Earley AI Podcast: Recent Episodes

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Why Applying AI to Drug Development Is One of the Most Technically Demanding Problems in the Industry - and What Is Finally Making It Solvable

Guest: Patrick Leung, Chief Technology Officer at Faro Health

Host: Seth Earley, CEO at Earley Information Science

Published on: August 4, 2026

In this episode, Seth Earley speaks with Patrick Leung, Chief Technology Officer at Faro Health, who spent over a decade at Google including working on Google Duplex before bringing that technical depth to one of the most regulated and high-stakes domains in medicine. They explore why generative AI is in the trough of disillusionment in pharma, what the vibe coding fallacy costs organizations that believe they can build clinical software by prompting, how classical machine learning models and modern LLMs are working together to forecast trial outcomes, and why every day of clinical trial delay can cost up to half a million dollars in lost revenue. Patrick shares candid and specific insights on prompt injection as the new SQL injection, why human experts cannot be removed from clinical AI workflows, and what bending Eroom's Law would mean for patients worldwide.

Key Takeaways:

• Generative AI is in the trough of disillusionment in pharma - the initial hype that AI could automate entire clinical processes has collided with the real complexity of the domain and the limits of the technology.
• Vibe coding hits an event horizon of complexity - demo apps are achievable by prompting, but real enterprise software requires proper engineering, security design, testing discipline, and architectural decision-making that AI cannot replace.
• Prompt injection is the new SQL injection - any tool that uses AI to process user input is now vulnerable to a class of attacks that did not exist before, and most organizations are not yet protecting against them.
• Classical machine learning models and modern LLMs are more powerful together than either is alone - survivor curve models from insurance analytics proved directly transferable to clinical trial forecasting with strong results.
• Every day of clinical trial delay can cost up to half a million dollars in lost revenue - and a typical amendment forcing a trial redesign and resubmission runs three to six months.
• Generic general-purpose models cannot replace domain-specific knowledge engineering in clinical contexts - the claim that they can is an easy sales pitch that does not survive contact with the actual complexity of the problem.
• The goal is not to automate clinical professionals out of existence but to remove the rote and repetitive work so they can focus on the judgment calls that only they can make.

Insightful Quotes:

"There's no escaping the fact that you need to test software. There's no escaping the fact that you need to have specs that are really well thought out. As you add more features to a codebase, it gets more complex and unwieldy and difficult to maintain. You can't vibe code your way out of those key design decisions." - Patrick Leung

"I found myself applying models I'd learned about in a completely different domain. Survivor curve models we used for predicting insurance policy claims worked pretty well when applied to clinical trials. Transferability is really a thing." - Patrick Leung

"Eroom's Law is not sustainable. Any exponential increase in cost is not sustainable by definition. So we want to bend Eroom's Law - and hopefully reverse it. Why not?" - Patrick Leung

Tune in to discover why AI in clinical drug development is one of the hardest and most consequential problems in the field - and what is finally making it tractable.

Links

LinkedIn: https://www.linkedin.com/in/puiwah/

Website: https://www.farohealth.com

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Why Making Complex Revenue Simple at Scale Requires More Than Throwing Contracts Into a Chat Interface

Guest: Deepak Bapat, Co-Founder and CTO at Tabs

Host: Seth Earley, CEO at Earley Information Science

Published on: July 30, 2026

In this episode, Seth Earley speaks with Deepak Bapat, Co-Founder and CTO at Tabs, a revenue and accounts receivable management platform built for B2B companies. They explore why dropping contracts into a general-purpose AI tool is not a strategy for enterprise scale, what generative AI unlocked that OCR and legacy machine learning could never solve, why context engineering beat fine-tuning for contract extraction, and why newer and larger models are not always better for specialized tasks. Deepak shares candid and specific insights on building atomic AI pipelines, the provability requirement that financial compliance demands, and what finance and data leaders consistently underestimate before deploying AI on their contracts.

Key Takeaways:

• Dropping contracts into a chat interface is a reasonable experiment but not an enterprise strategy - doing things at scale requires specific tooling, specific expertise, and integration across systems.
• The SaaSpocalypse framing misses the point - the more interesting question is not whether chat replaces UI, but how platforms can understand intent and preempt the actions users would otherwise have to click through manually.
• Generative AI solved the contract problem by reasoning over ambiguous natural language at document level - something OCR and rules-based systems fundamentally could not do.
• Context engineering beat fine-tuning at Tabs because merchant preferences vary so significantly that fine-tuning per merchant became cost-prohibitive - a well-prompted generalized model proved faster and more elastic.
• Newer and larger models are not always better for specialized tasks - Deepak's eval sets show that models from six months ago outperform newer versions on certain contract extraction jobs, likely due to overfitting on coding.
• Provability is the non-negotiable requirement in financial AI - it is not enough to produce correct output, you must be able to prove the output is correct and traceable back to the source contract.
• Organizations that want to deploy AI on their contracts first need to standardize internally on what outcomes they actually want - two people on the same team asking the same question about the same contract should not produce two different answers.

Insightful Quotes:

"The misconception is that difficult problems can just be solved by throwing something into ChatGPT and having the answer come out the other side. In our case, the at-scale piece is everything. Those intelligence tools are still individualized tools - to do things at scale for an entire enterprise still takes specific tooling, specific thought, and specific expertise." - Deepak Bapat

"What we're trying to do is move from a place of unstructured data to provable and correct structured data. That is what Tabs is built around - and that is what most of these other systems simply cannot handle." - Deepak Bapat

"When you think about the legacy players that were more rigid SaaS tools with manual entry and brittle connectors - what was intractable about that model is exactly what generative AI made solvable. The ability to reason over the words in a document, understand what they meant, and understand what the output should be - that changed everything." - Seth Earley

Tune in to discover what it actually takes to build AI that is accurate enough, auditable enough, and elastic enough to handle enterprise revenue data at scale - and what most organizations underestimate before they start.

Links

LinkedIn: https://www.linkedin.com/in/deepakbapat/

Website: https://www.tabs.inc

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How the Threat Landscape Is Being Rewritten and What Organizations Need to Do Before It Gets Ahead of Them

Guest: Taylor Hersom, Founder of Eden Data and Managing Director at Riveron

Host: Seth Earley, CEO at Earley Information Science

Published on: July 28, 2026

In this episode, Seth Earley speaks with Taylor Hersom, Founder of Eden Data and Managing Director at Riveron, a cybersecurity and compliance firm he built and grew before its acquisition in 2025. They explore why security is still treated as a cost center when it should be treated as a sales motion and competitive differentiator, how AI has exponentially expanded the attack surface, why most organizations have adopted AI with almost no security program around it, and how the subscription model Taylor pioneered is now reshaping how professional services firms price and deliver work. Taylor shares candid and specific insights on AI governance standards, the limits of automated threat detection, and why information architecture is the foundation security professionals are finding missing everywhere they go.

Key Takeaways:

• Security is still treated as a cost center by most organizations when it should be viewed as a trust-building and sales motion that directly impacts revenue and brand reputation.
• Pre-revenue startups are now arriving with a million lines of AI-generated code - the attack surface has expanded exponentially and security programs have not kept pace.
• Most organizations have adopted AI across the enterprise without any AI-specific security program, controls around LLM access, or governance over what models are allowed to do.
• ISO 42001 and the NIST AI Risk Management Framework are the clearest starting points for organizations that want to de-risk their AI environment without reinventing the wheel.
• AI in security has shifted the human role from doing the work to supervising it - but final judgment on whether a threat is legitimate still requires a human and always will.
• Unorganized, incorrect, or inaccessible data creates systemic risk in AI environments - poor information architecture is what leads to the snowball effect of cascading security failures.
• The subscription model for security services - pricing for outcomes rather than hours - has proven durable across market conditions and is now becoming the industry expectation.

Insightful Quotes:

"We naturally leaned into AI from a technology standpoint, and there is almost no security around it to speak of. If you go ask the average company that's using AI across their enterprise, they probably don't have an AI-specific program where they have controls around their LLM and their processes and their access - and that is terrifying." - Taylor Hersom

"Rather than go the FUD route - fear, uncertainty, and doubt - you can look at security as a way to build your brand and make it a part of your identity, and be proactive in how you use this when educating customers about how you protect their data." - Taylor Hersom

"There's no AI without IA. Security requires information architecture - access controls, data organization, knowing what you have and where it lives. When you start losing control of your data, you start to create risks you don't even know about." - Seth Earley

Tune in to discover why cybersecurity in the AI era is no longer just a technical problem - and what organizations need to put in place before the threat landscape gets ahead of them.

Links

LinkedIn: https://www.linkedin.com/in/taylorhersom/

Website: http://www.riveron.com

Website: https://www.edendata.com

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Why the Gap Between an AI Translation Demo and Enterprise Production Is Wider Than Most Organizations Realize

Guest: Olga Beregovaya, VP of AI at Smartling
Host: Seth Earley, CEO at Earley Information Science
Published on: June 17, 2026

In this episode, Seth Earley speaks with Olga Beregovaya, VP of AI at Smartling, who brings 25 years of experience across every major evolution in natural language processing - from rules-based systems through statistical models, neural translation, and now LLMs. They explore why plugging into a commercial model at token-level pricing is not a translation strategy, how brand voice fractures at 300,000 employees, why information architecture is just as essential for language pipelines as it is for retrieval, and what it actually takes to deliver consistent, on-brand, multilingual content at enterprise scale. Olga shares candid and specific insights on language complexity, the human-in-the-loop imperative, and why the organizations that are finally succeeding with AI have stopped treating it as art for art's sake.

Key Takeaways:
The price of a commercial model's tokens is not the cost of enterprise AI translation - data integrity, pipeline architecture, linguistic assets, and human review are the real cost drivers.

Brand voice fractures the moment every employee can generate content autonomously - a Fortune 10 company discovered it had 300,000 voices overnight after deploying a co-pilot tool.

Information architecture is equally essential for language pipelines as for retrieval - nested HTML tags, tokenization failures, and unstructured content break translation before the model ever sees the text.

LLMs unlocked context that neural machine translation never had - resolving pronouns, disambiguating terminology, and working at document level instead of sentence by sentence.

The assumption that AI translation works equally across all languages is one of the most dangerous misconceptions in the space - morphological complexity, writing systems, and training data representation vary enormously.

Human review is not optional even in fully automated pipelines - it is how models learn, how ground truth is established, and how brand consistency is maintained over time.

The organizations now succeeding with AI translation have moved from implement-and-fail to measured deployment - defining use cases, respecting prerequisites, and matching tooling to actual requirements.

Insightful Quotes:
"Yes, you can totally consume your million tokens at a super low price point, but what exactly are you buying for this money? Everybody can totally produce a translation or generate copy, but is it going to represent your brand? That's a different question." - Olga Beregovaya

"He installed a co-pilot tool and said, it's great, except my company has 300,000 employees and now my company has 300,000 voices. That's not necessarily what I was prepared for in different countries." - Olga Beregovaya

"If you want your models to evolve, and if you want your models to learn, you obviously need somewhere for these models to learn from - and this is where human review comes in. It is always twofold: guaranteeing the quality to your customers, and helping your models evolve." - Olga Beregovaya

Tune in to discover why AI translation at enterprise scale requires far more than a model and an API key - and what the organizations getting it right have built that their competitors have not.

Links
LinkedIn: https://www.linkedin.com/in/olga-beregovaya-04b5/
Website: https://www.smartling.com

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Why Supply Chain Visibility Is One of the Most Consequential and Underestimated Applications of AI in the Enterprise

Guest: Ilya Levtov, Founder and CEO at Craft.co

Host: Seth Earley, CEO at Earley Information Science

Published on: June 1, 2026

In this episode, Seth Earley speaks with Ilya Levtov, Founder and CEO of Craft.co, a supplier intelligence platform that uses AI and knowledge graphs to give enterprises and government agencies visibility into their full supply networks. They explore why most organizations believe they have adequate supply chain visibility when they do not, why a simple risk score will always mislead, and how cross-correlating data streams surfaces risks that no human - and no generic LLM - would ever find alone. Ilya shares candid and specific insights on building knowledge graphs for mission-critical infrastructure, why only one percent of enterprise knowledge exists inside today's LLMs, and how the give-to-get model is turning supply chain intelligence into a shared strategic asset.

Key Takeaways:

• Most enterprises believe their top-supplier relationships give them adequate visibility - but the middle and long tail of a supply network, which can run to 20,000 or 30,000 suppliers, remains almost entirely opaque.
• Supply chain is a misnomer - it is a complex, multi-dimensional network where companies are simultaneously suppliers, customers, and competitors to each other.
• A simple risk score is not meaningful and not actionable; supplier risk is deeply contextual and requires human judgment to weigh cost, probability, and consequence together.
• Cross-correlating data streams reveals hidden risks that no single source can surface - including correlations between employee morale and cybersecurity vulnerability that have proven highly predictive.
• Only approximately one percent of enterprise knowledge exists inside today's LLMs - which is exactly why a specialized knowledge graph grounded in proprietary data is essential before applying AI.
• AI has compressed analyst work on a supplier report from eight hours to under 30 minutes - but the decision of what to do with those findings still requires human judgment and always will.
• The give-to-get model and supplier passporting allow enterprises to share intelligence across a shared supply network without compromising their own competitive position.

Insightful Quotes:

"Only 1% of enterprise knowledge approximately exists inside the LLMs today. Companies don't want to give all of their data to the LLMs. Data providers don't want to give it for free either. That's why you need a specialized approach - leverage the power of the models on your own data set and on your knowledge graph." - Ilya Levtov

"A financially vulnerable supplier becomes a target for adversarial capital - entities coming in from unfriendly nations looking to survive. You're connecting two different data sets, connecting entities, and getting to a very significant risk insight you need to act on before it becomes a problem for your enterprise." - Ilya Levtov

"Organizations compete on their knowledge - knowledge of customers, knowledge of solutions, knowledge of supply chains, knowledge of routes to market. Those are competitive advantages. You do not want those inside an LLM. That is why doing this in a way that is internal and proprietary is so important." - Seth Earley

Tune in to discover why supply chain visibility is one of the most important and most underestimated applications of AI in the enterprise today - and what it actually takes to build intelligence at the scale the problem demands.

Links

LinkedIn: https://www.linkedin.com/in/ilya-levtov/

*Website*: https://www.craft.co

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Why Voice Is Not a Solved Problem - and What Real-Time Audio Intelligence Changes for Enterprise AI

Guest: Mike Pappas, CEO at Modulate

Host: Seth Earley, CEO at Earley Information Science

In this episode, Seth Earley speaks with Mike Pappas, CEO of Modulate, whose work began in gaming - one of the most demanding environments for real-time voice intelligence - and has since expanded to enterprise applications including fraud detection, customer abuse prevention, AI agent guardrails, and sales coaching. They explore why transcription is not the same as understanding, what gets lost when audio is reduced to text, and why voice is the most powerful tool fraudsters have. Mike shares candid and specific insights on deepfake detection, the fine line between safety and surveillance, and what organizations need to put in place before deploying voice AI at scale.

Key Takeaways:

• Transcribing voice and understanding voice are not the same thing - intonation, emotion, cadence, and timbre carry information that transcripts cannot capture.
• Voice AI demos are typically built for pristine environments; the real challenge is building systems that hold up under noise, jargon, and emotional complexity in production.
• Real-time intervention changes behavior more effectively than after-the-fact review - feedback delivered in the moment produces measurable reductions in repeat offenses.
• Voice is the most powerful tool for manipulation because it bypasses rational judgment by triggering emotional responses - and AI is now making voice fraud scalable.
• AI voice agents cannot introspect - they cannot tell when a call is going wrong, which is why a separate supervisory layer is essential for any enterprise voice deployment.
• The line between safety systems and surveillance systems is real; collecting and storing only what is necessary for the specific risk being addressed is both a privacy and a trust requirement.
• Before deploying any voice AI, organizations need to define their KPIs clearly - if the system is driving customer satisfaction down, the deployment is failing regardless of what else it is doing.

Insightful Quotes:

"When you hear a voice, you hear the intonation, you hear the emotion, you hear pregnant pauses - there is so much information being carried in that audio that gets lost when you pull down to a transcript. And whenever we talk to someone who professionally works in a contact center, they are always saying, we know these transcripts are losing tons of good value." - Mike Pappas

"If I am actively harassing you and the platform is able to come in and put a stop to it live in the conversation, that feedback actually systematically changes behavior. Getting an email 30 minutes later saying we noticed you did something wrong - that just infuriates people, it does not lead to change." - Mike Pappas

"There is a fine line between safety systems and surveillance systems. How do you design voice AI that improves safety and trust but does not cross that boundary that makes users and employees uncomfortable?" - Seth Earley

Tune in to discover why real-time voice intelligence is one of the most consequential and least understood frontiers in enterprise AI - and what organizations need to get right before they deploy.

Links

LinkedIn: https://www.linkedin.com/in/mike-pappas-9a30a858/

Website: https://www.modulate.ai

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Why Centralization Is the Wrong Foundation for AI - and What Organizations Need to Build Instead

Guest: Todd Barr, CEO at Axonis.ai
Host: Seth Earley, CEO at Earley Information Science
Published on: May 13, 2026

In this episode, Seth Earley speaks with Todd Barr, CEO of Axonis.ai, a company spun out of a government defense integrator that is bringing federated AI and decision intelligence to high-consequence enterprise workflows. They explore why the demo-to-production gap is one of the most costly misconceptions in enterprise AI today, why centralization was built for business intelligence and not for AI, and what it really means to send your AI to your data rather than the other way around. Todd shares a candid and direct perspective on decision artifacts, AI cost exposure, the risks of vendor lock-in, and why enterprises that give away how they make decisions may be giving away the most valuable thing they own.

Key Takeaways:

The demo-to-production gap is a form of malpractice - polished AI demos built on curated data create executive expectations that production reality cannot meet.

Centralized data infrastructure was built for business intelligence, not AI - it is optimized for reporting, not reasoning or prediction.

The premise of agentic AI is decentralization - if agents have to wait for data to be synced and centralized before acting, the architecture is working against itself.

Data resists centralization for three distinct reasons: technical constraints, regulatory and compliance requirements, and organizational politics.

Decision artifacts - cryptographically sealed records of data used, model applied, and reasoning followed - turn AI-assisted decisions into auditable, improvable corporate assets.

Enterprises now face a clear choice: pay in tokens, pay in vendor lock-in, or invest in owning their own AI infrastructure through open source models.

How an organization makes decisions is its most proprietary asset - giving that context to a third-party AI platform may be the most consequential thing enterprises are doing right now without fully understanding it.

Insightful Quotes:
"The misconception is really the gap between prototype and reality, and that's where a lot of these things are falling down right now. Getting people excited about something they can't have is almost malpractice." - Todd Barr

"Centralization is almost a fallacy in itself. Whenever you are using data you are changing it, enriching it, doing something with it. It is a fractal nature of data that defies the whole concept of centralization." - Seth Earley

"If I'm an enterprise, what do I own in this day and age? I own how I make decisions. Which data I use to make those decisions. If we are just going to give that away, that is like giving our brain away." - Todd Barr

Tune in to discover why the most important AI infrastructure decision an enterprise can make right now is not which model to use - but whether they are building a foundation they actually own.

Links
LinkedIn: / tbarr
Website: https://axonis.ai

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Guest: Steven Woo, Fellow and Distinguished Inventor at Rambus
Host: Seth Earley, CEO at Earley Information Science
Published on: May 5, 2026

In this episode, Seth Earley speaks with Steven Woo, Fellow and Distinguished Inventor at Rambus, where he has spent over 30 years at the frontier of memory technology. They explore why memory - not compute - is the binding constraint on AI performance today, how moving data between chips consumes more than half of all power in a high-end AI processor, and what the rise of agentic AI means for infrastructure planning.

Steven shares a rare long-view perspective on the innovation curve for memory technology, the supply-demand dynamics driving prices higher, and the questions enterprise leaders should be asking before signing their next infrastructure contract.

Key Takeaways:

Memory, not compute, is the primary bottleneck limiting AI performance - and the gap between processor speed and memory speed is widening, not closing.

Over 50 percent of the power consumed by high-end AI processors is spent simply moving data on and off the chip, not performing computation.
Stacking memory components closer together can reduce energy costs dramatically but introduces new challenges around heat dissipation and power delivery.

Training and inference have very different memory profiles - understanding both is essential for organizations architecting AI infrastructure at scale.
Agentic AI compounds the memory challenge significantly, because one user can spin up multiple agents that each spawn further agents, multiplying context and capacity demands.

Memory prices have risen sharply due to supply-demand imbalance - organizations are now signing long-term supply agreements to lock in capacity, just as they do for power.

The most important question enterprise leaders can ask their infrastructure providers is how much experience and demonstrated reliability they have - downtime during model training can be catastrophic.

Insightful Quotes:
"Memory has become a big bottleneck. In many cases, in AI, your speed at which you can actually process information and create new large language models is really gated by the speed and availability of memory." - Steven Woo

"More than 50 percent of the power is spent in circuits just trying to move data on and off the processor. It's pretty astounding to think that as companies plan how much power they need, a lot of it is really related to simply moving data back and forth." - Steven Woo

"People think of compute in terms of gigawatts. But it turns out it's really the movement of that data - and nobody talks about that. It's the silhouette behind the curtain that's actually constraining everything else." - Seth Earley

Tune in to discover why the future of AI depends as much on memory engineering as it does on model development - and what enterprise leaders need to understand about the infrastructure constraints shaping every AI investment they make.

Links
LinkedIn: https://www.linkedin.com/in/stevencwoo/
Website: https://www.rambus.com

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From Meeting Intelligence to Personal AI: How Digital Twins Are Reshaping How We Work

Guest: David Shim, Co-Founder and CEO at Read AI

Host: Seth Earley, CEO at Earley Information Science

Published on: April 27, 2026

In this episode, Seth Earley speaks with David Shim, Co-Founder and CEO of Read AI, the fastest-growing meeting intelligence platform globally with over 5 million monthly active users. They explore how AI is moving beyond summarization toward recommendation and autonomous action, what it really means to build a digital twin grounded in your actual work history, and why the organizations getting the most from AI are the ones that treat it like a trainable intern rather than an out-of-the-box solution. David shares candid insights on agentic guardrails, data privacy, workforce transformation, and why access to personal AI may one day be considered a basic human right.

Key Takeaways:

• AI is moving from task execution to recommendation - the next frontier is AI that proactively surfaces what you should do next.
• A digital twin is only as good as its context; weighting recent activity more heavily produces responses that actually reflect how you think and work today.
• Treating AI like a trainable intern - feeding it your emails, files, meetings, and tools - is what separates high-value users from disappointed ones.
• Native permissions are the cleanest foundation for digital twin privacy; building new rules for every edge case creates the vulnerabilities you are trying to avoid.
• Agentic guardrails should be built in from the start, not bolted on - autonomy without oversight erodes trust and adoption faster than it builds them.
• The tension between organizational IP and individual work style is real; your tone, voice, and preferences belong to you, even when the content belongs to the company.
• AI is a great leveler - emerging markets and individuals with access to these tools are already competing on equal footing with developed market counterparts.

Insightful Quotes:

"It's not plug and play today. You have to give it more context - your emails, your files, your CRM, your meetings. When you have all that data, now your intern is learning as you go, and it's pulling from your experience as the mentor." - David Shim

"Your digital twin knows I hate meetings after three hours straight. After three hours, my engagement goes down, my sentiment goes down - so it puts in a buffer. That's the first part. Then it starts asking: what happens when people ask you a question?" - David Shim

"You can't take the AI's version of the world as a representation of your version of the world. What's more valuable is your secret sauce, your knowledge, your expertise - you have to give it examples of your work, give it your perspective, not just take the LLM's." - Seth Earley

Tune in to discover how digital twins and agentic AI are transforming the way individuals and organizations work - and what it takes to get real value from the technology before it gets ahead of you.

Links

LinkedIn: https://www.linkedin.com/in/davidshim/

Website: https://read.ai

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Why Knowledge, Not Technology, Is the Foundation of Successful AI-Driven Data Migration

Guest: Dominik Wittenbeck, Group CTO at SNP Group
Host: Seth Earley, CEO at Earley Information Science
Published on: April 20, 2026

In this episode, Seth Earley speaks with Dominik Wittenbeck, Group CTO at SNP Group, a 1,600-person global software and solutions firm with 30 years of SAP-centric data migration expertise. They explore why AI is only as good as the institutional knowledge behind it, how agentic AI is transforming high-stakes enterprise migrations, and why organizations must treat data migration as a strategic opportunity rather than a cost-reduction exercise. Dominik shares hard-won insights on semantic architecture, governance, and what executives consistently get wrong when applying AI to critical enterprise processes.

Key Takeaways:

AI is not a silver bullet for data migration - it requires deep, domain-specific knowledge to produce deterministic, auditable results.

Enterprise data migration is a team sport requiring cross-functional specialists; AI accelerates the work but cannot replace that expertise.

The real opportunity in migration is not just moving data - it is cleaning it up and optimizing processes while the organization is already changing.

Agentic AI is transforming the full migration lifecycle, from pre-sales solutioning and blueprint generation to rule creation and automated testing.
Governance established once without ongoing enforcement decays quickly - organizations must build continuous oversight into critical processes from the start.

Value mapping, not just structural mapping, is the dominant challenge in SAP migrations, and AI can significantly accelerate semantic alignment work.

Executives should focus AI investments on problems that truly matter, not easy wins - meaningful impact comes from finding where differentiation really counts.

Insightful Quotes:
"In order to run complicated systems which have a critical impact on your business, they need enough grounding. You actually need to feed the knowledge into the agentic system that you're building on top of, in order to make sure that you get deterministic results in the end." - Dominik Wittenbeck

"Rather than re-architecting the whole thing, try to identify what the critical processes really are, that if they are not exercised correctly, really hurt your business. Find where the value lies - or if you can't find that, find where your risk lies." - Dominik Wittenbeck

"Sometimes cheap is quite costly, and sometimes slowing down speeds things up. If you're moving stuff from one system to another and you say, we'll clean it up later - that's never going to happen. It's like moving from one house to another with an attic full of boxes and junk." - Seth Earley

Tune in to discover why successful AI-driven enterprise migration depends less on technology and more on institutional knowledge, governance, and treating transformation as a strategic opportunity.

Links
LinkedIn: https://www.linkedin.com/in/dominik-wittenbeck-61a64669/
Website: https://www.snpgroup.com

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How Grafana Labs Built a Competitive Edge Through Openness, Agentic AI, and Engineering Culture

Guest: Tom Wilkie, VP of Product at Grafana Labs
Host: Seth Earley, CEO at Earley Information Science
Published on: April 17, 2026

In this episode, Seth Earley speaks with Tom Wilkie, VP of Product at Grafana Labs, a leading observability platform serving 25 million users across 50 global regions. They explore how Grafana's open source "big tent" philosophy creates unexpected competitive advantages in the AI era, why agentic AI is transforming how engineers respond to production incidents, and how the build-versus-buy debate is shifting with AI-assisted development. Tom shares candid insights on engineering culture, remote-first work, and why junior engineers may be more valuable than ever.

Key Takeaways:

Grafana Labs' open source strategy gave AI foundation models deep familiarity with their software, creating a powerful and unexpected competitive advantage.

Agentic AI is transforming observability by automating root cause analysis of production incidents, reducing engineering response time significantly.
Adaptive telemetry technology automatically identifies unused data, enabling organizations to cut observability costs dramatically without sacrificing coverage.

The build-versus-buy debate is shifting, but the real hidden cost is long-term maintenance - not the initial development effort.

Emergent engineering standards outperform top-down mandates; leaders consistently overestimate how much centralized consolidation is actually needed.

Remote-first engineering works when companies deliberately engineer collaboration rather than relying on spontaneous hallway interactions that rarely happen anyway.

AI-powered LLMs may solve the remote junior engineer onboarding problem by providing a low-ego, always-available resource for learning and guidance.

Insightful Quotes:
"By having 25 million users worldwide, they're out there blogging, publishing examples, tweeting, publishing videos - generating so much content on the open web about how to use Grafana. These foundation models are trained on that data. They know how to use our software better than proprietary competition." - Tom Wilkie

"The cost of consolidation is often underestimated. And it's often dangerous to the culture, because as soon as you start telling engineers that have poured their heart and soul into this project to drop it - that's devastating to people." - Tom Wilkie

"Openness - whether it's open source, open standards, open culture - is not just a philosophy. It really is a competitive strategy. It lowers switching costs, builds trust, and in the area of AI, it turns out to be the best way to make sure your models know how to use your technology." - Seth Earley

Tune in to discover how Grafana Labs turned open source philosophy into a winning AI-era strategy - and what engineering leaders can learn about culture, observability, and building for the long term.

Links
LinkedIn:https://www.linkedin.com/in/tomwilkie/
Website: https://grafana.com

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Why Security Teams Are Being Asked to Do Three New Jobs - and What to Do About It

Guest: Rob Lee, Chief AI Officer and Chief of Research at SANS Institute

Host: Seth Earley, CEO at Earley Information Science

Published on: March 27, 2026

In this episode, Seth Earley speaks with Rob Lee, Chief AI Officer and Chief of Research at SANS Institute, about why AI governance is broken in most organizations - and what it actually takes to fix it. They explore why security teams are being asked to simultaneously govern, adopt, and defend AI, why the default framework of no is driving shadow IT rather than preventing risk, and what a practical reset of AI governance actually looks like. Rob also shares why agents should be treated like workers rather than software, and why executives cannot afford to outsource their understanding of AI to anyone else.

Key Takeaways:

• Security teams are now being asked to do three new jobs at once - evaluate AI tools for the organization, drive their own AI transformation, and manage governance and regulatory compliance.
• The default framework of no does not prevent AI use - it drives it underground, creating shadow IT that is far harder to monitor and control than sanctioned tools.
• Governance needs a stoplight model - green means experiment freely, yellow means involve security as a lifeguard, red means stop - with the default answer being yes unless there is a clear reason to say no.
• AI governance documents written before generative AI arrived are already outdated - most say nothing about agentic workflows, human-in-the-loop requirements, or connector permissions.
• Agents should be treated like workers, not software - they reason, improvise, and operate 24-7, which means they require the same zero-trust principles, oversight structures, and ethical guardrails as human employees.
• Executives cannot outsource their understanding of AI to security teams - AI literacy at the C-suite level is a competitive requirement, not an optional capability.
• Good governance is not about documenting every possible bad outcome - it is about establishing overarching goals and building a culture of trust with enough guardrails to prevent the truly stupid risks.

Insightful Quotes:

"The framework security teams are using is a framework of no. And that framework of no is causing people to use AI secretly, regardless of what the security team says." - Rob Lee

"An agent in the future - and some organizations are already treating it this way - is a worker. Everything you ask about governing agents, replace that with a human who just got hired. The same rules apply." - Rob Lee

"You can't automate what you don't understand - and with agents, the stakes are even higher. An agentic mistake isn't a wrong paragraph, it's a blocked critical system." - Seth Earley

Tune in to discover how security and executive leaders can move from a governance posture of restriction to one that enables innovation, manages real risk, and keeps organizations competitive in the age of agentic AI.

Links:

LinkedIn: https://www.linkedin.com/in/leerob/

Website: https://www.sans.org

Sponsor: Vector - https://www.vktr.com/

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• VKTR
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Accuracy, Trust, and the Interface Revolution: How AI is Transforming Legal Workflows

Guest: Mike Anderson, Chief Product Officer, Filevine

Host: Seth Earley, CEO at Earley Information Science

Published on: March 20, 2026

In this episode, Seth Earley speaks with Mike Anderson, Chief Product Officer at Filevine, about what it takes to bring AI into one of the most demanding and high-stakes environments in the enterprise - legal operations. They explore why AI will not replace attorneys but will dramatically extend what legal professionals can accomplish, how real-time deposition analysis is transforming courtroom preparation, and why information architecture remains the critical foundation beneath every AI capability. Mike also shares why the interface - not the model - is the biggest unlock AI offers the legal industry.

Key Takeaways:

• AI will not replace attorneys or paralegals - legal services are already severely undersupplied, and AI's role is to extend what existing professionals can accomplish.
• The billable hour model is evolving, but the bigger opportunity is eliminating non-billable administrative burden so attorneys can focus on higher-order legal thinking.
• Real-time deposition analysis - live transcription cross-referenced against case files - is one of the most powerful and practical AI applications in legal today.
• Boolean search cannot be replaced in legal because accountability for document populations requires transparent, auditable logic that external parties can evaluate.
• Effective AI in legal requires three information retrieval lenses: semantic search, Boolean search, and attribute-based filtering - all three are necessary.
• Information architecture - defining the is-ness and about-ness of legal objects like matters, contracts, depositions, and clients - remains the foundation for AI to work accurately.
• The interface is the single biggest unlock AI offers legal professionals - the ability to ask a question in natural language rather than navigate complex click paths changes everything.

Insightful Quotes:

"The demand for legal services already outpaces supply, and it has for some time. We should be talking about the productivity and extensibility of legal professionals - not obsolescence." - Mike Anderson

"If only I had this analysis of the deposition during the deposition. That one customer comment kicked off an entire depositions platform for us." - Mike Anderson

"You still need the is-ness and about-ness. The interface changes, but the underlying information architecture is still what makes AI work correctly." - Seth Earley

Tune in to discover how legal teams are moving past AI skepticism and building the foundations that make AI accurate, trustworthy, and transformative in practice.

Links

LinkedIn: https://www.linkedin.com/in/michael-anderson-374299163/

Website: https://www.filevine.com

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From Vision to Value: How Leaders Can Close the Gap Between AI Ambition and Operational Reality

Guest: Brian Stafford, CEO at Diligent

Host: Seth Earley, CEO at Earley Information Science

Published on: March 9, 2026

In this episode, Seth Earley speaks with Brian Stafford, CEO of Diligent, a $700 million global software and AI company focused on governance, risk, and compliance. They explore why most organizations understand that AI is transformative but still struggle with the how of actually getting there, and what it takes to move beyond pilots into real operational change. Brian shares how Diligent is helping clients in compliance, audit, and risk functions do more with less through AI-wired software and agents, and why context, leadership, and process understanding are the real drivers of successful AI transformation.

Key Takeaways:

• Most organizations have crossed from asking what AI can do to struggling with how to actually execute and drive measurable transformation.
• Calling initiatives pilots gives organizations an excuse to fail - framing AI as transformation from the start changes accountability and outcomes.
• AI maturity is less about sector or company size and more about the quality and commitment of executive leadership driving change.
• Compliance, risk, and audit functions face a structural mandate - increasing obligations with flat or shrinking budgets - making AI adoption a necessity, not a choice.
• Agents should be thought of like a smart new associate - trained gradually, checked in with frequently at first, then trusted to operate with more autonomy over time.
• Context is the key differentiator for AI solutions - partners who already understand your domain, regulatory environment, and workflows will deliver faster, better outcomes.
• AI-native employees who are intellectually curious and fluent in modern tools can deliver 5 to 10 times the output of peers who resist adopting new capabilities.

Insightful Quotes:

"I hate the term pilot. Pilot gives organizations the license to call something unsuccessful. You're not piloting a transformation - you're either driving it or you're not." - Brian Stafford

"Most of our clients don't care if I ever said the word agent. They care about an outcome. The technology is just what helps deliver it." - Brian Stafford

"You can't automate what you don't understand. And once you do understand it, agents change everything - but the process clarity has to come first." - Seth Earley

Tune in to discover how forward-thinking leaders are closing the gap between AI ambition and real operational impact across governance, risk, and compliance functions.

Links

LinkedIn: https://www.linkedin.com/in/brian-k-stafford/

Website: https://www.diligent.com

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This episode welcomes Sujay Dutta and Siddharth Ragagopal, co-authors of Data as the Fourth Pillar. With extensive experience guiding global organizations on aligning data strategy with real-world business outcomes, Sujay (based in Stockholm) and Siddharth (based in the Netherlands) offer deep insights into AI adoption, data governance, and scaling artificial intelligence responsibly. Hosted by Seth Earley, the conversation explores how businesses can move beyond AI experimentation and develop a mature, impactful data strategy.

Key Takeaways:

• AI Is More Than Technology: AI impacts people, processes, and data—not just IT. Leaders must approach AI holistically.
• Not Every Problem Needs AI: Business leaders should carefully evaluate which challenges truly require AI solutions, and distinguish between traditional AI and generative AI use cases.
• Overcoming Pilot Mode: Successful organizations plan experimentation as part of a longer maturity journey, connecting short-term MVPs to strategic goals.
• The Supply and Demand Gap: Bridging business needs (demand) and technical capabilities (supply) is essential for effective AI integration.
• Stages of AI Maturity: The episode introduces a three-stage maturity model—Foundational, Scaled, and Automated—and explains how organizations can assess their position.
• Data Quality Is Contextual: Data quality requirements should be based on the needs of specific use cases, recognizing dimensions like completeness, timeliness, and relevance.
• Human Factor Is Crucial: Organizational structure, culture, and incentive models must support AI adoption. Preparing people for AI is as important as preparing AI for people.
• Cross-functional Collaboration: Embedding AI and data practices into broader business strategy, and fostering collaboration between business and IT teams, helps avoid siloed efforts.
• Next AI Opportunities: Productivity gains are just the beginning; capturing tacit knowledge and reimagining business processes will drive greater value in coming years.

Featured Quote from the Show:

"One of the key challenges with AI is not about AI being ready for people, but are people ready for AI? ... Ultimately it will land upon the people of the enterprise. How the leaders are clarifying that incentive model to each individual." — Sujay Dutta

Tune in to learn how to build a solid data foundation, avoid common AI pitfalls, and prepare your organization—and your people—for the future of intelligent business.

Links

LinkedIn: https://www.linkedin.com/in/sujaydutta

LinkedIn: https://www.linkedin.com/in/sidd-rajagopal/

Website: https://datathefourthpillar.com

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In this episode of the Earley AI Podcast, host Seth Earley welcomes Krishna Rangasayee, Founder and CEO of SiMa.ai, for a grounded conversation on what it takes to make AI work in real world environments. The discussion focuses on moving beyond hype to address the practical challenges of deploying AI systems that are efficient, scalable, and reliable at the edge.
Krishna brings decades of experience across hardware, software, and AI systems design. He shares why many AI initiatives struggle outside controlled environments and how organizations must rethink architecture, performance, and context when deploying AI closer to where data is created and decisions are made. The episode explores why efficiency is not just a cost concern but a core enabler of real time intelligence across industries.

Key Takeaways from this Episode:
Common misconceptions about AI readiness and why scaling models alone does not lead to success

Why edge AI is critical for real time decision making, latency reduction, and operational reliability

How efficiency at the hardware and system level unlocks new AI use cases
The importance of aligning AI architecture with real world constraints such as power, bandwidth, and deployment conditions

Why organizations must rethink the balance between cloud and edge computing

How leadership and culture influence whether AI experimentation turns into production impact

Insightful Quotes from the Show:
"AI success is not about chasing bigger models. It is about understanding the environment where AI actually has to operate and designing systems that work within real constraints." - Seth Earley

"If you want AI to deliver value in the real world, efficiency has to be designed in from the start. Otherwise, intelligence never makes it past the lab." - Krishna Rangasayee

Links
LinkedIn: https://www.linkedin.com/in/krishnarangasayee/
Website: https://sima.ai

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• VKTR
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In this episode, host Seth Earley welcomes Brandon Lucia, CEO of Efficient Computer, for a deep dive into how AI advancements are reshaping the future of computing—particularly with a focus on energy efficiency, sustainable infrastructure, and real-world applications.

Brandon Lucia brings almost 20 years of experience in computer architecture, having served as an academic at Carnegie Mellon University and led significant research at the boundary of hardware and software innovation. He and his team have pioneered a new kind of hardware architecture designed to drastically reduce power consumption for AI workloads without sacrificing performance or versatility. Their work has far-reaching implications for data centers, edge AI, robotics, automotive, and large-scale infrastructure monitoring.

Key Takeaways from this Episode:

• AI’s energy demands are accelerating rapidly and require rethinking not just bigger models, but architectural efficiency at every level.
• Effective AI infrastructure goes beyond mathematical optimization (like linear algebra); it includes real-world complexity and physical deployment.
• Specialized hardware architectures (CPU, GPU) are evolving, but general-purpose solutions with built-in efficiency—like those from Efficient Computer—can unlock new application domains.
• Edge computing and “physical AI” (as distinguished from legacy IoT) require extremely efficient processing to enable long device lifetimes and advanced capabilities.
• Efficient Computer’s chips offer exponential gains in energy efficiency compared to market-leading CPUs and embedded GPUs—sometimes up to hundreds of times better.
• Enterprises should focus on hardware-software co-design and apply principles like Amdahl’s Law: you are limited by what you can’t optimize, so balancing all types of computation is critical.
• Fine-grained personalization and retraining of AI at the edge will be increasingly important for future applications.
• Organizations that deal in manufacturing, logistics, automotive, infrastructure, or robotics stand to benefit greatly from advances in efficient hardware and architecture.

Insightful Quote from the Show:

"We're not going to meet these energy requirements with the existing hardware and software—we have to change." - Seth Earley

"We are vastly ahead of our competition when it comes to energy consumption. Batteries last longer. You can do more under a power cap. You're not limited by thermal constraints. Those convert directly into capabilities into lifetime. So you can do more than you could do today." - Brandon Lucia

Tune in for a conversation that not only explores the technical side of AI hardware, but also the practical, business, and societal impacts of powering tomorrow’s intelligent systems with greater efficiency.

Links

LinkedIn: https://www.linkedin.com/in/brandon-lucia-0767792/

Website: https://www.efficient.computer/

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• VKTR
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In this episode of the Earley AI Podcast, host Seth Earley sits down with Forrest Zeisler, co-founder and Chief Technology Officer at Jobber. With years of experience building technology for service professionals, Forrest Zeisler has played a pivotal role in empowering small businesses—from landscapers and plumbers to cleaners and contractors—to harness AI and automation for streamlined operations and growth.

Discover how Forrest Zeisler and his team scaled Jobber from three customers to over 300,000, delivering more than $100 billion in services, and learn how their journey demonstrates the transformative impact AI can have on businesses of all sizes.

Key Takeaways:

• Small businesses can benefit enormously from AI, especially for reducing administrative tasks and boosting productivity.
• Adopting new technology isn't just about features—it's about building trust and reliability for the end user.
• Jobber’s growth began with direct customer conversations, leading to a highly configurable platform supporting over 55 industry verticals.
• The journey from manual onboarding and white-glove service to sophisticated self-serve and AI-driven automations took years of iteration and customer feedback.
• Integrating AI isn’t just about chatbots or flashy features; the real impact comes from making technology disappear in the background, allowing users to focus on their craft.
• Reliable automation, rooted in real customer behavior and best practices, is key to driving widespread adoption of AI across industries.
• Building trust with AI systems should mirror how you onboard new employees: review, supervise, and gradually increase autonomy as reliability is proven.
• Orchestrating multiple AI models and agents allows platforms like Jobber to deliver context-aware, intelligent assistance that feels human and personalized.

Insightful Quotes:

"AI is beginning to simplify that work and reduce administrative overhead and reduce those efforts and help small companies provide more consistent and more efficient and more reliable results." - Seth Earley

“Our goal is not to stick a lot of chatbots in front of our customers. It's to make Jobber just magically always seem like it knows what you need when you need it. We want to measure our success by how little we're sticking in front of our customers.”- Forrest Zeisler

Tune in for a behind-the-scenes look at building scalable, reliable AI for small business—and the lessons you can apply whether you're an entrepreneur or driving digital transformation in a larger enterprise.

Links

LinkedIn: https://www.linkedin.com/in/forrestzeisler/

Website: https://www.getjobber.com

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• VKTR
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Join us for a compelling episode of the Earley AI Podcast as host Seth Earley sits down with George Swetlitz, CEO and Co-Founder of RightResponse AI. George brings decades of expertise in natural language technologies, enterprise AI adoption, and building advanced models to solve real business challenges—especially in the realm of customer engagement, feedback, and competitive analysis.

Tune in as George shares how AI-powered systems are changing the way organizations capture, understand, and act on customer feedback to deliver more relevant, personalized, and valuable experiences. He discusses why sounding “human” isn’t enough, the importance of contextual relevance, and how to transform the review response process at scale for both efficiency and revenue growth.

Key Takeaways:

• Relevance Over Sounding Human: The real power of AI in customer experience lies in delivering contextually relevant responses, not just in mimicking human conversation.
• Granular Sentiment Analysis: Advanced AI systems can break down reviews into meaningful phrases, better identify true intent and sentiment (even with sarcasm), and map feedback to business KPIs.
• Building Fact Repositories: Onboarding AI involves creating a dynamic library of facts drawn from reviews, responses, and website content, enabling responses that are tailored to specific, high-value customer concerns.
• Operational Impact at Scale: Large organizations can redeploy significant resources by automating repetitive review responses, freeing up staff to focus on complex, high-touch customer problems.
• Personalized Review Requests: AI can personalize review requests by incorporating context from customer interactions, dramatically improving conversion rates and generating more insightful customer feedback.
• Competitive Insights: AI-driven analysis of both your reviews and your competitors’ can highlight where you’re outperforming or falling short—especially at the hyperlocal level.
• Future of AI in CX: As AI models become more advanced, onboarding and implementation will become smoother, and the quality of customer engagement will only improve.

Insightful Quote:

“What you’re trying to do with AI is get the best of both worlds. You’re trying to be relevant to somebody in the space or in the place that they’re in… The best customer service rep would do that. And now, at scale, AI can help organizations truly meet customers where they are.” George Swetlitz

Listen now and discover how leveraging AI in customer feedback can transform both experience and outcomes!

Links:

LinkedIn: https://www.linkedin.com/in/george-swetlitz-7b43812/

Website: https://www.rightresponseai.com

Thanks to our sponsors:

• VKTR
• Earley Information Science
• AI Powered Enterprise Book

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This episode of the Earley AI Podcast features Betsy Mello, a seasoned retail executive whose career includes leadership roles at Dorel Home, Levi’s, Old Navy, Sears, and several retail startups. With deep expertise in merchandising, inventory management, and eCommerce strategy, Betsy brings a pragmatic perspective shaped by years of leading digital transformation and managing major marketplace relationships with Amazon, Bed Bath & Beyond, and others.

HostSeth Earley talks with Betsy about how retail leaders can navigate the ongoing shift from traditional operations to AI-driven business models. Their discussion explores how structured data, process discipline, and organizational alignment form the foundation of successful digital and AI initiatives—and why the fundamentals still matter, even in the age of automation.

Key Takeaways:

• How the move from brick-and-mortar to digital commerce has transformed consumer expectations and the pace of retail innovation.
• Why marketplaces are data supply chains—and how brands must adapt content, taxonomy, and product positioning across diverse channels.
• The importance of clean data, standardized terminology, and clear use cases before adopting AI solutions.
• Strategies for breaking down silos, aligning KPIs, and ensuring cross-functional collaboration around data and insights.
• Leading through change: the value of transparency, experimentation, and learning from failure during AI-driven transformation.
• What leaders often overlook when preparing for AI—and how to make foundational data work visible and measurable.
• The building blocks for sustainable AI success: information architecture, governance, and accountable data ownership.

Show Quotes:

"You need to have everything standard. You need to have clean data and very clear workflows and accountabilities... The key is having the team set in place and very clear defined processes and roles and responsibilities. It’s incredibly critical to make sure your foundation is correct. You need to be always starting at the basics." – Betsy Mello

"Supply chain is an information supply chain. And every time you have a new way of distributing your product, you have to think about, how do I distribute the data with that product?" - Seth Earley

Tune in to hear how retail and eCommerce leaders can turn complexity into clarity—and build the cultural and data foundations that make AI work.

Links

LinkedIn: https://www.linkedin.com/in/betsymello/

Website: https://www.dorelhome.com

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• VKTR
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Bharat Guruprakash, Chief Business Officer at Algolia, joins host Seth Earley on this episode of the Earley AI Podcast. With years of experience helping organizations leverage AI to connect people with information, Bharat brings deep insight into the evolving world of AI-powered search, retrieval, and agentic technologies. At Algolia, a global leader in AI-driven search and retrieval, he helps shape what’s next in unifying data, building intelligent systems, and designing platforms that understand real-time context.

Key Takeaways:

• Misconceptions about Search and AI: Many organizations think large language models (LLMs) can handle all search needs, but true effective AI solutions require robust retrieval systems beneath the surface.
• The Role of RAG and Memory: Retrieval Augmented Generation (RAG) remains important, but the future is moving toward agentic architectures that require "memory" and stateful interactions, not just stateless search.
• Clean, Structured Data is Crucial: The importance of having clean, accessible, and normalized data stores as the backbone for any successful AI and search initiative.
• Experimentation and Innovation: Enterprises struggle with a culture of experimentation and face challenges in safely running and scaling AI experiments. Autonomous experimentation, where AI can test and optimize different approaches, is emerging as a solution.
• The Rise of Agentic Technologies: The distinction between generative AI (focused on content creation) and agentic AI (focused on task automation and execution), and how agents will soon drive more dynamic, event-driven workflows.
• Guardrails and Risk: Implementing proper protocols (like MCP and Google’s A2A SDKs) and guardrails is essential to ensure agents act safely and within business parameters.
• Privacy and the Future: As agents learn more about users than users know about themselves, privacy, transparency, and identity become critical concerns. The speed of change is challenging, but small, iterative steps help organizations evolve responsibly.

Insightful Quote from the Show:

"It's very risky to say, let's just boldly go forth into the unknown, right? I think you have to have experiments, right? You have to control them, experiments, but you have to Be careful about that and have a mechanism for managing that and for controlling it and for monitoring the results." Seth Earley

"It's okay to start small. Find the small places where you can improve...and keep multiplying them. Over time, when you look back after a year or two, you'll have a very different company from when you started." – Bharat Guruprakash

Tune in for a thoughtful deep dive into the challenges, opportunities, and responsible strategies for embracing AI, search, and agentic technologies in your organization.

Links:

LinkedIn: https://www.linkedin.com/in/bharatguruprakash/

Website: https://www.algolia.com

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• VKTR
• Earley Information Science
• AI Powered Enterprise Book

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In this episode of the Earley AI Podcast, host Seth Earley welcomes Eric Rehl, Vice President of Digital Customer Experience in North America at Schneider Electric. With over 25 years of expertise in digital strategy and customer experience, Eric has guided global organizations through complex digital transformations, always keeping business outcomes and customer needs at the core. Drawing on his deep industry knowledge, Eric shares how large enterprises can move beyond buzzwords like “digital transformation” and “AI,” instead choosing a pragmatic, data-driven approach to drive real business value.

Join Seth and Eric as they discuss the evolving role of digital capabilities in business strategy, the foundational importance of high-quality data, the unique challenges faced by B2B organizations, and how AI can power truly personalized customer experiences—from the ground up.

Key Takeaways:
Digital transformation should be rooted in business outcomes, not technology hype; focus on the “so what” for your customer and organization.

Strong, clean, accessible data is critical for scaling digital experiences and enabling AI-driven personalization—without it, even the best tools will fail.
B2B companies often lag in digital maturity due to legacy data architectures and complex customer relationships, but can catch up by investing strategically in foundational capabilities.

A robust digital journey relies on operationalizing and continually improving product and customer data, rather than one-off fixes.
Maturity in B2B digital experiences evolves from simply “doing no harm,” to enabling ease of business, and ultimately leveraging digital platforms for growth and commercial impact.

AI’s promise lies in moving from segmented personalization to real-time, dynamic customer engagement powered by integrated data and knowledge.

Preparing for AI-driven customer discovery means syndicating high-quality, semantically-structured content across channels—both on and off your own domain.

The next frontier is operationalizing knowledge (not just product or customer data) to fuel AI tools for differentiation and problem-solving.
Continuous experimentation and responsible opportunism allow organizations to discover new outcomes and business value.

Insightful Quotes:
"I think as you start building maturity, you're learning how to orchestrate those pieces. You're getting more of that harmonization of organizing principles across those disparate departments, across knowledge and content and customer experience and product information. And so that becomes kind of the holistic journey that you're thinking about." - Seth Earley

“We always start with the outcome. Like, why are we talking about capabilities here? Why are we talking about AI? What are we actually going to do with it to get to what the business outcome we’re trying to drive or the experience outcome we’re trying to drive?” - Eric Rehl
Don’t miss this in-depth conversation packed with practical advice and forward-looking insights for anyone leading or navigating digital transformation initiatives in the AI era.

Links
LinkedIn: https://www.linkedin.com/in/ericrehl/
Website: https://www.se.com/us/en/

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This episode of the Earley AI Podcast features Rudy Abitbol, a recognized expert in B2B commerce and AI. With years of hands-on experience helping global enterprises with digital transformation—especially around product information management, large-scale AI adoption, and making cutting-edge technology truly practical—Rudy brings a pragmatic view to how AI is revolutionizing B2B e-commerce. He’s passionate about making AI accessible and effective for tackling real-world business challenges.

Key Takeaways:

• AI-Driven Transformation: AI is dramatically streamlining the content generation and management process in B2B commerce, especially when dealing with vast, complex product catalogs.
• Platform Flexibility Matters: Tools like Shopify are lowering barriers for B2B companies by offering user-friendly interfaces and robust ecosystems that allow for rapid configurations—often with no coding required.
• Vibe Coding & Rapid Prototyping: “Vibe coding” empowers product owners to quickly move from conversations and requirements to working wireframes and functional specs, tightening feedback loops and boosting innovation.
• Balancing Efficiency & Differentiation: While AI tools help standardize processes, true competitive advantage comes from infusing proprietary business knowledge into digital solutions.
• Data Readiness & Quality: AI can enrich incomplete or messy data, but organizations must still focus on good data structure and master data management to fully capitalize on AI and automation.
• AI Adoption Hurdles: The most common blockers are cultural—resistance to change and undertraining. Hands-on learning, POCs, and fostering a culture of curiosity are essential.
• Empowering Teams: True AI readiness isn’t just about tools but about giving teams the time, training, and encouragement to experiment—and even risk failure—as they integrate AI into daily workflows.
• Avoiding Over-Reliance on AI: While AI enhances productivity, it can’t replace human judgment. Insightful, context-driven use of AI yields the biggest returns.
• Building Core Competency: Don’t outsource the heart of your digital transformation; instead, build internal expertise and maturity to stay ahead in a rapidly evolving landscape.

Insightful Quote from Rudy Abitbol:
"All the insight within the phrasing, within the way that it’s done...still needs to come from you. You still need to have someone that is a product owner with a great vision, because that's the sole person that is able to infuse [the business]."

Tune in to learn how AI is fundamentally reshaping B2B commerce and how leaders can stay ahead of the digital curve.

Links

LinkedIn: https://www.linkedin.com/in/rudyabitbol/

Website: https://www.trustinsights.ai

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In this episode of Earley AI Podcasts, host Seth Earley welcomes Mark Anderson, co-founder and CEO of Pattern Computer, for a fascinating exploration of what lies beyond the current AI mainstream. With a career grounded in technology, strategy, and scientific innovation—including receiving the Alexandra J. Nobel Award for his contributions to computing and medicine—Mark brings nearly a decade of experience in developing proprietary pattern recognition technologies that move far beyond traditional machine learning models.

Together, Seth and Mark dive deep into the journey of Pattern Computer, unveiling its revolutionary Pattern Discovery Engine—a platform with the unique ability to make discoveries in data that have eluded conventional approaches. Mark explains how his passion for science and the shortcomings of the classic scientific method sparked the creation of new mathematical and architectural foundations in AI, leading to major breakthroughs not only in medicine but also across enterprise applications.

Key Takeaways:

• Origins of Pattern Computer: The story behind the formation of Pattern Computer and its foundational mission to turn pattern discovery from an art into a science.
• A New Approach to AI: How the Pattern Discovery Engine goes beyond finding incremental improvements, enabling true discovery by flipping the traditional scientific method.
• Breakthroughs in Medicine: The real-world impact of Pattern Computer’s approach, including the discovery of gene patterns and the development of new drugs for triple negative breast cancer.
• Pattern Discovery vs. Large Language Models: The critical differences between pattern discovery engines and LLMs, and how these technologies can work together to combine human-friendly communication with genuine scientific discovery.
• Explainable AI and Ethics: Why true explainability, interpretability, and ethical data are at the heart of next-generation AI—and how Pattern Computer is leading the way with interpretable outputs and transparency.
• Enterprise & Science Applications: Use cases in aerospace, mining, healthcare, and more, where Pattern Computer’s approach has led to major discoveries in seconds—successes that eluded brute-force methods for years.
• Advice for Organizations: How businesses and innovators can access and test the Pattern Discovery Engine for their own complex data challenges.

Insightful Quote from Mark Anderson:

“Instead of having a hypothesis and then you run, you want to go again, it’s the opposite. You’re not allowed to have any hypothesis. You can’t bring your bias to the game. And instead of that, you have good data. You run the data and you generate the hypothesis. That’s the right way to solve problems.”

Links

LinkedIn: https://www.linkedin.com/in/markandersonpredicts/

Website: https://www.patterncomputer.com

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In this episode of the Earley AI Podcast, host Seth Earley sits down with Christopher Penn, co-founder and Chief Data Strategist at Trust Insights. Widely known as an authority on analytics, data science, and AI, Chris brings a wealth of practical experience, thought leadership, and cutting-edge perspective to the conversation. With a proven track record as an author, keynote speaker, and trusted advisor in digital transformation, Chris delves deep into the realities of AI for today’s enterprises.

Join Seth and Chris as they cut through the hype surrounding generative AI and focus on what truly matters for organizations: effective AI adoption, data strategy, and delivering measurable value.

Key Takeaways:

• AI Content Detection is Overrated: Most people don’t care if content is AI-generated; what matters is whether it solves their problem or meets their needs.
• Effective AI Use Is About Differentiation: Real business value comes from leveraging your unique voice, expertise, and data—not simply automating generic processes.
• Enterprise AI Adoption Challenges: Organizational inertia, politics, and risk-averse cultures are frequently the biggest barriers to successful AI projects, not technology itself.
• Failure is Essential for Innovation: Enterprises with zero tolerance for failure will struggle with AI: experimentation and learning from failure are critical.
• AI’s Impact on Jobs: Entry-level roles are rapidly changing or disappearing with automation, especially for routine and repetitive tasks.
• Data Quality Still Reigns: The success of tools like Microsoft Copilot depends on having clean, well-organized foundational data—bad data in, bad results out.
• Agentic AI and Model Context Protocols: The future of AI involves building robust agentic infrastructure, understanding APIs for AI, and navigating security/privacy in enterprise integrations.
• Transform, Don’t Just Optimize: Leaders must ask if they’re truly transforming or just making old processes faster; meaningful change starts with people and process—not tech-first thinking.

Insightful Quote from Christopher Penn:

"In the enterprise, it is actually more important to navigate the people and the politics than it is the technology. The technology is easy. It is the humans that are the hard part."

Links

LinkedIn: https://www.linkedin.com/in/cspenn/

Website: https://www.trustinsights.ai

MAICON 2025 Code: PENN200

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In this episode of the Earley AI Podcast, host Seth Earley welcomes Charlie Betz, Principal Analyst at Forrester Research. With an extensive background in digital operating models, enterprise architecture, and the future of work, Charlie brings a systems thinking approach to how digital initiatives are planned, governed, and scaled. As a leading expert covering a $250 billion segment of the global IT market—including vendors like ServiceNow, Atlassian, and Dynatrace—Charlie provides invaluable perspective for technology and business leaders facing the complexities of AI enablement and digital operations in large organizations.

Together, Seth and Charlie dive deep past buzzwords to uncover practical, actionable insights about harnessing AI, operationalizing feedback loops, and navigating legacy technical debt. Charlie shares his real-world experiences wrangling with generative AI tools—including building systems with Anthropic's Claude as a "junior developer"—and distills lessons for executives on aligning business needs with technological advancements.

Key Takeaways:

• How AI, particularly generative models like Claude, has moved from simple code autocompletion to accelerating the development of full-fledged applications—and the challenges and opportunities this creates for non-developers and professionals alike.
• The architecture of the $250 billion IT control plane market, including IT Service Management (ITSM), AIOps, and the massive influence these domains have on enterprise performance and boardroom-level decision-making.
• Why the ultimate business value of AI lies in accelerating feedback loops and continuous learning, not just automation or chatbot deployments.
• Lessons from continuous improvement (lean, Deming cycles, etc.) and why previous attempts struggled at scale—plus how modern AI may finally make the learning organization a reality.
• The importance of architectural governance, data stewardship, and feedback loop closure in successful AI integration—plus concrete calls to action for executives and enterprise architects.
• A nuanced discussion of legacy systems and technical debt: why simply layering new technology on top of old can lead to "technical bankruptcy," and practical strategies for managing (and paying down) technical debt before it becomes existential.
• Cutting through the hype around AI agents and swarms: separating realistic enterprise use cases from risk-laden hype, and the current limitations and essential guardrails needed for safe, effective agentic operations.

Insightful Quote from the Episode:

"If you held my feet to the fire and you told me, 'Charlie, there’s only one point,' I would say look for the feedback loop... What AI is enabling is essentially a faster feedback loop than we've ever had before in industry. And this is where the old becomes new."
– Charlie Betz

Tune in for an unvarnished, deeply practical conversation on making AI real in complex enterprise environments—packed with tangible guidance no matter where you are on your digital transformation journey.

Links:

LinkedIn: https://www.linkedin.com/in/charlestbetz/

Website: https://www.forrester.com

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This episode features a fascinating conversation with Sid Sheth, CEO and Co-Founder of d-Matrix. With a deep background in building advanced systems for high-performance workloads, Sid and his team are at the forefront of AI compute innovation—specifically focused on making AI inference more efficient, cost-effective, and scalable for enterprise use. Host Seth Earley dives into Sid’s journey, the architectural shifts in AI infrastructure, and what it means for organizations seeking to maximize their AI investments.

Key Takeaways:

• The Evolution of AI Infrastructure: Sid breaks down how the traditional tech stack is being rebuilt to support the unique demands of AI, particularly shifting from general-purpose CPUs to specialized accelerators for inference.
• Training vs. Inference: Using a human analogy, Sid explains the fundamental difference between model training (learning) and inference (applying knowledge), emphasizing why most enterprise value comes from efficient inference.
• Purpose-built Accelerators: d-Matrix’s approach to creating inference-only accelerators means dramatically reducing overhead, latency, energy consumption, and cost compared to traditional GPU solutions.
• Scalability & Efficiency: Learn how in-memory compute, chiplets, and innovative memory architectures enable d-Matrix to deliver up to 10x lower latency, and significant gains in energy and dollar efficiency for AI applications.
• Market Trends: Sid reveals how, although today’s focus is largely on training compute, the next five to ten years will see inference dominate as organizations seek ROI from deployed AI.
• Enterprise Strategy Advice: Sid urges tech leaders not to be conservative, but to embrace a heterogeneous and flexible infrastructure strategy to future-proof their AI investments.
• Real-World Use Cases: Hear about d-Matrix’s work enabling low-latency agentic/reasoning models, which are critical for real-time and interactive AI workloads.

Insightful Quote from Sid Sheth:

“Now is not the time to be conservative and get comfortable with choice. In the world of inference there isn’t going to be one size fits all... The world of the future is heterogeneous, where you’re going to have a compute fleet that is augmented with different types of compute to serve different needs.”

Tune in to discover how to rethink your AI infrastructure strategy and stay ahead in the rapidly evolving world of enterprise AI!

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In this episode of the Earley AI Podcast, host Seth Earley sits down with Charles Migos, a veteran toolmaker whose career has spanned animation, post-production, visual effects, and large-scale media systems. Charles is recognized for his innovative approach to making emerging technologies—particularly generative AI—intuitive and accessible for creatives at every level, not just technical experts. Drawing on decades of experience, Charles shares what it means to design tools that empower storytellers and production teams, accelerate creativity, and address industry-specific needs.

Key Takeaways:

• The media and entertainment industry has been under stress due to macroeconomic changes and the disruptive impact of generative AI.
• Creative jobs and processes are being transformed as AI tools dramatically speed up and democratize production workflows, bridging the gap between concept and execution.
• Traditional creative roles are shifting: Directors, producers, and even clients can now participate directly in visual storytelling without years of technical training.
• Instead of relying solely on prompt-based AI systems, innovation lies in giving creatives precise, fine-grained control for rapid exploration and content iteration.
• Asset repurposability, modularity, and the ability to build on prior creative work are becoming crucial for agencies and studios to scale, reduce costs, and increase creative output.
• Protecting intellectual property and ensuring ethical AI use—particularly around deepfakes and content control—is fundamental as generative tools become more powerful.
• The foundation of successful creative AI lies in deeply understanding and honoring existing workflows, enabling faster collaboration, clearer communication, and greater creative trust.

Insightful Quote from Charles Migos:

"Your ability to create faster and better than you ever have before, your ability to collaborate with your team and your stakeholders in ways you never have before, and the ability to communicate around what you create as that team effort is what matters most."

Tune in to explore how AI is reshaping the creative landscape and to gain actionable insight on building truly human-centered design into emerging tech.

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In this episode of the Earley AI Podcast, host Seth Earley sits down with Abhi Yadav, founder of iCustomer AI—a cutting-edge composable decision intelligence company. With a deep background in enterprise technology, Abhi pioneered the Customer Data Platform (CDP) category and is now leading innovation in advanced multigraph frameworks and customer intelligence. He is also an advisor to leading data management organizations and is passionate about helping brands reshape how they connect with customers by making data truly "AI-ready."

Together, Seth and Abhi explore the evolving landscape at the intersection of AI, data, and enterprise maturity. They dive deep into the challenges and opportunities organizations face as they strive for higher levels of decision intelligence, highlighting both the technical and strategic shifts driving the industry forward.

Key Takeaways:

• The evolution from traditional MDM (Master Data Management) and CDP platforms toward a “knowledge engineering era” powered by AI and multi-graph architectures.
• Why a single source of truth is more about a unified, reconcilable semantic layer than a single data repository.
• The importance of developing robust taxonomies, ontologies, and knowledge graphs to contextualize customer data and orchestrate intelligent decisioning at scale.
• Clear definitions and distinctions between zero-party, first-party, second-party, and third-party data—and why they matter for privacy, compliance, and effective marketing.
• Challenges enterprise leaders face in scaling decision intelligence, especially in determining the right balance of human and machine-led decisions.
• The emerging need for "decision lineage": tracking not only what data was used, but how and why decisions were made, for compliance and transparency.
• Advice for founders, technologists, and enterprise leaders: avoid the hype and focus on solving real, specific problems before building platforms or dreaming of category creation.

Insightful Quote from Abhi Yadav:
"Don't follow the hype, follow the real problem. There are still so many real problems—and the more specific that problem is, the more impact you can make. Platforms and categories come later; start with solving measurable problems today."

Tune in for a thoughtful, actionable conversation on unlocking the true potential of enterprise AI and creating data architectures that drive meaningful impact.

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In this episode of the Earley AI Podcast, host Seth Earley sits down with Yang Li, a leading figure in AI and software innovation. Yang is the Chief Operating Officer of Cosine, an advanced AI development firm, with deep experience driving startups, scaling organizations, and pioneering advancements in engineering and software development. Yang’s work focuses on leveraging AI to empower the next generation of developers, especially in navigating the increasingly complex landscape of modern and legacy codebases.

Yang and Seth dive into how AI is reshaping the role of software engineers, the evolving challenges of handling massive backlogs and legacy systems, and what creativity and efficiency really look like in an age of AI-powered software development.

Key Takeaways:

• AI’s Impact on Software Engineering: AI is shifting the developer’s role from hands-on coding to more creative, iterative, and strategic work.
• Tackling Legacy Code: Cosine is pioneering new ways for AI to handle outdated and complex codebases (like COBOL and Fortran) that most engineers—and AI models—struggle with.
• Augmenting, Not Replacing, Engineers: AI tools like Cosine’s Genie reduce ramp-up time for engineers, help address daunting backlogs, and act as creative partners rather than outright replacements.
• The Challenge of Benchmarks: Yang explains why public coding benchmarks can be misleading when bringing products to real-world enterprise environments, especially with diverse codebases.
• The Emergence of ‘Vibe Coding’: Idea-to-prototype time is shrinking, allowing non-technical team members to quickly bring their ideas to life using AI assistants.
• Risks & Limits: Over-reliance on AI, standardization versus differentiation, and the need for new evaluation criteria in engineering organizations.
• Future Skills: The importance of risk-taking, adaptability, and prompt engineering as software development evolves, plus insights into how organizations are rethinking career ladders and promotions in an AI-powered world.

Insightful Quote from Yang Li:

"Previously you had to use words and language to describe your idea, you can now show people your idea... The time between you having thought of an idea to actually be able to show people that idea has now reduced almost to zero because of vibe coding."

Tune in to discover what’s next for software engineering in the age of AI, and how to stay ahead in this rapidly changing landscape.

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In this episode of the Earley AI Podcast, host Seth Earley sits down with industry analyst and advisor Tony Baer, a seasoned expert in data, cloud, and analytics. With decades of experience guiding global tech leaders like AWS and Oracle, Tony brings a nuanced perspective on how knowledge engineering is evolving—and why context is the missing link in many enterprise AI initiatives.

Together, Seth and Tony explore the shift from static data models to dynamic knowledge frameworks, the renewed importance of governance, and how graph databases and generative AI are reshaping enterprise intelligence. This is a conversation packed with hard-earned lessons and actionable insight for data, IT, and transformation leaders aiming to make AI work in the real world.

Key Takeaways:

• Knowledge engineering today is about dynamic, adaptive structures—not static ontologies or rigid models.
• The role of the knowledge engineer is shifting: it’s less about technical mastery and more about bridging data, business, and domain expertise.
• Context is foundational. The five W’s—Who, What, When, Where, Why (and How)—unlock meaningful, actionable intelligence.
• Graph databases and AI are enabling real-time connections across data, turning static information into living knowledge.
• Generative AI delivers the most value when rooted in organizational context. RAG strategies demand clean data and strong information architecture.
• Successful AI initiatives are focused. Start with well-bounded, high-impact processes—avoid boiling the ocean.
• Core principles from previous data waves still apply. It’s about evolving governance, stewardship, and architecture for the AI era.
• Sustainable value comes from feedback loops, iteration, and alignment—not silver bullets.

Tune in to discover how to make AI practical, actionable, and intelligent for your organization.

Quote of the Show: "Just because something is old does not make it wrong. There are a lot of disciplines we've built up over the years—governance, data stewardship—that still matter. The principle was right. We just adapt it and use our learnings from each cycle to become more knowledgeable and proficient." Tony Baer

Links

LinkedIn: https://www.linkedin.com/in/dbinsight/

Website: https://www.dbinsight.io

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In this episode of the Earley AI Podcast, host Seth Earley welcomes two insightful guests from Anders, a top 100 CPA firm: David Hartley and Dave Blatt. David Hartley is a seasoned CPA with a profound understanding of the synergy between finance and technology. He advocates for how AI can enhance traditional accounting roles rather than replace them. Dave Blatt brings a wealth of knowledge in AI automation and analytics, focusing on empowering mid-sized companies to harness AI for competing with larger players.

Join us as we dive into the world of AI applications in the finance, accounting, and mid-market operations sectors. Our guests dispel common myths and fears surrounding AI, exploring how small and medium-sized enterprises can practically and effectively adopt AI technologies to drive transformation and growth.

Key Takeaways:

• Demystifying AI: Understanding AI in the context of mid-market companies and addressing misconceptions around AI replacing human jobs.
• AI for Mid-Sized Enterprises:** How AI is accessible and beneficial for mid-sized businesses, allowing them to compete with larger organizations.
• Impact on Accounting: Enhancing traditional accounting roles through AI and freeing up time for more value-added activities.
• Implementation Strategies: Best practices for implementing AI in mid-sized companies, focusing on education, small projects, and quick wins.
• Real-World Applications: Case studies in industries like construction and manufacturing, where AI has improved efficiency and productivity.
• Communication and Trust: The importance of communication and building trust among team members to ensure successful AI adoption.

Quote of the Show: "Start small and not make it so daunting... get some quick wins that will be a catalyst to doing more projects and bigger efforts." - Dave Blatt

Links:

LinkedIn: https://www.linkedin.com/in/davehartley/

LinkedIn: https://www.linkedin.com/in/daveblatt/

Website: https://anderscpa.com

Article: AI Adoption Is Not as Hard as You Think – Start Now or Fall Behind

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In this episode of the Earley AI Podcast, we welcome guest, Jack Lampka, an accomplished advisor and speaker with over 27 years of experience in corporate roles within the tech and pharma sectors. Now based in Munich, Germany, Jack specializes in enhancing data storytelling and cultivating a product mindset among technical employees. His extensive career journey includes living and working in countries like Poland and the United States.

Key Takeaways from this Episode:

• The importance of a product mindset for technical teams when developing AI solutions.
• Understanding the misconceptions and realistic expectations for AI and generative AI in businesses.
• How to successfully sell AI solutions internally by focusing on business needs and creating a comprehensive product marketing plan.
• The role of data storytelling in bridging the gap between technical and non-technical users.
• Insights into the hype surrounding Agentic AI and its relevance to current business applications.

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Join Seth Earley on Earley AI Podcasts as he welcomes Adam Honig, a pioneering force in the field of customer relationship management (CRM). As the founder of Spiro AI, Adam challenges the conventional need for CRM systems by offering an innovative AI-driven alternative. With a rich history of building one of the largest Salesforce consulting partners—eventually acquired by Accenture—Adam’s insights blend tradition with transformative technology, sparking discussions about the evolving landscape of CRM.

Key Takeaways:

• Revolutionizing CRM: Understand Adam's perspective on why traditional CRM systems are antiquated and how Spiro’s AI-driven approach offers a modern solution.
• Automated Data Capture: Learn how Spiro AI automates the tedious process of data entry and offers real-time insights for sales teams without manual input.
• Industry-Specific Challenges: Explore the unique hurdles faced by manufacturing and distribution sectors in adopting CRM and AI technologies.
• AI Implementation: Discover practical implementations of AI, including order entry automation, in revolutionizing traditional business models.
• Future of Work: Delve into a candid discussion on how AI could lead to significant job displacement, and the broader economic implications.
• Roadmap of Innovation: Get a sneak peek into future developments, including autonomous AI agents and enhanced integration with product data.

Quote from the show:

"Salespeople didn't go into sales to enter data. They want to meet with customers. They want to be where the action is, and they need the software to just get out of their way." – Adam Honig

Links:
LinkedIn: https://www.linkedin.com/in/adamhonig/

Website: https://spiro.ai

X: https://x.com/adamhonig

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In this episode, Seth Earley is joined by Brian Magerko, a professor of digital media at Georgia Institute of Technology and a pioneer in applied AI and computer-human interaction. Brian shares his journey through the academic realm and his fascinating experiences, including co-founding EarSketch, an educational platform that merges coding with music for nearly 2 million users. Together, Seth and Brian explore the bridging of technical language gaps, the role of AI in creativity, and unravel common misconceptions about generative AI. Listen in as they discuss the complexities of AI-driven creativity, the importance of fostering AI literacy in organizations, and the ethical considerations that come with the integration of cutting-edge technology. This episode is packed with insights that are sure to provoke thought and inspire innovation in any AI enthusiast or professional. Join us as we continue our journey into the evolving landscape of artificial intelligence.

Key Takeaways:

• The evolution of AI from aspirational concepts to today’s realities in the marketplace.
• Insights into the bridging of technical language gaps and the role of AI in creativity.
• Common misconceptions and myths about generative AI and its responsible use.
• Importance of a holistic organizational approach to adopting AI technology, including involving diverse stakeholders.
• Discussion on the implications of biases in AI and the significance of responsible data handling.

Quote from the show:

"They are tools, not oracles, you know, they're things that are great in the hands of people that know how to use them." - Brian Magerko

Links:
LinkedIn: https://www.linkedin.com/in/magerko/

Website: https://expressivemachinery.gatech.edu

X: https://x.com/thatmagerko

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In this episode, hosts Seth Earley and Chris Featherstone are joined by Don Gossen to explore the transformative potential of AI and blockchain in decentralized energy networks. This episode explores how AI and blockchain technologies work together to optimize energy consumption, secure transactions, and drive innovation in local energy management. Don shares real-world case studies showcasing the impact of AI-driven IoT solutions, from reducing costs to enabling energy sovereignty in European markets.

Key Takeaways:

• AI’s Role in Energy Optimization – How AI-powered agents connect with IoT devices to enhance efficiency and manage decentralized transactions.
• Blockchain for Security & Transparency – The role of blockchain in securing transactions and ensuring trust in energy markets.
• Real-World Applications – Case studies of AI-driven decentralized energy networks lowering costs and increasing sustainability.
• Future Trends – The emergence of AI-driven economic models that could redefine how energy is produced and consumed.

Tune in to hear Don’s insights on the cutting edge of AI, blockchain, and the future of decentralized energy.

Quote from the show:

"Blockchain technology offers elegant solutions to problems of provenance in data and ML models, enabling higher fidelity and trustworthiness. The integration of decentralized blockchain with centralized ML technology presents conflicts, but it is essential for developing better solutions and new economic models. It's about more than just replacing tasks; it's about redefining how we manage and transact value in an increasingly autonomous ecosystem." – Don Gossen

Links:
LinkedIn: https://www.linkedin.com/in/donald-gossen-40ab96/

Website: https://nevermined.io

X: https://x.com/dongossen

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In this episode, hosts Seth Earley and Chris Featherstone are joined by John Lenker, an accomplished expert in enterprise search and data governance. John, who has presented to technology leaders like the CTO of NASA and the CIO of the Marshall Space Center, shares his extensive knowledge and experiences from working with leading companies. Currently engaged with Big ID, John delves deep into the evolving landscape of search technologies, data security, and enterprise-specific solutions.

Key takeaways:

• Evolution of Search Technologies: Current search tools are transitioning from simple keyword-based systems to sophisticated question-based interactions, offering synthesized answers and enhancing data findability.
• Importance of Security: John underscores the critical need for granular security in modern search technologies, detailing the challenges of securing sensitive documents amidst cloud migration and generative AI implementations.
• Trust and Customization: Organizations can trust search vendors to enhance data findability while retaining existing systems. Companies must consider both common needs and unique requirements shaped by their environments and practices.
• Challenges with Legacy Systems: Accessing and modernizing data from outdated systems adds complexity to organizational workflows, necessitating a fine balance between old and new technologies.
• Retrieval Augmented Generation (RAG): The future of search lies in interactive models like GPT, evolving towards more contextually relevant search experiences.

Quote from the show:

"Effective information architecture is the backbone of superior search experiences. It’s not just about finding information—it’s about finding the right information securely and efficiently." – John Lenker

Links:
LinkedIn: https://www.linkedin.com/in/jlenker/

Website: https://bigid.com

X: https://x.com/LenkerITPro

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In this episode of the Earley AI Podcast Sahitya Senapthy, Founder and CEO of Endeavor, joins us for an episode filled with rich discussions on AI, data management, and the intriguing paths of technological advancement in industrial manufacturing. Sahitya's career has spanned work with the US Air Force, AWS, and Palantir.

Sahitya joins our hosts, Seth Earley and Chris Featherstone, as they have an in-depth discussion about the intersection of AI and industrial manufacturing.

Key takeaways:
Rethinking AI Implementation: The importance of moving beyond simple chatbot applications like ChatGPT to realize real ROI through generative AI for sales optimization and cost savings in industrial spaces.

Phased Approach to Tech Deployment: The "crawl, walk, run" methodology along with proof-of-concept (POC) phases is crucial in acclimating users and ensuring new tools deliver tangible ROI, avoiding "shelf-ware."

Data Privacy and Security: Amidst the integration of sophisticated AI solutions, maintaining data privacy and security remains paramount.

Quote from the show:
"Real ROI in industrial spaces comes from effectively using generative AI for sales and cost savings—not just relying on chatbots." - Sahitya Senapathy

Links:
LinkedIn: https://www.linkedin.com/in/sahityas/
Website: https://www.endeavor.ai

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In this episode of the Earley AI Podcast Ron Green, Founder of KUNGFU.ai joins host Chris Featherstone. Ron's expertise spans over 25 years in artificial intelligence and machine learning. Starting his journey in computer science during the early days of AI, he has witnessed and contributed to the evolution of the field from modest neural networks to today's complex, groundbreaking systems. With a master's degree in AI from Sussex, he has firsthand experience in areas ranging from protein folding predictions to advanced graph models.

Key takeaways:

• Feedback Loop in LLMs: Discover why training large language models (LLMs) using their own generated data can create a feedback loop reducing model capabilities.
• AI Accuracy and Trust: Insights into the importance of building trust in AI through accurate models and robust graph environments handling diverse data associations.
• Ethical AI Development: Discussions on the ethical challenges and necessary oversight in AI, emphasizing transparency and policy controls to mitigate biases.

Quote from the show:

"Accuracy in AI models builds trust, and that's crucial for their successful deployment. But it's vital to remember that all models have biases; the key lies in identifying and addressing the unfair ones early on." - Ron Green

Links:
LinkedIn: https://www.linkedin.com/in/rongreen/

Website: https://www.kungfu.ai

Podcast: https://www.kungfu.ai/resources/hidden-layers

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In this episode of the Earley AI Podcast Camden Swita, the Head of AI and ML Innovation at New Relic, joins us for an insightful discussion on the transformative role of AI in modern technology. With a rich background spanning journalism, product management, and cutting-edge AI/ML initiatives, Camden brings a unique perspective on leveraging artificial intelligence to enhance both professional and creative workflows.

Camden joins our hosts, Seth Earley and Chris Featherstone, as they navigating the complexities of data readiness and AI operations

Key takeaways:

• Understanding Vendor Offerings and Data Infrastructure: The importance of scrutinizing vendor promises and the potential of building simpler AI solutions, like basic chatbots, in-house. Consulting legitimate data scientists and engineers to assess data readiness before plunging into AI investments was highly recommended.
• Impact of Generative AI on Content Creation: Generative AI's role in automating content creation is rapidly evolving. While it offers incredible efficiencies in some areas, its application in creative writing is still experimental and requires a balance between traditional methods and automation.
• Modular Retrieval Augmented Generation (RAG): The concept of Modular RAG was explored as a way to enhance language models with relevant context. This system uses specific data sources to fill knowledge gaps, making responses more accurate and targeted. The discussion included how visual tools like heatmaps can identify and improve sparse contexts.

Quote from the show:

"Understanding the actual capabilities of AI systems and the state of your own data infrastructure is crucial. Investing in foundational data work isn't glamorous, but it's the backbone of any successful AI implementation. Without it, even the most advanced algorithms can't deliver meaningful results." - Camden Swita

Links:
LinkedIn: https://www.linkedin.com/in/camden-swita-54a41aa/

Website: https://newrelic.com

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In this episode of the Earley AI Podcast we are joined by guest David Marra a seasoned expert in AI-driven investment strategies and founder of Marken Asset Management. David brings invaluable insights into the transformative potential of AI, particularly in complex payroll workflows, ERP systems, and financial services.

David joins our hosts, Seth Earley and Chris Featherstone, as they explore the current state and future of AI, including the integration of large language models (LLMs) and the importance of robust data management.

Key takeaways:

• AI for Simplifying Workflows: AI, particularly chatbots, can streamline complex payroll tasks, improving productivity by up to 30% and reducing errors.
• AI in Financial Services: Though slower to adopt AI, financial services have vast potential, offering long-term growth opportunities through AI investments.
• Global AI & Investment: Japan’s AI market is booming, and companies with strong data infrastructures are best positioned to benefit from AI advancements.

Quote from the show:

"2023 was the wake-up call for realizing new technology's immense potential, and as we transition pilot programs to production systems in 2024, we’ll start seeing real, tangible outcomes by 2025. The application layer of AI isn’t just a technological enhancement; it’s poised to become a multi-trillion-dollar market, revolutionizing capabilities and creating workflows that weren't even conceivable before." - David Marra

Links:
LinkedIn: https://www.linkedin.com/in/david-marra-8693b44/

Website: https://markinfunds.com

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In this episode of the Earley AI Podcast we are joined by guest Yuri Dvoinos, the Chief Innovation Officer at Aura, a leading cybersecurity company based in Boston. With an extensive background in information security, AI applications, and data privacy, Yuri brings a wealth of knowledge on how to protect personal information in the digital age.

Join our hosts Seth Earley and Chris Featherstone as Yuri explains the intricacies of data privacy, the adverse effects of phone scams, and the critical role of AI in detecting and thwarting sophisticated fraud attempts, including deepfakes and phishing scams.

Key takeaways:
The Importance of Understanding Your Digital Footprint. Learn how your online activities impact your privacy and what steps you can take to protect yourself.
Identity Protection and the Impact of Scams. Delve into the psychological toll of phone scams, particularly on seniors, and how to safeguard against such threats.
The Role of AI in Scam Detection. Understand how AI can detect psychological pressure and scam tactics in real-time, and the challenges in distinguishing AI-generated chats from real conversations.
The Rise of Deepfakes. Explore the dangers of deepfakes in voice and video manipulation, and the need for heightened vigilance.

Quote from the show:
"Understanding one's digital footprint and protecting personal information online is no longer optional—it's essential. The rise of deepfakes and sophisticated scam tactics means that we need to be more critical and skeptical of the information we encounter. Leveraging AI to detect psychological pressure and scam tactics in real time can help us stay one step ahead, but ultimately, it starts with heightened awareness and constant vigilance." - Yuri Dvoinos

Links:
LinkedIn: https://www.linkedin.com/in/dvoinos/
Website: https://www.aura.com
Twitter: https://x.com/YuriyNos

Thanks to our sponsors:

• VKTR
• Earley Information Science
• AI Powered Enterprise Book

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In this episode of the Earley AI Podcast guest Tobias Zwingmann, an esteemed analytics and AI expert from Hanover, Germany, brings a wealth of experience from his work with SaaS platforms and consulting, and he shares invaluable insights on the practical intricacies of AI in business.

Join our hosts Seth Earley and Chris Featherstone as they discuss with Tobias the importance of business leaders understanding AI, the pitfalls of misleading sales tactics, and the necessity of organizational alignment for successful AI implementation. With topics ranging from data quality to the challenges of adopting generative AI, this episode is a treasure trove of actionable advice for anyone looking to navigate the complex world of artificial intelligence.

Key takeaways:

• The critical need for business leaders to educate themselves on AI and LLMs to ask the right questions and evaluate vendors effectively.
• The foundational role of good data in AI projects and examples of AI used to rectify data issues.
• The tendency of software vendors to oversell solutions and the issues arising from improper data formats and organizational misalignment.
• Challenges presented by fragmented processes, systems, and data in large enterprises and the benefit of small, targeted interventions.
• The importance of data labeling, taxonomies, ontologies, and metadata in effectively leveraging AI.
• Misconceptions about AI as pure software and the need to shift mindsets for working with generative AI.
• Difficulties in scaling generative AI and defining outcomes, leading to missed opportunities for customers.

Quote from the show:

"Understanding AI requires commitment at the senior level. You need those workshops. You need commitment and understanding because, without alignment, no AI implementation will truly succeed." - Tobias Zwingmann

Links:
LinkedIn: https://www.linkedin.com/in/tobias-zwingmann/

Website: https://www.rapyd.ai

Twitter: https://x.com/ztobi

Website: newsletter.tobiaszwingmann.com

Thanks to our sponsors:

• VKTR
• Earley Information Science
• AI Powered Enterprise Book

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In this episode of Earley AI Podcasts, we welcome Bartek Roszak, an expert in artificial intelligence and data science. With a career starting as an equity trader and evolving to lead AI strategy and implementation at STX Next, Bartek brings deep insights into the world of AI-driven trading and data quality improvement.

Join hosts Seth Earley and Chris Featherstone as they explore Bartek's experiences, the nuances of deploying AI in trading, and the future potential of generative models.

Key takeaways:

• Understanding the intricacies of modular rag for batch processing and data quality improvement.
• Insights into the use of AI bots in trading, and a critical view on publicized successful strategies.
• The importance of validating models to prevent data leakage and overfitting, and monitoring accuracy post-deployment.
• Challenges and solutions in training generative AI models, including the necessity for human validation.
• How advanced techniques like re-rankers and embedders enhance the accuracy of large language models (LLMs).
• The evolving trends in AI, including the hype around newer approaches like prompt engineering and multi-agent strategies.
• The significance of knowledge architecture and metadata in enriching content embeddings for better outcomes.

Quote from the show:

"Embracing opportunities across different industries and persevering through rejections is crucial. The field of AI is ever-evolving, and staying adaptable and curious will open doors you never imagined." — Bartek Roszak

Links:
LinkedIn: https://www.linkedin.com/in/bartekroszak/

Website: https://www.stxnext.com

Thanks to our sponsors:

• VKTR
• Earley Information Science
• AI Powered Enterprise Book

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In this episode of Earley AI Podcasts, we are joined by Nick Usborne, a veteran copywriter with over four decades of experience and an advocate for the sophisticated use of generative AI in human-centric communication. Nick provides a wealth of insights into the pitfalls and potentials of integrating AI into marketing and how businesses can leverage AI without losing their unique voice.

Key Takeaways:

Human Connection Matters:

The episode opens with Nick Usborne expressing disappointment and loss of trust in a brand due to an impersonal automated onboarding process, emphasizing the need for emotional intelligence in all interactions.

Education and Training in AI:

Nick stresses the importance of better educating employees and businesses on AI capabilities and limitations.

The Sameness Trap:

A discussion on the risk of using identical AI models to create content, leading to homogenization and a loss of brand uniqueness.

Limitations of AI:

Nick elaborates on AI's current inability to genuinely experience emotions and sensory input, stressing the crucial role of human supervision.

Structured Prompts for AI:

Nick shares methods, like the RACE framework, to optimize AI outputs and ensure relevance and quality.

Human Creativity in Marketing:

The necessity of human involvement to preserve creativity and innovation in marketing efforts despite the speed and efficiency of AI.

Quote from the show:

"AI will never replace the human touch in creating meaningful connections and fostering trust. It's a partner, not a tool, and our role is to guide it with emotional intelligence and creativity." – Nick Usborne

Thanks to our sponsors:

• VKTR
• Earley Information Science
• AI Powered Enterprise Book

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This episode of the Earley AI Podcasts features Jason Radisson, an expert in digital transformations who has worked with renowned companies such as McKinsey, eBay, and Uber.

Tune in as Jason shares his insights on the misconceptions surrounding AI startups and the importance of having a quality team with enterprise experience. He also highlights the agility of startups in implementing new features and integrations, challenging the notion of slow processes.

Key takeaways:

-AI startups benefit from having a quality team with enterprise experience, allowing them to be agile and quickly implement new features and integrations.

• Non-tech companies often struggle to adapt to automation due to cultural barriers and legacy thinking, despite automation not requiring lengthy change management processes.

• The gig economy presents challenges in optimizing large workforces, requiring a balance between employer and employee perspectives to create win-win solutions.

• Organizations need to actively seek out innovative strategies and technologies to stay competitive, rather than relying on traditional approaches such as enterprise data warehouses and data lakes.

Quote from the show:

"The common approach of starting with an enterprise data warehouse and data lake is a fallacy. It's crucial to work backwards from customer-first use cases and focus on initiatives that will drive business value. By making quick developments and enabling additional investment, companies can harness the power of AI and machine learning technologies to transform their operations and stay ahead of the curve." - Jason Radisson

Links:
LinkedIn: https://www.linkedin.com/in/jason-radisson/
Website: https://www.movo.co

Ways to Tune In:
Earley AI Podcast: https://www.earley.com/earley-ai-podcast-home
Apple Podcast: https://podcasts.apple.com/podcast/id1586654770
Spotify: https://open.spotify.com/show/5nkcZvVYjHHj6wtBABqLbE?si=73cd5d5fc89f4781
iHeart Radio: https://www.iheart.com/podcast/269-earley-ai-podcast-87108370/
Stitcher: https://www.stitcher.com/show/earley-ai-podcast
Amazon Music: https://music.amazon.com/podcasts/18524b67-09cf-433f-82db-07b6213ad3ba/earley-ai-podcast
Buzzsprout: https://earleyai.buzzsprout.com/

Thanks to our sponsors:

• VKTR
• Earley Information Science
• AI Powered Enterprise Book

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With extensive experience in AI and machine learning dating back to 1998, Cohen-Dumani brings valuable insights into the historical and present-day landscape of AI, emphasizing the importance of foundational knowledge, expertise, and knowledge management in making AI work effectively within organizations.

Tune in to this enlightening conversation as they discuss the attention and resources that must be invested in unstructured data and knowledge to leverage the full potential of AI.

Key takeaways:

• A foundational reference architecture is critical for making sense of data and discerning between vendors' aspirational capabilities and reality.
• Traditional long-term technology planning is no longer applicable in the age of AI and large language models (LLMs) due to the unpredictable nature of AI's uses and leveraging capabilities.
• Executives should personally experiment with AI tools and allow more freedom for workers to adopt AI, rather than stifling innovation.
• Building an extensible and expandable data foundation and good enterprise architecture is crucial to avoid data silos and maintain consistency in data.

Quote from the show:
"I think one of the challenges that organizations have is they're not investing the time, the effort, the money, the resources, and the attention on unstructured data, on knowledge. You know, if you look at any accounting department, they spend inordinate amount of time and resources on numbers, on transactional data. But if you look at how much effort is put on unstructured data, it's night and day. And yet unstructured data is 80+% of the data most organizations have." - Daniel Cohen-Dumani

Links:
LinkedIn: https://www.linkedin.com/in/dcohendumani/
Website: https://www.withum.com

Thanks to our sponsors:

• VKTR
• Earley Information Science
• AI Powered Enterprise Book

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A tech innovator with profound expertise in AI and its applications, our guest Alex Gurbych joins us with rich insights into how AI integrates into varying industries. Alex has extensive experience addressing the challenges and potentials of AI, especially in legacy systems and organizations.

Tune in to this enlightening conversation as they dissect how AI can be aligned with business value, explore the nuances of AI consciousness, and discuss the necessity of thinking like a data scientist in today's fast-evolving tech landscape.

Key takeaways:
- The Overestimation of AI: Alex Gurbych tackled the common hype surrounding AI capabilities and stressed the importance of understanding its limitations and training.

• Challenges in AI Integration: The hurdles encountered when integrating AI into legacy systems and the necessity of defining clear use cases for practical applications were discussed.

• AI in Healthcare and Biosciences: The role of AI in drug design and development, protein folding, and target discovery was dissected with a focus on both its potential and limitations.

• AI and Consciousness: A fascinating exploration of whether AI can achieve consciousness ensued, with thoughts on the complexity of the human brain and AI's rapid evolution.

• Behavioral Change and AI Adoption: The discussion highlighted a case of AI adoption success among technicians versus challenges faced by doctors, showcasing the importance of behavior change driven by AI use cases.

• The Role of Data Scientists: The critical investment in data scientists and the growing necessity for everyone to adopt a data scientist's mindset were underscored.

Quote from the show:

"The biggest challenge is really to define your use case. What do you actually want to get out of AI? And when you have a very crisp definition of that, then it's much easier to actually make it work. But if you just say, oh, I want to use AI because everybody's using AI and it's a hot topic, then it's not going to help you much." - Alex Gurbych

Links:
LinkedIn: https://www.linkedin.com/in/ogurbych/
Website: https://blackthorn.ai

Thanks to our sponsors:

• VKTR
• Earley Information Science
• AI Powered Enterprise Book

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Seth Earley and Chris Featherstone are joined by special guest Bob Levy. Bob Levy, Founder and CEO of Immersion Analytics, brings a wealth of experience in technology and data visualization, having worked with top companies such as IBM, Rational Software, and Mathworks. He shares his profound insights on integrating multidimensional visualization technology using virtual and augmented reality to tackle complex data challenges.

Bob Levy is Founder & CEO of Immersion Analytics. With extensive experience in R&D and product management at companies like IBM and Rational Software. Bob is an expert in AI and data visualization. He's been a speaker at prestigious events like MIT Technology Review’s EmTech Caribbean and Reilly Strata Data Conference, and has won competitions like MIT’s Reality Virtually hackathon and Tableau’s DataDev Competition.

Key Takeaways:

• Examples of how visualization tools help investors make more informed decisions based on a multitude of data attributes.

• The transformative potential of VR and AR in business settings and educational environments, backed by partnerships with tech giants like Microsoft and Apple.

• Visualization technology as a tool for simplifying the understanding of AI-related compliance and emerging standards.

• The discussion on the lack of global compliance standards and the need for potential new standards or refinement of existing ones.

• Use cases in derivatives trading, financial performance metrics, and real-time pricing data for detecting anomalies and opportunities.

• Innovative ways to visualize artificial neural networks and understand the training processes via VR.

• Visualization tools for web and enterprise-level applications, including programming languages and hardware requirements.

• The crucial role of visualization in making AI systems comprehensible to non-technical stakeholders like regulators.

Quote of the Show:
"Seeing all the data points and complexity is crucial for understanding the true nature of the data and avoiding misinterpretation." - Bob Levy

Links:
LinkedIn: https://www.linkedin.com/in/boblevy/
Website: https://www.immersionanalytics.com/

Ways to Tune In:
Earley AI Podcast: https://www.earley.com/earley-ai-podcast-home
Apple Podcast: https://podcasts.apple.com/podcast/id1586654770
Spotify: https://open.spotify.com/show/5nkcZvVYjHHj6wtBABqLbE?si=73cd5d5fc89f4781
iHeart Radio: https://www.iheart.com/podcast/269-earley-ai-podcast-87108370/
Stitcher: https://www.stitcher.com/show/earley-ai-podcast
Amazon Music: https://music.amazon.com/podcasts/18524b67-09cf-433f-82db-07b6213ad3ba/earley-ai-podcast
Buzzsprout: https://earleyai.buzzsprout.com/

Thanks to our sponsors:

• CMSWire
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• AI Powered Enterprise Book

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Seth Earley sits down with Moritz Müller, a distinguished figure with a rich background in consulting and a leader in artificial intelligence applications. Before carving out his niche at Squirrel AI, Moritz Müller honed his skills at a prestigious consulting firm in Switzerland and spearheaded an ambitious venture by setting up an office in Singapore.

As the head of product management at Squirrel, Moritz brings a wealth of experience from digital transformation programs and a deep understanding of AI technologies across various industries. His insights into the burgeoning world of retrieval augmented generation (RAG) and large language models (LLMs) are second to none, offering listeners an in-depth look at the future of information access and management.

Moritz brings his expertise full-circle by stressing the importance of metadata, vector similarity searches, and the need for ongoing maintenance of knowledge bases to ensure that emerging technologies truly enhance our search capabilities and knowledge utilization.

Key Takeaways- A thorough exploration of retrieval augmented generation and how it's poised to reshape data handling in the digital era.

• The importance of ACLs, knowledge graphs, digital body language, and conversational search for personalizing organizational data access.

• The need for supervised fine-tuning of LLMs to ensure relevance and accuracy in data retrieval processes.

• How to troubleshoot LLM errors and why successful information retrieval is critical for the effective implementation of RAG.

• The ongoing challenges and considerations in using AI for effective document search and retrieval within organizations.

• The significance of structuring content, tailoring prompts, and understanding the user context to harness the full potential of language models.

Quote from the show:

"The pairing of information retrieval technology with large language models isn't just a minor improvement; it's a revolutionary step forward. It's about reaching into that vast ocean of data and pulling out the exact details you need – that's the game-changer."
- Moritz Müller

Links:

LinkedIn: https://www.linkedin.com/in/moritzbmueller/
Website: https://squirro.com/

Ways to Tune In:
Earley AI Podcast: https://www.earley.com/earley-ai-podcast-home
Apple Podcast: https://podcasts.apple.com/podcast/id1586654770
Spotify: https://open.spotify.com/show/5nkcZvVYjHHj6wtBABqLbE?si=73cd5d5fc89f4781
iHeart Radio: https://www.iheart.com/podcast/269-earley-ai-podcast-87108370/
Stitcher: https://www.stitcher.com/show/earley-ai-podcast
Amazon Music: https://music.amazon.com/podcasts/18524b67-09cf-433f-82db-07b6213ad3ba/earley-ai-podcast
Buzzsprout: https://earleyai.buzzsprout.com/

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Erdem Özcan is an esteemed expert with a rich background in computer science, focusing on innovations in AI. With a PhD in computer science and significant industry experience, including work on IBM's Watson and at Elemental Cognition, Dr. Özcan has been at the forefront of blending symbolic AI and deep learning systems. Today, he is actively engaged in developing solutions that enhance the reliability and explainability of AI applications.

Tune in to this enlightening conversation and gain deeper insights into the future trajectories and current challenges within the world of artificial intelligence as explained by one of the leading thinkers in the field.

Key takeaways:

• Symbolic vs. Statistical AI: Erdem discusses the critical differences and applications of symbolic AI versus statistical methods, emphasizing the need for reliably representing concepts for efficient AI outcomes.

• The Role of Cogent English: Insight into how Cogent, a platform developed by Erdem, assists in translating complex business knowledge into APIs and conversational interfaces using a subset of English tailored for formal reasoning.

• Challenges in Generative AI: Exploration of issues that arise with generative AI, particularly around reliability and the operational deployment of reasoning systems.

• Development of Neurosymbolic AI: Erdem predicts a significant shift towards hybrid AI architectures that combine both symbolic and deep learning approaches to handle real-life complex scenarios more efficiently.

• Importance of Explainability in AI: A discussion on why explainability and the ability to audit AI decisions are crucial, especially as AI systems become more integrated into critical decision-making processes.

• Comparison of Formal Reasoning Systems and LLMs: Erdem explains why formal reasoning systems can be more reliable than large language models (LLMs) in complex problem-solving scenarios.

Quote from the show:

"Translating human expertise into AI systems is not just about feeding data into algorithms. It’s about creating structures that allow machines to reason and make decisions transparently and reliably." – Erdem Özcan

Links:

LinkedIn: https://www.linkedin.com/in/aerdemozcan/
Website: https://ec.ai/

Ways to Tune In:
Earley AI Podcast: https://www.earley.com/earley-ai-podcast-home
Apple Podcast: https://podcasts.apple.com/podcast/id1586654770
Spotify: https://open.spotify.com/show/5nkcZvVYjHHj6wtBABqLbE?si=73cd5d5fc89f4781
iHeart Radio: https://www.iheart.com/podcast/269-earley-ai-podcast-87108370/
Stitcher: https://www.stitcher.com/show/earley-ai-podcast
Amazon Music: https://music.amazon.com/podcasts/18524b67-09cf-433f-82db-07b6213ad3ba/earley-ai-podcast
Buzzsprout: https://earleyai.buzzsprout.com/

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Lief Erickson brings expertise in technical writing and content strategy consultation. Having steered numerous organizations through the integration of AI and coherent information architecture, making the complex accessible. With a voice of authority in AI and content management the EIS Podcast is thrilled to have him on the show.

Tune in to this episode for a comprehensive understanding of how structured content and precise prompt engineering are pivotal to leveraging AI in the realm of content creation and management.

Key Takeaways:

• Large language models (LLMs) require clear prompts and structured content to produce accurate and trustworthy responses.

• The importance of structured content in enabling effective retrieval and utilization by generative AI, akin to finding a book in a library.

• Misconceptions about generative AI’s capabilities in content management, highlighting the need for careful curation and validation.

• Real-world applications of AI that can help increase brand loyalty, efficiency, reduce support calls, manage risk, and boost revenue.

• The emerging role of prompt engineering and its significance in ensuring the relevance and accuracy of AI-generated content.

• Legal and ethical considerations in using AI for content creation, with insights on copyright and the ownership issues surrounding machine-generated content.

Quote of the Show:

"Understanding structured content is like understanding the blueprint of a building—it's what allows us to scale and architect information in ways that align with our strategic goals." - Lief Erickson

Links:

• LinkedIn: https://www.linkedin.com/in/lief-erickson/
• Website: https://www.intuitivestack.io/

Ways to Tune In:

• Earley AI Podcast: https://www.earley.com/earley-ai-podcast-home
• Apple Podcast: https://podcasts.apple.com/podcast/id1586654770
• Spotify: https://open.spotify.com/show/5nkcZvVYjHHj6wtBABqLbE?si=73cd5d5fc89f4781
• iHeart Radio: https://www.iheart.com/podcast/269-earley-ai-podcast-87108370/
• Stitcher: https://www.stitcher.com/show/earley-ai-podcast
• Amazon Music: https://music.amazon.com/podcasts/18524b67-09cf-433f-82db-07b6213ad3ba/earley-ai-podcast
• Buzzsprout: https://earleyai.buzzsprout.com/

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Manish Sharma is the co-founder of Resolve AI. With a rich history spanning over two decades in the technology industry, Manish offers profound perspectives on the intersection of artificial intelligence, information architecture, and knowledge management.

Tune in as Manish dissects popular AI myths, underscores the importance of bridging the technological gap, and emphasizes the need for robust security measures in today's digital landscape.

Key takeaways:

• When implementing AI solutions like large language models, CISOs should ask questions around data security, access controls, model guarantees, and emerging risks like prompt hacking to properly manage risks.

• Information architecture is critical for data privacy, security, and ensuring AI systems can only access appropriate data sources and provide the right information to different user groups.

• Retrieval augmented generation using a knowledge graph or index is important to avoid hallucinations and ensure AI systems can only respond based on curated data sources.

• Scripted responses may be needed in some cases like legal to provide verbatim answers instead of generated responses.

• User personas and metadata are important to ensure AI systems understand the context and privileges of different user groups to provide appropriate and non-confusing information.

• When integrating AI solutions with knowledge repositories like SharePoint, only curated subsets should be connected instead of entire repositories, and information should be properly tagged and structured.

Quote of the show:"A key to successful AI integration is not just in understanding the technology itself but in grasping the nuances of user needs, processes, content, and knowledge that remains timeless, no matter the advancements in tech. Coming to grips with that is where the real value lies."
- Manish Sharma

Links:

• LinkedIn: https://www.linkedin.com/in/manish-sharma-rezolve/
• Website: https://www.rezolve.ai/

Ways to Tune In:

• Earley AI Podcast: https://www.earley.com/earley-ai-podcast-home
• Apple Podcast: https://podcasts.apple.com/podcast/id1586654770
• Spotify: https://open.spotify.com/show/5nkcZvVYjHHj6wtBABqLbE?si=73cd5d5fc89f4781
• iHeart Radio: https://www.iheart.com/podcast/269-earley-ai-podcast-87108370/
• Stitcher: https://www.stitcher.com/show/earley-ai-podcast
• Amazon Music: https://music.amazon.com/podcasts/18524b67-09cf-433f-82db-07b6213ad3ba/earley-ai-podcast
• Buzzsprout: https://earleyai.buzzsprout.com/

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• CMSWire
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Thomas Blumer is a renowned expert in AI-driven transformation with extensive experience in implementing groundbreaking artificial intelligence and knowledge strategies within complex business environments. Echoing a profound understanding of metrics-driven governance of AI systems, Thomas has made significant strides in aligning AI applications with overarching business goals. As a strategic advisor and consultant, he has facilitated diverse organizations in their journey to integrate AI to optimize efficiency, enhance user experiences, and drive actionable business outcomes. His expertise is instrumental in developing robust AI governance frameworks that ensure data, algorithms, and knowledge are in strict adherence to driving value and enterprise strategy.

Key takeaways:

• Defining and measuring KPIs tailored to customer and user lifecycle is crucial to drive business outcomes with AI and knowledge systems.

• The transition from proof of concept to proof of value in AI implementations often encounters hurdles due to artificial environments and upstream data issues.

• AI's implementation should focus on improving specific tasks and processes, ensuring tangible improvements rather than the technology's mere presence.

• Storytelling and emotional resonance play a pivotal role when data alone does not suffice in persuading stakeholders about AI initiatives.

• Governance structures need to strike a balance between centralized standards and decentralized, data-driven decision making.

• Large language models have brought about a revolution in accessing corporate knowledge and productivity, highlighting the need for responsible usage.

Quote of the show:"Bringing AI into the fold isn't just about technology; it’s about shaping an ecosystem that thrives on data integrity, governance, and context to create impactful narratives."
- Thomas Blumer

Links:

• LinkedIn: https://www.linkedin.com/in/thomasblumer/
• Website: https://www.zyris.com

Ways to Tune In:

• Earley AI Podcast: https://www.earley.com/earley-ai-podcast-home
• Apple Podcast: https://podcasts.apple.com/podcast/id1586654770
• Spotify: https://open.spotify.com/show/5nkcZvVYjHHj6wtBABqLbE?si=73cd5d5fc89f4781
• iHeart Radio: https://www.iheart.com/podcast/269-earley-ai-podcast-87108370/
• Stitcher: https://www.stitcher.com/show/earley-ai-podcast
• Amazon Music: https://music.amazon.com/podcasts/18524b67-09cf-433f-82db-07b6213ad3ba/earley-ai-podcast
• Buzzsprout: https://earleyai.buzzsprout.com/

Thanks to our sponsors:

• CMSWire
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• AI Powered Enterprise Book

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Trent Fitz holds over 20 years of experience in the tech industry. Currently a C-level Product Strategy and Technical Marketing Leader at Zenoss. He is an expert in global marketing, product strategy, business development in cloud computing, cybersecurity, and AI. Repeatedly proving his effectiveness in the industry by leading solutions to projects in innovative company’s such as IBM, Sailpoint, Trustwave and other various startups.

Key takeaways:

• APM tools such as Dynatrace, AppDynamics, and New Relic are key, and their integration has been aided by standards like open telemetry.

• AI governance is crucial on technical, business process, and enterprise strategy levels.

• The maturity models for AIOPs involve governance, decision making, and data/information architecture.

• There is a general lack of appreciation for data and content within IT organizations.

• AIOPs includes machine learning, and there's a need to educate about structured data and AI capabilities.

Quote of the show:"At the core of AIOPs lies a fundamental need to not just visualize but truly understand the staggering complexity of modern IT environments. It's not just about piles of data or sophisticated algorithms; it's about cultivating a genuine appreciation for the significance of that data and how we can harness it to drive smarter, more proactive operations." — Trent Fitz

Links:

• LinkedIn: https://www.linkedin.com/in/trent-fitz/
• Website: https://www.zenoss.com

Ways to Tune In:

• Earley AI Podcast: https://www.earley.com/earley-ai-podcast-home
• Apple Podcast: https://podcasts.apple.com/podcast/id1586654770
• Spotify: https://open.spotify.com/show/5nkcZvVYjHHj6wtBABqLbE?si=73cd5d5fc89f4781
• iHeart Radio: https://www.iheart.com/podcast/269-earley-ai-podcast-87108370/
• Stitcher: https://www.stitcher.com/show/earley-ai-podcast
• Amazon Music: https://music.amazon.com/podcasts/18524b67-09cf-433f-82db-07b6213ad3ba/earley-ai-podcast
• Buzzsprout: https://earleyai.buzzsprout.com/

Thanks to our sponsors:

• CMSWire
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Ian Hook is an exemplary professional whose journey spanned from an early career as a blacksmith and preschool teacher to becoming a seasoned expert in knowledge management and artificial intelligence (AI) at Nevartis. His unorthodox path and hands-on experience have endowed him with a deep understanding of the intricacies of knowledge management and its pivotal role in leveraging generative AI tools efficiently and effectively within operational teams. Ian's significant contributions have led to remarkable operational efficiencies, including an $18 million savings for his company by centralizing market research resources.

Key Takeaways:

• Knowledge management and generative AI are integral to improving the speed and accuracy of issue detection and remediation in operational teams.

• Understanding the lineage and flow of data is vital for data scientists to fulfill their responsibility effectively.

• Ian Hook illustrates the considerable impact of having a centralized knowledge management platform on efficiency and cost savings within a corporate setting.

• The importance of governance in the context of utilizing generative AI is highlighted to mitigate unreliable outcomes due to ungoverned data.

• Knowledge graphs are presented as sophisticated tools that visualize expertise and the relationships between different domains of knowledge.

• The episode explores the limitations of large language models and emphasizes the importance of human oversight to prevent inaccuracies.

Quote of the Show:

"In our quest to harness AI, we must remember that the texture of human knowledge and expertise is the bedrock upon which these systems must be built." - Ian Hook

Links:

• LinkedIn: https://www.linkedin.com/in/ianhook1/
• Website: Novartis.com

Ways to Tune In:

• Earley AI Podcast: https://www.earley.com/earley-ai-podcast-home
• Apple Podcast: https://podcasts.apple.com/podcast/id1586654770
• Spotify: https://open.spotify.com/show/5nkcZvVYjHHj6wtBABqLbE?si=73cd5d5fc89f4781
• iHeart Radio: https://www.iheart.com/podcast/269-earley-ai-podcast-87108370/
• Stitcher: https://www.stitcher.com/show/earley-ai-podcast
• Amazon Music: https://music.amazon.com/podcasts/18524b67-09cf-433f-82db-07b6213ad3ba/earley-ai-podcast
• Buzzsprout: https://earleyai.buzzsprout.com/

Thanks to our sponsors:

• CMSWire
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• AI Powered Enterprise Book

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Mark Pickren currently serves as the President of Next Net Media. With over 25 years of experience as a seasoned entrepreneur and business leader, he possesses expertise in marketing-focused technology companies. Mark has demonstrated a consistent track record of building and managing successful ventures, with leadership experience spanning various industries, including Fintech, SaaS, and Digital Marketing. He has effectively overseen hundred-million-dollar P&Ls at large public corporations and Madison Avenue agencies. Remaining at the forefront of the dynamic digital landscape, Mark consistently delivers innovative solutions for consumers and businesses.

Takeaways:

• Organizations need to prepare for around a 25% decline in organic search traffic as search becomes more personalized.
• Marketers need to focus on multi-dimensional targeting and providing value to specific customer personas to optimize content for search.
• As repetitive tasks are automated, career paths will focus more on managing autonomous agents and leveraging AI effectively.
• Large language models pose risks if not properly overseen by humans, and differentiation requires responsible use of proprietary data and knowledge.
• Emerging technologies like retrieval-augmented generation will have major impacts on enterprises by improving information access.

Quote of the Show:

• "Don't be a cynic. Lean into the better angels of technology, and be part of the solution." (Advice for graduates on how to approach emerging technologies.)

• Marc Pickren

Links:

• LinkedIn: https://www.linkedin.com/in/marcpickren/
• Website: https://nextnetmedia.com/
• Marc's Website: https://www.marcpickren.com/

Ways to Tune In:

• Earley AI Podcast: https://www.earley.com/earley-ai-podcast-home
• Apple Podcast: https://podcasts.apple.com/podcast/id1586654770
• Spotify: https://open.spotify.com/show/5nkcZvVYjHHj6wtBABqLbE?si=73cd5d5fc89f4781
• iHeart Radio: https://www.iheart.com/podcast/269-earley-ai-podcast-87108370/
• Stitcher: https://www.stitcher.com/show/earley-ai-podcast
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Our guest this episode is Kristina Francis, a Executive Director at JFFLabs. Jobs for the Future (JFF) is a nationwide nonprofit dedicated to reshaping U.S. education and workforce systems for inclusive economic progress.

Kristina is a experienced professional with a rich background spanning management consulting, software development, engineering, and cybersecurity. She began in database administration at the American Institutes for Research, evolving from an individual contributor to leading a 120-member development team for the Department of Defense. In 2016, a pivotal moment led to a dual career path, involving founding a consulting company, angel investing in women-owned tech ventures, and engaging in workforce opportunities. Currently serving as the Executive Director for JFFLabs at Jobs for the Future, Kristina provides a distinctive perspective on the present and future of workforce and education, emphasizing innovation, disruption, and foresight into the implications of emerging technologies.

Takeaways:

• AI has the potential to both disrupt jobs and create new job opportunities, but ensuring access to skills training will be important for workforce development.
• Personalized learning and career discovery tools that integrate assessments and map out skills pathways could help more people navigate changing job opportunities.
• Addressing systemic barriers and biases will be important to ensure all populations can benefit from new economic opportunities.
• Regions and employers can play a role in workforce development through public-private partnerships, on-the-job training programs, and investing in employees' skills.

Quote of the Show:

• " How do we get more innovators, school systems, programs, and employers to get on board and provide the support and systems needed so that everyone in our communities is able to discover and navigate through our system to achieve their highest potential? "

• Kristina Francis

Links:

• LinkedIn:https://www.linkedin.com/in/kristinaharrisonfrancis/
• Website: https://www.jff.org/
• Email: KFrancis@JFF.org

Ways to Tune In:

• Earley AI Podcast: https://www.earley.com/earley-ai-podcast-home
• Apple Podcast: https://podcasts.apple.com/podcast/id1586654770
• Spotify: https://open.spotify.com/show/5nkcZvVYjHHj6wtBABqLbE?si=73cd5d5fc89f4781
• iHeart Radio: https://www.iheart.com/podcast/269-earley-ai-podcast-87108370/
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Our guest this episode is Alexander Schober, a data & AI project owner at Motius. He manages a diverse team of tech experts, focusing on Machine Learning, Knowledge Graphs, and Data Analysis.

Alexander previously worked at Siemens Technology which involved pioneering research in Federated Learning and Self-Supervised Methods for anomaly detection. He used algorithms like Federated Averaging and SimCLR to address data privacy and label sparsity. Alexander joins Seth Earley and Chris Featherstone to the discuss knowledge graphs, metadata modeling for data engineering, using large language models to build data pipelines and more.

For more content related to LLM's and Knowledge Graphs: https://www.earley.com/case-studies

Takeaways:

• AI Enhancements with Knowledge Graphs: While not strictly required, knowledge graphs enhance the capabilities of AI, particularly large language models. The ability to provide context and resolve conflicts within the data contributes to more accurate and reliable AI outcomes.
• Unified Metadata Model: There's a need for a unified metadata model across different tools and platforms in the data engineering and AI landscape. Disjointed metadata tools can lead to inefficiencies, and efforts should be made to integrate and unify metadata for better collaboration.
• AI-Powered Data Pipeline Construction: Large language models can be used to generate data pipelines based on provided metadata. This approach can streamline the data engineering process, ensuring that quality checks, governance attributes, and privacy classifications are integrated into the pipeline.

Quote of the Show:

• " All of these things are interconnected. Knowledge graphs, ontologies and semantics. They are all very important."

• Alexander Schober

Links:

• LinkedIn:https://www.linkedin.com/in/alexander-schober/
• Website: https://www.motius.com

Ways to Tune In:

• Earley AI Podcast: https://www.earley.com/earley-ai-podcast-home
• Apple Podcast: https://podcasts.apple.com/podcast/id1586654770
• Spotify: https://open.spotify.com/show/5nkcZvVYjHHj6wtBABqLbE?si=73cd5d5fc89f4781
• iHeart Radio: https://www.iheart.com/podcast/269-earley-ai-podcast-87108370/
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Today’s guest is Rachad Najjar, working the forefront of innovation in the fields of organizational learning and knowledge management for nearly a decade. Prior to this, he served as a knowledge management advisor for the Dubai Land Department, where he played a pivotal role in achieving the EFQM Excellence Award. Notably, he's also a co-author of a recent book on knowledge management and research innovation, alongside numerous scientific publications in prestigious journals. In his ground breaking thesis, he introduced a framework to configure collaboration for virtual collectives, improving effectiveness across various professional contexts. Rachad joins Seth Earley and Chris Featherstone to the discuss his insights on AI, knowledge management, enterprise strategy implementation and more.

Takeaways:

• Seven guiding principles for a successful AI strategy, including a strong business case, process integration, quality training data, continuous supervision, powerful computing infrastructure, and AI and ML skills.
• AI governance should involve diverse expertise, including legal, supply chain, project management, and knowledge management.
• Focus on how generative AI is adding value in knowledge management and learning, particularly in areas such as customer support, search, learning, and marketing.

Quote of the Show:

• "AI models heavily depend on the quality of the training data, so quality in and quality out."

• Rachad Najjar

• Links:

  • LinkedIn: https://www.linkedin.com/in/rachadbn/
  • Article: 7 Guiding Principles of a Successful Enterprise AI Strategy
  • Article: A conversation between a knowledge sharing advocate and a knowledge sharing skeptic
  • Article: AI Integration Strategy for Learning and Knowledge Management Solutions
• Ways to Tune In:
  • Earley AI Podcast: https://www.earley.com/earley-ai-podcast-home
  • Apple Podcast: https://podcasts.apple.com/podcast/id1586654770
  • Spotify: https://open.spotify.com/show/5nkcZvVYjHHj6wtBABqLbE?si=73cd5d5fc89f4781
  • iHeart Radio: https://www.iheart.com/podcast/269-earley-ai-podcast-87108370/
  • Stitcher: https://www.stitcher.com/show/earley-ai-podcast
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Today’s guest is Amar Goel, founder of Bito. Amar joins Seth Earley and Chris Featherstone to the discuss the increase in new A.I. tools, LLMs and the journey behind forming Bito! The A.I. assisted software developing tool.

Takeaways:

• Converting AI prototypes into reliable, production-ready products is a non-trivial task, often requiring significant effort and expertise.
• AI has the potential to assist developers in various ways, from code refactoring to code migration, helping to address issues related to legacy code and modernization.
• The cost of running AI models can be significant, and businesses need to consider the expenses involved in deploying AI tools in their products and services.
• AI can play a pivotal role in streamlining developer processes, such as enhancing code quality, security, and test coverage, while allowing developers to maintain their creative freedom. However, it's essential to strike a balance between automation and creativity in the development process.

Quote of the Show:

• "We don't know what we don't know yet" about AI ethics and privacy, as everyone is learning on the job." - Amar Goel

Links:

• LinkedIn:https://www.linkedin.com/in/amargoel/
• Website: https://https://bito.ai/

Ways to Tune In:

• Earley AI Podcast: https://www.earley.com/earley-ai-podcast-home
• Apple Podcast: https://podcasts.apple.com/podcast/id1586654770
• Spotify: https://open.spotify.com/show/5nkcZvVYjHHj6wtBABqLbE?si=73cd5d5fc89f4781
• iHeart Radio: https://www.iheart.com/podcast/269-earley-ai-podcast-87108370/
• Stitcher: https://www.stitcher.com/show/earley-ai-podcast
• Amazon Music: https://music.amazon.com/podcasts/18524b67-09cf-433f-82db-07b6213ad3ba/earley-ai-podcast
• Buzzsprout: https://earleyai.buzzsprout.com/

Thanks to our sponsors:

• CMSWire
• Earley Information Science
• AI Powered Enterprise Book

Thanks to our sponsors:

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• Earley Information Science
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Today’s guest is Sanjay Mehta, Head of Industry Commerce for LucidWorks. Sanjay joins Seth Earley and Chris Featherstone to the discuss the rapidly evolving hype of generative AI and how it can be applied to your industry.

Takeaways:

• Sanjay points out that emerging AI is "not turn key". Maybe from a consumer side but when it comes to B2B there are many hoops to jump through before it's easy and effective.
• Data is the lifeblood of modern businesses, and its true potential shines when we connect the dots between customer behaviors, product attributes, and user experiences. At the heart of this transformation is the concept of ingesting good product data into the vector space.
• There are many preceded knowledge graphs for certain industries. When you build your index of data it is important to know your users context and application. Using a knowledge base to build your own vector space can be helpful.

Quote of the Show:

• “AI is Not Turn Key" - Sanjay Mehta

Links:

• LinkedIn:https://www.linkedin.com/in/sanjaymehta/
• Website: https://lucidworks.com

Ways to Tune In:

• Earley AI Podcast: https://www.earley.com/earley-ai-podcast-home
• Apple Podcast: https://podcasts.apple.com/podcast/id1586654770
• Spotify: https://open.spotify.com/show/5nkcZvVYjHHj6wtBABqLbE?si=73cd5d5fc89f4781
• iHeart Radio: https://www.iheart.com/podcast/269-earley-ai-podcast-87108370/
• Stitcher: https://www.stitcher.com/show/earley-ai-podcast
• Amazon Music: https://music.amazon.com/podcasts/18524b67-09cf-433f-82db-07b6213ad3ba/earley-ai-podcast
• Buzzsprout: https://earleyai.buzzsprout.com/

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Today’s guest is Doug Kimball, Chief Marketing Officer for Ontotext . Doug joins Seth Earley and Chris Featherstone to the discuss the rapidly evolving world of knowledge graphs and AI.

Takeaways:

• Doug Kimball's statement about knowledge graphs being an "add to" and an "enhancement of" data is spot on. In the world of modern data management and analytics, knowledge graphs are a game-changer.
• There is a proper way to ask the right questions when communicating with Generative AI models. It is important to include the correct context and parameters.
• Knowledge graphs have many applications to a variety of different business models and use cases. Doug mentions an example where a mass migration of population from one place to another could be an opportunity for businesses to track and profit based off of user demographics utilizing knowledge graph practices.

Quote of the Show:

• “Knowledge graphs are not a rip and replace, they are an add to/enhancement of" - Doug Kimball

Links:

• LinkedIn:https://www.linkedin.com/in/dougkimball/
• Website: https://www.ontotext.com/
• Twitter: https://twitter.com/TheDKimball

Ways to Tune In:

• Earley AI Podcast: https://www.earley.com/earley-ai-podcast-home
• Apple Podcast: https://podcasts.apple.com/podcast/id1586654770
• Spotify: https://open.spotify.com/show/5nkcZvVYjHHj6wtBABqLbE?si=73cd5d5fc89f4781
• iHeart Radio: https://www.iheart.com/podcast/269-earley-ai-podcast-87108370/
• Stitcher: https://www.stitcher.com/show/earley-ai-podcast
• Amazon Music: https://music.amazon.com/podcasts/18524b67-09cf-433f-82db-07b6213ad3ba/earley-ai-podcast
• Buzzsprout: https://earleyai.buzzsprout.com/

Thanks to our sponsors:

• Marketing AI Institute
• CMSWire
• Earley Information Science
• AI Powered Enterprise Book

Thanks to our sponsors:

• CMSWire
• Earley Information Science
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Today’s guest is Ben Clinch, Head of Information Architecture for BT Group . Ben joins Seth Earley and Chris Featherstone to the discuss the rapidly evolving world of data science in organization.

Takeaways:

• An intriguing aspect is the common practice of Large Language Models (LLMs) utilizing generic data models Ben and Seth discuss more effective ways to harness the power of LLMs through specialized data models and organization.
• Companies will realize quickly that they cant do any sensible Generative AI without a core of useful referential data to utilize, train and not hallucinate.
• If people lean on Generative AI, that accelerates things rapidly, but all it does is deferring knowledge to somebody else's data model.
• Some people ask if we really need a data model. Can't we just get an industry standard view and follow that? Do you want to buy an org chart? Do you want to defer how you structure your teams to somebody else's view of how you should? This may be a good starting point, but a terrible ending point.
• What is the ROI on data modeling? Think of data as an asset for your organization, and think of people as an asset for your organization. Everybody from the chairman to the guy sweeping the floor understand an org chart. They understand you have to organize your people. Otherwise, there will be involuntary anarchy.

Quote of the Show:

• “Taxonomy is a chart of accounts for knowledge" - Seth Earley

Links:

• LinkedIn:https://www.linkedin.com/in/benclinch/
• Website: https://www.bt.com/
• Twitter: https://twitter.com/BritishTelecom

Ways to Tune In:

• Earley AI Podcast: https://www.earley.com/earley-ai-podcast-home
• Apple Podcast: https://podcasts.apple.com/podcast/id1586654770
• Spotify: https://open.spotify.com/show/5nkcZvVYjHHj6wtBABqLbE?si=73cd5d5fc89f4781
• iHeart Radio: https://www.iheart.com/podcast/269-earley-ai-podcast-87108370/
• Stitcher: https://www.stitcher.com/show/earley-ai-podcast
• Amazon Music: https://music.amazon.com/podcasts/18524b67-09cf-433f-82db-07b6213ad3ba/earley-ai-podcast
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Today’s guest is Glenn Gow, CEO of Coaching at The Peak Performance CEO Coach. Glenn joins Seth Earley and shares how people should start leaning into what technology is advancing today. Glenn expresses the importance of learning these new materials to create opportunities for you and your company. Be sure to listen in on Glenn giving his advice on how larger companies should incorporate AI into their business!

Takeaways:

• Glenn believes that the enhanced value that Predictive AI and Analytical AI can bring to CEOs can create a crucial aspect of the evolution. By harnessing AI approaches, CEOs can gain insights that can drive decision-making and strategic planning. Glenn advocates for adopting AI methodologies to empower CEOs in navigating the rapidly evolving business landscape.
• Glenn created a concept known as "Winner Takes All." This concept is if you excel in AI, both you and your direct competitor will consistently accumulate data about your customers. This resource empowers you to gain insights into your customer base, enabling you to enhance your understanding and knowledge. The stakes are high, as falling behind your competitor could lead to setbacks and missed opportunities.
• An example of the vast impact Chat GPT and AI have on our world is Chegg—software designed to provide students with vital information to excel in school. However, when Chat GPT came to light, the AI world dramatically shifted. In a single day, the creation of Chat GPT caused Chegg's stock to plummet by 45%. Today, AI is globally, revolutionizing to assist with education, essay writing, tests, and countless other domains. Its pervasive influence continues to reshape the way we approach and engage with knowledge.
• Glenn believes enterprises will find it effortless to gather information about open-source technologies their competitors developed. By integrating the resources into their frameworks and incorporating their data, businesses will gain access to carry out operations within their competitors' organizations that were once beyond their reach. Glenn thinks people should take advantage of these opportunities to safeguard their data.
• Glenn describes prompting by taking a large language model and condensing it to a specific area of focus. This act of shrinking allows the model to channel toward a defined domain or subject matter. By honing the model's attention on a particular area, it targets outputs that align with the desired scope. This makes it simple for users to leverage the language model while meeting specific objectives.

Quote of the Show:

• “Become good at all the tools that are being made available to us, because that's going to create opportunity for you.” - Glenn Gow

Links:

• LinkedIn: https://www.linkedin.com/in/glenngow/
• Website: https://www.glenngow.com/
• Twitter: https://twitter.com/glenngow1

Ways to Tune In:

• Earley AI Podcast: https://www.earley.com/earley-ai-podcast-home
• Apple Podcast:

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Today’s guest is Kirk Marple, Technical Founder and CEO at Unstruk Data. Kirk joins Seth Earley and Chris Featherstone to discuss organizing historical data and long-term memory. Kirk emphasizes the importance of organizing data in a manner that allows for seamless integration with novel models and shares valuable advice on understanding data.

Takeaways:

• The semantic web serves as a powerful tool for optimizing business applications and data organization.
• A prevalent misconception surrounding AI is that individuals need to construct their own models and be data science experts. Advancements unfold at a rapid pace. People need to harness the power of AI and employ it strategically within their business operations.
• Data lies at the core of everything. To optimize the utilization of emerging models effectively it is important to organize data in a way that seamlessly integrates with novel models. AI implementation needs to be approached with a practical mindset.
• In the last 6-9 months large language models have developed the ability to engage in meaningful conversations with their underlying data. This aspect of interactive communication tends to be overlooked. The focus often leans towards retrieval and entity extraction.
• Over the years, people have addressed the issue of non-equalization of data intent through the provision of taxonomies. In the future Kirk anticipates that AI will play a pivotal role in enhancing this process.

Quote of the Show:

• “It’s a data set. Not just a hard drive.” (03:50)

Links:

• LinkedIn: https://www.linkedin.com/in/kirkmarple/
• Website: https://www.unstruk.com/
• Twitter: https://twitter.com/unstruk

Ways to Tune In:

• Earley AI Podcast: https://www.earley.com/earley-ai-podcast-home
• Apple Podcast: https://podcasts.apple.com/podcast/id1586654770
• Spotify: https://open.spotify.com/show/5nkcZvVYjHHj6wtBABqLbE?si=73cd5d5fc89f4781
• iHeart Radio: https://www.iheart.com/podcast/269-earley-ai-podcast-87108370/
• Stitcher: https://www.stitcher.com/show/earley-ai-podcast
• Amazon Music: https://music.amazon.com/podcasts/18524b67-09cf-433f-82db-07b6213ad3ba/earley-ai-podcast
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Today’s guest is Alex Babin, Co-Founder and CEO at ZERO Systems. Alex joins Seth Earley and Chris Featherstone and shares the two biggest misconceptions of AI. Alex also discusses the new and upcoming AI metric data tool. Alex explains what it does and what we can expect from it. Be sure to listen in on Alex giving his advice on how to keep track of your data using AI!

Takeaways:

• The biggest misconceptions about AI are:
  • AI can work out of the box. Chat GPT shows people what it can do but, it does not mean that AI can do everything.
  • You can throw data at AI and it will be able to do the things you need it to do perfectly. AI technology is changing, but it hasn’t met this level of expertise yet.
• A best practice to avoid these misconceptions of AI is to start from the beginning. Figure out your company's ROI, and reconstruct all the steps required.
• Alex mentions a new layer of Metric Data that has never existed before. This metric data is user-generated data or a feedback loop. When you interact with the tool, a new type of metadata is born with more data to the tool is reinforced to provide better results.
• Alex explains how the two anthologies aren’t connected. He believes that it should be connected to help provide better results for the client. Alex believes that in the future, AI can be used as a teaching tool between the two anthologies.
• Alex is looking forward to the advancement of AI in the future. He is also nervous about AI and how it will be controlled. Since everyone doesn’t know where AI will go, it is less predictable and controllable.

Quote of the Show:

• “Throwing Chat GPT on top of your problems will not solve it.” (04:51)

Links:

• LinkedIn: https://www.linkedin.com/in/alexbabin/
• Website: https://zerosystems.com/
• Twitter: https://twitter.com/zeromailapp?lang=en

Ways to Tune In:

• Earley AI Podcast: https://www.earley.com/earley-ai-podcast-home
• Apple Podcast: https://podcasts.apple.com/podcast/id1586654770
• Spotify: https://open.spotify.com/show/5nkcZvVYjHHj6wtBABqLbE?si=73cd5d5fc89f4781
• iHeart Radio: https://www.iheart.com/podcast/269-earley-ai-podcast-87108370/
• Stitcher: https://www.stitcher.com/show/earley-ai-podcast
• Amazon Music: https://music.amazon.com/podcasts/18524b67-09cf-433f-82db-07b6213ad3ba/earley-ai-podcast
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Today’s guest is Maxim Serebryakov, Co-Founder and CEO at Sanas. Max joins Seth Earley and Chris Featherstone and shares what influenced him to start his company. Max also discusses what it was like to study artificial intelligence at Stanford and how it created a broad perspective on how things work. Be sure to listen in on Mike giving his advice on how you can go above and beyond to help anyone!

Takeaways:

• Max was born in New York then shortly after, he moved to Russia where his family is from. When he came back to coming to the US, the accents around him and being an immigrant were the brewing grounds for the creation of his company, Sanas.
• Max studied at Stanford and he spent a lot of time doing research into artificial intelligence and studying artificial intelligence. Through these experiences, he ended up building Sanas.
• Artificial intelligence shows the limitations of modern-day voice conversion research. Max learned that you're not just modulating the pitch and tone, you're changing the underlying phonings that are present within it.
• Max and his team chose to do their deployments initially with contact centers and enterprises. They saw that their pain point is initially with these deployment verticals because speech is very structured.
• Max’s company, Sanas, works with some of the largest contact centers and top enterprises in the world. They work with them to better help their customer service interactions since it is a crucial part of these companies.

Quote of the Show:

• “We ended up building an algorithm that really doesn't exist in the research world. It's very innovative. It works on the edge, works with clients, and it's very efficient.” (11:02)

Links:

• LinkedIn: https://www.linkedin.com/in/maximser/
• Website: https://www.sanas.ai/

Ways to Tune In:

• Earley AI Podcast: https://www.earley.com/earley-ai-podcast-home
• Apple Podcast: https://podcasts.apple.com/podcast/id1586654770
• Spotify: https://open.spotify.com/show/5nkcZvVYjHHj6wtBABqLbE?si=73cd5d5fc89f4781
• iHeart Radio: https://www.iheart.com/podcast/269-earley-ai-podcast-87108370/
• Stitcher: https://www.stitcher.com/show/earley-ai-podcast
• Amazon Music: https://music.amazon.com/podcasts/18524b67-09cf-433f-82db-07b6213ad3ba/earley-ai-podcast
• Buzzsprout: https://earleyai.buzzsprout.com/

Thanks to our sponsors:

• Marketing AI Institute

Thanks to our sponsors:

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Today’s guest is Michael Todasco, Writer about Generative AI. Mike joins Seth Earley and Chris Featherstone and shares how he got his start in Generative AI and how much it has changed his life. Mike also discusses why he thinks people should embrace AI. Be sure to listen in on Mike giving his advice on how to build a better connection with your customers!

Takeaways:

• While he was at PayPal, Mike was responsible for innovation for the company about, how they get more out of the people who work there.
• Michael wants people to embrace AI. He believes that when you work with AI or a computer, it will result in better solutions.
• It is important for everyone to know what their competitive advantage is and what their end goal is or what they want to achieve.
• Michael believes that in order to get proprietary information about your customers, you have to stage a gated experimentation process.
• Michael has experimented and wrote a book using an Excel spreadsheet. He would take what was written in Excel and paste it into Chat GPT to craft 56 different writing genres.

Quote of the Show:

• “Your job is not gonna be replaced by AI. It's gonna be replaced by a human who's using AI.” (08:17)

Links:

• Twitter: https://twitter.com/todasco
• LinkedIn: https://www.linkedin.com/in/todasco/
• Website: https://medium.com/@todasco

Ways to Tune In:

• Earley AI Podcast: https://www.earley.com/earley-ai-podcast-home
• Apple Podcast: https://podcasts.apple.com/podcast/id1586654770
• Spotify: https://open.spotify.com/show/5nkcZvVYjHHj6wtBABqLbE?si=73cd5d5fc89f4781
• iHeart Radio: https://www.iheart.com/podcast/269-earley-ai-podcast-87108370/
• Stitcher: https://www.stitcher.com/show/earley-ai-podcast
• Amazon Music: https://music.amazon.com/podcasts/18524b67-09cf-433f-82db-07b6213ad3ba/earley-ai-podcast
• Buzzsprout: https://earleyai.buzzsprout.com/

Thanks to our sponsors:

• Marketing AI Institute
• CMSWire
• Earley Information Science
• AI Powered Enterprise Book

Earley AI is produced and sponsored by Ringmaster, on a mission to create connections through B2B p

Thanks to our sponsors:

• Marketing AI Institute
• CMSWire
• Earley Information Science
• AI Powered Enterprise Book

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Today’s guest is Gordon Hart, Co-Founder and Head of Product at Kolena. Gordon joins Seth Earley and Chris Featherstone and shares how ​​machine learning algorithms are a challenge from different perspectives. Gordon also discusses the core problem in his company before they turned it around. Be sure to listen in on Gordon giving his advice on how to validate models in order to have a successful product!

Takeaways:

• Gordon noticed that developing algorithms internally or buying from other model vendors, has really had a constant unexpected model behavior. It made him feel he couldn’t trust the models to behave sensibly.
• Gordon started his company because he noticed that time after time, he was getting blindsided. So he thought to himself that there has to be a better way to develop models and be able to validate what they were doing.
• The key challenge that Gordon and his team ran into was that when you have all the data when they were looking at that one number, they were looking at that aggregate metric computed across their entire benchmark.
• Gordon expresses the importance of going through scenarios with your products. He found that when you break down your evaluation into these different scenarios, the test gives you an understanding of how this model improves in the aggregate over previous models and how are the failures distributed.
• Gordon believes that testing data versus training data is more critical since your testing data is what you're using to decide if your new model has the behaviors it needs to have.
• Testing the full pipeline from pre-processing through post-processing rather than testing the model component will oftentimes improve the visibility into how your product is actually going to work when you put it out there.

Quote of the Show:

• “Having your evaluation metrics align with the way that your system is going to be evaluated in the field is a key thing that you can do to get a better understanding of ‘is this model better for what I set out to do?’” (22:36)

Links:

• Twitter: https://twitter.com/kolenaIO
• LinkedIn: https://www.linkedin.com/in/gordon-hart/
• Website: https://www.kolena.io/

Ways to Tune In:

• Website: https://www.earley.com/earley-ai-podcast-home
• Apple Podcast: https://podcasts.apple.com/podcast/id1586654770
• Spotify: https://open.spotify.com/show/5nkcZvVYjHHj6wtBABqLbE?si=73cd5d5fc89f4781
• iHeart Radio: https://www.iheart.com/podcast/269-earley-ai-podcast-87108370/
• Stitcher: https://www.stitcher.com/show/earley-ai-podcast
• Amazon Music: https://music.amazon.com/podcasts/18524b67-09cf-433f-82db-07b6213ad3ba/earley-ai-podcast
• Buzzsprout: https://earleyai.buzzsprout.com/

Thanks to our sponsors:

Thanks to our sponsors:

• Marketing AI Institute
• CMSWire
• Earley Information Science
• AI Powered Enterprise Book

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Today’s guest is Daniel Faggella, Head of Research and CEO at Emerj Technology Research. Dan joins Seth Earley and Chris Featherstone and shares how martial arts influenced him to get into artificial intelligence. Dan also discusses what his experience was like with surveillance technology creation technology. Dan had a machine that could generate the next 10 slides of your desired moving picture. Be sure to listen in on Dan giving his advice on how you should properly use open AI!

Takeaways:

• Dan got into artificial intelligence by practicing the martial art, Jujitsu. He started a Jujitsu gym which helped support him when he was in school. Jujitsu helped motivate him and keep his mind balanced.
• Dan mentions how generative AI has been starting to bubble up since the spark of ChatGBT. He sees people starting to experiment with social, and experiment with proposals.
• With AI in general, people are looking at junctures within the workflow. At this juncture, they might be able to push a button and make this portion of what their company does slightly better and faster.
• Generative AI finds the juncture pockets and knows exactly where those settle in.
• Dan believes that eventually, people will somehow evolve in the way that we use ChatGBT and structure different FAQs.
• Dan believes that one day with Generative AI, we will be able to create some kind of feedback loop for humans to say what's wrong, and what's right to be able to train that AI system.

Quote of the Show:

• “The dust has yet to settle on the early cluster of those use cases in Generative AI.” (19:06)

Links:

• Twitter: https://twitter.com/danfaggella
• LinkedIn: https://www.linkedin.com/in/danfaggella/
• Website: https://emerj.com/
• Podcast: The AI and Business Podcast
• Article: Lotus Eaters and World Eaters

Ways to Tune In:

• Website: https://www.earley.com/earley-ai-podcast-home
• Apple Podcast: https://podcasts.apple.com/podcast/id1586654770
• Spotify: https://open.spotify.com/show/5nkcZvVYjHHj6wtBABqLbE?si=73cd5d5fc89f4781
• iHeart Radio: https://www.iheart.com/podcast/269-earley-ai-podcast-87108370/
• Stitcher: https://www.stitcher.com/show/earley-ai-podcast
• Amazon Music: https://music.amazon.com/podcasts/18524b67-09cf-433f-82db-07b6213ad3ba/earley-ai-podcast
• Bu

Thanks to our sponsors:

• Marketing AI Institute
• CMSWire
• Earley Information Science
• AI Powered Enterprise Book

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Today’s guest is Michelle Zhou, Co-Founder and CEO at Juji, Inc. Michelle joins Seth Earley and Chris Featherstone and dives into what proprietary data is and how it can be used correctly. Michelle also discusses the one lesson she has learned is that you have to build a product that can help people. You want to achieve your customers' outcomes, not your outcomes. Be sure to listen in on Michelle giving her advice on how to pick out the golden nuggets in AI data to show a coherent and meaningful summary!

Takeaways:

• When Michelle first started with computer science, she wasn't fond of it until she attended Michigan State University where two professors changed her perspective on computers. They gave her the opportunity to work on building graphical user interfaces for power management and worked on projects that dealt with AI data storytelling.
• Michelle explains that the AI data storyteller gives a set of data and tasks of the user which then gives the user visual preferences. It also consists of a series of animated data visualization.
• During Michelle’s first 15 years of research, she was working on understanding users in a task context. For example, what their tasks are, what they're looking for, what their visual preferences are, and what their verbal preferences were.
• Michelle has noticed a lot of students will strive for a degree that their family has done in the past. Michelle says that you don’t always have to follow any degree you don’t want. There are so many unique degrees to pick from.
• Michelle believes that transparency drives responsibility and since they have a powerful AI system, she wants to make sure that they use their AI in a responsible way.
• The one lesson Michelle has learned is that you really have to build a product that can help people. Make sure to achieve your customers' outcomes and not yours. You don’t want to waste their time.

Quote of the Show:

“I want to really democratize the use of this cutting-edge technology.” (23:41)

Links:

• Twitter
• LinkedIn
• Website

Ways to Tune In:

• Website
• Apple Podcast
• Spotify
• iHeart Radio
• Stitcher
• Amazon Music
• Buzzsprout

Thanks to our sponsors:

• Marketing AI Institute
• CMSWire
• Earley Information Science
• AI Powered Enterprise Book

Earley AI is produced and sponsored by Ringmaster, on a mission to crea

Thanks to our sponsors:

• Marketing AI Institute
• CMSWire
• Earley Information Science
• AI Powered Enterprise Book

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Today’s guest is Juan Sequeda, Principal Scientist at data.world and Co-Host of the Catalog & Cocktails Podcast. Juan joins Seth Earley and Chris Featherstone and shares how to understand the problem that you are trying to solve. Juan also discusses how your company's success should be defined differently. Don’t focus on just the goal being to save money and make money, have the focus be on solving a problem. Be sure to listen in on Juanl giving his advice on how to understand who you report to in order to speak the same language!

Takeaways:

• Juan believes that the market is pretty immature when it comes to, what they want or what they think they want. This is where data catology comes in to help since the majority of the data a company as they don’t know where to look.
• From the perspective of the data management world, it’s focused on only technology. The problems that they had been trying to solve 30 years ago continue to be the same problems they’ve been trying to solve.
• A good tactic that Juan has used before is if you are on the technical side of your business, it is important to understand who you should be reporting to. Every position has a different outcome on who you report to so it is key to grab that understanding early on.
• Juan’s definition of a knowledge graph is representing real-world concepts and the relationships between those real-world concepts end up forming a graph. The reason why the graph is really valuable is because you can integrate data coming from many diverse sources.

Quote of the Show:

“Keep working on the same vision.” (07:50)

Links:

• Twitter
• LinkedIn
• Website
• Juan’s Podcast
• Juan’s Portfolio
• Book

Ways to Tune In:

• Website
• YouTube
• Apple Podcast
• Spotify
• iHeart Radio
• Stitcher
• Amazon Music
• Buzzsprout

Thanks to our sponsors:

• Marketing AI Institute
• CMSWire
• Earley Information Science
• AI Powered Enterprise Book

Thanks to our sponsors:

• Marketing AI Institute
• CMSWire
• Earley Information Science
• AI Powered Enterprise Book

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In this episode, our guest is Andy Fitzgerald and Information Architecture & Content Strategy Consultant.

Highlights:
1:40 - Getting from Ph.D. in English and Literature in information architecture and knowledge graphs
9:23 - Schema.org
14:30 - How can we get search to be like "Google"?
19:00 - The trouble with self-organizing information
20:40 - The KFC debacle in Germany and case for keeping humans in the loop
22:15 - Knowledge graphs and AI
29:35 - Role of linguistics
33:00 - What happens when you don't apply knowledge graphs to AI projects
37:00 - Boutique knowledge graph - UXMethods.org
48:00 - Value of smaller scale knowledge graphs and simplicity

Links:

• Connect with Andy on Linkedin
• andyfitzgeraldconsulting.com
• UX Methods - a community powered, linked data driven knowledge graph for learning about the techniques of user experience design.
• Jim Hendler's papers
• Gall's Law

Thanks to our sponsors:

• Marketing AI Institute
• CMSWire
• Earley Information Science
• AI Powered Enterprise Book

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In this episode, Seth and Chris talk with Peter Voss, Founder, CEO, and Chief Scientist at AGI Innovations & Aigo.ai.

Highlights:
2:58 "Software is quite dumb"
3:51 "What is reality?"
5:00 Coining the phrase "Artificial General Intelligence" - what it means
9:00 On understanding cognition in the deepest terms
11:10 What is consciousness?
15:20 Difference between "Artificial Intelligence" and "Artificial General Intelligence"
19:00 The 3 waves of AI
29:45 What is cognitive architecture?
34:30 Quality of data vs quantity of data
38:00 Practical applications for building personalization systems
39:20 What can organizations do to prepare for AI driven systems?
46:30 One corporate bot or multiple bots?
53:45 Automation should be able to deliver the superior customer experience, not the cheaper second class option

Links:

• Connect with Peter on Linkedin
• Visit the Aigo.ai website

Thanks to our sponsors:

• Marketing AI Institute
• CMSWire
• Earley Information Science
• AI Powered Enterprise Book

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In this episode, Seth and Chris talk with Dan Turchin, CEO & Founder of PeopleReign.

Highlights:
4:30 What drives Dan's mission to impact a billion lives at work?
11:15 Disruption and the future of work
17:50 What will happen when people can have a day a week back from automation?
20:00 Work will change more in the next 30 years than in the previous 300
24:30 AI is really still in its infancy - how can we use it for good as it grows up?
29:00 What are the pre-requisites to success with AI?
37:50 How do you sell the business on the need to address their data?
43:00 Choosing use cases to get started with
51:00 What's next?

Links:

• That episode of his podcast with Seth that Dan mentions
  https://peoplereign.io/2022/09/seth-earley-author-of-the-ai-powered-enterprise-discusses-the-future-of-knowledge-management
• Dan's LinkedIn Profile
  https://www.linkedin.com/in/dturchin/
• PeopleReign
  https://peoplereign.io/

Thanks to our sponsors:

• Marketing AI Institute
• CMSWire
• Earley Information Science
• AI Powered Enterprise Book

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In this episode, Seth and Chris talk with Dr. Mark Maybury, former CTO with Stanley, Black & Decker.

Highlights:
3:30 Mark's early influences
9:10 What he does with his "spare" time - it is planned
16:30 His experience at Stanley, Black & Decker - making the elephant dance
22:50 Transitioning from analog to mixed reality (physical and digital)
31:00 AI - doing the early foundational work without existing ML systems
40:00 Early development of sentiment and intent analytics
45:00 Future projects including movie project getting students excited about careers in AI in the service of the public good
48:45 Ready Robotics
51:00 Development of a COVID De-activization protocol

Links:

• LinkedIn Profile
  https://www.linkedin.com/in/dr-mark-maybury-28532/
• Ready Robotics
  https://ready-robotics.com/

Thanks to our sponsors:

• Marketing AI Institute
• CMSWire
• Earley Information Science
• AI Powered Enterprise Book

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In this episode, Seth and Chris talk with Steve Stesney, Senior Product and Data Practice Lead at Predictive UX.

Highlights:

3:30 What is predictive user experience and how did Steve get there?
5:30 Grasping disambiguation and the move to graph data
18:00 Building trust in the data
19:26 Explaining taxonomy, ontology and knowledge graph to executives
21:30 Connecting UX and knowledge graphs
28:00 Managing the open floodgates once users discover what knowledge graphs can do
36:30 What is "predictive ux" ?

Links:

• LinkedIn Profile
  https://www.linkedin.com/in/stephenstesney/
• Website
  https://www.predictiveux.com/

Thanks to our sponsors:

• Marketing AI Institute
• CMSWire
• Earley Information Science
• AI Powered Enterprise Book

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In this episode, Seth and Chris talk with Scott Taylor, the "Data Whisperer" about telling stories about data management.

Highlights:
2:00 Data Whisperer origin story
9:30 Translating complex dry material into a story that resonates
11:30 Why master data is the most important data and how to help execs understand it
18:15 Bad data + AI = AS (Artificial Stupidity)
22:30 Every system demos perfectly
26:25 Don't say "data quality"
27:30 Definition of digital transformation
32:00 Ugly babies and the reality of bad data
38:30 About the book, "Telling Your Data Story" 99% buzzword free (coupon code in show notes)
47:00 Data management is macro trend agnostic
49:00 What's next - more puppets and dad jokes
52:00 Influencing the next generation of data managers

Links:

• LinkedIn Profile
  https://www.linkedin.com/in/scottmztaylor/
• Website
  www.MetaMetaConsulting.com
• BOOK
  Telling Your Data Story - Data Storytelling for Data Management
  20% off with code: DATAWHISPERER (publisher site only)
  https://technicspub.com/data-storytelling/
• Top Content
  Too Much Tech Talk? (A puppet service announcement)
  The Little Red Data Hen - A Cautionary Tale
  Connecting Data Management to the Essence of Your Business
  Data Has Got to Move to Have Value
  The Super Hero Adventures of Master Data
  Scott Taylor - The Data Whisperer YouTube Channel

Thanks to our sponsors:

• Marketing AI Institute
• CMSWire
• Earley Information Science
• AI Powered Enterprise Book

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In this episode, Seth and Chris talk with Henrik Hahn about driving innovation and change in his role as Chief Digital Offer at global chemical specialty giant, Evonik.

Highlights:

12:30 Organizational change and culture science
14:30 Augmented intelligence vs artificial intelligence
20:00 On deciding where to start
29:00 Using data to measure success
39:00 Organizational design
44:00 Dealing with information gatekeepers
48:30 What kinds of tools are getting best traction
52:00 Lessons learned while tackling big change

Links:

• https://corporate.evonik.com/en
• https://www.linkedin.com/in/henrik-hahn/

Thanks to our sponsors:

• Marketing AI Institute
• CMSWire
• Earley Information Science
• AI Powered Enterprise Book

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In this episode, Seth and Chris talk with Jim Iyoob about using data and AI to deliver customer experience that matters.

Highlights:
2:25: Our celebrity guest…Named top 20 influential people to follow on twitter and who has been inducted into the CX Hall Of Fame
8:10: Adding value & being a great person….Let’s look at the customer experience through the customer’s lens
17:10: AI as a plug and play solution….myth or reality?
19:10:…the insights and the understanding. The mechanisms and methods to extract more insights than the average bear.22:55: What goes into setting up controlled vocabularies, architecture, etc…how well are organizations prepared?
27:55: First things first: human intelligence. What is the outcome we want to drive? What is the hypothesis we want to drive? Is the data there to help us make insightful decisions?
34:35: Transparency driving accountability
40:25: What are you seeing at organizations when you go in and start looking at their knowledge bases?

Links:

• Etech Website
• Jim on Instagram
• Jim on Linkedin
• 22 Influential Leaders to Watch in 2022
• The CX Hall of Fame
• Books:
  • Quality Monitoring in a Contact Center: Making Sure Your Contact Center Works for Your Business
  • The Future of Contact Centers: Leveraging People, Processes & Artificial Intelligence
  • Delivering Customer Experience That Matter: Combining Human Intelligence with Artificial Intelligence to Delight Your Customers and Gain Market Share

Thanks to our sponsors:

• Marketing AI Institute
• CMSWire
• Earley Information Science
• AI Powered Enterprise Book

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In this episode, Seth and Chris talk with Tom Davenport, author of The AI Advantage How to put the artificial intelligence revolution to work.

4:40 On getting started in this space

7:47 What is most important today for organizations that are trying to operationalize AI

16:05 How organizations are doing in the human collaboration space and knowledge space

21:26 What is slowing organizations down…is it infrastructure or understanding?

31:40 Do organizations need to get ahead of the curve for augmented reality

34:12 Tom's take on Gartner’s prediction on Knowledge Management for AI…the largest growing segment and the largest spend

40:08 Customer experience and personalization…are you seeing organizations doing that effectively?

46:56 Democratization of AI

50:30 What is in store for 2022

Links:

• The AI Advantage
• Only Humans Need Apply
• TomDavenport.com

Thanks to our sponsors:

• Marketing AI Institute
• CMSWire
• Earley Information Science
• AI Powered Enterprise Book

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In this episode, Seth and Chris talk with Michael Schrage, Fellow at MIT Sloan School's Center for Digital Business and author of the book, "Recommendation Engines."

Highlights:3:44: checkered past
12:50: covering the overwhelming nature of technology
15:50: why do people make decisions
18:21: Perfect choice-what are the right choice architectures?
24:25 Recommendation engines: The hand you're delt - how the bluff matters
26:15: How do we use tools and technologies to come up with better and more reliable advice?
32:50: Ethical topics
49:06: What impact does good advice have on agency and autonomy
52:30: What Michael is working on next

Links:

• Book: Recommendation Engines

Thanks to our sponsors:

• Marketing AI Institute
• CMSWire
• Earley Information Science
• AI Powered Enterprise Book

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In this episode, Seth and Chris talk with Stephanie Lemieux, President of Dovecot Studio about the nuts and bolts of taxonomy and information architecture.

Highlights:
1:42 - Stephanie Lemieux background & Relationship with IA & Taxonomy

7:30 - Complexities & Foundational Problems

15:05 - Graph data and knowledge graphing

22:15 - Dynamic roles within an organization

37:27 - Leveraging weak signals and solutions

48:48 -What's the "excuse case"

50:59 - Value difference between large global and integrated organizations vs. a focused and niche organization

53:35 - Opinion on finding talent

Links:

• KMWorld Conference
• Taxonomy Boot Camp
• Dovecot Studio

Thanks to our sponsors:

• Marketing AI Institute
• CMSWire
• Earley Information Science
• AI Powered Enterprise Book

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In this episode, Seth and Chris talk with Jeff Coyle about how AI is impacting content strategy.

Highlights:
2:55 - Craft beer - on winning the award for a Mexican lager
10:00 - Jeff's path to a passion for SEO
14:30 - On using data to decide what to write about
20:00 - What does Google think you think is important on your site?
25:00 - Publishing low quality content is like a time bomb
36:00 - Existential risk of not using data to drive content decisions
39:00 - If you're not there at the top of the funnel you don't deserve to be at the bottom
41:45 - Owning a content space - what it takes
56:00 - Future of AI's role for content strategy

Links:

• MarketMuse website
• Content Strategy Guides & Case Studies
• Jeff's Content Strategy vlog
• MAICON

Thanks to our sponsors:

• Earley Information Science
• CMSWire
• Marketing AI Institute

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In this episode, Seth and Chris talk with Mark Anderson about the new field of pattern discovery and its impact on AI.

Highlights:11:45 Path to pattern discovery
17:15 Eliminating the hypothesis and focus on the data with a Y value
30:00 Solving the most challenging problems with pattern discovery
40:00 Making sure this technology is only used for good
42:15 Identifying a COVID test that is 98% effective within minutes
44:30 Pattern recognition processors
48:30 The importance of clean data and making the most of the data you have
52:00 Best use cases for pattern discovery

Links:
Book: The Pattern Future: Finding the World’s Great Secrets and Predicting the Future Using Pattern Discovery https://www.amazon.com/gp/product/B07659RJGB/ref=dbs_a_def_rwt_bibl_vppi_i0

About Pattern Computer https://www.patterncomputer.com/

Paper: Learning from learning machines: a new generation of AI technology to meet the needs of science https://arxiv.org/abs/2111.13786

Contact Mark:

• https://www.linkedin.com/in/markandersonpredicts/

Thanks to our sponsors:

• Earley Information Science
• CMSWire
• Marketing AI Institute

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In this episode, Seth and Chris talk with Tim Huckaby about the his experience as a software developer in the 90s and his take on the future of AI and computer vision.

Highlights:

4:30 Developing software at Microsoft in the 90s
11:00 Hollywood stories
14:30 Leaving Microsoft and building an app dev firm
17:00 Building CNN's "Magic Wall"
22:30 Predictions gone wrong and right
29:00 Pace of change in ML - quantum computing
38:00 Augmented reality and computer vision
48:00 AI and ethics

Contact Tim:

• https://www.linkedin.com/in/timhuckaby/

Thanks to our sponsors:

• Earley Information Science
• CMSWire
• Marketing AI Institute

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In this episode, Seth and Chris talk with Sean Martin about the development and practical applications of knowledge graphs.

Highlights:

5:30 – First online sports scoring website launched
9:00 – First forays into semantics applications
13:00 – Getting through scaling issues
16:30 – On needing to build the entire stack for knowledge graphs 
18:00 – The business problems that Cambridge Semantics solves
24:45 – Dealing with and making sense of unstructured content
29:30 – Data models for natural language queries
32:00 – About the book “The Rise of the Knowledge Graph”
34:00 – What is an ontology and how does it relate to knowledge graphs
42:30 – What’s next?

Contact Sean:

• https://www.linkedin.com/in/seanjmartin/

Get the book: The Rise of the Knowledge Graph

• https://info.cambridgesemantics.com/the-rise-of-the-knowledge-graph-oreilly

Thanks to our sponsors:

• Earley Information Science
• CMSWire
• Marketing AI Institute

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In this episode, Seth and Chris talk with Paul Zhao about making AI more accessible for everyone.

Highlights:

5:00 on being a tech entrepreneur
30:00 after the buy out challenges - what now?
39:00 advice to the non-technical on gaining business value with AI/ML
50:00 on build vs buy 

Contact Paul:

• https://www.linkedin.com/in/ppzhao/

Thanks to our sponsors:

• Earley Information Science
• CMSWire
• Marketing AI Institute

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In this episode, Seth and Chris talk with Paul Lasserre about his experience developing applied AI applications. 

Highlights:
4:15 Link between AI and chasing pirates in the Navy
7:00 On getting into customer experience as a ML problem
10:00 On the challenges of internally selling new ideas
15:00 On measuring success
18:30 On rules vs machine learning
26:30 Creating a better customer experience
28:00 On leaving Genesys and moving to AWS
35:30 How to give people the support they want
42:00 What he's working on now and what's next
Contact Paul:

• https://www.linkedin.com/in/paul-lasserre/

Thanks to our sponsors:

• Earley Information Science
• CMSWire
• Marketing AI Institute

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In this episode, Seth and Chris talk with Henrik de Gyor about his research on synthetic media.

Highlights:11:15 Defining synthetic media
13:30 Rights management
19:14 Nefarious applications
21:30 Provenance & Ethics
37:00 Applications and tools today
48:30 Future applications
Links

Book: Synthetic Media: The Next Reality
https://www.amazon.com/gp/product/B09MJW7BX1/

Podcast: Synthetic Media
https://open.spotify.com/show/5N7Qnx1qI1QOo6Q6T5jziJ

Synthetic Futures

Contact Henrik:

• https://www.linkedin.com/in/hdegyor/

Thanks to our sponsors:

• Earley Information Science
• CMSWire
• Marketing AI Institute

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In this episode, Seth and Chris talk with Linda Andersson, Founder & CEO of Artificial Researcher about AI powered semantic search.

Highlights:5:00 - Linda's journey to her work
14:20 - Domain knowledge and ontologies
17:07 - Knowledge extraction
20:05 - Why we need ontologies
20:50 - Bias and not knowing what you don't know
25:40 - Structuring and curating the knowledge base
30:00 - Supervised vs semi-supervised models
38:15 - What is Academia missing
43:30 - Getting the right start for AI projects

Links

Information about Artificial Researcher

• https://artificialresearcher.com/
• www.linkedin.com/company/artificialresearcher

Demo pages for index and the ontologies generated by the Artificial Researcher Data pipeline solution:

• https://passageretrieval.artificialresearcher.com/
• https://graph-demo.artificialresearcher.com/

Contact Linda:

• https://www.linkedin.com/in/linda-andersson-76483916/
• https://calendly.com/artificial-researcher

Thanks to our sponsors:Earley Information Science
CMSWire
Marketing AI Institute

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In this episode, Seth and Chris talk with Adam Sutherland about AI and machine learning in media and content.

Highlights:2:20 Adam talks his journey from Asian studies to Amazon.
12:00 A day in Adam's life and cool problems customers are solving
17:30 Why we still can't find what we want on streaming services
21:00 Biggest barrier to entry to AI enhanced solutions
24:00 Best practices for tagging media assets
25:00 When developing a bespoke model is the right decision
28:30 AI isn't perfect but sometimes that's fine
30:00 Personalization and recommendation engines
34:30 Data lakes plus content metadata
37:00 Emerging trends and techniques (really cool AI stuff in media)
42:45 Predictions for the future

Links

Contact Adam:https://www.linkedin.com/in/adamrsutherland/

Thanks to our sponsors:Earley Information Science
CMSWire
Marketing AI Institute

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In this episode, Seth and Chris talk with Massood Zarrabian, CEO at BA Insight about how enterprise search is evolving - getting better (bringing greater value) and costing less.

Congratulations to BA Insight on receiving the KMWorld 2021 Readers' Choice Award - Best Enterprise Search!

Highlights:1:15 Massood's road from a Civil Engineering degree from MIT to BA Insight.
5:00 Massood's philosophy on growing teams and companies - and how theater has influenced him
8:25 Can enterprise search be like Google?
13:30 Unstructured vs structured data
17:45 The maturing of enterprise search
26:00 The last mile in enterprise search
32:00 Building, maintaining, training your Index
39:00 Bots and users don't care where the content lives
42:30 Role of an information reference architecture
49:00 Role of automation for tagging
52:30 The future of enterprise search

Contact Massood:mzarrabian@bainsight.com
https://www.linkedin.com/in/massoodzarrabian/ Links:BA Insight website
The AI Powered Enterprise

Thanks to our sponsors:Earley Information Science
CMSWire
Marketing AI Institute

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In this episode, Seth and Chris talk with Mike Kaput, Chief Content Officer at Marketing AI Institute, about how out-of-the-box AI is providing immediate value to businesses of all types.

Highlights:4:04 Mike's day to day & background
11:30 Use cases & Planning
28:02 Ethics & Bias
36:45 Where do you start with AI in marketing?
40:00 Differentiation vs Standardization
43:30 Barriers to entry
47:35 Humans in the AI loop

Contact Mike:Mike@pr2020.com
https://www.linkedin.com/in/mikekaput/ Links:Marketing AI Institute
MAICON 2022
State of Marketing AI Report
The AI Powered Enterprise

Thanks to our sponsors:Earley Information Science
CMSWire

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Many companies are turning to chatbots and virtual assistants to improve customer experience and increase operational efficiency. In the past text and voice channels were distinct. Now, tools and technologies are emerging to support omnichannel virtual assistants that seamlessly blend text and voice. However, voice and text interactions are quite different and have specialized design requirements. 

In this episode, Chris and Seth talk about core principles for building chat and voice assistants and review key considerations for each channel. 

• How true intelligent virtual assistants (IVA’s) differ from chatbots
• The unique challenges of designing for voice
• Selecting the right problems to solve with IVA’s
• Skills and talents needed for success
• Getting past stumbling blocks in order to scale