Impact of AI: Recent Episodes
Melissa Drew
'Impact of AI (& Data)' podcast is an exploration of how AI impacts us daily, both professionally and personally, with an emphasis on education, innovation, trends, lessons learned, and insights from women leaders and influencers around the world.
Join Melissa Drew, your podcast host, to rethink how we understand and utilize AI technologies in our constantly changing global landscape. If you are new to the topic, looking to upskill, or simply enjoy a good story, this is the right place for you.
This episode discusses an often overlooked area of data cleansing and its enduring value, even in the age of AI technologies. In our data-driven world, companies are racing to adopt AI to automate processes at the expense of pausing longer to evaluate potential adverse side effects. This is why the human element remains a critical component.
This conversation aims to dive deeper into the benefits of data cleansing with human expertise, challenging the premise that AI is the only solution. With our guest this week, Susan Walsh, we explore how human intervention continues to add value.
While AI can offer speed and efficiency, it cannot fully understand the contextual use of the same data set across industries. Leveraging human intelligence in conjunction with automation tools strengthens the outcomes.
Sprinkled with real-world stories, the conversation will uncover the 'behind the scenes' human-driven data cleansing methodologies. This episode promises to offer a different perspective on AI and explain why the human element will be around for a while.
Julienne B. Ryan shares valuable insights on the importance of effective communication in a digital world post-COVID. She highlights the impact of AI technologies on communication, including the limitations and potential inaccuracies of AI language models like ChatGPT. Ms. Ryan emphasizes the need to balance technology's benefits with the importance of the human experience. In her discussion, Ms. Ryan stresses the significance of critical thinking, trust, and authenticity in using technology for communication. She also explores creativity, experimentation, and failure in the context of technology and communication. Furthermore, Ms. Ryan highlights the importance of empathy and nuance in effective communication. Drawing from her consultant practice, Ms. Ryan encourages the audience to approach technology-assisted communication with a critical mindset while trusting their instincts and experiences. Her insights are more relevant than ever as we navigate a virtual and digital world.
Adita Karkera, "..motivated and inspired in how the public sector can cater to the needs of our citizens. My heart lies in the public sector."Data.gov was launched in 2009 and is the United States government's open data website. It provides access to datasets published by agencies across the federal government. It wasn't until 2018 that the federal government's mandate became a clear sign acknowledging the value and effectiveness of data.The 2018 Foundations for Evidence-Based Policymaking Act is a United States law that requires the federal government to modernize its data management practices. Its goal was to change the way data is used in the government to create policy. The law aims to improve access to federal data so agencies and policymakers can craft better, more effective policies and programs and deliver on services promised to the country.In 2019, as a follow-up to the prior 2018 Foundations Act, the head of each agency was tasked to appoint or designate a qualified #chiefdataofficer (CDO) without regard to political affiliation. Since then, a Federal Data Council has been established, bringing # CDOs across all federal government agencies. This interagency council has a charter establishing best practices to leverage data as a strategic asset. As of this recording, around 80+ individuals make up that council. Some of the other topics explored with Adita: - Are there challenges for the CDO in the public and private sectors?- Skills for a data-literate workforce- Understanding the 360-degree view of the citizen- Perception the public sector moves slowly may be an advantage rather than a hindrance- Potential risks in leveraging data can not be ignored, no matter how excited we are about emerging technologies such AIWe need more women in the public sector to help shape our future. I am interested in serving the public after listening to Adita speak so passionately. Reach out directly to discuss the role of Women in Data in the Public Sector. / aditakarkera
The strategy and executive of marketing is not commonly considered in the early part of the product development and lifecycle. The most common reasons include, but not limited to: 1. to ensure the product and service works before taking it commercial (internally or external) and 2. the product or service can evolve during the product development cycle. Our guest, Isabella has an expertise is working with new and emerging products after the fact. Often 1-4 years after the product or service has been created. "...a lot of times we (the marketer) miss the actual problem the product or services was created to solve, " Isabella mentions, "it is common to have a disconnect between engineering and marketing but also sales and marketing,"During this episode we discuss a marketing methodology:1. Business Challenge - What was this product created for? 2. Refining the buyer persona - who are we targeting ? Who is going to use this product?3. Validate with Customers - Schedule time with potential buyers for feedback.4. Translate into what will resonate - Breaking through the noise.5. Educating value - What is the value proposition? It is more than features & capabilities6. Establish the story, but stay consistent.Other topics discussed were: - Educating new emerging technologies- Working around disadvantages: - What about good and negative publicity. Is it all good?- Startups. Are they spending their money wisely with lack of resources in marketing?- What do we mean when we talk about 'the story'- What approaches do we want to avoid? - Customer attention spans- Prioritize marketing efforts- Supplementing expertise with external content- Generation Z perspectives- Women on board of advisors at the university level, supporting the next generation- Lessons learned - women having a voice. You are there for a reason. You are where you are supposed to be. It is outFeel free to DM Isabella: https://www.linkedin.com/in/isabellarichard/
From housing data on a single server to global, distributed storage with zettabytes of available data. Over the past 20 years we have continued to modify the underlying architecture to match our desire to store more data. We use this data in consumer hyper personalization, detect vulnerabilities and data breaches, and to have a higher level of confidence in the answers to our questions. As we collect more data to address the questions from today, consumer expectations shift again and have more complicated questions tomorrow. Just when we think we have enough data, the line in sand moves again. In fact, the line is moving so much these days, one could say it doesn't really exist anymore. My guest today is Laura Ellis, who originally thought she was destined to become a teacher. Today she is a data engineering & platform analytics executive who started her professional career in technology and recently received CDO executive certificate. She is also the founder of the Blog 'Little Miss Data', focusing on living out loud and talking about all things data. In her spare time, Laura produces and hosts the annual event (for the past 3 years) 'Data Mishaps'. This event is a virtual, safe space where data mistakes are shared to learn from each other. With ~400 participating in the last event it is becoming a much anticipated event each year. Topics discussed today are: - What is cybersecurity - the simple definition - Is the role of data engineering different than a data scientist - How has the interpretation of data changed over the years - Is there a business case for dark data - Pivoting into a data profession - The value of returning to school after 15+ years
We hear and understand that step 1 in developing a #dataorganization is to clean up the data. This often includes data harmonization from multiples sources, classification, mapping, labeling, etc. Most often it usually stops there. We cleaned it... we are done, right?No, not really. In my conversations with Himali Kumar an IT and Data Executive, new data is constantly be added into the enterprise data set. From information provided by new customers, inclusion of new data sources, changes in data sources such as POS system, customer preferences, etc. Question: How do you gather and ensure value in enterprise data if the data is constantly changing?If you are not leveraging a #datagovernance program with resources dedicated to continue to re-evaluate the data, then the organization is already falling behind or creating a negative perception between the company and the customer.Examples of challenges that can occur after the initial #datacleansing include: - duplicate customer data leading to sending the same message multiple times not realizing it was the same person (from a data perspective)- data schema issues leading to many man hours of rework- #dataaccuracy issues causing outages to internal and external stakeholdersA decision-based organization is only as good as its data. If the data is constantly changing, then it is critical to continue to evaluate (or audit) the data to minimize gaps and mitigate data strategy challenges.My conversation with Himali, also highlighted: - Impacts of not performing regular reviews (audit) of the data - Three (3) areas critical to developing a data organization - the minimum to get started with - Establishing tolerances / risk thresholds to enterprise data - Who is really the end user? - Achievability of #dataquality
Lloyd Skinner , CEO Greyfly.ai interviews Melissa to discuss how AI technologies are impacting project management. Specifically providing 'project intelligence' to the organization and reducing project risks before the project even starts.
Bhuva Shaki is the Chief Sustainable Innovation Officer of Bhuva's Impact Global and Global Chief Ethics & Culture Officer (CECO) | Director of the Americas (Not-for-Profit), Women in AI. She embraces the concept of continuous learning and opportunities for personal growth: sustainability, entrepreneurships, private equity, venture capital. All of which intertwine to support her advocacy of others, helping startups expand sustainable innovation and championing women's financial inclusion with technology with a goal to eliminate exclusion.With such purposeful engagement in her continuous learning, I had to ask, which is first the education or the experience? Bhuva, replied experience first, then focus on specific areas of education that empahsizes your interest. This lead to the bulk of our conversation today, covered across the following four (4) questions: 1. How can we eliminate bias and integrate ethics into financial services industry?2. 21st Century Skills are suppose to outline the grouping of skill sets our next generation needs as they enter the workforce. The phrase literacy tends to emphasis media, technology, and information literacy. There are so many other types of literacy the next generation should also focus on. How do we ensure the balance from those literacy skills while adding in others such as data literacy, data bias, financial literacy?3. Do governments need to play a role in order to improve of gender equity? More specficially, a recent article was referenced that real gains in gender equity will require technical equity as well and the role of government agencies is another variable required in the larger equation. 4. Do you have recommendations to close that gap in the middle management of the workplace? More specifically, when we look at where women are in leadership roles today, we have improved at the level of VP and higher. We also see a lot of women leaving college and entering technical roles. There is a clear gap in recent studies in the middle section. If we don't have women throughout the oerganization do we risk our progression falling backward?5. Generative AI - from a textual perspective (not generating art). Is this going to help or hinder the financial customer, specifically from the lens of women-owed startups and financial inclusion. Finally, two (2) key takeaways for women-owned startups. What can they start doing after listening to this podcast to really change their financial equity?https://www.linkedin.com/in/bhuvashakti/
https://www.linkedin.com/company/bhuvas-impact-global
/https://www.bhuvas-impact.global/
Data is a rich asset. As we push the boundaries of available data beyond Zettabytes and into Bronobytes and Geobytes, how will we achieve accurate data intelligence? According to Forbes, 90% of the world's data was generated in the last two years with 2.5 quintillion bytes data being created each day. Is all data equal, and how much data do we need to address our questions? In talking with Kamal Distell, from Toyota Motor Corporation, "there are similarities in how we think about data governance, collection, labeling, architecture, etc., across all industries." These foundational elements remain static, regardless of which industry or company you work for. "It is the context that is different across each industry. Yet, the context of the data can be learned." Kamal adds, "As we increase how we gather more information, there will be a hypothesis, and the data will either refute or accept." In the 1990s, the term Data Mining became more mainstream in the database communities. It was one of the foundational concepts of my Master's degree in Management Information Systems, focusing on Data Management. I was more vocal about the declining expectations as data increased in ways we could no longer imagine; i.e. how could we store and report on meaningful data. As we continue to collect and store beyond terabytes of data (this was in 1996), the relational architecture would no longer be sufficient. What was working, in theory, would not work in the real world. Moving forward to 2022, there are many more levels above a Terabyte just so we can quantify the amount of data accessible today. We are having the same conversations we started in 1996. How do we wrap our arms around the right balance of relevant data to support the business challenges, to create an effective decision at a time when that decision has the most impact? Kamall and I discuss the concept of minimal viable data, which will be a critical component to the future success of data intelligence. Additional topics in under 30 minutes include: - Value of working with data across industries - Are we able to solve any business challenge with data alone? - Future of Data Trends - Minimal Viable Data - 80/20 rule from the past. Is this still relevant today? - Using data differently - Lessons learned
We talk about challenges in moving AI solutions from testing into production or commercialising AI solution for all users internall / externally. We haven't really focused a lot of attention on the other extreme: the data collection and developing the proof of concept.
Where do you start ? Ask yourself, what is the outcome you want to achieve? What is the business problem you are trying to solve? Not understanding this is the #1 blocker to success.
Prerequisities: There are a lot of moving parts just to get started, some examples, but not limited to: 1. what data is needed & where are you going to get it 2. availabilty of data; do you have access to this data in a repeatable way 3. what about data privacy, white room (data can not leave the site) 3. how will the data be organized 4. who is going to own the data goverance 5. do the data scienists have the right technologies to do their job 6. is the organization ready for this to start Additional topics discussed: - How to priortize where to start - 80% of the work by data scientist is data rangling - Example use cases in the insurance sector - Example use cases in asset management companies - Proof of concept validates if the outcomes we want to or expect to see, can really happen - Establishing the architecture in a way we can scale if the POC is sucessful - Technologies data scientist should have access to - Monitoring data drift effectively - Different languages across the roles. Takes a lot of change management to get this right.
Ultlimately is the organization 'data ready'?
My podcast recordings are usually under 30 minutes, but sometimes there is topic that is so fundemental and foundational to how we live our lives every day, the clock is just thrown out. This is one of those topics.
While there were many 'Ah ha' moments, one moment of enlightenment was just how much carbon is produced for the inefficiency in training an AI model. We looked it up during this podcast. It was shocking. AI is used to support research in reducing carbon emissions, but we may not be fully aware how much AI technologies act as a carbon emitter.
Our goal with this discussion is by then end you are asking yourself questions you may not have thought about and/or starting asking new questions at your job tomorrow.
Topics covered today: What is Circular Supply Chain? Impact of on primary, secondary, and other supply chain...markets, materials, and waste How do you wrap your arms around all that data? Considerations when building new products?
Why should we care about Circular Supply Chain? Impact of carbon footprint when training AI technologies
Redefine the definition of your carbon footprint to include systems, AI technologies, etc.
Are we really closer to a zero carbon footprint? What is a true circular economy? What is Zero-waste really? The defintion from 5 years doesn't exist today. In 3 years from now, we will want to redefine this again.
Visible impact & roles for a citizen of the earth - what can I do? Cultivating the right mindset - the negative perception of refurbished vs OEM Roles and responsibilities - what you thought your role was today is completely changing
Do we take a serious look at scrap & how it can be resused in new creative ways? We illustrate a real world example. -----------------------
Sneha's passion is leading supply chain process improvements while focusing on the reduction of carbon footprint and improving sustainability.
AI technologies are not new. They only became popular after some time. They have been around for decades. As a result of supporting components such as cloud architecture and semiconductor chips, using AI technologies has become more accessible. This accessibility brings this topic into the mainstream for discussing positive and negative impacts.
Aruna Pattam, Head of AI & Analytics for the Asia Pacific region, is my guest on this episode. In 2021, she was Linked in Top Voice 2021 for Technology and Innovation and recently named of the 2023 Top 100 Brilliant Women in AI Ethics. Additionally, she continuously contributes her thought leadership as a group member of the Responsible AI Think Tank.
In Jan 2023, Aruna published her perspectives on the Top AI Trends for 2023. Today's discussion discusses those trends and why we should be watching these more closely in 2023. 1. Impact of AI and Cybersecurity 2. Democrationalization of AI 3. Edge AI 4. Generative AI 5. Responsible AI
This interview, our discussion, and our views are now a part of the global data set.
According to Laura, data is impartial. It doesn't have human-like qualities. The unclean data, the unstructured data is the best mirror to ourselves. Ultimately, we are responsible for the data-sets that are pulled into other organizations. i.e. we buy, we post, we upload, we like, we repost.
We can clean data to redefine a better 'prettier' answer, but if we want to fix the data, we may want to shift that focus to ourselves. "... you play an active role whether you have known it or not. You have been in it all along", Laura. "We focus on all the things that can go wrong (when discussing AI technologies), but there are benefits that can be used for good with the right gaurd rails. We have to sift through the not good in order to acheive the good", Laura.
Additional Topics Discussed: - Definition of ethics - Applying ethic concepts to AI technologies - What does it mean to be a human in today's world - Moral decisions and consequences - Bias exists comes from us, the human interaction - Broken mirror theory, Laura's theory based on years or observation and reflection - AI algorithms are not entirely responsible for all the bias themes - Ethics look at the harm when the result provide the wrong answer that is harmful - Ethics-washing - we always want the pretty version without the work - Books: How to Lie with Statistics & Merchants of Doubt - Transparency Fact Sheet - Are we missing anything? - Regulations are written after innovation, i.e. Copyright Act, written in 1976 - Policies.The future requires dedicated resources to revise policies on demand or every quarter. - Right, good, fair, and just - Laura' answers the question, 'what can someone do to start thinking differently about ethics, that impacts us personally' - Protecting your images, what your have contributed yourself you may not have thought much about until now
https://www.linkedin.com/in/lmiller-ethicist/
AI-related projects are not typical IT projects with a traditional waterfall approach. My conversation with Lisa Palmer, Chief AI Strategist & Ethicist, AI Leaders focused around her recent doctorial thesis and research on 'Artifical Intelligence: Are For-Profit Entities using the "Do the right thing' goverance to drive business results"
When her customers claimed they were 'doing the right thing', Lisa wanted to take that further i.e. 'What does that really mean and more importantly, what does the customer believe that means? This led to her doctoratal statement and how companies are:
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Making decisions
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Taking action
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The results of those actions
AI Technologies are still reletatively new in how they are being used in for-profit entities. She turned to podcasts to collect the foundational data related to her thesis statements for recent and relevant information. Listening to 172 podcast episodes and narrowing this down to 46 specific scenarios included in her study, covering a date range from the last 3 years up to April 2022.
There are 3 ways AI technologies are being used in enterprise companies:
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Efficiency of processes
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Revenue opportunities
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Risk Avoidance
Other questions discussed during this episode: -Self-adopted policies - What are companies actually doing from a policy perspective? -What are the types of companies which are doing this well? -Is the AI champion a benefit to the enterprise organization, or are there unintended consequences -What about companies that are focused too broadly? -What is a reoccurring theme for successful companies? -How important is the quality of diversity in viewpoints or successful companies? -How influential is company culture when executing AI-related technologies? -Can AI policy and regulation restrict our ability to innovate? -Is there a disadvantage to the US which is not writing AI-related policies and regulations but instead -This remains a fluid topic. What do we need to address in 2022-2023?
A recent graduate from Nichols College in Boston, MA and now working as an Automation Engineer at Paragus IT, Skyla started her educational journey with an Accounting degree, but later changed that concentration to technology and automation.
In a conversation with her advisor, it was suggested perhaps Accounting wasn't the right fit. These discussions led to an internship through Center of Intelligent Automation (CIPA) program at Nichols College. Skyla defines automation as the improvement of manual processes that are rule based and repetitive. Mostly data entry that was occuring daily or weekly. She using Power Automate with Microsoft to support her automation Two (2) examples where she has made a visible impact to her internal stakeholders are:
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The same email is sent every week, from the same supervisor with the same body of content to remind folks about their time sheet.
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Employees submit reimbursement requests via paper. Through the use of mobile app, employees can send those request very quickly and can managers can quickly approve or deny.
Other topics discussed wtih Skyla:
- What prompted the change - the appeal and challenge - Types of activities as an automation engineer - Not every process can be solved by automation. - Do stakeholders reach out to the department or is it a struggle for internal stakeholders to embrace automation? - Listening is the #1 skill set. The process is explained differently by different people or doesn't exist on paper. - The value of current state processes - Skyla's recommendations for other women just starting their career journey. https://www.linkedin.com/in/skyla-w/ https://www.linkedin.com/company/paragus-it/
AI4Good. We talk about this often but haven't focused a lot of time on understanding the 'good' part of this phrase. It is women like Shiran Somech who are using AI technologies that truly have a global social impact. She has found the perfect blend of her two (2) primary passions of AI technologies and wants to make a visible social impact on women's rights. Our discussion talks about her initial idea and carrying that forward from concept to reality, leveraging AI Technologies to create something that may not have been possible five (5) years ago.
Social Media Campaign 'Listen to my Voice' An AI-based campaign speaking up against domestic violence uses AI technologies to bring the voices of women murdered by their initimate partners who are no longer here to tell us their stories. Our conversation covers the partnerships, the impact, and some technology challenges she had overcome in bringing this campaign to life. This could not have been possible without the support of the families. To realize this concept, collaborate with non-profit government partners, tech creators, and sponsors. As a result of its extraordinary success, the campaign is now being duplicated around the world: https://www.listentoourvoices.co/en/.
Volunteer Special Projects Tochnit Saleet, is a program sponsored by the Ministry of Welfare & Tel Aviv Municipality to help women break out of the sex-work cycle and integrate into the hi-tech workforce. In this role, Shiran helps to raise funds, provide mentorships, and upskill women, enabling them to enter the Israeli technology market.
"Best Friends is transforming the animal welfare industry and data is transforming Best Friends", Michelle Dunivan, PhD Dunivan, Analytics Director of Best Friends Animal Society. As we approach the 2022 holidays in the United States, the adoption of kittens & puppies increases as gifts to family members.
What we don't see is everything behind the scenes to ensure your local animal shelter or rescue center has the right balance of animals to service local demand. We never see the amount of data which needs to be collected, to resolve key business challenges for the betterment of animal welfare.
In talking with Michelle, data provides her organization with the ablilty to ask questions no one thought to consider five (5) years ago because the data wasn't as accessible as it is now. In the future, data can help Animal Welfare Industry to be proactive instead of reactive to really understand the pyschology of adoptions.
What if data could help us understand (or predict) the outcome when a type of dog breed is accepted by the local shelter in a geographical area, at a certain time of year, and possibly the time of month.
We packed a lot information in just under 30 minutes. Other topics during our discussion include: - Roles & reponsibilities supporting data and analytics in animal welfare - Research in the real world compared to the obstacles of academia research - Studies: Animal Behavior (kpeeing animals living longer) - Research: Community cat programs - Supply and Demand: what animals are needed in which states - Myths: Pandemic Puppies are bring returned. The data refutes this perception - Data collection: Collecting from animal shelters across the United States - Establishing the right amount of data to achieve our business goals - Reporting to bring actionable decisions not just to have lots of data - Constituents, volunteers, donnations, and local state and county policies
"There is no such thing as a bad project, only poor project planning." Personally, this has been my quote for 15+ years. Executing a good project plan is a balance between decisions and data. The decisions we make before and during the project and data availability facilitate those decisions.
We have all been there. Using XLS instead of Microsoft Project to create a project plan with just enough detail to allow you to track the progress, and yet somehow, we cannot make accurate decisions with a high level of confidence about the success or failure of the project. Yes, we can say the project is ahead or behind, and we know when a project goes longer than expected, we can use the existing run rate of our resources to estimate how much more money will be needed to finish the project.
What about the majority of projects that continue to go over budget or run long multiple times before the project is completed? How do we know when and where to shift resources to other workstreams, so the project is back on track? More importantly, can we predict a project will be successful or fail before the kick-off and explain why?
With the accessability of AI technologies, Greyfly.ai combines the foundational concepts of project management to generate true project intelligence. This software solution enables project managers to predict proactively the expected success or failure of a project and, during the project, outline what project parameters need to be changed to bring the project back on track.
In talking with Marcia Williams, Chief Product Officer of Grefly, leveraging AI technologies on top of the data enables predictive analytics to address project questions with higher confidence. The fact-based decisions reduce the risk that can drive potential savings (or cost-avoidance. The historical data can be used to highlight if a project has the potential to be a failure and address the 'why.'
There is a US cartoon in the 1980s I used to watch on Saturday mornings called GIJoe. Their slogan was 'knowledge is power and 'knowing is half the battle. With improved project intelligence, using AI technologies is helping to harness the power of project data and to answer the one question we could not figure out before 'Why?'.
Other Topics Discussed:
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a professional journey from IT auditor to Product Development
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data quality -
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drive a fluid business culture to allow the customer to take you where you need to go
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the product officer's role
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resource capacity through an organic growth structure
According to a World Economic Forum study, it would take 152 years to close the gender gap if we did nothing more than what we do today.
This was just one insight during my discussion with Debbie Botha, Global Chief Partnership Officer, who further describes the Women in AI organization as a 'Do-Tank', not just another 'Think-Tank.
"Even with the hardest efforts, we will still be biased human beings ....it is important teams build the future of our world with AI applications to embrace internal diversity as much as possible to create an inclusive AI and represent a right image of a multicultural society. That’s why we need... more women in AI, but (also) more diversity in general. This can extend to skin color, religion, education, country, family, age, etc.," Founders of Women in AI
Started from a grassroots organization, Women in AI has grown into a global organization in 150 countries with boots on the ground in over 40 countries.
Our conversations dive deeper into the following:
- making a visible impact on minimizing bias and closing the gender gap
- five (5) ways to increase (or speed up) the process
- how does culture impact, hurts, or help the progress
- example such as job description, where a small change can make a big difference
Is customer service really servicing the customer if the priority is capturing data rather than understanding the customer?
Think about the last time you called customer service and spoke with someone who spent more time on data entry than really helping you, or what about a non-profit organization whose support center is manned by volunteers or interns? Perhaps the customer service agent does not want to ask the customer to repeat themselves and thereby miss out on documenting key information, or perhaps the agent is so busy hearing the conversation for data entry and not listening to the customer's needs.
Wyser is a 2-year-old start-up using AI technologies to document, in real-time, the voice of the customer. This reduces data entry errors while increasing data accuracy. More importantly, it allows the customer service agent to provide service to the customer rather than risk escalating customer frustration even further.
Donna, "we are processing data in the most intelligent way to fully use it without someone having to manually look at it.... able to identify patterns and keywords to establish trends and then adapt accordingly."
Additional topics discussed are:
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Shifting into an early-stage start-up from a long career in the industry
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Is this value of what you are doing immediately understood
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Types of bias in the traditional customer service approach
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Cultural challenges in off-shore call centers
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Loss of valuable information due to individuals who are slow at data entry
TBDWith the increase of AI technologies and, at the same time, a decrease in the cost of using those technologies, we are seeing a rise in startups and small businesses that can focus attention on solving specific business challenges.
In this episode, Erin McFarlane, VP of Product Innovation at the Boston, MA startup Fairmarkit is doing just that with a new round of funding in 2022, bringing 'fairness to the market is possible. Three (3) years ago, Erin took a risk to shift from working on the buy-side, most of her professional career, to the supply side and work with a very young start-up company.
Erin, "Policies often state using procurement at a threshold of $X. P-card is reserved for smaller but more frequent spending. The middle ground is where Fairmarkit excels, supporting both direct & indirect spending."
Additional topics covered: - What lured you away from industry to a start-up? - Fairmarket overview - What use cases is Fairmarkit solving for? - Does it really cost more for the organization to use diverse suppliers? - What and where are the AI technologies used? - Lessons Learned in working with Startups from Erin
As older generations (last of the baby boomers) are expected to be fully out of the workforce in the next 5-8 years, the organization will be left with a void of resources with deep expertise expanding over a career of 25 years.
Their replacements? A new wave of resources leveraging data and AI technologies. Many of us already in the workforce have learned on the job or been taught by co-workers. What about the generation in college/university today? Can we continue to expect the current formal education will be enough?
Kerry Calnan, VP of Innovation & External Affairs at Nicols College in MA, USA, leads by experience, fostering capabilities such as the Center for Intelligent Process Automation (currently going into its 3rd year) and in Fall 2022, will launch a new 4-year degree 'AI Automation.'
Intending to link real-world experiences at the undergraduate level, Nicols College is continuing its efforts to remain at the forefront of education and emerging technologies combined with the 21st-century skill needed in our constantly changing global landscape.
Want to know more: https://www.linkedin.com/in/kerry-calnan-75693a12/ https://www.nichols.edu/offices/center-for-intelligent-process-automation/
Our guest today is Mara Pometti. She outlines her professional journey from Journalist to AI Strategist and covers topics: - Predictive maintenance solutions in the energy sector.
Specifically, failures in high voltage cables routing energy from the wind farm to the mainland - Translating data through technical KPIS using visual data story - Perspectives on how the EMEA is embracing AI technologies - Human-centered AI discipline - Multi-disciplinary team throughout the AI lifecycle - Lessons learned along the way.
About Mara:
Mara is IBM’s first-ever AI Strategist. She defines herself as a data-savvy humanist who sits at the intersection of AI, data journalism, and design. Mara’s method uses human needs as a lens to uncover overlooked opportunities hidden in companies’ data and transform that data into stories that illuminate the vision of a new scenario that aligns AI projects with human values and business intents. As a polymath whose experience spans from the humanities to AI, Mara acts as a translator who bridges these worlds through design, strategy, and data storytelling.
Mara’s work focuses on humanizing AI, that is, responsibly weaving AI into the dynamics that we, as human beings, live in every day.
Mara’s book: Communicating with data: information between data journalism e data visualization https://amzn.eu/d/9TjDgQO
User experience (UX) design defines a user's experience when interacting with a digital product, application, or website. It is the primary layer between the user and the technology.
Design decisions in UX design are often driven by research, data analysis, and test results rather than aesthetic preferences and opinions. Software applications focus on UX design to bridge the gap between the results of the application and how the end user interacts with those results.
There hasn't been a lot of focus on (UX) User-Centric Design Experience with AI-driven results.
Laura's past 6 years as a UX Designer - and recently focused on AI applications in the past 3 years -has uncovered additional value to the stakeholder by including UX Design as part of the AI Lifecycle.
Find Laura on LinkedIn: https://www.linkedin.com/in/laura-dohle-559aba121
I love food & I have embraced AI Technologies. Therefore, I was really excited to discover Andrew Yip, Head of Future Foods at Monde Nissan Singapore. In this conversation with Andrew we take a discovery perspective of breaking down food to its chemical components, understanding taste and texture, and where AI technologies are getting deployed today and where we might be seeing AI Technologies in the future. Think kitchen in a restaurant or research. With new roles such as a Food Technologist, technology and food are fusing together more and more.
More about Andrew Yip - https://www.linkedin.com/in/andrew-yip/
What if using AI technologies could remove the unconcious human bias when evaluating & segmenting unstructured data? What if we are no longer required to store petabytes (1 petabyte = 1 million gigabtyes), of historical, industry specific data to reference and compare to?
That is the focus of the discussion with Adriano Garibotto, Co-founder - Chief Sales & Marketing Officer at Creactives SpA.
Humans look for patterns in data to cluster like-data together, then use queries or a grammar-based machine learning approach that trains models based on existing data sets. When using deep learning combined with natural language processing, to create a more realistic neural network of connections, those older approaches are no longer necessary.
Creactives provides AI-powered solutions to provide real-time procurement insights and analytics, optimize inventories, procurement processes, and to render business data usable by overcoming geographical, linguistic, and ERP/PLM/S2P system barriers.
Discussion today focuses on how AI is slowly started to making its way into the legal system in India, as well as, trends Divya is observing as a practising advocate (lawyer / attorney). A constant learner, she has recently has focused on AI policy and auditing for AI processes. Divya also talks about global resources and connections she has used which can assist others in starting or continuing your education journey in AI privacy, policy, etc.
Divya (दिव्या द्विवेदी) Dwivedi is a practising advocate at the Supreme Court of India. An Engineer turned Lawyer with experience in ensuring the legality of commercial transactions of Corporate world involving fields like AI, IoT, Data Privacy, IPR (Patent Drafting, Patents, Trademark Filing, Copyright Maintenance, IP Portfolio Management), Company Law, Technology Laws, Cyber Laws, Cyber Forensics, Environmental Compliances, Gender Justice, Legal due diligence.
This episode explores from point in time when an executive leader say 'This is our goal for this year' to understanding how to identify & 'right size' the enterprise data to support those strategic goals.
I 1st heard the term 'right-size' when I worked in waste management. The goal of 'right-sizing' is to ensure you have the right solution to meet the customer's needs at a cost that is acceptable. I feel this term is appropriately used here as well. How much data and at what cost (time, bias) is the right fit to support value to the organization.
Additional topics discussed with Samta Kapoor, from Accenture include: - Avoiding data paralysis - Linking data directly to corporate goals focusing on valuable outcomes - leading with value first - Recognizing enterprise data may be in mulitple data sources and may have overlapping data across those sources - Governance and specifically identifying an individual who owns the governance is critical to sucess - Data is and should be treated like an asset - Start small with proof of concept for buying with business stakeholders and leadership - Not everyone will understand data. Taking time to fold in a data driven culture - What does it really mean when we say 'value to the organization' - Lessons learned
In this episode, we explore the obstacles & value of deploying AI-infused (in-country) weather solutions with a goal to impact 195 countries worldwide. Anna Prouse is an internationally recognized foreign correspondent with expertise in Middle Eastern, African, European, and Indonesian culture, politics, and business.
International Red Cross Delegate and advisor to governments and industry in post-conflict and emerging nations. Knighted with the highest-ranking honor of the Italian Republic: the Order of Merit of the Italian Republic. Given the title of “Honorary Man” by the most senior Iraqi military elite.
Author of four books, athlete, and adventurer. Anna has combined her expertise in collaboration with Atmo who developed weather technology utilizing AI to provide higher confidence in the predictability of weather across the globe.
Brilliant storyteller, this episode takes you deeper into countries and cultures we may never have an opportunity to experience in our lifetime.
https://www.linkedin.com/in/anna-prouse-4106b239/
https://www.linkedin.com/company/atmo-ai/