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Even well-designed algorithms can fail and end up hurting the people they're meant to help. In this episode, Dr. Ben Green, Assistant Professor of Information and Public Policy at the University of Michigan, explains what it takes to build AI that works for humans. He shares what keeps data from being used for good, why AI regulation can't be left to AI companies, and what organizations risk losing when they hand too much of their thinking to AI.   Key Moments:  The Hidden Cost of Outsourced Thinking (03:40): Students using AI ace their homework but fall behind on exams. Ben explains why powerful AI doesn't always make people better at their jobs.Why Algorithms Belong in the Loop (06:31): Ben argues "human in the loop" sidelines people. Flipping it to "algorithm in the loop" keeps AI in a support role for human decision-makers.Finding the Real Problem at Boston EMS (08:09): Brought in to cut ambulance response times, Ben found the real driver was homelessness and mental health calls, which led to a new specialized team.The Data Scientist's Most Underrated Skill (16:45): Asking questions builds trust with stakeholders and forces the humility needed to understand a problem before solving it.Why AI Companies Can't Police Themselves (24:48): Ben calls voluntary AI safety pledges a PR move and argues for legal liability, pre-deployment testing, and a new direction for AI innovation.  Key Quotes:  “The ways in which data scientists and AI developers tend to judge whether a system is good or bad has a quite tenuous relationship with what the reality of the impacts that the system will create down the road if it's implemented.” - Dr. Ben Green“ We need to not let these companies off the hook by placing agency on the tools. It is the companies that are building these tools and failing to safeguard them.” - Dr. Ben Green“We need to develop an entirely new direction for developing AI systems that actually work for the betterment of humanity.” - Dr. Ben Green  Mentions:  NEW: Algorithmic Realism by Dr. Ben GreenSeeing like a State by James C. ScottThe Public is Getting Fed Up With Data Centers. Politicians Need to Take Notice Guest Bio:  Ben Green is an Assistant Professor of Information and Public Policy at the University of Michigan. He is also a Faculty Associate at the Berkman Klein Center for Internet & Society at Harvard. Ben holds a PhD in Applied Mathematics from Harvard University, with a secondary field in Science, Technology, and Society. Through his research, writing, and advocacy, Ben contributes practical lessons for achieving a world in which AI improves society rather than exacerbating inequality and oppression. He is the author of two books from the MIT Press: just released Algorithmic Realism: Data Science Practices to Promote Social Justice (2026) and The Smart Enough City: Putting Technology in Its Place to Reclaim Our Urban Future (2019).  Ben regularly engages policymakers and the public to promote equitable technology adoption and regulation. He has published op-eds in outlets including The New York Times, The Wall Street Journal, and The Hill, and has been quoted in newspapers such as The New York Times, The Washington Post, and the Los Angeles Times. Hear more from Cindi Howson here. Sponsored by ThoughtSpot.

Every leadership team is arguing about which AI model is best, but that debate is a distraction from the real problem. In this episode, Jeff McMillan, founder of McMillanAI and former head of firmwide AI at Morgan Stanley, breaks down the real bottlenecks behind enterprise AI strategy. He shares why fixing your data matters more than picking a model, why your best AI leader is probably already inside your company, and why AI eliminates tasks rather than entire jobs. Key Moments: Why the Chief AI Officer's Real Job Is to Teach (01:41): Jeff explains why he left Morgan Stanley: build standards and infrastructure, then get out of the way.Your Next AI Leader Already Works for You (11:10): Hiring an outsider usually loses to teaching an A-plus insider who already knows the organization.Why Model Choice Matters Less Than You Think (15:11): Jeff compares the AI model debate to arguing over whose car is better without ever driving it; the models are capable enough already.How to Triage a Broken Data Foundation (17:54): Jeff's framework is to find your most critical data attributes, measure accuracy, then invest where it counts.Why AI Eliminates Tasks, Not Entire Jobs (28:27): The companies that dominated after the Industrial Revolution reorganized work; they did not just have better tech. Jeff argues AI eliminates tasks, not jobs.Key Quotes: “The chief AI officer is not there to build AI for the company; they are to teach the company how to build AI.” - Jeff McMillan“AI does not eliminate jobs; it eliminates tasks.” - Jeff McMillan“ Don't just talk about AI, don't just prompt it. Build something that's real, that has actual measurable outcome, and then share that with the world.” - Jeff McMillanMentions: McMillanAI: Everyone Is BehindCollected Writings of Mary Parker FollettGuest Bio: Jeff McMillan is one of the most experienced AI leaders in financial services. As the former Head of Firmwide AI at Morgan Stanley, he led the development and deployment of AI across the entire enterprise — overseeing hundreds of GenAI use cases from concept to production. Jeff is on faculty at Columbia Business School, where he teaches AI strategy to the next generation of business leaders. He founded McMillanAI to help organizations cut through the noise and build AI strategies that actually work. Hear more from Cindi Howson here. Sponsored by ThoughtSpot.

Most AI playbooks are built for a workforce tethered to a desk, but many workers are not near a keyboard. In this episode, Beth Miles, Chief AI Officer at Advantage Solutions, breaks down how she's building an AI-ready culture across a distributed workforce of more than 60,000 teammates. She shares why AI literacy has to start with leadership, why redesigning a broken process matters more than simply automating it, and how she measures AI success well beyond adoption metrics. Key Moments: From Chief of Staff to Chief AI Officer (05:00): Beth explains how her chief of staff role gave her the cross-functional view needed to build Advantage's AI office.Hands-On Keyboards: AI Training in the C-Suite (11:00): Beth breaks down 10+ hours of executive AI training, including a CEO who sat down for a coding workshop.Inside the AI Champions Build Summit (22:00): Over 40 teammates spent five hours building AI use cases together, breaking down silos across functions.Why You Shouldn't Optimize a Bad Process (26:00): Redesigning broken processes before automating them matters more than speed.Adoption Is Step One, Not the Finish Line (31:00): Usage is only step one. Beth points to service excellence and time to complete as the metrics that matter.Key Quotes: “AI can be an accelerant, but you have to have all the mechanics and the process and the data to actually make it run really well.” - Beth Miles“It may require moving slower now to move faster later, to actually redesign and rethink work so you’re not optimizing a bad process.” - Beth Miles“ To lead a change, we need to understand it.” - Beth MilesMentions: AI Transformation Requires Redesigning Work, Not Cutting RolesEast of Eden by John SteinbeckShoe Dog by Phil KnightGuest Bio: Beth Miles leads Advantage Solutions’ AI strategy, enablement, and governance to drive value for the company’s clients and teammates. She drives AI deployment across the enterprise, alongside the Data Office and Growth & Strategy Office. Beth joined Advantage in early 2024 and has served the last year as Chief of Staff, leading the company’s AI workstreams from diagnostic through scaled execution, driving enterprise transformation initiatives, and overseeing CEO Office operations. Before Advantage, Beth spent more than a decade leading technology and corporate transformation at global software firms, including senior roles at Sage and Cerner (now Oracle Health). At Cerner, she drove the redesign of Cerner’s portfolio management process for their $400m R&D investment. At Sage, she led GTM execution across new revenue streams and managed a large cloud partnership, leading cross-functional teams across governance and delivery. Hear more from Cindi Howson here. Sponsored by ThoughtSpot.

In this special episode of The Data & AI Chief, host Cindi Howson sits down with three incredible authors to explore what it really takes to lead through the AI era, from scaling innovation to building AI-ready data foundations to designing systems that keep humans at the center.  Get ready for a deep dive into: Scaling innovation and leading through uncertainty with Linda Hill, Harvard Business School Professor and author of Genius at Scale: How Great Leaders Drive InnovationBuilding AI-ready data foundations with Sanjeev Mohan, Principal at SanjMo and author of Designing the AI-Driven Data FoundationsDesigning accountable, human-centered AI with Harveer Singh, Co-Founder and CIO of Rizz Wireless and author of When Data MovesConsider this your fall reading list for leading through the next phase of AI. Check out our 2026 must-read list for data and AI leaders! Key Moments: Why Scaling Innovation Requires a Culture Shift (01:03): Linda Hill, author of Genius at Scale, unpacks her ABC model of architects, bridgers, and catalysts in scaling AI innovation.The Timeless Fundamentals Behind AI-Ready Data (32:40): Sanjeev Mohan, author of Designing the AI-Driven Data Foundations, explains why data fundamentals haven't changed even as the tools have.Why Every Data Pipeline Decision Has a Human Consequence (58:38): Harveer Singh, author of When Data Moves, shares personal stories showing the human stakes behind every data decision.Key Quotes: “Innovation is almost always the result of collaboration, experimentation, learning of people who are different, who have different perspectives, different expertise.” - Linda Hill“I strongly believe that to achieve the promises of AI, we should really focus on the foundational pieces, because those things never go away.” - Sanjeev Mohan“The more and more we are embedding these AI agents into our technology, we need to make sure that there is an accountability factor, there is a responsibility factor that is put into it before these decisions are made.” - Harveer SinghMentions: Genius at Scale: How Great Leaders Drive InnovationDesigning the AI-Driven Data FoundationsWhen Data MovesThe AI Fairness Test Nobody RunsGuest Bios: Linda Hill Linda A. Hill is the Wallace Brett Donham Professor of Business Administration at Harvard Business School and Faculty Chair of the Leadership Initiative. She is widely recognized as one of the world’s foremost experts on leadership and innovation. Hill is the coauthor of Genius at Scale: How Great Leaders Drive Innovation (March 2026), which introduces three essential roles for leading innovation across organizations and ecosystems: architect, bridger, and catalyst. The book was shortlisted for the 2025 Thinkers50 Innovation Award. She is also the coauthor of the award-winning books Collective Genius and Being the Boss. Her TED talk on leading collective creativity has garnered more than three million views. Sanjeev Mohan Sanjeev Mohan is a recognized thought leader in cloud technologies, modern data architectures, analytics, and artificial intelligence. With a keen focus on emerging trends and technologies, Sanjeev hosts It Depends podcast and authors regular Medium blogs. He is also the author of Data Product for Dummies. Formerly a Vice President at Gartner, Sanjeev was renowned for his in-depth research and strategic insights, shaping the research agenda for data and analytics globally. Over the past three years, he has led SanjMo, a consultancy specializing in technical advisory services that elevate category and brand awareness for clients. Sanjeev is the author of Designing the AI-Driven Data Foundations, a practical guide to building modern data foundations for the AI era. Harveer Singh Harveer Singh is a globally recognized data, AI, and fintech leader who bridges enterprise-scale leadership with entrepreneurial execution. Across 25+ years, he has transformed financial institutions spanning banking, payments, telecom, and Web3. A recognized industry leader, Harveer's work has earned DataIQ Top 10 CDOs, American Banker  Innovation of the Year, and American Asian Outstanding 50 honors. In 2025, Harveer co-founded Rizz Wireless, an AI-native wireless company, where he serves as Co-Founder, CIO, and Board Member. He is also co-creator of RZTO, the company's Solana-based rewards token, which reimagines how loyalty and telecom rewards work and is listed on Gate.io. Harveer is the author of When Data Moves, which challenges the tech industry to remember that data systems serve humans, not machines. Hear more from Cindi Howson here. Sponsored by ThoughtSpot.

Semantic layers and ontologies have moved from nice-to-have data modeling tools to the foundational engine required for enterprise AI. In this episode, Raman Tallamraju, Senior Director and Head of Enterprise Data Architecture and Engineering at Vanguard, breaks down how Vanguard is architecting its AI semantic layer to turn scattered institutional knowledge into reliable, agent-ready context. He shares why autonomous agents expose decades of hidden data debt, how to bridge domain-specific definitions like clients versus prospects, and how to balance building a unified semantic layer with a pragmatic, federated data operating model. Key Moments: Why Data Is Your Differentiator (02:36): Across four asset managers, Raman shares the one lesson that holds: data is the real differentiator in an AI-first world. Why Semantic Layers and Ontologies Are a Priority (14:12): Autonomous agents remove the human workaround, exposing years of technical debt in data modeling that tribal knowledge used to hide. Why Context Makes or Breaks Your AI Agents (19:18): Context is everything. Agents act confidently on wrong answers when terms like client, prospect, and lead go undefined. Launching an AI-Ready Data Program: Where to Start (24:54): Raman advises starting with a real business use case tied to points of economic leverage, rather than trying to boil the ocean. Why You Need a Federated Data Model (36:51): Raman explains why a single central platform isn't practical at a global firm, and the four levers he uses to earn real business ownership of data. Key Quotes: “If we're going to go in an AI-first world and everybody has access to the same frontier models… well, what is going to be differentiated about you? Data will be your differentiator, and the companies that bring the best data game are going to have enduring advantages over those that don't.” - Raman Tallamraju “You want your data to be well-defined. You want your data to be trusted. You want your data to be well-connected. You want your data to be contextualized. You want your data to be consumed in a multimodal way, and you want it to be ready for humans and machines at scale.” - Raman Tallamraju “If you're going to start with tech and you're going to go towards the shiny toys, those are good, but without the data foundations, they're going to sit in the garage. So I started calling it the Ferraris in the garage problem. Unless you get the data strategy running ahead, these Ferraris are going to run out of gas.” - Raman Tallamraju Mentions: The Innovator's Dilemma by Clay Christensen 57% of enterprises traced a wrong AI answer to missing business context — Credible bets portable, open-source semantic code beats proprietary metadata Guest Bio: Raman Tallamraju has twenty years leading enterprise technology and data strategy across four of the world’s largest asset managers — Vanguard, T. Rowe Price, Capital Group, and Fidelity. Appointed Officer and Group Vice President at T. Rowe Price, where Raman built and led a 300-person global technology organization responsible for the firm’s enterprise architecture and digital transformation. Wharton CTO Program, 2024. Raman has operated across the full investment value chain — research and trading platforms, client experience, distribution technology, and enterprise data infrastructure — with portfolio accountability exceeding $100M. Raman’s career has been defined by taking on complex, high-stakes technology transformations and delivering measurable business outcomes: modernizing legacy estates, building scalable platforms, and creating the organizational structures that sustain them. Hear more from Cindi Howson here. Sponsored by ThoughtSpot.

Imagine redesigning your most important processes from a blank sheet of paper, with no legacy systems to hold you back. Alpa Lally, Chief Product Officer at Grant Thornton, shares how the company is transforming into a frontier firm by reimagining core processes instead of automating them. She unpacks how embedded analytics reshapes client conversations, why AI governance is what helps you move faster, and how to reinvent your career in the age of AI. Key Moments: From Structural Engineering to Product Leadership (01:36): Alpa explains how designing buildings taught her the vision, strategy, and foundation-up thinking she now applies to product. Becoming a Frontier Firm (06:27): Rethinking how work gets done for 13,000-plus professionals means recognizing that automating a broken process just does the wrong thing faster. Embedding ThoughtSpot Analytics (14:57): By putting insights in auditors' hands on day one, Grant Thornton shifts client conversations from data gathering to strategy. The AI Proof Gap: Governance Enables Speed (19:32): Alpa reframes governance as what lets you move faster, sharing that 78% of firms doubt they'd pass an AI governance audit. Is Product Management Dead? (24:33): The role is evolving, not dying, and AI raises the value of deciding which opportunities are actually worth pursuing. Key Quotes: “A frontier firm uses AI not just to improve how work gets done, but fundamentally rethinks how the work gets done. It's moving beyond productivity gains and creating entirely new ways to deliver value.” - Alpa Lally “The challenge is if you simply automate a broken process, you end up doing the wrong thing faster.” - Alpa Lally “What ThoughtSpot has done is [give us] our ability to embed them immediately into the flow [which] has opened up a whole level of different insights that we can offer, not to our practitioners only, but imagine an auditor showing up on day one to your client and having these insights already calculated, already delivered.” - Alpa Lally Mentions Grant Thornton’s 2026 AI Impact Survey Report Wall Street is debating the AI buildout. Enterprises just answered: More than 80% say their GPUs run at half capacity or less Hers for the Taking by Tracey Newell Guest Bio Alpa Lally is the Chief Product Officer at Grant Thornton. Alpa is an experienced product management executive with several years in designing, developing, and launching consumer and business products for large to mid-sized to small organizations, fintechs, startups, and Fortune 500. She has led global cross-functional teams to drive high growth targets while staying laser-focused on solving customer and consumer problems. Alpa leverages data and analytics to drive successful business outcomes that deliver results and achieve corporate growth goals. Hear more from Cindi Howson here. Sponsored by ThoughtSpot.

For most enterprises, autonomous AI agents still feel like a risk waiting to happen. Andi Gutmans, Vice President and General Manager for Data Cloud at Google, joins Cindi Howson to explain what it takes to build the data foundation that trustworthy agentic AI depends on. He breaks down how organizations can finally activate the 90 percent of enterprise data that's unstructured, why tokenmaxxing is the wrong way to measure AI value, and how open standards like Apache Iceberg are helping leaders tear down fragmented, multi-cloud data silos and unify data across clouds. Key Moments: Perception vs. Reality in Trusting Autonomous AI (03:55): Waymo is safer than a human driver by the numbers, but most people still choose Uber. Andi explains why closing that trust gap takes time. Activating the 90% of Unstructured Data (09:40): Most enterprise data sits in images, PDFs, and contracts, never touched by traditional analytics. Andi explains how AI finally makes that dark data usable. Crawl, Walk, Run: Scaling Autonomous Agents in Production (16:37): Virgin Voyages cut weather disruption rebooking from six hours to eleven minutes with autonomous agents. Andi explains the thresholds that made scaling safe. Token Efficiency Over Tokenmaxxing (19:54): Andi pushes back on tokenmaxxing as a measure of progress. Real success means the best outcome with the fewest tokens and the cheapest model for the job. Breaking Down Data Silos with a Borderless, Open Lakehouse (27:01): Most enterprises run on a messy mix of multiple clouds. Andi explains how open standards like Apache Iceberg tear down those walls. Key Quotes: “Over ninety percent of enterprise data is actually unstructured data... AI is really good at reasoning around this unstructured data, and so we can make a hundred percent of the data state light up.” - Andi Gutmans “If you take a Waymo, you're actually eighty percent less likely to get into a hurtful accident than if you take a service with a human driver. Still, most people prefer to take the Uber versus the Waymo…  It's going to take time to earn the trust to understand what quality outcomes we can drive.” - Andi Gutmans “ The way we're thinking about the data platform is the best context is the context that drives the outcomes you need with the least amount of tokens and processing.” - Andi Gutmans Mentions Blue Ocean Strategy by W. Chan Kim and Renée Mauborgne Virgin Voyages case study (Google Cloud) Vodafone, Google Cloud and TM Forum Unveil Framework for Self-Optimising Autonomous Networks Guest Bio Andi Gutmans is the General Manager and Vice President for Data Cloud at Google, which includes operational databases, analytical data platforms, open lakehouse technologies, and  Business Intelligence. His core focus involves building, managing, and scaling highly innovative data services to deliver a leading AI-native data platform for businesses. Before joining Google Cloud, Gutmans was VP Analytics at AWS, where he managed services such as Amazon Redshift, AWS Glue, and Amazon EMR. Prior to that, he co-founded and served as CEO of Zend Technologies, a company built around the open-source PHP language, of which he was a co-creator. Andi Gutmans is a highly experienced leader with over 20 years dedicated to open source contributions. He is particularly renowned as a co-creator of the open-source PHP language, which has become one of the most widely used languages for web development. He is an emeritus member of the Apache Software Foundation and served on the Eclipse Foundation’s board of directors. Gutmans holds a bachelor's degree in Computer Science from the Technion, Israel Institute of Technology. Hear more from Cindi Howson here. Sponsored by ThoughtSpot.

For most data leaders, governance feels like the thing standing between them and progress. Bharathi Rajan, Vice President of Digital, AI, and Data at Swire Coca-Cola, joins Cindi Howson to share how she built the data foundation powering a $3 billion supply chain operation. She breaks down how to turn data quality into a business accelerator, get ahead of demand signals, and build the foundation every AI initiative actually depends on. Key Moments: Building a Data Foundation Across a $3B Supply Chain (06:39): The starting point wasn't strategy. It was figuring out where the data was, who had it, and how to get it into the hands of actual decision-makers. Reframing Data Governance (10:22): Governance is the foundation that makes better decisions possible. Bharathi shares how education and relationship-building drove the mindset shift at Swire Coca-Cola. AI as an Enablement Factor (11:50): Bharathi reframes AI as a tool for operational efficiency and more impactful work, not a headcount threat. Building the Plant of the Future (14:41): A new $475 million Colorado facility gives Swire Coca-Cola a rare chance to design data and AI infrastructure from scratch. The Skills That Will Matter Most in an AI World (17:58): Bharathi breaks down what she tells young people and aspiring data leaders about building a career that AI can't replace. Key Quotes: “Governance is not red tape. It is important. If you want to make decisions with the right data, you need to have governance. You have to have that quality data flowing in.” - Bharathi Rajan “If you're passionate about something and then you know how to use technology to enable that, that makes the difference.” - Bharathi Rajan “AI, it's an enablement factor for us internally as to how can I help the enterprise really grow, create operational efficiencies, but also have people do more impactful work.” - Bharathi Rajan Mentions Swire Coca-Cola to Build $475 Million Bottling Plant in Colorado Springs, CO DataIQ100:  The most influential people in data and AI 2025 AI Women Power List Honorees The Let Them Theory by Mel Robbins Guest Bio Bharathi Rajan is a results-driven and experienced Chief Data Officer and AI strategist.  In her current role as Vice President Digital, AI & Data at Swire Coca-Cola, USA, Bharathi has saved multi-millions by adopting new tech stack and bringing in capabilities critical for Enterprise growth and performance. While consistently leading innovation and enabling AI & data literacy, Bharathi consecutively drives data and systems architecture confluence across the enterprise. Prior to joining Swire Coca-Cola, USA, Bharathi operated as a Senior Director of Operations in Data and Infrastructre for three years, focusing on Enterprise Reporting and Analysis (ERA), where she was specifically hired for her distinctive capabilities in data strategy, cloud migration, and creative efficiencies.  Bharathi is currently ranked #3 DataIQ100 North America for 2026. She has attended several panel discussions, in Emory University, Women in Tech, AI & Manufacturing and DataIQ. In 2026, Bharathi delivered a keynote on Data Leader as the Transformation Architect at the DataIQ summit. Additionally, Bharathi has spoken at various summits like Microsoft Ignite, Databricks, AI&Data summit and Snowflake summit over the years. Bharathi is also a mentor with WLDA Ventures and Women Tech Council. Hear more from Cindi Howson here. Sponsored by ThoughtSpot.

What happens when a software company building AI tools for HR teams uses those same tools to transform itself? Josh McKenzie, Chief Technology Officer at ELMO Software Group, shares how his team rebuilt their entire software development lifecycle around AI agents and redrew the boundaries of every engineering role. He breaks down how to lead that shift without losing people's trust, why domain expertise is the real SaaS moat, and how the right analytics partner unlocks decisions HR teams have never been able to make before. Key Moments: The SaaS Moat: What AI Can't Erode (06:37): Josh argues SaaS value runs deeper than software. Accountability, compliance, and domain expertise keep purpose-built platforms irreplaceable. How ELMO's AI Journey Started (10:23): ELMO started by mapping every role against AI impact. Turning that lens on their own engineering team set the full transformation in motion. Why ELMO Chose ThoughtSpot Over Building Its Own Analytics (18:42): A homegrown tool requiring too much user expertise led ELMO to look elsewhere. ThoughtSpot Spotter and natural language capabilities closed the gap. Why HR Teams Are the Most Underserved (20:21): Payroll here, benchmarking data there, performance data somewhere else. HR teams have been drowning in spreadsheet hell for years. Josh explains how AI finally closes that gap. From Engineer to CTO: Build a Team of Complements (24:17): Josh reflects on the mindset shift that defined his path to the C-suite. Great leadership means building a team whose strengths cover your blind spots. Key Quotes: “ ThoughtSpot was particularly interesting for us… The big thing for us was the Spotter product. Allowing users to bridge that data analyst gap was really important. So, that product has yielded really, really great results for us.” - Josh McKenzie “I think it's really important that we instill a culture where it's okay to fail, and it's okay to make a mistake. You want to be vocal about your mistakes so others don't repeat the same mistake.” - Josh McKenzie “My belief is you want to focus on your secret sauce. So, what is the thing that makes your business super successful? And for us, that's where we came to look at ThoughtSpot. It has a really nice visual user interface and allows you to create some great dashboards.” - Josh McKenzie Mentions Hiring and Onboarding Taking Longer Despite Widespread AI Adoption, New Australian Research Finds The 5 Levels of AI Coding (Why Most of You Won't Make It Past Level 2) WireGuard: Next Generation Kernel Network Tunnel | Jason A. Donenfeld Guest Bio As the Chief Technology Officer, Josh McKenzie is responsible for both technical strategy and delivery (build, release and operation) of the ELMO product suite. Josh has a proven track record of successfully leading technology teams and implementing transformative strategies that enhance efficiency, drive growth, and elevate overall technological capabilities. Josh has 20 years of experience in technology, primarily in FinTech. Before joining ELMO in 2024, Josh held executive and senior positions at Lendi Group, OFX, ASX and Westpac. Josh holds a Bachelor of Computer Science from the University of Newcastle and an MBA from the University of Sydney. Hear more from Cindi Howson here. Sponsored by ThoughtSpot.

Explore how a global supply chain company turned its data platform into a customer-facing product designed to operate at the speed of disruption. Boris Rabkin, Chief Information Officer at Ligentia, shares how the company executed that shift through a deliberate phased approach and a partnership with ThoughtSpot. He breaks down how to build a data foundation that scales, what it takes to embed analytics where decisions happen, and how to structure AI ownership and governance across a global regulatory environment. Key Moments: From Reactive to Proactive with Agentic AI (04:46): Supply chain disruption response has changed from slow email chains and fragmented data to agentic systems that flag issues and test decisions in real time. Boris illustrates how Ligentia navigated that shift firsthand. Embedding Analytics Into the Customer Platform (09:00): Boris explains why bolting analytics onto a separate tool creates friction and why embedding intelligence directly into the existing customer platform is the better call. How to Phase a Data Transformation That Sticks (12:12): Boris outlines three phases: stabilize the foundation, standardize definitions, then build a usable experience. Skipping the plumbing is where most transformations fail. Where AI Ownership Really Belongs in the Enterprise (14:03): Understand why AI ownership should sit where value is created. Learn how centralized governance ensures data accuracy and security across the organization. What the Asyad Acquisition Unlocks for Ligentia (22:49): Boris shares how the new investment opens doors to scale the platform globally, automate logistics workflows, and monetize data beyond services. Key Quotes: “ We wanted to control the brand experience, the same login for our customers. Removing the friction and having the experience of being in one trusted platform for making those decisions… This is where [ThoughtSpot] came in.”  - Boris Rabkin “I think AI should be owned where value is created. It shouldn't be a centralized  function inside a lab. If it's not close to the product and the people that are using it, AI won't create the value.” - Boris Rabkin “Speed is one thing, but confidence in the data is something that really drives decisions.” - Boris Rabkin Mentions The EU AI Act’s ‘Wait and See’ Window Is Closing Asyad Group and Ligentia Join Forces to Accelerate Global Growth and Enhance Technology-Driven Supply Chain Solutions ThoughtSpot Supply Chain Solutions & Case Studies The Acquired Podcast: Formula 1 | From Bankrupt Teams to a Global Sports Empire  The Acquired Podcast: Costco | How a Wholesale Club Built a Customer Fanaticism  Guest Bio Boris Rabkin is the Chief Information Officer at Ligentia. As a Chief Information Officer and Board Member, he brings a distinctive blend of strategic vision and execution capabilities to drive business growth and operational excellence through digital transformation. With extensive experience leading global teams and technology initiatives, Boris is driven by a passion for leveraging data, AI, and automation to build scalable, secure, and resilient enterprises that deliver lasting value. Hear more from Cindi Howson here. Sponsored by ThoughtSpot.

Understand how to close the gap between AI experimentation and enterprise production. Shub Agarwal, Founder of the AI Trust Lab at USC and author of Successful AI Product Creation: A Nine-Step Framework, shares his AI product management framework for taking enterprise AI strategy from demo to production, drawing on two decades of product leadership at Amazon and Fortune 50 firms. He breaks down why experimentation must tie directly to business OKRs, the four mindset shifts leaders need to scale AI responsibly, and how the AI Trust Lab is building a benchmark evaluation framework for AI model trust and governance. Key Moments: Why 80% of AI Projects Never Reach Production (02:13): Shub traces the root cause of stalled AI programs to a missing system for moving from demo to deployment. Most teams have no repeatable path to production. Shub's Nine-Step Framework for Building AI Products (06:00): Most AI projects start with a cool model instead of a painful problem. Shub walks through the three phases of his framework: discovery, execution, and excellence. The Case Against "Fix Your Data First" (12:41): Conventional wisdom says clean your data before building AI. Shub challenges that, arguing modern LLMs offer far more flexibility with imperfect data. Four Mindset Shifts for Scaling Enterprise AI (16:35): Shub outlines the four shifts separating organizations that scale AI from those that stall, from measuring AI performance differently to embedding trust from day one. Inside Shub's AI Trust Lab at USC (23:54): Major foundation models are already being benchmarked on trust and safety. Shub explains the lab's mission to build a standardized evaluation framework for AI model governance. Why Enterprise AI Governance Needs Multiple Disciplines (28:36): AI models can be sycophantic, manipulative, or lack candor. Shub argues that building trustworthy AI demands an interdisciplinary approach. Key Quotes: “I think the fundamental problem that organizations are facing today… is not that they have a lack of experimentation in the demo aspect. The challenge is they don't know how to take those demos to production, and that is where I saw the gap.” - Shub Agarwal “I do think data is the fuel for AI… But I think today organizations are crippled by this ‘fix your data, and then we'll build AI’, and they never build AI. They never build use cases that are adding value.” - Shub Agarwal “There's no FICO scores for models, so I decided to create one. I built this lab… bringing the computer scientists, the researchers, the applied AI researchers, the policy, and the communication people together to think of what is trust, define it, and ultimately measure and evaluate it.” - Shub Agarwal Mentions USC AI Trust Hub Successful AI Product Creation: A Nine-Step Framework by Shub Agarwal Four Steps to Epiphany: Successful Strategies for Products That Win by Steve Blank Masters of Scale podcast with Reid Hoffman Guest Bio Shub Agarwal is an associate professor of professional practice at the University of Southern California, an industry executive, and an advisor to start-ups and academic institutions. He holds an MBA from the University of California, Los Angeles (UCLA), and an MS from Carnegie Mellon University (CMU). He is the author of two books: Solve Catch-22 of Product Management and Successful AI Product Creation: A 9-Step Framework. He has made significant contributions to the fields of artificial intelligence and machine learning, holding several U.S. and global patents for his work, and is also a published author of several technical research papers. With around two decades of extensive experience in product management and leadership, his journey has been marked by a relentless pursuit of leveraging AI technologies to create impactful products that redefine industry standards. His industry experience includes leadership roles at Amazon, Silicon Valley start-ups, and other Fortune 50 firms. Hear more from Cindi Howson here. Sponsored by ThoughtSpot.

Learn what happens when the executive accountable for data strategy is also the executive accountable for the business results that depend on it. Saugata Saha, President of S&P Global Market Intelligence and Chief Enterprise Data Officer at S&P Global, shares how he manages one of the world's largest financial data estates while driving business outcomes across public and private markets. He breaks down the four pillars of S&P Global's data strategy, the federated organizational model that connects data teams to business value, and why capturing ROI from AI requires deliberate workflow transformation. Key Moments Why Data Strategy Must Follow Business Strategy (04:57): Saugata challenges the idea that data and business strategy can run in parallel. Market trends, customer pain points, and existing capabilities must come first. Building an AI-Ready Financial Data Estate (15:10): Scale alone does not create intelligence. Saugata explains why semantic layers and graph databases are the hard work behind connected financial data. How AI Compresses Post-Acquisition Data Integration (18:29): Manual reconciliation of millions of records is no longer the only path. Discover how AI entity matching accelerated post-acquisition integration. The Federated Model That Connects Data to Value (22:49): Most large organizations either over-centralize data teams or leave them too embedded to scale. Saugata outlines the federated model that actually bridges both. Rethinking AI Productivity: From Marginal to Transformative (28:29): Most AI programs stop at training and tooling. Saugata explains why deliberately redesigning workflows is the missing step between AI investment and real ROI. Key Quotes “Data strategy and business strategy have to be very tightly connected. And if they're not, that's when value capture does not happen. In fact, I would go so far as to say data strategy actually follows from business strategy.” - Saugata Saha “Stop treating data as an afterthought or byproduct, but start thinking about data as a key ingredient for value creation and competitive advantage.” - Saugata Saha “We don't want everybody to become 10% more productive, because that's a little squishy. We want 10% of the people to become a hundred percent more productive so they can do other things.” - Saugata Saha “If a company can really use data at scale for better decision making, better client service, [and] better outcomes, that creates a lasting edge over the competition.” - Saugata Saha Mentions S&P Global Agrees to Acquire With Intelligence from Motive Partners for $1.8 Billion, Establishing Its Leadership in Private Markets Intelligence The Data & AI Chief: Why a Federated Data Team is Crucial for Business Value, with Dow Private Companies Wait Too Long to Go Public The Lex Fridman Podcast Guest Bios  Saugata serves as President of S&P Global Market Intelligence, leading the division's efforts to deliver essential insights and intelligence to clients worldwide. He is also S&P Global’s Chief Enterprise Data Officer, responsible for driving innovation and excellence in the company’s enterprise data strategy. Saugata is a member of S&P Global’s Executive Leadership Team, contributing to the strategic direction and growth of the organization. Before joining S&P Global, Saugata was a consultant at McKinsey & Company’s New York office, where he advised clients on strategy, mergers and acquisitions, corporate finance, and operational improvements across various industries, with a strong focus on financial services. Hear more from Cindi Howson here. Sponsored by ThoughtSpot.

Learn why an organization’s ontology, a structured framework for how a business defines, connects, and makes sense of its data and knowledge, is the most valuable and most overlooked asset in any AI strategy. Jessica Talisman, CEO and Founder of The Ontology Pipeline, and Tony Seale, Founder of The Knowledge Graph Guys, break down what it actually takes to build trusted AI, covering everything from semantic layers and knowledge graphs to why provenance is non-negotiable. They explain how organizations can start building their knowledge infrastructure for AI, and make the case for why their ontology is their most defensible competitive asset. Key Moments BI Semantic Layers vs. AI Context Layers (02:21): Explore the evolution from 1990s business vocabularies to modern AI context layers. Learn why ontologies are essential for connecting data points beyond traditional BI. Why Knowledge Graphs are Essential for AI (09:22): Understand why relational databases fail AI's needs. Tony explains how knowledge graphs turn data relationships into "first-class citizens" using open standards. How to Build Your First Business Ontology (17:04): Stop over-modeling and start delivering. Learn how to anchor your data strategy to high-value use cases and business-language competency questions. Solving the AI Provenance & Lineage Gap (34:19): Why LLMs lack built-in reliability. Jessica discusses the necessity of injecting data lineage at the retrieval layer to verify AI accuracy and prevent hallucinations. Why Your Ontology is Your Most Valuable IP (39:27): In the age of commodity AI, your internal data relationships are your only moat. Discover why hosting your ontology with third parties puts your core assets at risk. Key Quotes “If you let somebody else take your ontology, learn the essence of what it is that you know that's out of distribution with the rest of the world, you've just given them everything valuable about your company.” - Tony Seale “The accuracy of the information you receive is reliant upon the lineage or the provenance of the information received from an LLM. It's so important." - Jessica Talisman “As a business leader, you need to be looking below the surface to the data infrastructure. The key trick to do right now is to turn the power of the models that we've got back upon your own internal infrastructure to build out these rich ontologies and to connect your information.” - Tony Seale "Your ontology is like your thumbprint, your digital thumbprint for your organization. It's unique to each organization, and how you define things may not be the same as an LLM might define something." - Jessica Talisman Mentions The Ontology Pipeline® - A Semantic Knowledge Management Framework | Jessica Talisman How the Ontology Pipeline Powers Semantic Knowledge Systems | Jessica Talisman Why Early Knowledge Graph Adopters Will Win the AI Race | The Knowledge Graph Guys Spec-First Development: Why LLMs Thrive on Structure, Not Vibes | The Knowledge Graph Guys The Knowledge Graph Academy Guest Bios  Tony Seale For over a decade, Tony has been passionate about linking data. His creative vision for integrating Large Language Models and Knowledge Graphs within large organisations has gained widespread attention, particularly through his popular weekly LinkedIn posts, earning him the reputation of ‘The Knowledge Graph Guy.’ Today, as the founder of The Knowledge Graph Guys, Tony is dedicated to helping organisations harness the power of their data. His consultancy develops cutting-edge Knowledge Graphs that fuel innovation and growth in the rapidly evolving Age of AI. Jessica Talisman Jessica Talisman has dedicated her 25-year career to exploring the dynamics of information and knowledge—how it flows across systems, evolves through context, and powers intelligent technologies. Her work spans historical research, educational frameworks, and enterprise-scale applications of artificial intelligence. Previously a Senior Information Architect at Adobe, Jessica led the development of semantic knowledge graphs to enrich content and contextual understanding. She now serves as CEO and Founder of Ontology Pipeline, where she leads efforts to bridge the worlds of library science and data management - building robust, scalable knowledge systems for the AI era. Hear more from Cindi Howson here. Sponsored by ThoughtSpot.

Discover how WHOOP is building an AI-powered health data infrastructure that is redefining how we understand human health. Emily Capodilupo, Senior Vice President of Research, Algorithms, and Data at WHOOP, explains how continuous physiological data is uncovering new opportunities in predictive health through AI, from presymptomatic disease detection to biological age scoring. She examines the governance challenges of deploying AI in a regulated environment and what it takes to build the data trust required to make it work at scale. Key Moments: How WHOOP Built Its AI and Data Foundation (00:57): Emily explains how WHOOP's early focus on elite athlete performance shaped the data collection rigor and multidisciplinary science organization that now powers its predictive health capabilities. She outlines the model she built across AI, machine learning, clinical research, and digital signal processing, and why starting with the highest-demand use case created a data foundation built to scale. The Power of Continuous Data (06:21): Emily draws on WHOOP's sleep research to show how continuous physiological data reveals patterns that would be invisible without longitudinal tracking. She shares findings linking sleep architecture to metabolic disease, cancer risk, and cognitive decline, illustrating why the depth and continuity of a data set determine what insights are actually possible. The Data Governance Challenge of Acting on Sensitive Data (13:17): Emily shares how WHOOP's respiratory rate data could detect COVID infection up to three days before symptom onset in over 80% of cases, but a denied FDA application left the company holding actionable insights it was legally prohibited from sharing. She examines the governance tension that emerges when your data capabilities move faster than the regulatory frameworks designed to govern them. Turning Complex Multi-Signal Data Into a Single Actionable Metric (27:32): Emily introduces WHOOP's Healthspan feature, which translates physiological and behavioral data across nine components into a single biological age score tied to all-cause mortality risk. She explains why distilling complex data into one number is more motivating than presenting raw risk statistics, pointing to research that shows how age-based framing drives stronger behavior change. Building Data Trust and Privacy Infrastructure at Scale (31:40): As WHOOP moves into FDA-cleared products and more sensitive data collection, Emily outlines the governance principles that underpin member trust. She argues that for any organization building on sensitive personal data, the asymmetry between earning trust and losing it should be a foundational design constraint. Key Quotes: "It takes 13 years to earn the trust and one mistake to lose it. And that kind of asymmetry is constantly top of mind." - Emily Capodilupo "We were able to show that we could detect COVID up to three days before symptom onset in over 80% of cases." - Emily Capodilupo “ WHOOP has been collecting data [for] over 12 years. We're working on a lot of new types of algorithms that are able to help people understand their bodies in ways that we might not have appreciated…even just a couple years ago.” - Emily Capodilupo "One of the ways that AI has advanced the product... is this ability to chat with WHOOP in natural language, the way you might chat to a doctor or a trainer or a coach." - Emily Capodilupo Mentions Harvard Study | Analyzing changes in respiratory rate to predict the risk of COVID-19 infection  Cornell Study Uses WHOOP Sleep Data to Monitor Patients at Risk for Alzheimer’s Can Data Help Us Sleep Better? | WHOOP There's More to Sleep than Sleep Need: The Importance of Sleep Consistency | WHOOP Cribsheet & Expecting Better 2 Books Collection Set By Emily Oster  The Family Firm: A Data-Driven Guide to Better Decision Making in the Early School Years By Emily Oster  Guest Bio  Emily Capodilupo is an award-winning AI and research leader with more than 13 years of experience building and scaling science-driven organizations in fast-paced startup environments. She began her career as an emergency medical technician before studying neurobiology and human sleep at Harvard University and conducting research at Brigham and Women’s Hospital. Emily is driven by a passion for using data to solve hard problems and advance our understanding of human physiology. Along the way, she "accidentally" became a data scientist, recognizing that the biggest breakthroughs in health require not just rigorous science, but big data and bold technology.  As WHOOP’s first employee, Emily founded and now leads the company’s science organization, pioneering a new model of health that begins long before diagnosable illness and is continuous, personalized, AI-powered, and designed to empower individuals to take the driver’s seat in their own well-being. She has built and scaled multidisciplinary teams across artificial intelligence, machine learning, digital signal processing, clinical research, and engineering to translate real-time physiological data into actionable insights that improve performance, resilience, and long-term health. Emily’s work sits at the intersection of wearable technology, digital biomarkers, and predictive health, helping shift healthcare from reactive treatment to proactive optimization. Hear more from Cindi Howson here. Sponsored by ThoughtSpot.

Learn why scaling AI is as much a human challenge as it is a technological one. Stefano Puntoni, Co-Director of Wharton Human-AI Research and Professor at The Wharton School, examines the limits of data-driven decision making in the age of AI and why insights so often fail to translate into action. He breaks down the psychology behind AI resistance and outlines the leadership and change management strategies needed to turn AI potential into real organizational impact. Key Moments: Why More Data Doesn’t Lead to Better Decisions (02:26): Stefano challenges the assumption that smarter algorithms automatically produce smarter decisions. He argues that decision quality depends on rigorous conceptual thinking before turning to data. Without clearly defining objectives, alternatives, and success criteria, analytics efforts rarely translate into meaningful action. Conversational AI and the Lowering of the Cost of Action (07:26): Stefano explains how conversational AI brings decision makers closer to data by reducing friction. By lowering the cost of experimentation, AI enables managers to test hypotheses in real time instead of waiting days for analysis. This shift moves organizations from analysis paralysis to faster, more confident action. Rethinking Your Role in the Age of AI (17:16): For professionals navigating disruption, Stefano outlines two paths forward. One is becoming a complement to AI by upskilling and using the technology as a productivity multiplier. The other is pivoting toward skills AI is less likely to replace, such as strategy, orchestration, and human judgment. The AWARE Framework: Pairing Technical Rollout with Human Rollout (22:41): Stefano introduces the AWARE framework to help leaders anticipate and manage the human reactions to AI transformation. He argues that every technical implementation must be matched with structured communication, identity support, and organizational alignment. Without this dual-track approach, even well-designed AI systems can fail to gain traction. Change Management, AI Literacy, and the Gap in Organizational Readiness (31:11): Only a small percentage of organizations have formal AI change management programs. Stefano questions whether companies are truly prepared for large-scale AI transformation. He emphasizes that AI literacy, leadership accountability, and structured change management will determine whether AI investments translate into sustained performance. Key Quotes: “ The leaders need to know why we are doing AI. AI is not a strategy; AI is just a tool. So what is it that we're trying to achieve?” - Stefano Puntoni “ I think the problem is that technology is almost like taking all the oxygen from the room. There's so much attention and urgency around the tech itself that we often forget the people around it.” - Stefano Puntoni “You don't want to be the substitute to the technology because if that is what you do, then there's no future. But if you're a complement, the technology might be a multiplier of your productivity.” - Stefano Puntoni Mentions Decision-Driven Analytics: Leveraging Human Intelligence to Unlock the Power of Data The Wall Street Journal: The Boss Has a Message: Use AI or You’re Fired 2025 Report Accountable Acceleration: Gen AI Fast-Tracks Into the Enterprise How AI Affects Our Sense of Self Why Gen AI Feels So Threatening to Workers Conversational AI: The Next Frontier of Digital Platform Monetization Guest Bio  Stefano Puntoni is the Sebastian S. Kresge Professor of Marketing at The Wharton School. Prior to joining Penn, Stefano was a professor of marketing and head of department at the Rotterdam School of Management, Erasmus University, in the Netherlands. He holds a PhD in marketing from London Business School and a degree in Statistics and Economics from the University of Padova, in his native Italy. His research has appeared in several leading journals, including Journal of Consumer Research, Journal of Marketing Research, Journal of Marketing, Nature Human Behavior, and Management Science. He also writes regularly for managerial outlets such as Harvard Business Review and MIT Sloan Management Review. Most of his ongoing research investigates how new technology is changing consumption and society, including how humans are adopting and evolving with AI. Hear more from Cindi Howson here. Sponsored by ThoughtSpot.