The Data & AI Chief

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1. Separating AI Hype From Reality with Capgemini’s Danielle Crop

   28 oktober 2026 · 30 min

   Afspelen
   Between the doom headlines and the vendor hype, it’s hard for data and AI leaders to see what enterprise AI can actually do. In this episode, Danielle Crop, Head of Strategic Foresight & Influence at Capgemini and former CDO at Albertsons and American Express, explains why a strong AI strategy starts with treating AI like a tool. She shares how data leaders can earn a seat at the business strategy table, which AI myths are holding organizations back, and what really breaks when agentic AI goes into production. Lees meer

   Key Moments:

   How CDOs Elevate Their Seat at the Table (04:39): A problem-first mindset, curiosity, and courage help data leaders move into business strategy.

   AI Doom vs. Hype: The Case for Pragmatic Optimism (09:47): History is another form of data. Better tools grew the number of data scientists, and AI will likely create new jobs we can’t yet imagine.

   What Leaders Misunderstand About AI (16:28): Because AI feels human, leaders misjudge it and overtrust it. Danielle’s fix: stop seeing it as a colleague and treat it as a library in your pocket.

   Building AI Agents: Why Guardrails Belong in the Platform (20:42): As everyone builds AI agents, security practices lag. Platforms need built-in guardrails, since agents do exactly what they’re asked, at scale.

   The Hugging Face Incident: Did AI Go Rogue? (23:55): Danielle explains what really happened when an AI agent broke into Hugging Face, and what leaders should learn about AI security at scale.

   Key Quotes:

   “For me, I always was about how do I use data, analytics, and machine learning… to solve a particular problem. I was always problem first, technique and data second.” - Danielle Crop

   “We have to remember that it's a statistical compression of the sum total of human knowledge. That's what an LLM is. And it feels magical, but it's just really good science.” - Danielle Crop

   “Saying AI went rogue is giving it a personality that it doesn’t have.” - Danielle Crop

   Mentions:

   Economic Scenarios for Transforma…

2. Why AI for Good Fails and How Leaders Can Fix It

   14 oktober 2026 · 40 min

   Afspelen
   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. Lees meer

   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

   Menti…

3. What Most Companies Get Wrong About AI Strategy with Jeff McMillan, former Morgan Stanley AI Leader

   30 september 2026 · 44 min

   Afspelen
   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. Lees meer

   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 McMillan

   Mentions:

   McMillanAI: Everyone Is Behind

   Collected Writings of Mary Parker Follett

   Guest 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 deploym…

4. AI Literacy Starts in the C-Suite

   16 september 2026 · 37 min

   Afspelen
   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. Lees meer

   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 Miles

   Mentions:

   AI Transformation Requires Redesigning Work, Not Cutting Roles

   East of Eden by John Steinbeck

   Shoe Dog by Phil Knight

   Guest 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 & St…

5. 3 Data and AI Books Every Leader Should Read in 2026

   2 september 2026 · 1 uur 27 min

   Afspelen
   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. Lees meer

   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 Innovation

   Building AI-ready data foundations with Sanjeev Mohan, Principal at SanjMo and author of Designing the AI-Driven Data Foundations

   Designing accountable, human-centered AI with Harveer Singh, Co-Founder and CIO of Rizz Wireless and author of When Data Moves

   Consider 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 in…

6. How Vanguard is Architecting its AI Semantic Layer

   19 augustus 2026 · 48 min

   Afspelen
   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. Lees meer

   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-…

7. How Grant Thornton Reimagines Core Processes in the AI Era

   5 augustus 2026 · 43 min

   Afspelen
   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. Lees meer

   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 d…

8. How Google Is Building Enterprise Trust in Agentic AI

   22 juli 2026 · 39 min

   Afspelen
   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. Lees meer

   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…

9. How Swire Coca-Cola Turned Governance Into an AI Growth Engine

   8 juli 2026 · 25 min

   Afspelen
   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. Lees meer

   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: …

10. How to Transform a SaaS Company with AI from ELMO CTO

    24 juni 2026 · 36 min

    Afspelen
    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. Lees meer

    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 d…

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