katie shares insights on non-AI focused sessions and the importance of remembering that technology is just a tool. Chris discusses legal [...]Read More... from In-Ear Insights: MarketingProfs B2B Forum 2023 Takeaways

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In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss how to vet and hire the right analytics expert or analytics agency for your business. We cover the importance of having your processes and goals clearly defined before bringing on help, so you can assess if a potential hire or agency [...]Read More... from In-Ear Insights: How to Vet and Hire an Analytics Agency or Employee

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In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss the impact of generative AI on businesses, especially hourly businesses like agencies. They explore the problems with time tracking and utilization rates, and how it breeds inefficiency. Katie shares her experience being forced to innovate on command. Chris talks about the [...]Read More... from In-Ear Insights: Generative AI and the Future of Work

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In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss the important issue of who owns your data. They talk about why companies need to be aware of who has access to their data, especially when using tools like Google Analytics or generative AI models. Katie and Chris explain how you [...]Read More... from In-Ear Insights: Who Owns Your Data?

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In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris provide a guide to generative AI for the C-Suite and discuss how CEOs should approach understanding and using large language models and AI, starting with identifying business problems first before considering technology solutions. Watch the video here: Can’t see anything? Watch it [...]Read More... from In-Ear Insights: The C-Suite Guide to Generative AI

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In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss how to mitigate gender bias in AI systems by assessing risk, implementing human review processes, and building an equitable company culture. Watch the video here: Can’t see anything? Watch it on YouTube here. Listen to the audio here: Download the MP3 [...]Read More... from In-Ear Insights: Gender Bias and Fairness in Generative AI

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In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss how to leverage large language models like ChatGPT with your business analytics data. We talk through real examples of using AI to analyze CRM data for buyer personas, extract tables from PDFs, summarize PowerPoints into executive briefs, transform your data into [...]Read More... from In-Ear Insights: Analytics Use Cases for Large Language Models

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In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss how large language models with perfect memory can assist with competitive analysis. They explain how these tools can quickly synthesize vast amounts of public data to provide insights into competitors’ offerings, differentiation, and performance. Katie and Chris note the importance of [...]Read More... from In-Ear Insights: How To Use Generative AI For Competitive Analysis

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In this episode of In-Ear Insights, Katie and Chris discuss how to pick the right AI tools, answering the common question, “Which AI tool should I be using?” We explain starting with the problem, not the tool. Katie recommends the 5 P’s to evaluate AI readiness. We emphasize choosing AI intentionally, testing tools against requirements. [...]Read More... from In-Ear Insights: Which AI Tool Should I Be Using?

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In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss takeaways from the recent Marketing AI Conference, MAICON 2023. We talk about the growth of the event, Chris’s generative AI keynote, and share highlights from other speakers on AI regulation, ethics, and impact on jobs. We debate whether agile development processes [...]Read More... from In-Ear Insights: Marketing AI Conference 2023 In Review

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Since its release in November 2022, ChatGPT has been lauded as a groundbreaking AI chatbot. However, recent research indicates its capabilities may be declining over time. In this episode of the Trust Insights podcast In-Ear Insights, hosts Christopher Penn and Katie Robbert ask the question: is ChatGPT getting dumber? They discuss findings that ChatGPT appears [...]Read More... from In-Ear Insights: Is ChatGPT Getting Dumber?

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In this episode of In-Ear Insights, the Trust Insights podcast, Katie Robbert and Christopher Penn tackle a common workplace challenge – how to keep teams focused on priorities instead of getting distracted by drama or interpersonal issues. As Katie explains, acknowledging that some level of gossip or emotional venting will happen is key. Trying to [...]Read More... from In-Ear Insights: Workplace Communication and Productivity

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In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss the limitations of data science skills. They explore the various aspects of data science and what it truly means to be a data scientist. They touch upon the importance of understanding the scientific method and how it applies to data science. [...]Read More... from In-Ear Insights: Limitations of Data Science Skills

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In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss the challenge of marketing products and services that are considered boring or mundane. They emphasize the importance of identifying the right audience for such products and reframing the marketing approach to make it more appealing to that specific audience. They provide [...]Read More... from In-Ear Insights: How to Market Boring Products

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In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss the age-old question of whether to buy or build. They explore various aspects of this decision, including business strategy, in-house skills, regulatory requirements, long-term considerations, and the value of owning versus renting. They also touch on the growing importance of large [...]Read More... from In-Ear Insights: Buy Or Build Marketing Technology?

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In this week’s In-Ear Insights, Katie and Chris discuss the six dimensions of expertise, and what constitutes someone being an expert or not. Learn what the 6 dimensions are and whether or not someone should be considered an expert. Watch the video here: Can’t see anything? Watch it on YouTube here. Listen to the audio [...]Read More... from In-Ear Insights: In-Ear Insights: What Qualifies Someone As An Expert?

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In this week’s In-Ear Insights, Katie and Chris review marketing mix modeling, media mixed modeling, and whether Tiktok’s Marketing Mix Modeling study has any insights marketers can learn from. Tune in to find out more! Watch the video here: Can’t see anything? Watch it on YouTube here. Listen to the audio here: Download the MP3 [...]Read More... from In-Ear Insights: Tiktok and Marketing Mix Modeling

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In this week’s In-Ear Insights, Katie and Chris discuss a thought-provoking question raised during a recent talk: Is there a genuine risk with the use of AI? Will it make us lazy and reduce the quality of our work? We explore the impact of AI on human behavior and the potential consequences of over-reliance on [...]Read More... from In-Ear Insights: Will AI Make Us Lazy and Stupid?

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In this week’s In-Ear Insights, Christopher Penn and Katie Robbert discuss B2B influencer marketing, which is becoming more prominent in the B2B marketing space. B2B influencer marketing is an endorsement where a B2B marketer uses their influence to endorse a product or service because of the reputation they have. Unlike B2C, B2B influencer marketing is [...]Read More... from In-Ear Insights: What Is B2B Influencer Marketing?

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In this week’s In-Ear Insights, Katie and Chris answer the big question that people are afraid to ask for fear of looking silly: what IS a large language model? Learn what an LLM is, why LLMs like GPT-4 can do what they do, and how to use them best. Tune in to learn more! Watch [...]Read More... from In-Ear Insights: What Is A Large Language Model?

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In this week’s In-Ear Insights, Christopher Penn and Katie Robbert discuss the impact of artificial intelligence (AI) on jobs. They explore whether generative AI will take over jobs, especially for junior members of an organization. They discuss the aspects of jobs that AI can and cannot replace, such as repetitive tasks versus human creativity and [...]Read More... from In-Ear Insights: AI Will Take Your Job

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In this week’s In-Ear Insights, Katie and Chris talk through how to improve your prompt engineering for large language models like ChatGPT, GPT-4, and other services through the use of the software development lifecycle. Learn how to apply the SDLC to your individual work with AI tools, and why it’s so important. Watch the video [...]Read More... from In-Ear Insights: How To Improve Prompt Engineering With the Software Development Lifecycle

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In this week’s In-Ear Insights, Katie and Chris talk vendor and partner management, aka third party relationship management. What is it? How do you choose vendors and partners who fit your requirements best? How do you position yourself as the vendor of choice? Tune in to find out! Watch the video here: Can’t see anything? [...]Read More... from In-Ear Insights: Third Party Relationship Management

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In this week’s In-Ear Insights podcast episode, Katie and Chris talk about one of the secrets of the C-Suite, based on a recent LinkedIn article. What does one set of executives do to be more productive that others don’t, and what lessons we can learn from it? Tune in to find out! Watch the video [...]Read More... from In-Ear Insights: Secrets of the C-Suite

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In this week’s In-Ear Insights, Katie and Chris tackle how to present analytics and data to stakeholders in a world where opinion and emotion increasingly triumphs over data and basic facts. How important is accuracy? How do we stay true to our commitments to be data-driven when decisions are made with emotions? Tune in to [...]Read More... from {PODCAST} In-Ear Insights: How Important Is Accuracy?

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In this episode of In-Ear Insights, Katie and Chris answer one of the most common questions for jobseekers: “What should my salary be?” Learn the two different methods for determining how much compensation to ask for, and ways to handle salary negotiations in interviews. Key points from this episode: 1. The first step in determining [...]Read More... from {PODCAST} In-Ear Insights: What Should My Salary Be?

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In this week’s In-Ear Insights, Katie and Chris tackle the question on every advertiser’s mind: how to adapt for the cookieless future? What should we do about the cookieless future? What things should marketers know – what’s going away? What’s still available? What strategies should marketers pursue to no longer rely on third party data [...]Read More... from {PODCAST} In-Ear Insights: How To Adapt For The Cookieless Future

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In this episode of In-Ear Insights, Katie and Chris talk about personalization and customization of content. In an era when privacy regulations are throttling data marketers can obtain and the media environment is in total chaos, how do we still manage to customize and personalize? In order to customize your content without data about people, [...]Read More... from {PODCAST} In-Ear Insights: How to Customize Content Without the Data?

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In this week’s episode, Katie and Chris tackle news media’s usage of data. Is what you read believable? How would you go about proving it? We examine some recent claims in Bloomberg and Business Insider about racial slurs on Twitter and the process for verifying that claim, extending it to the role data-savvy organizations should [...]Read More... from {PODCAST} In-Ear Insights: Can I Believe What I Read?

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In this episode, Katie and Chris discuss the evolution of SEO and where it’s going, where AI and machine learning fit in, and what trends to be aware of as you create content and optimize it for search in 2023. Tune in to learn more! Watch the video here: Can’t see anything? Watch it on [...]Read More... from {PODCAST} In-Ear Insights: Do You Need To Be an SEO Expert in 2023?

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In this week’s episode, Katie and Chris run through a list of some of their key takeaways from the MarketingProfs B2B Forum 2022 conference. Influencer marketing may be a good way to reach your target audience, as data privacy restrictions make it more difficult to buy lists or use data from social media platforms. The [...]Read More... from {PODCAST} In-Ear Insights: MarketingProfs B2B Forum 2022 Key Takeaways

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In this episode of In-Ear Insights, Katie and Chris discuss the sudden re-emergence of people promoting go to market strategy (GTM) and what it means. Why is this suddenly a thing again, when GTM is as old as product marketing itself? Go to market strategy is a subset of marketing strategy that is focused on [...]Read More... from {PODCAST} In-Ear Insights: Go To Market Strategy

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In this fireside chat with attorney Ruth Carter, we dig into how copyright law applies to content created by artificial intelligence. Who owns these fabulous works of art generated by systems and models like OpenAI’s DALL-E or Stability.ai’s Stable Diffusion? What about blog content created by tools like GoCharlie or Copy.ai? Watch the interview on [...]Read More... from AI and Copyright Law: How Copyright Applies to AI-Generated Content

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In this week’s In-Ear Insights, Katie and Chris tackle how to sell the value of data to stakeholders like the C-Suite. How do you convince people of the merits of data, of investing in data programs and projects, and ultimately to become champions for the analytics work you do? Tune in to find out! Watch [...]Read More... from {PODCAST} In-Ear Insights: Selling The Value of Data To Stakeholders

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In this episode, Katie and Chris dig into what data-driven marketing is and is not. What is data-driven marketing? Who is data-driven? Is there a safe balance between opinion-led marketing and data-driven marketing? Tune in to find out! Data driven means making decisions with data when the data doesn’t tell you what you want to [...]Read More... from {PODCAST} In-Ear Insights: What is Data-Driven Marketing?

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In this episode, Katie and Chris revisit the essential basic B2B marketing metrics. They discuss the importance of metrics such as engagement rate, bounce rate, and conversion rate. They also talk about the sales funnel and how it is a tiny portion of the overall customer journey. Watch the video here: Can’t see anything? Watch [...]Read More... from {PODCAST} In-Ear Insights: 2022 B2B Marketing Metrics

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In this episode, Katie and Chris tackle content attribution. What is content attribution, and how does it differ from normal marketing attribution? You’ll learn about two different ways to do content attribution and one way you can get set up and running right away. Watch the video here: Can’t see anything? Watch it on YouTube [...]Read More... from {PODCAST} In-Ear Insights: What Is Content Attribution?

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In this week’s In-Ear Insights, Katie and Chris discuss market research capabilities. What kinds of resources should an organization allocate towards market research? How often should you be doing market research? What are the different kinds of market research? These questions and many more answered – tune in to find out! Watch the video here: [...]Read More... from {PODCAST} In-Ear Insights: Market Research Capabilities

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In this week’s episode, Katie and Chris look at the use of predictive analytics to forecast trends and learn when to put marketing campaigns into market. They examine some of the data around pumpkin spice season as well as other seasonal trends. Ever wonder when the best time is to put a seasonal campaign into [...]Read More... from {PODCAST} In-Ear Insights: Predictive Analytics and Trend Forecasting

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In this week’s episode, Katie and Chris tackle the question of how to determine if someone has a good attitude and aptitude for marketing, business, or any professional even if they don’t have professional experience. The labor market is constrained for “hit the ground running” candidates, but the actual labor pool is much, much larger [...]Read More... from {PODCAST} In-Ear Insights: Attitude and Aptitude in Hiring

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In this week’s episode, Katie and Chris walk through what big data analytics are. What criteria separates regular data and big data? What are the four Vs of big data? How do big data analytics play a role in marketing analytics? Why don’t more marketers use big data and big data analytics to improve marketing? [...]Read More... from {PODCAST} In-Ear Insights: What is Big Data Analytics?

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In this week’s episode, Katie and Chris talk about marketing measurement strategy. What is measurement strategy in the context of marketing? Why do so many marketers and especially consultancies and agencies mix up strategy, tactics, and execution when it comes to measurement? How should you think about your measurement strategy? Tune in to find out! [...]Read More... from {PODCAST} In-Ear Insights: Marketing Measurement Strategy

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In this week’s episode, Katie and Chris talk SEO and how to deal with diminishing returns in SEO. What should you do when your efforts aren’t yielding as much juice for the squeeze? How should you balance pillar content? What’s the role of content recycling? Tune in to find out the answers to these questions [...]Read More... from {PODCAST} In-Ear Insights: Diminishing Returns in SEO

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In this episode, John and Chris discuss a roundup of the most commonly identified content marketing trends. Which trends are real, actual trends and which trends are simply recycled advice? What are the trends marketers are not paying attention to? Tune in to find out! Watch the video here: Can’t see anything? Watch it on [...]Read More... from {PODCAST} In-Ear Insights: Content Marketing Trends (2022)

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In this episode, Katie and Chris discuss personal branding considerations and challenge the notion of what a personal brand is. Do you have to be the loudest person in the room? How do you build a personal brand without being perceived as arrogant and full of yourself? Tune in to find out! Watch the video [...]Read More... from {PODCAST} In-Ear Insights: Personal Brand Building Considerations

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Analytics is inherently rearward-looking, looking at what happened. Yet if we want to increase the value and impact of analytics, we have to look forward, to help plan. How do we do this? In this episode, learn 3 different approaches for improving the value of analytics. Watch the video here: Can’t see anything? Watch it [...]Read More... from {PODCAST} In-Ear Insights: Increasing the Value of Analytics

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In this week’s In-Ear Insights, Katie and Chris discuss how artificial intelligence impacts corporate culture and vice versa. Learn about the two different types of organizations, use cases for AI in corporate management, and the hidden danger of AI and institutional knowledge in your corporation. Watch the video here: Can’t see anything? Watch it on [...]Read More... from {PODCAST} In-Ear Insights: Artificial Intelligence and Corporate Culture

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In this week’s In-Ear Insights, Katie and Chris discuss whether AI and machine learning will imperil the career of the data scientist. What is data science, and how much of it can be automated and handled by machines? Tune in to find out. Watch the video here: Can’t see anything? Watch it on YouTube here. [...]Read More... from {PODCAST} In-Ear Insights: Will AI Wipe Out Data Science Jobs?

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In this week’s In-Ear Insights, Katie and Chris answer a listener question about proving the value of brand awareness. Sophie asks, “Our marketing strategy as a young startup is to drive sales, to try and make money. But how does brand awareness affect that? I could just run lead generation ads to try and get [...]Read More... from {PODCAST} In-Ear Insights: The Value of Brand Awareness

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In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris talk about questioning and challenging marketing best practices. From the best times to send emails to how much curated content you should share on social media, sacred cows are on the table. Tune in to find out what other marketing best practices [...]Read More... from {PODCAST} In-Ear Insights: Challenging Marketing Best Practices

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In this episode of In-Ear Insights, Katie and Chris discuss the impact of the Great Resignation on organizations and how marketers should be thinking about preserving institutional knowledge. How do you anticipate and protect your organization from unexpected (or expected) departures, ensuring you can still meet your goals and create the kind of impact you [...]Read More... from {PODCAST} In-Ear Insights: Organizational Change and the Great Resignation

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In this episode of In-Ear Insights, Katie and Chris examine discrepancies between Google Search Console data and third-party SEO tool data. What are the major differences? What actions should we take with each of the datasets? What purposes and functions are appropriate for each dataset? How should we better use Google Search Console data? Take [...]Read More... from {PODCAST} In-Ear Insights: Google Search Console and SEO Tool Data

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In this episode of In-Ear Insights (the Trust Insights podcast), Katie and Chris discuss the data available to marketers for social media analytics around TikTok, plus an in-depth discussion of influencer analytics and how to identify influencers on TikTok. Tune in to find out how – and get your copy of the new paper at [...]Read More... from {PODCAST} In-Ear Insights: TikTok and Social Media Analytics

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In this week’s In-Ear Insights, Katie and Chris walk through effective process development for marketing, especially marketing technology. Listen in as they work out the process for setting up a Twitter hashtag monitoring bot for Social Media Marketing World. Watch the video here: Can’t see anything? Watch it on YouTube here. Listen to the audio [...]Read More... from {PODCAST} In-Ear Insights: Process Development for Marketing Technology

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In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris dig into data analytics. What is data analytics? How is it different than, say, marketing analytics? What are the prerequisites for data analytics? Learn all this and much more in this episode. Watch the video here: Can’t see anything? Watch it on [...]Read More... from {PODCAST} In-Ear Insights: What is Data Analytics?

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In this week’s In-Ear Insights, Katie and Chris discuss how marketers and business folks should approach data visualization, reports, and dashboards. What are some of the best – and worse – practices in data viz? You’ll also learn the secret of great data visualization: user stories. Tune in to find out how! Watch the video [...]Read More... from {PODCAST} In-Ear Insights: Data Visualization Principles and Basics

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In this week’s In-Ear Insights, Katie and Chris walk through the software development life cycle – the SDLC – and how we can apply it to marketing operations. From Data Studio dashboards to Google Ads to SEO, the overall process behind the SDLC applies well to any kind of marketing operations that require planning. Tune [...]Read More... from {PODCAST} In-Ear Insights: Applying the SDLC to Marketing Operations

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In this week’s In-Ear Insights, Katie and Chris dig into the four pillars of content marketing strategy – creation, distribution, amplification, and performance. You’ll also learn about why backlinks are so important to both SEO and content marketing generally. Tune in now! Watch the video here: Can’t see anything? Watch it on YouTube here. Listen [...]Read More... from {PODCAST} In-Ear Insights: Content Marketing Strategy

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In this week’s In-Ear Insights, Katie and Chris take a walk on the wild side of affiliate marketing. What is affiliate marketing? What is affiliate marketing’s relationship with influencer marketing? How do you know if affiliate marketing is something to consider for your marketing mix? Tune in to find out! Watch the video here: Can’t [...]Read More... from {PODCAST} In-Ear Insights: Affiliate Marketing Overview

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In this episode of In-Ear Insights, Katie and Chris talk about business strategy. From popular frameworks like SWOT analysis to concepts like Simon Sinek’s Start With Why, as well as the Trust Insights 5P Framework, learn how to approach business strategy and distill down the massive concept into actions you could take. Watch the video [...]Read More... from {PODCAST} In-Ear Insights: Business Strategy

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In this episode of In-Ear Insights, Katie and Chris talk about local SEO and the four key sets of local SEO basics – technical SEO, on-site local SEO, content, and off-site local SEO. Learn the basics and what companies local SEO benefits most. Watch the video here: Can’t see anything? Watch it on YouTube here. [...]Read More... from {PODCAST} In-Ear Insights: Local SEO Basics

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In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris walk through the AI and Machine Learning Lifecycle. When you’re thinking about deploying artificial intelligence and machine learning in your organization, especially for marketing purposes, what are the steps in the process you need to consider? Listen in and discover what basic [...]Read More... from {PODCAST} In-Ear Insights: Exploring the AI/ML Lifecycle

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In this episode of In-Ear Insights, Katie and Chris review a case study of a client who was missing attribution data on more than 75% of their website visitors. When your data is that dirty, making good, data-driven decisions is all but impossible. Watch or listen as they talk through what happened and some ways [...]Read More... from {PODCAST} In-Ear Insights: Google Analytics Audit Case Study Review

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In this episode of In-Ear Insights, Katie and Chris discuss the evolution of the Trust Insights 6C Data Quality Framework into the Data Quality Lifecycle. Learn what the Data Quality Lifecycle is, why it matters, and how to start applying the concept to your own marketing data. Watch the video here: Can’t see anything? Watch [...]Read More... from {PODCAST} In-Ear Insights: The Data Quality Lifecycle

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In this week’s In-Ear Insights, Katie and Chris tackle Tiktok. What data is available to marketers and creators? What data is not available by normal means? What kind of analytics does Tiktok provide – and what kind of analytics do you need to obtain for yourself? Tune in to find out. Watch the video here: [...]Read More... from {PODCAST} In-Ear Insights: Basics of Tiktok Analytics and Tiktok Data

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In this week’s In-Ear Insights (the @trustinsights podcast), @katierobbert and @cspenn answer the most common question in our inboxes: what are the 2022 marketing trends we should be paying attention to? Find out why trend spotting is unreliable, what data sources to check for actual trends, and what Katie and Chris think are actual trends [...]Read More... from {PODCAST} In-Ear Insights: 2022 Marketing Trends

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In this week’s In-Ear Insights, Katie and Chris answer a question from the MarketingProfs B2B Forum on how YouTube analytics data can be used for B2B marketing – and how YouTube itself fits into your B2B marketing strategy. Watch and listen for tips on how to do YouTube research to inform your YouTube strategy as [...]Read More... from {PODCAST} In-Ear Insights: YouTube Analytics for B2B Marketers

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