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149 afleveringen, van nieuw naar oud
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Separating AI Hype From Reality with Capgemini’s Danielle Crop
28 oktober 2026 · 30 min
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…
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Why AI for Good Fails and How Leaders Can Fix It
14 oktober 2026 · 40 min
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…
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What Most Companies Get Wrong About AI Strategy with Jeff McMillan, former Morgan Stanley AI Leader
30 september 2026 · 44 min
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…
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AI Literacy Starts in the C-Suite
16 september 2026 · 37 min
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…
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3 Data and AI Books Every Leader Should Read in 2026
2 september 2026 · 1 uur 27 min
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…
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How Vanguard is Architecting its AI Semantic Layer
19 augustus 2026 · 48 min
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-…
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How Grant Thornton Reimagines Core Processes in the AI Era
5 augustus 2026 · 43 min
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…
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How Google Is Building Enterprise Trust in Agentic AI
22 juli 2026 · 39 min
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…
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How Swire Coca-Cola Turned Governance Into an AI Growth Engine
8 juli 2026 · 25 min
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: …
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How to Transform a SaaS Company with AI from ELMO CTO
24 juni 2026 · 36 min
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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How AI is Scaled Across Global Supply Chains with Ligentia
10 juni 2026 · 31 min
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. Lees meer
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 shoul…
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Why Most Enterprise AI Pilots Fail: Lessons on Trust and Deployment
27 mei 2026 · 38 min
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. Lees meer
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 …
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S&P Global’s Chief Data Officer on Turning Data into Business Outcomes
13 mei 2026 · 41 min
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. Lees meer
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…
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How Semantic Layers and Ontologies Create Trusted AI
22 april 2026 · 53 min
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. Lees meer
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 informa…
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Inside WHOOP's Wearables AI Engine for Predictive Health
8 april 2026 · 41 min
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. Lees meer
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 tran…
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A Wharton AI Research Leader's Formula for Responsible AI
25 maart 2026 · 42 min
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. Lees meer
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 sy…
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How a Serial CDAO Scales AI in Insurance with Verisk
11 maart 2026 · 47 min
Discover how enterprise AI and data strategy are operationalized at scale in one of the most highly regulated industries in the world. Louis DiModugno, Global Chief Data Officer at Verisk, shares how he builds AI-ready data foundations across 40+ petabytes of insurance and risk data, and the best practices behind embedding AI into enterprise products. He discusses unstructured data, deepfakes, and the shift from governance to observability, offering practical insights for data leaders scaling AI responsibly. Lees meer
Key Moments:
From Military Leadership to Chief Data Officer: Data Integrity as a Competitive Advantage (03:02): Louis shares how his experience as a U.S. Air Force Colonel has shaped his approach to data governance, data quality, and enterprise AI leadership. He explains why integrity, service, and operational excellence are essential foundations for modern CDOs building trusted, decision-ready data environments.
Building AI-Ready Data Foundations at a 40+ Petabyte Scale (17:13): Managing more than 40 petabytes of insurance and risk data, Louis breaks down how Verisk transforms complex, multi-source data into AI-ready infrastructure. From entity resolution and master data management to benchmarking and predictive analytics, he outlines what it takes to prepare enterprise data for AI and advanced analytics at scale.
Designing an AI-First Data Strategy for Enterprise Decision Intelligence (20:00): Louis breaks down how Verisk evolved toward an AI-first data strategy across more than 150 insurance and analytics products. Rather than treating AI as an add-on, he explains how embedding AI into core workflows enables smarter underwriting, pricing, regulatory reporting, and risk management. He also discusses the strategic role ThoughtSpot plays in delivering natural language search, embedded analytics, and scalable AI-driven decision making.
AI Fraud, Deepfakes, and Risk Management in Financial Services (26:11): As AI-generated images and synthetic claims become …
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How AI Augments Human Creativity at Scale: The WPP Blueprint
25 februari 2026 · 41 min
Learn how AI agents are reshaping enterprise decision-making, AI governance, and brand creativity. Daniel Hulme, Chief AI Officer at WPP & CEO of Satalia/Conscium, explains how AI agents, decision intelligence, and his concept of “brand brains” (AI systems designed to create brand-specific, production-grade content) are changing how organizations operate. He shares why companies don’t have data problems but decision-making problems, and how AI can augment human creativity at scale. Lees meer
Key Moments:
From Academic AI Research to Enterprise AI Systems (01:50): Daniel traces his 25-year journey in AI, from studying intelligence and consciousness at UCL to building real-world systems inside global enterprises. He explains how curiosity about what it means to be human ultimately shaped his approach to building practical, responsible AI at scale.
AI Agents and Risk: Why AI Needs Governance (05:50): Daniel introduces a defining metaphor, describing AI agents as intoxicated graduates—confident, fast, and often wrong. He uses this framing to explain why unchecked agent deployment is risky and why governance, testing, and supervision are essential as organizations scale AI.
What Most Organizations Get Wrong About AI Testing: (14:00): Daniel breaks down the difference between testing for knowledge versus testing for real capability. He argues that most companies stop at surface-level validation, creating a false sense of safety and trust.
How AI Changes Business Decision-Making (24:45): Daniel challenges the traditional analytics mindset, arguing that dashboards and insights rarely lead to better decisions. He explains why AI should be designed to make decisions directly and why humans are fundamentally bounded when dealing with complex optimization problems.
Brand Brains and the Future of Creative Differentiation (30:25): Daniel introduces the concept of “brand brains,” explaining why generic generative AI content won’t create competitive advantage. He shows how agentic sys…
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How Nasdaq Architected a $90 Trillion Data Ecosystem
11 februari 2026 · 43 min
Discover how Nasdaq uses data platforms at a massive scale to power markets and prepare for AI. Angie Ruan, Nasdaq’s CTO of Capital Access Platforms, explains how large-scale data systems support market integrity, transparency, and decision-making across public and private markets. She defines what it really means to be AI-ready, how leaders should modernize data platforms, and how market fundamentals help separate real AI value from hype. Lees meer
Key Moments:
Why Nasdaq Is More Than a Stock Exchange (06:10): Angie reframes Nasdaq as a global technology company rather than a traditional exchange, explaining how data, platforms, and engineering underpin trust, resilience, and transparency across public and private markets.
The Scale of Market Data Powering the Global Financial System (11:15): Angie breaks down the massive scale of Nasdaq’s data ecosystem, including hundreds of billions of market messages per day and platforms supporting more than $90 trillion in assets. She explains how data quality and reliability are foundational to market integrity and decision-making.
Building a Unified Data Intelligence Platform at Nasdaq (16:35): Angie explains how Nasdaq approaches data architecture, governance, and platform design to create a unified data intelligence layer. She shares why access control, operational efficiency, and data trust matter more than raw data volume when enabling analytics and AI at scale.
The AI-Ready Playbook for Data and AI Leaders (19:20): Drawing on her experience across startups and large enterprises, Angie outlines a practical framework for data and AI transformation. She emphasizes cloud adoption, breaking down silos, listening to business needs, and treating platform modernization as both a technical and organizational challenge.
Is AI a Bubble? Using Market Data to Separate Hype from Reality (31:00): Angie applies a data-driven lens to the AI bubble debate, examining earnings growth, margins, return on equity, and capital investment. She ex…
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How OneDigital Is Reframing AI Agents as Coworkers
28 januari 2026 · 48 min
Discover why the future of AI at work is more human than you think. Vinay Gidwaney, Chief Product Officer at OneDigital, shares how treating AI agents as talent rather than technology is changing AI adoption at work. He explains people-first change management, managing AI coworkers, building trust in human AI teams, and why real transformation depends on equity, access, and better decision-making. Lees meer
Key Moments:
AI as a Coworker (01:45): Vinay introduces “Ben,” an AI benefits consultant at OneDigital, to illustrate a fundamentally different way of thinking about AI at work. Instead of positioning AI as automation or replacement, he explains how AI coworkers are designed to augment human expertise and support better decision-making.
Hiring, Training, and Managing AI Like Employees (04:20): Vinay walks through OneDigital’s intern-to-apprentice model for AI, including job descriptions, cultural fit, human managers, and ongoing supervision. He shows how applying HR rigor to AI builds trust, accountability, and clarity while helping employees see AI as part of the team, not a threat.
Why AI Projects Fail: The Misguided Focus on Tools over Talent (10:45): Vinay argues that AI fails when treated like a traditional IT rollout. He emphasizes that AI adoption is fundamentally a people and change-management challenge, calling on HR leaders to lead the shift in how humans and AI work together.
Recognizing the Limits and Risks of AI Automation (23:10): Vinay explains why fully autonomous AI agents often fall short in knowledge-based organizations. He cautions leaders to be skeptical of automation-first promises and introduces a more realistic model centered on cognition, human oversight, and thoughtful ROI evaluation.
The Future of Work in an AI World (38:10): Vinay reflects on his career and argues that software alone is no longer a defensible moat. He emphasizes speed, insight, services, and human judgment as the true sources of lasting value.
Key Quotes:
“ If you treat…
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Preparing for Agentic AI: Top Trends in Data and AI 2026
14 januari 2026 · 56 min
In this season premiere of The Data Chief podcast, host Cindi Howson sits down with three industry leaders to unpack what’s next for AI, and the concrete moves data and AI leaders need to make in 2026—many of which are detailed in ThoughtSpot’s Top Data & AI Trends of 2026 ebook. Lees meer
Get ready for a deep dive into:
Agentic AI goes mainstream with Paul Baier, CEO and Co-Founder of GAI Insights
AI-ready data and the rise of the AI manager with Jennifer Belissent, Principal Data Strategist at Snowflake
Scaling agents with trust and control with Rory Blundell, CEO of Gravitee
Consider this your field guide to navigating AI in 2026.
Key Moments:
Agentic AI Goes Mainstream with Paul Baier, GAI Insights (1:50): Paul Baier, CEO and Co-Founder of GAI Insights, explains why enterprises that already have GenAI in production are pulling decisively ahead, how agentic AI is reshaping enterprise operating models, and why leadership alignment and AI literacy will determine winners in 2026.
AI-Ready Data and the Rise of the AI Manager, Jennifer Belissent, Snowflake (19:16): Dr. Jennifer Belissent, Principal Data Strategist at Snowflake, breaks down why data quality, transparency, and governance remain the foundation of AI success, and why the next critical enterprise skill is learning how to manage AI agents as part of the workforce.
Scaling Agents with Trust and Control with Rory Blundell, Gravitee (35:11): Rory Blundell, CEO of Gravitee, shares how the agentic era is redefining API integration, why most enterprises are stuck at early AI maturity stages, and how agent management and security frameworks will unlock real action in 2026.
Key Quotes:
“Yo u have to treat AI as a capability and not an IT project.” - Paul Baier
“ Transparency as a requirement is not slowing down adoption. It's actually accelerating it.” - Jennifer Belissent
“My prediction is that companies that adopt robust security frameworks in 2026 will be the companies that accelerate fastest.” - Rory Blunde…
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GenAI Best Practices: What Early Adopters Have Learned
17 december 2025 · 45 min
In this special episode, host Cindi Howson pulls together the most useful, and hard-won, lessons from a year of conversations with Data Chiefs leading the GenAI charge. With generative and agentic AI no longer a side experiment, this episode spotlights five practices early adopters can rely on to move from pilots to profit. Expect straight talk on what to prioritize, how to bring people with you, and how to scale AI with the trust, literacy, and guardrails that make impact stick. Lees meer
Key Moments:
Tying AI to Real Dollars with Anand Iyer, Ecolab (02:10): Anand cuts through the GenAI FOMO and brings everything back to a simple survival test: if you can’t draw a straight line from an AI initiative to top-line growth or bottom-line savings, it won’t last. His lesson is a sharp reminder that “cool” doesn’t scale, value does.
Leading Through Ambiguity with Karen Stroup, WEX (06:01): Karen names what everyone’s feeling: ambiguity is paralyzing. She explains how leaders earn trust by shrinking the unknown into learnable, bite-sized experiments and creating the psychological safety people need to engage instead of resist.
Building Practical AI Literacy at Scale with Josh Cunningham, Lloyds Banking Group (12:42): Josh shares how Lloyds Banking Group makes literacy impactful by meeting people where they are. Rather than one-size-fits-all training, they pair broad fundamentals with role-specific learning so every business unit can build confidence in ways that match their actual work.
Scaling Responsible Agentic AI with Noelle Russell, AI Leadership Institute (25:09): Noelle steps in with a practical framework for building agentic systems that don’t go rogue. She walks through the POET framework and stresses that responsible AI isn’t a final checkpoint. It’s something you embed from the first idea to production, with guardrails that protect people and outcomes.
Embedding AI Where Work Happens with Ilan Twig, Navan (32:35): Ilan tells a classic early-adopter story: start with…
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How Yum! Brands is Using Data & AI as a Recipe for Innovation with CDO Cameron Davies
3 december 2025 · 40 min
Learn how one of the world’s biggest restaurant companies is turning data and AI into a recipe for global innovation. Cameron Davies, Chief Data Officer at Yum! Brands, shares how he’s combining strategy, technology, and change management to drive gobal growth. He explains how Yum! is building AI literacy from the top down, reimagining operations with generative AI, and partnering with NVIDIA to scale innovation. Cameron reveals what true data leadership looks like, balancing bold ideas with business impact, and proving transformation starts with people, not technology. Lees meer
Key Moments:
Start with the Business Problem, Not the Tech (04:27): Cameron recalls advice from a mentor, “start with the business problem down, not the technology up.” He emphasizes that innovation only matters when it solves real business challenges, reminding data leaders not to get enamored with the “cool” factor of technology at the expense of impact.
Balancing Global Scale with Local Agility (07:45): Cameron unpacks the challenge of scaling analytics across 160 countries and four major brands, 98% of which are franchise-owned. He explains how Yum! balances centralization and autonomy, ensuring smaller markets have a voice while global teams leverage shared technology and insights.
Building AI Literacy from the Top Down (13:44): Cameron describes Yum!’s investment in digital upskilling, from Harvard-led training for executives to hands-on AI workshops for employees. He outlines how the company is embedding AI tools, like Microsoft Copilot and ChatGPT, into daily workflows to build confidence and accelerate adoption.
Digitizing the Restaurant: Byte By Yum! (17:18): Cameron introduces Byte By Yum!, a suite of proprietary software that simplifies restaurant operations. He explains how it unifies e-commerce, point-of-sale, voice AI, and kitchen systems to make running a restaurant easier and more efficient in an increasingly complex digital environment.
Partnering with NVIDIA to Power the Fut…
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Alghanim Industries on Why the Middle East Is the New Frontier for Data-Driven Innovation
19 november 2025 · 45 min
Step inside a conversation on the art and architecture of AI transformation. Vedran Karamani, Group Data Analytics & Agentic AI – CDAIO at Alghanim Industries, shares how he’s embedding intelligence across one of the Middle East’s largest conglomerates. He breaks down what it takes to make AI approachable in the boardroom, why data literacy is now a business imperative, and how to balance innovation with operational reality. Discover how Vedran is redefining the role of data, technology, and human insight in shaping the next generation of enterprise transformation. Lees meer
Key Moments:
From Deterministic to Probabilistic Thinking (10:19): Vedran explains how today’s AI shifts decision-making from predictable, rules-based systems to adaptive, probabilistic ones. He shares how leaders must learn to balance control with flexibility to build trust in AI’s potential while managing its unpredictability.
AI Literacy in the Boardroom (12:55): Vedran emphasizes that real transformation begins with leadership understanding. He shares how he uses storytelling and analogies to educate executives on AI fundamentals, turning abstract concepts like “deterministic vs. probabilistic” into relatable, actionable insights.
The Readiness Factor (17:29): Not every business is equally prepared for AI. Vedran breaks down the difference between change management and change readiness, urging leaders to assess cultural alignment, technical infrastructure, and data maturity before diving into transformation.
Data as the Differentiator (25:15): Vedran argues that as algorithms become commoditized, competitive advantage will come from the quality and context of a company’s data. He outlines how clean, well-modeled, and contextualized data will form the backbone of any successful AI strategy.
The Middle East’s AI Momentum (32:05): Vedran highlights how ambition, experimentation, and government investment are fueling rapid AI growth across the Middle East. He contrasts this energy with slower-movin…
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When Navan Chose to Build, Not Buy: The AI Decision That Changed Everything
5 november 2025 · 48 min
In this episode of The Data Chief, Ilan Twig, co-founder and CTO of Navan, shares why large language models will revolutionize our relationship with technology—just like the mouse did for the keyboard. From pushing AI to its limits to launching Navan Cognition, built for zero critical hallucination, Ilan reveals what it really takes to lead through change and build AI that people can trust. He also dives into a critical question every company must face: Will you build AI from scratch, or build with AI partners?? And if you're curious about the next frontier, Ilan paints a bold vision of agent-to-agent communication—where AI services talk to each other and your admin work disappears into the background. A must-listen for anyone building the future of AI-powered user experiences. Lees meer
Key Moments:
Agent-to-Agent Communication (A2A) (17:00): Ilan envisions a future where dedicated AI services communicate with each other in natural language, without the need for an API. This "mother of all bots" would manage administrative tasks by talking to other bots, simplifying complex tasks for the user.
AI as a "Human" Experience (27:16): Ilan was surprised by the release of ChatGPT in 2022 because it was the first time a technology felt human. This led him to spend four months building and testing the technology's boundaries, including its ability to lie or be "jailbroken" with creative prompts.
Identifying the Core Business (31:43): Ilan advises companies to decide if they want to become an "AI company" or simply use AI. He explains that building a core AI platform requires a huge commitment.
A Case Study in Building (35:32): The conversation furthers, as a light is shed on the building of “Navan Cognition”, because no solution existed at the time to prevent critical hallucinations in AI models. This system includes a supervisor agent that works to catch and correct undesirable responses, creating a "zero critical hallucination" experience for its users.
Key Quotes:
"LLMs wo…
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Canva’s Moe Kiss on Why Creativity Is the Missing Ingredient in Data
22 oktober 2025 · 40 min
Join us for a masterclass in building data-driven marketing cultures. Moe Kiss, Director of Data Science in Marketing at Canva, shares how she bridges analytics and storytelling to make data approachable across teams. She breaks down the shift from attribution to experimentation, why storytelling is a must-have skill for every data professional, and how to balance intuition with evidence. Discover how Moe’s redefining the balance between technology, trust, and empathy in data leadership. Lees meer
Key Moments:
Data as “Part of the Meal” (09:09): Using a cooking analogy, Moe describes how data should be integrated into decision-making, not as a garnish or afterthought, but as part of the meal itself. She highlights Canva’s culture of balancing intuition with data, ensuring both creative instinct and analytics inform every decision.
Storytelling as a Data Superpower (10:57): Moe breaks down her “insight headline” method, turning flat dashboards into stories that drive action. She argues that data storytelling isn’t about aesthetics but understanding, and that trust comes from how clearly insights connect to business impact.
Technical Fluency in the Age of AI (18:16): While some leaders claim programming is obsolete, Moe insists it’s more important than ever. Understanding what’s under the hood helps data professionals vet AI outputs, maintain data quality, and confidently guide stakeholders through rapid technological change.
Buy vs. Build: The Cost–Benefit Equation (20:11): Moe breaks down how Canva evaluates when to build internal tools versus buy off-the-shelf solutions. She stresses that the best answer depends on scalability, cost, and time to market — and that implementation costs are often underestimated.
AI’s New Role in Marketing (24:48): Moe shares how AI is reshaping marketing teams and their relationship with data. She highlights how generative AI tools can democratize access to insights, but warns that without trusted, high-quality data foundations, AI can j…
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3 Must-Read Data and AI Books for 2025 with Geoff Woods, Wendy Batchelder, and Malcolm Hawker
8 oktober 2025 · 1 uur 21 min
Welcome to a special author's episode of The Data Chief, where we delve into the minds of three influential authors who are shaping the conversation around data and AI. First, Geoff Woods, author of The AI-Driven Leader, shares his philosophy of prioritizing strategy over technology to make faster, smarter decisions. Next, Wendy Batchelder, author of The Data Governance Handbook, discusses how to transform governance from a rigid bureaucracy into a business accelerator by focusing on business outcomes. Finally, Malcolm Hawker, author of The Data Hero Playbook, challenges data leaders to adopt a heroic mindset by becoming customer-driven and aligning their incentives with business success. Join us to learn how to lead effectively in the AI era by building a strategy-driven, governed, and customer-centric data function. Lees meer
The Data Chief Podcast: Author Episode Key Moments
Geoff Woods: The AI-Driven Leader
From "IT Problem" to Strategic Partner (06:20): Woods advocates for viewing AI as a "strategic thought partner" rather than an assistant or replacement, and emphasizes that AI strategy must align with business strategy.
The CRIT Framework for Smarter Prompts (12:25): He introduces the CRIT framework for prompt engineering: Context, Role, Interview, Task. This method helps leaders get non-obvious, high-impact strategies from AI by having the AI ask the right questions.
Beyond the Bottom Line: AI's Human Impact (22:17): Woods discusses the ROI of AI, including a case where AI identified savings equivalent to 2% of a company's revenue.
Wendy Batchelder: The Data Governance Handbook
Data Governance as an Accelerator (32:33): Wendy Batchelder addresses the myth that data governance is a "dirty word" or a code for "no," arguing that its true purpose is to be an accelerator.
Speaking the Language of Business (35:17): Batchelder emphasizes that data governance should be embedded from the start of a project, not as an afterthought. She provides an example of "bad" vs. …
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Why CDO of WEX Puts People Before Technology in the Age of AI
24 september 2025 · 43 min
Join us for a compelling conversation on leading through change with Karen Stroup, Chief Digital Officer at WEX. In this episode of The Data Chief, Karen shares her insights on navigating the complexities of AI adoption. She discusses why focusing on the customer problem is more important than simply applying new technology, how to build trust by accounting for the "human side" of data, and why her team uses a "two-way door decision" to combat the rapid pace of tech change. Discover her "hot take" on the future of analytics, where dashboards become a "backdrop" and an interactive, predictive experience takes center stage. Lees meer
Key Moments:
Navigating Ambiguity with People (06:02): Karen argues that leaders' primary role is to build confidence and help people navigate ambiguity. She explains that people fear the unknown more than change and suggests involving employees in the journey to help alleviate that fear.
Trust and the Human Side of Data (14:30): She highlights that giving a correct answer is not the same as building confidence that it's the right answer. Karen discusses the importance of accounting for the "human side" of how people feel about the information they receive.
Prioritizing AI use cases (22:48). Karen describes WEX's approach, which starts with a top-down cultural shift from the CEO, and then uses filters like value, feasibility, and desirability to prioritize projects.
The "Two-Way Door Decision" for Technology (28:13): To combat the disconnect between rapid technology evolution and slow cultural change, Karen discusses using a "two-way door decision". This approach involves architecting solutions to be vendor-agnostic, allowing the company to pivot if a technology proves to be unsuccessful.
"Hot Take" on the Future of Analytics (35:34): Karen's "hot take" is that the user experience will fundamentally change. She predicts that dashboards will become a "backdrop," while an interactive, predictive experience will move to the foreground.
Key Quo…
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Unlocking the Autonomous Enterprise, with ThoughtSpot CEO
10 september 2025 · 46 min
The next generation of analytics is here. In this episode of The Data Chief, ThoughtSpot CEO Ketan Karkhanis explains why AI is the new BI, and the future of analytics is autonomous. Karkhanis shares his vision for the autonomous enterprise, where AI agents act on insights and automate workflows. He also explains why a culture of trust and experimentation is crucial for unlocking AI's full potential. Don't miss this discussion on how to fundamentally rethink how organizations interact with data to drive better business outcomes and build an autonomous enterprise. Lees meer
Key Moments:
AI is the New BI (08:35): Ketan explains that AI represents a “foundational rewiring” of the entire technology stack, a shift he calls Cloud 2.0. He predicts the BI market is on the verge of an “upgrade super cycle,” leaving legacy players behind.
AI Becomes the Only UI (20:45): Ketan shares his vision that in the future, AI will become "the only UI you will need". He explains that ThoughtSpot’s MCP host can bring together structured data, unstructured data, and world knowledge to provide better context for a user's question.
Progress over Perfection (25:56): Leaders are reminded not to let “perfection be the enemy of progress.” For Ketan, a culture of trust and openness to experimentation is more important than having perfectly defined KPIs or flawless dashboards.
Training Comes First (29:02): One of the biggest lessons learned was the importance of investing in people before chasing the promise of AI outcomes. After rolling out mandatory generative AI training, new use cases began emerging organically from across the business—proof that education fuels innovation.
Outcomes Over Tech (38:47): Despite mountains of legacy technology, many organizations remain starved for actionable insights. Ketan points to EasyJet as an example of getting it right: rather than focusing on systems and infrastructure, they designed their AI initiative around a tangible outcome—avoiding flight cancellations…
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From Building at Pinterest and Airbnb to Revolutionizing AI at Kumo.AI
20 augustus 2025 · 33 min
Hear how data is rewriting the rules—and driving the future of innovation. Dr. Vanja breaks down his approach to enterprise AI, and the key strategies for success. He shares what it means to be a “data-forward organization” and why data must sit at the heart of how a company operates. Discover why integration speed is the new competitive edge Lees meer
Key Moments:
Data as a Core Enterprise Asset (6:04): Dr. Vanja defines what it means to be a “data-forward organization,” emphasizing that data shouldn’t be an afterthought—it must sit at the heart of how a company operates, makes decisions, and serves customers.
Relational Foundation Models for Predictive AI (16:26): Dr. Vanja explains how Kumo uses transformer architectures on raw relational data to eliminate 95% of traditional ML tasks like feature engineering—bringing pre-trained, multi-purpose prediction models into the structured data world.
Integration Speed as a Competitive Edge (27:03): It's no longer just about who builds the fastest—but who integrates the fastest. Dr. Vanja shares how companies that can quickly adopt and scale best-in-class third-party tools will define the next generation of winners.
The Case for Rebuilding at 10x Advantage (29:02): Dr. Vanja urges leaders to rethink old systems if a new approach offers a 10x cost or performance improvement. Sticking with outdated architectures out of fear or inertia risks falling behind during pivotal transitions.
Key Quotes:
“The key asset is the data that you have… You need to build the whole enterprise around this data. It’s not an afterthought — it’s at the center of the organization and how it functions.” - Dr. Vanja
“Companies that integrate the fastest — not just build the fastest — will win.” - Dr. Vanja
“If you really value your data, you need to stay in the cloud… If the cost is your only driver, then do what you want — but you’ll miss out on the majority of new technologies.” - Dr. Vanja
Mentions:
Build AI Models for Relational Data
The New …
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Why "Garbage In, Disaster Out" is AI's New Reality: Ecolab CDO
6 augustus 2025 · 40 min
Join us for expert insights on driving data-led change. Anand Iyer, Senior Vice President and Chief Data Officer at Ecolab, breaks down his approach to AI-powered innovation. He shares how to ensure AI initiatives directly impact the bottom line, why an engineering approach is key for prioritizing AI use cases, and that "garbage in, disaster out" is the new reality for data at AI scale. Discover how self-service analytics with AI is transforming data access and why AI is now a critical "business forcing function" in today's volatile world. Lees meer
Key Moments:
The "Value First Mindset" for AI (03:13): Despite the hype around AI, initiatives must directly impact the top or bottom line in a measurable way. Sustained investment requires a clear link between the AI initiative and its financial value, moving beyond "soft benefits."
Engineering Mindset for Prioritization (11:27): Anand discusses how an engineering approach is applied to prioritizing AI use cases, which helps teams focus on thoroughly understanding the problem and desired outcome before selecting a solution.
"Garbage In, Disaster Out" (14:27): A new take on an old adage is introduced: "in the AI world is garbage in and disaster out". This highlights the magnified risks of bad data when leveraged at AI scale.
Advocacy for Self-Service Analytics with AI (24:10): Self-service analytics is championed, describing how the integration of AI and conversational AI allows users to ask questions regarding the data. This removes the need for IT involvement in report generation and simplifies the learning curve for data structures.
AI as a Business Forcing Function (33:38): In today's volatile global environment, near real-time data and AI-driven insights are no longer optional but a "business forcing function". Rapid reactions to market disruptions, policy changes, and supply chain issues are critical for a company's survival and success.
Key Quotes:
"If you want to have a seat at the table, you've got to be able to …
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Why Leaders Need to Master Responsible AI: Insights from Pioneer Noelle Russell
23 juli 2025 · 58 min
Prepare for game-changing AI insights! Join Noelle Russell, CEO of the AI Leadership Institute and author of Scaling Responsible AI: From Enthusiasm to Execution. Noelle, an AI pioneer, shares her journey from the early Alexa team with Jeff Bezos, where her unique perspective shaped successful mindfulness apps. We'll explore her "I Love AI" community, which has taught over 3.4 million people. Unpack responsible, profitable AI, from the "baby tiger" analogy for AI development and organizational execution, to critical discussions around data bias and the cognitive cost of AI over-reliance. Lees meer
Key Moments:
Journey into AI: From Jeff Bezos to Alexa (03:13): Noelle describes how she "stumbled into AI" after receiving an email from Jeff Bezos inviting her to join a new team at Amazon, later revealed to be the early Alexa team. She highlights that while she lacked inherent AI skills, her "purpose and passion" fueled her journey.
"I Love AI" Community & Learning (11:02): After leaving Amazon and experiencing a personal transition, Noelle created the "I Love AI" community. This free, neurodiverse space offers a safe environment for people, especially those laid off or transitioning careers, to learn AI without feeling alone, fundamentally changing their life trajectories.
The "Baby Tiger" Analogy (17:21): Noelle introduces her "baby tiger" analogy for early AI model development. She explains that in the "peak of enthusiasm" (baby tiger mode), people get excited about novel AI models, but often fail to ask critical questions about scale, data needs, long-term care, or what happens if the model isn't wanted anymore.
Model Selection & Explainability (32:01): Noelle stresses the importance of a clear rubric for model selection and evaluation, especially given rapid changes. She points to Stanford's HELM project (Holistic Evaluation of Language Models) as an open-source leaderboard that evaluates models on "toxicity" beyond just accuracy.
Avoiding Data Bias (40:18): Noelle wa…
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How Lloyds Banking Uses Data Escape Rooms to Build a Data Culture
9 juli 2025 · 50 min
Tune in for a masterclass in data leadership. Josh Cunningham, Group Head of Data and AI Culture at Lloyds Banking Group, unveils how this distinguished institution is boldly innovating with cutting-edge AI. Josh provides a riveting look into his unique role, which is dedicated to accelerating talent, building capabilities, and articulating data and AI's profound impact across the organization. Learn how Lloyds is rapidly expanding its data and AI graduate scheme and learn more about their ambitious quest to be the "most data literate bank". Hear how their innovative five-persona literacy framework, and engaging initiatives like the "Data and AI Summer School" and a physical "Data Escape Room," are driving their business forward on data and AI. Lees meer
Key Moments:
The "People Side" of Data and AI (12:33): Reflecting on his career, Josh highlights his passion for the "people side" of data and AI, focusing on building teams and fostering career development. This addresses a critical industry gap where technology readiness often outpaces human capability.
Scaling Data and AI Talent (17:23): Lloyds Banking Group significantly scaled its data and AI graduate scheme from approximately 10 to 100 graduates annually over three years, while demonstrating a proactive approach to balancing between AI training and tool adoption for their increasing talent pool.
Data and AI Literacy Framework (22:52): Lloyds developed a data and AI literacy framework with five personas, from "data beginner" to "citizen," representing an evolving maturity lifecycle. This framework helps map and track colleagues' literacy levels over time.
The Data and AI Summer School (29:06): Josh highlights this major, two-month, virtual program that offers over 200 live sessions hosted by internal and external experts. It covers diverse data and AI topics for all colleagues, from beginners to practitioners, and has attracted over 42,000 sign-ups in the previous year.
The Physical Data Escape Room (32:18): Lloyd…
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How a CDAO goes from baseball to insurance with Don Vu - New York Life
25 juni 2025 · 40 min
Step inside the world of data innovation as Don Vu, SVP and Chief Data and Analytics Officer at New York Life, reveals how a 180-year-old institution is embracing cutting-edge AI. Don, shares insights from his unique background, spanning Major League Baseball and retail startups, now applied to transforming the insurance industry. Hear how New York Life leverages AI to make experiences proactive and intelligent, addressing challenges like the "last mile problem" in data operationalization. Lees meer
Key Moments:
MLB Data Insights (07:28): The conversation delves into how every baseball stadium is extensively instrumented with high-speed camera and radar technology, meticulously tracking every object on the field. This massive trove of data is then shared across all baseball clubs for in-depth analysis and the optimization of strategies.
The Last Mile Problem (09:38): A critical challenge in data and AI is identified as the "last mile problem," emphasizing that the primary hurdles often lie in the operationalization, change management, adoption, and acceptance of solutions, extending far beyond the mere building of models.
Data & AI in Business Strategy (13:08): The discussion highlights that data serves as the fundamental underpinning for seamless operations, while AI actively transforms experiences, making them proactive and intelligent. This deep integration of AI and data is central to New York Life's core business strategy.
Data Readiness & Quality (20:08): Persistent data readiness issues are addressed, underscoring that data quality, latency, governance, and stewardship—with business owners held accountable—are absolutely crucial for both structured and unstructured data environments.
AI Interoperability & Agent-Driven Future (22:43): The episode explores the importance of tracking emerging AI protocols such as MCP (Model Context Protocol) and agent-to-agent protocols. A compelling vision of the future is also shared, where AI agents act on behalf of consumers. R…
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How the Met Police Uses AI to Prevent Crime and Protect Communities
11 juni 2025 · 45 min
Step into the future of policing where data is a mission-critical asset. Cindi Howson talks with Aimee Smith, Director of Data at the Metropolitan Police, about the Met’s bold data transformation—from digitizing records dating back to 1829 to using AI and cloud tech for smarter, faster decisions. Hear how initiatives like the V100 program and real-time analytics help improve city safety. Lees meer
Key Moments:
Genesis of the Met’s Data Strategy (03:35) - The Met's data strategy's origin is traced to former Commissioner Cressida Dick's leadership, who envisioned leveraging data to transform policing, leading to a program building data capabilities and broadening analytics use beyond traditional intelligence and performance applications.
Mission with Data and AI (13:34): The Met's overarching mission to use data and AI for precise decision-making is articulated, acknowledging the complexity of policing's multiple goals: crime prevention, incident response, organized crime intervention, victim service, and custody safety.
Infrastructure Evolution (15:18): The transformation of the Met's data infrastructure over 5 years, from 8 separate operational systems to an integrated one with cloud technology adoption, is described, enhancing analytics and data science capabilities.
V100 Initiative (19:58): The V100 initiative, a data and analytics effort to reduce violence against women and girls by prioritizing individuals with a history of harm, is explained.
Concert Security Powered by Analytics (27:50): The use of ThoughtSpot by frontline officers is illustrated with a sergeant's innovative application for analyzing crime data around events like the Taylor Swift Eras tour to improve policing plans.
AI Agent Development (36:37): An innovative project to build an AI agent that assists frontline officers at crime scenes by providing real-time guidance is outlined, aiming to improve public protection and investigative outcomes.
Key Quotes:
“So if an officer wants to start being a…
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Why Hyatt is Investing in AI and Self-Service to Transform the Future of Hospitality
21 mei 2025 · 41 min
Embark on an insightful exploration of the hospitality sector, powered by data-driven analysis. Cindi Howson and Hyatt's data trailblazer, Ray Boyle (Vice President, Data and Analytics), are charting a course through Hyatt's data innovation. Witness how Hyatt's four-pillar data strategy is revolutionizing everything from employee empowerment to guest personalization and operational efficiency. Discover how Hyatt is democratizing data with self-service tools and pioneering an AI-powered frontier to redefine the very essence of hospitality! Lees meer
Key Moments:
Data as an Asset (08:26): Ray emphasizes the importance of shifting the organizational mindset to view data not as a cost center, but as a critical asset. He discusses how data should be cared for, invested in, and stored like any other valuable asset, with the expectation of generating value for the business.
Hyatt's Data Strategy Pillars (13:00): Ray outlines the four key pillars that form the foundation of Hyatt's data strategy. These pillars include cultivating people and building a data-driven culture, personalizing the guest and customer experience in a high-trust environment, operating with excellence by ensuring operational efficiency and information consistency, and growing with intent by integrating new businesses and data flows.
Key Milestones in Hyatt's Data Transformation (16:42): Ray details the significant milestones in Hyatt's data transformation journey. These include clarifying the data strategy, establishing the data and AI operating model, building data governance capabilities, modernizing the data platform and infrastructure, expanding data assets, and releasing new services like personalization and forecasting.
Data Democratization and Data Fluency (23:00): Ray explains Hyatt's strong emphasis on self-service analytics to empower users across the organization. He discusses the importance of data accessibility, trustworthiness, and usability, as well as the potential of generative AI to furth…
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How Macquarie Bank Uses AI and Data to Enhance Customer Experience
12 mei 2025 · 41 min
Prepare to see banking in a new light! Cindi Howson and Macquarie Bank's data trailblazer, Ashwin Sinha (Chief Data Officer), go deep into the AI revolution transforming financial services. Discover how one of Australia's most dynamic financial institutions, Macquarie Bank, is wielding the disruptive force of generative AI, not just for efficiency, but to combat high-stakes threats like fraud. Plus, discover the remarkable evolution of the data analyst from report-generator to AI-powered strategic powerhouse! Lees meer
Key Moments:
Drivers of Digital Transformation (04:36): Ashwin outlines the key factors driving a digital transformation and early cloud adoption, emphasizing customer obsession, improving turnaround times, and ensuring technology reliability.
Leveraging Dual Cloud Providers (12:25): Ashwin discusses Macquarie Bank's use of AWS for infrastructure and core applications and Google Cloud (GCP) for its digital and data stack, including AI capabilities.
The Power of Gen AI in Analytics (14:16): Ashwin explores the role of generative AI in enhancing productivity for data analysts, particularly through prompt engineering and tools like ThoughtSpot.
Empowering Analysts Through Evolution (16:56): Ashwin details Macquarie Bank's successful strategy for evolving the data analyst role by proactively introducing self-service analytics, emphasizing upskilling, and enabling analysts to concentrate on higher-impact activities
Combating Data Risk and Fraud Prevention (26:04): Ashwin discusses the increasing threat of scams and fraud and details Macquarie's two-pronged approach: educating customers and employing AI and machine learning to detect and prevent fraudulent activities.
Importance of Prompt Engineering (32:57): Ashwin stresses the significance of prompt engineering as a general-purpose technology that can drive productivity across various business functions, not just within technical roles.
Key Quotes:
"There is always a big backlog in most organizations, whi…
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How SeaWorld Uses Data to Create Unforgettable Guest and Marine Life Experiences
23 april 2025 · 45 min
Ever wondered how data powers the magic behind your favorite theme park experiences? Join Cindi Howson and Gavin Hupp, VP of Technology, Enterprise Architecture, Data and Martech, E-commerce and Analytics at United Parks and Resorts, as they explore the complex data ecosystem of a theme park, from e-commerce and guest experience to AI's role in shaping the future of entertainment. Lees meer
Key Moments:
Theme Park Business Model (03:12): Theme parks are described as a mix of multiple businesses, including e-commerce for ticket sales, animal experiences, entertainment venues, culinary and restaurant services, and retail operations. This combination creates a complex ecosystem, similar to city planning, within a single physical location.
Data Ecosystem Challenges (03:37): Gavin highlights the challenge of managing data within theme parks due to the variety of business areas. Each area generates unique data, leading to disparate and sometimes siloed data sets across different business applications.
AI as an Innovation Driver (11:24): AI is viewed as a key driver of innovation within the theme park industry, capable of creating new products and services, such as augmented reality experiences, and enhancing personalization at scale.
AI for Process Optimization (11:24): Beyond guest-facing innovation, AI is also seen as a tool to optimize business processes, streamline operations, reduce costs, and identify opportunities for revenue growth through personalization and increased efficiency.
Data-Driven Decision-Making (17:30): United Parks and Resorts emphasizes the importance of guest feedback, collected through surveys and other means, and uses it to inform decision-making and guide the company's overall strategy.
Agile Development Approach (28:50): Gavin explains how the company employs agile development principles, using "skateboards" as a metaphor for quickly delivering initial solutions and value while simultaneously iterating and building more comprehensive and scalabl…
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How SharkNinja Drives Business Value: The Power of Data for All
9 april 2025 · 41 min
How does SharkNinja use data to fuel its rapid growth and product innovation? Join Cindi Howson and Elpida Ormanidou, VP of Analytics and Insights at SharkNinja, as they dissect SharkNinja's data-driven culture, Elpida's journey in the data space across CPG and retail, and her insights on AI in the workplace. Lees meer
Key Moments:
Data-Driven Culture (03:36): SharkNinja strongly emphasizes data in its culture, utilizing it to inform decision-making processes. The company is committed to using customer feedback gathered through data to drive the development and refinement of its product offerings.
CEO's Data Focus for Customer-centric Innovation (05:43): SharkNinja's CEO demonstrates a notable dedication to data by actively engaging with it. This involvement includes closely reviewing customer feedback and using data insights to guide product discussions and challenge teams to improve.
Data Ethics and Privacy (09:17): SharkNinja places a high priority on data ethics and privacy, emphasizing the importance of earning customer trust. Elpida shares how the company is committed to using customer data responsibly and has implemented strong controls to protect privacy.
AI and the Future of Work (20:31): Elpida discusses the transformative impact of AI on the future of work, characterizing it as a revolution. She emphasizes the importance of proactively addressing the changes by reskilling and upskilling the workforce to adapt to new roles and technologies.
Key Quotes:
"Value gets created at the time of consumption. We create value for the business when data gets consumed, not when it gets connected, not when it gets processed, not when it gets synthesized, only when it's being used to drive decisions that create value for the company." - Elpida Ormanidou
"Think of a company as a chain, where everything is interlinked to level up. Today's struggle is that while we have good AI applications, it's an art to connect them to create the next level of experience, particularly for…
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Data-Driven Personalization: Sephora's Winning Strategy
19 maart 2025 · 39 min
How is Sephora using data to create personalized experiences that customers love? Join Cindi Howson and Manbir Paul, VP of Engineering, Data Insights & MarTech at Sephora, as they explore the role of data and AI in understanding customer needs, predicting preferences, and delivering impactful moments. Lees meer
Key Moments:
Micro-Moments that Matter (3:30): Sephora leverages data to create impactful moments for customers, like sending a timely reminder to a traveler about their moisturizer.
Modern Data & AI Stack (5:00): Manbir discusses Sephora’s best-of-breed data and AI stack, spanning cloud data platform, BI solution, cataloging, and machine learning democratization.
The Power of the Semantic Layer (7:30): The semantic layer is crucial for enabling meaningful data discovery and governance. Sephora's investment in ThoughtSpot was driven by the need to enhance their semantic layer and drive intelligence in their BI space.
Collaborative Data Governance (10:00): Sephora fosters a collaborative approach to data governance, with data stewards playing a key role. They identify individuals who are subject matter experts in their areas and are passionate about data to help drive governance and enrichment.
Unlearning and Relearning (16:30): The challenge of keeping up with the evolving data landscape requires unlearning old practices and embracing new ones. Manbir highlights the importance of giving individuals the opportunity to look at the changing landscape from a new lens and empowering them to drive transformation.
The Importance of Continuous Learning (20:30): Manbir acknowledges the challenge of balancing learning with delivering results, but stresses the importance of continuous learning in a rapidly evolving field. She notes that individuals are often willing to go above and beyond if there is a learning opportunity.
Building High-Performing Teams (22:30): Manbir discusses the nuances of creating a high-performing, nimble team that can adapt to change and drive in…
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Bridging the Gender Gap in Data & AI with Databricks & Women in Data UK
5 maart 2025 · 54 min
Join host Cindi Howson as she dives into the critical topic of diversity and inclusion in the data and AI space with Roisin McCarthy, founder of Women in Data UK, and Robin Sutara, Field CDO at Databricks. They discuss the challenges of recruiting and retaining diverse talent, the importance of male allies, and the role of AI in creating a more inclusive workforce Lees meer
Key Moments:
The Power of Community: Building a Network for Women in Data: Roisin McCarthy shares the story behind founding Women in Data, inspired by her mother's advice to "put up or shut up." She highlights the organization's growth to 80,000 members in 120 countries and emphasizes the importance of male allies in achieving gender representation. (2:41)
From Apache Helicopters to Chief Data Officer: A Non-Traditional Journey: Robin Sutara shares her unique career path, starting with repairing Apache helicopters in the US Army and eventually becoming a CDO. She discusses the challenges she faced as a woman in tech and the importance of fixing systemic issues to achieve equity. (6:19)
The Talent Crunch: Addressing the Data Skills Gap: The conversation shifts to the shortage of qualified individuals in data and technology. Roisin McCarthy highlights the need for organizations to rethink their recruitment strategies and remove unnecessary barriers to entry. (11:31)
Closing the Pay Gap: A Shared Responsibility: Roisin and Robin discuss the persistent pay gap in the data industry and the risk of it widening further. They emphasize the importance of both individual and systemic action to achieve pay parity. (20:46)
Generative AI: A Double-Edged Sword for Recruiting: Roisin McCarthy shares a cautionary tale about the potential for bias in AI-generated job descriptions. She stresses the importance of human oversight and highlights Women in Data's work to develop technology that removes bias from job descriptions. (46:30)
The Future of Data and AI: Embracing Innovation and Inclusion: Robin Sutara expresse…
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Why a Federated Data Team is Crucial for Business Value, with Dow
19 februari 2025 · 51 min
Join host Cindi Howson alongside Chris Bruman, Chief Data and Analytics Officer at Dow, and Dan Futter, Chief Commercial Officer at Dow, as they explore how data-driven innovation is reshaping business and customer experiences. From the hub-and-spoke model for data management to the power of real-time insights, they discuss the role of data literacy, leadership, and AI-driven decision-making in driving success. Don’t miss this conversation on the future of AI, data strategy, and innovation. Lees meer
Discover the innovations that inspire Chris Bruman and Dan Futter, how data has shaped their careers, and which tech leaders they admire most.
Key Moments:
Revolutionizing Data: The Hub-and-Spoke Model: The Dow team highlights the shift to digital, while Cindi Howson reflects on IT’s evolution. They explain Dow’s decentralized hub-and-spoke model, balancing governance with agility for faster insights, accuracy, and career growth. (9:04)
Why Data & Business Literacy Matter: Our guests stress understanding business needs, defining clear roles within the hub-and-spoke model, and supporting skill development. This approach simplifies processes, builds confidence in analytics, and drives value for Dow. (18:02)
The Integrated Data Hub: A Game-Changer: Dow’s data hub slashes data science time from months to a day. Prioritizing quality over speed prevents tech debt, ensuring strong governance. Now the go-to source for innovation, it plays a crucial role in Dow’s data strategy. (28:03)
Balancing Competing Demands in Industry: Our knowledgeable leaders in the industry underscore the importance of prioritizing data projects for impact. Decentralization eases bottlenecks, but demand remains high. Dow now requires senior sponsorship to ensure measurable value and optimize resources. (36:06)
Key Quotes:
"Too often, we wait until the project's done to figure out how to get the value and who’s going to sponsor it. We have to flip that around and secure senior sponsorship before we even …
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Data Trends Shaping AI’s Future with NVIDIA’s Agentic AI CTO
5 februari 2025 · 50 min
Your host, Cindi Howson, and CTO of NVIDIA AI agentic software, Bartley Richardson, discuss the transformative potential of generative and agentic AI in business, focusing on customer service, HR, and workplace innovation. They explore real-world use cases, the challenges of managing diverse data sources, and the tools and technologies shaping the future of AI which lead to…. Lees meer
Data Challenges: Cindi and Bartley discuss the complexities of managing structured, semi-structured, and unstructured data in the context of generative AI. They explore the challenges and opportunities presented by different data formats.
Tools and Technologies: Bartley provides guidance for AI and tech leaders on evaluating and building AI agents, emphasizing the importance of listening to employee needs and selecting the right tools for specific use cases.
Real-World Use Cases: The conversation digs into practical applications of agentic AI, with a focus on customer service and software development. Bartley highlights examples of how companies are using AI agents to improve efficiency and productivity.
The Future of AI: The episode concludes with a look ahead at the future of AI, with Bartley sharing his optimism for the transformative potential of agentic AI and offering advice for data and AI leaders
Discover the creative facets that inspire Bartley and how data has been a driving force in his life since earning his PhD.
Key Moments:
Understand agentic AI: Bartley explains how agentic AI is one of the most exciting and transformative developments in the AI space, evolving from generative AI and LLMs (large language models) to create systems capable of taking actions on behalf of users. (2:20)
Use Case Summary - AI-Powered Agentic Workflows at NVIDIA: NVIDIA has embraced agentic AI workflows to enhance both employee efficiency and customer experience. A prime example is their implementation of Agent Morpheus, a system designed to streamline software delivery and security processes. …
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Data and AI Predictions for 2025 with Matt Turck, Steve Nouri, and Joe Reis
22 januari 2025 · 1 uur 22 min
In this season premiere of The Data Chief podcast, your host Cindi Howson sits down with three industry visionaries to explore the trends, predictions, and must-take actions for data leaders in 2025. Get ready for a deep dive into: Lees meer
The generative AI revolution with Matt Turck, Partner at FirstMark Capital
The future of data science and genAI with Steve Nouri, Founder of GenAI Works and AI for Diversity
Data Engineering in the Age of AI with Joe Reis, author of "Fundamentals of Data Engineering" and the upcoming "Mixed Model Arts."
Plus: Hear their fun predictions for everything from sports to space travel!
Key Moments:
The generative AI revolution: Matt Turck, Partner at FirstMark Capital shares his insights on the evolving AI landscape, the rise of unstructured data, and why now is the time for enterprises to embrace AI. (1:40)
The Future of Data Science: Steve Nouri, Founder of GenAI Works (an 8-million-strong community!) and AI for Diversity, discusses the impact of GenAI on data science roles, the ethical considerations of AI, and exciting trends like embodied AI and agentic AI. (29:36)
Data Engineering in the Age of AI: Joe Reis, author of "Fundamentals of Data Engineering" and the upcoming "Mixed Model Arts," provides his expert perspective on the importance of data modeling, the need for upskilling in data teams, and the potential for a universal semantic layer. (1:00:00)
Key Quotes:
“I would predict that there's going to be a number of big acquisitions in our general space in 2025. This whole tension between the public markets doing very well, especially in tech, but the private markets still recovering - I think lends itself well to a wave of consolidation.” - Matt Turck
“Anything that requires democratization, I'm a big fan of. And certainly, the ability to query natural language databases and all things, making that available to everyone is a very powerful idea. You guys at ThoughtSpot know this better than anyone.” - Matt Turck
“We are seein…
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Five Best Practices to Succeed with Data and GenAI: Lessons from Leaders
13 november 2024 · 31 min
Key Moments: Lees meer
Focusing on Value with Bill Schmarzo 1:48
Unlocking the Collective Genius with Walid Mehanna 4:07
Building a Data-Literate Workforce with Valerie Logan 5:58
Creating a Human-Centric AI Strategy with Sadie St. Lawrence 7:40
Selecting the Right Tools with Katie Russell 11:23
Implementing tools responsibly with Robert Garnett 16:00
Why Clean Data Matters with Barr Moses 19:36
Ensuring Responsible AI for the Long-Term with Dr. Gary Marcus 25:45
Key Quotes:
“Data-driven is not important. Value-driven—that’s what’s important. We should focus on value.” — Bill Schmarzo, Head of Customer Data Innovation at Dell Technologies
“Our role was rather to activate the organizational muscle… to try things out and tell us what has the highest opportunity and possibility.” — Walid Mehanna, Chief Data and AI Officer at Merck Group
“It’s really a mindset and a muscle… we need to foster this kind of lasting change.” — Valerie Logan, CEO of the Datalodge
“Teaching people to ask better questions is more about critical thinking than technology.” — Sadie St. Lawrence, Founder of the Human Machine Collaboration Institute
“We wanted to make analytics accessible to everyone, combining real-time data and intuitive tools so every team member can gain insights and contribute to our mission to decarbonize.” — Katie Russell, Head of Data and Analytics at OVO Energy
As we are looking at applications of AI within our environment, we are focused first on responsibility, making sure that we have a broad enough data set when we're building machine learning models, for instance. And so that's at the heart of anything that we do.” – Robert Garnett, Vice President for Government Analytics and Health Benefits Cost of Care at Elevance Health
“Our world is moving towards a place where data is the product—and in that world, directionally accurate just doesn’t cut it anymore.” — Barr Moses, CEO and Co-Founder of Monte Carlo
“The tech policy that we set right now is going to really …
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How to Master the Future of Work and AI Upskilling, with Marni Baker Stein at Coursera
30 oktober 2024 · 36 min
Description: Lees meer
Marni Baker Stein, Chief Content Officer at Coursera, joins host, Cindi Howson, and dives into the impact of Generative AI on skills, diversity in tech, and the future of upskilling.
Key Moments:
The impact GenAI and the surge of learning demand (05:45)
Why employers must prioritize AI literacy (10.32)
The gender gap in AI learning and why it matters (19:40)
Leveraging data to drive personalization and learner success (24:00)
Predictions for the future of AI in the labor market (29.53)
Key Quotes:
“Generative AI is going to require us to all be a lot more emotionally intelligent because it's going to create such disruption and change. And we're all going to have to navigate the complexities of this change. We're going to have to bring our organizations through this change. That's going to take emotional intelligence as the one thing this technology isn't, is human. Understanding and human empathy is going to remain paramount.”
“In terms of data and AI skills, what is extraordinary is that the demand for these skills in the last year has grown over a thousand percent. We now have seven individuals a minute enroll in GenAI content.”
“Millions of people globally are deciding that it's time to upskill and reskill in these AI, regardless of whether their employer is telling them to or not. People see it happening. They're reading about it. They're hearing about it. And they're actively going out and chasing down those skills.”
Mentions:
Caste: The Origins of Our Discontent by Isabel Wilkerson
From Academia to EdTech: The Path to an Equitable Education in the Digital Age
Girls Who Code
Marni Baker Stein Bio:
Marni Baker Stein is Coursera’s Chief Content Officer, where she oversees the company’s content and credential strategy and partner relationships. Marni has more than 25 years of experience in producing and scaling online and hybrid education programs. Prior to joining Coursera, she was Chief Academic Officer and Provost at Western Gover…
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How Can Data Leaders Shift from Data-First to Business-First Mindset? With Deepak Jose, Global Data, Analytics and AI leader
16 oktober 2024 · 46 min
Key Moments Lees meer
From Engineer to Data Leader (03:05)
A Mindset Shift: Business Problem First, Data Second (9:31)
Learning From Missteps (11:00)
The Gazelle and the Lion Analogy (14:53)
The Role of AI: Do Things, Do Things Better, and Do Better Things (25:58)
Built to Last versus Built to Adapt (40:01)
Key Quotes
"Instead of a data-first mindset, you need to have a business problem-first and data-second mindset. That has helped me transform myself as a leader quite a bit."
"It’s more important to define the problem right than solving the problem. How can we understand what you’re trying to solve, and how it impacts the stakeholder?"
“The head of data analytics functions need to be business problem driven, empathy driven, and not technology-first minded or AI-first minded. Our objective is to solve the business problems of the organization. Data, AI, and tech are the enablers.”
"In the past, we built capabilities to last. Now, the mindset has to be to build capabilities to adapt."
Mentions
Is Data Quality the Biggest Threat to Humanity? With Barr Moses and Olga Maydanchik
Responsible AI Institute
Two-Pizza Rule
Girl Scouts STEM Classes
From Cancerman to Ironman: A Police Officer's Journey of Arresting Illness
Hans Zimmer
Deepak Jose Biography
Deepak Jose is Vice President, Head of Data Sciences & Business Intelligence at Niagara Bottling. He is a member of the Forbes Tech Council, AWS Retail and CPG Executive Advisory Forum, industry standards associations, Editorial Board for CDO Magazine, and an advisor for startups and AI analytics service companies.
Before Niagara, Jose was part of global brands like Coca-Cola, Mars, ABB Group, Asurion and Mu Sigma in strategic roles driving business growth. He was named to the 2023 Consumer Good Visionaries by Consumer Goods Technology and Retail Info Systems News, the 2023 40 under 40 by CDO Magazine, the 2022 and 2023 Top 100 Innovators in Data & Analytics by Corinium Global Intelligence, the 2023 100 Most Inf…
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Can We Tame AI Before It’s Too Late? With Dr. Gary Marcus
2 oktober 2024 · 45 min
Key Moments: Lees meer
Disappointment With Today’s AI Systems (4:00)
Congressional Inaction And The Need for AI Regulation (9:00)
The Seduction of AI Propaganda (15:00)
The Misguided Hypothesis of "Scale is All You Need" (23:00)
Don’t Be Fooled by the Masters of AI Hype (27:00)
The Global AI Race and the Need for International Cooperation (33:00)
Key Quotes:
“This matters. It matters as much as immigration policy or financial policy. The tech policy that we set right now is going to really affect the rest of our lives.”
“We should want to have AI that can be like an oracle that can answer any question. There is value in trying to build such a technology. But, we don't actually have that technology. A lot of people are seduced into thinking that we do. But it may be decades away.”
“Nobody can look you in the eye and say, ‘I understand how human intelligence works’. If they say that, they're lying to you. It's still an unexplored domain.”
Mentions:
Taming Silicon Valley: How We Can Ensure AI Works for All Of Us
Kluge: The Haphazard Construction of the Human Mind
The Algebraic Mind: Integrating Connectionism and Cognitive Science (Learning, Development, and Conceptual Change)
The EU AI Act
AI Generates Covertly Racist Decisions About People Based On Their Dialect
Dr. Gary Marcus Bio:
Gary Marcus is a leading voice in artificial intelligence. He is a scientist, best-selling author, and serial entrepreneur (Founder of Robust.AI and Geometric.AI, acquired by Uber). He is well-known for his challenges to contemporary AI, anticipating many of the current limitations decades in advance, and for his research in human language development and cognitive neuroscience.
An Emeritus Professor of Psychology and Neural Science at NYU, he is the author of six books, including, The Algebraic Mind, Kluge, The Birth of the Mind, the New York Times Bestseller Guitar Zero, and most recently Taming Silicon Valley: How We Can Ensure AI Works for All of Us. He has often contributed …
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Three Must-Read 2024 AI and Analytics Books with Jeremy Kahn, Sol Rashidi, and Bernard Marr
18 september 2024 · 1 uur 24 min
Key Moments: Lees meer
Jeremy Kahn
The history of AI: the Turing Test and the Eliza Effect with Jeremy Khan (1:50)
Jeremy’s view on how we can learn from lessons of the past (9:00)
It starts with data and people: leveraging AI to increase productivity (16:00)
Sol Rashidi
Sol Rashidi on failing to succeed in AI (31:00)
The need for rogue executives (37:00)
Sol’s view on prioritizing GenAI use cases and measuring ROI (42:50)
Bernard Marr
Bernard Marr on demystifying AI (60:02)
Is society ready for AI’s impact on augmenting jobs? (66:00)
AI’s impact on personalization of medicine, treatment and drug discovery (72:00)
Key Quotes:
Jeremy Kahn
“We're in a position where we can take action while this technology is still being shaped, to try to set some sensible guardrails. If we do that, we will see a lot of benefit from this technology. If we wait, we’re going to be in a situation like with social media. We will have a deskilling of essential human cognitive abilities.”
We don’t talk enough about how to train people to use AI software. The organizations that think hardest about that are going to be very successful.”
Sol Rashidi
“Usually I start the conversations of how ROI shouldn't just be a financial measure. There's three ROI's in my opinion. There's a financial ROI, there's a cultural ROI, and there's a relevancy ROI.”
“I am adamant that business value is not the number one marker. Everything needs to be scored and graded by criticality and complexity. And your criticality is a measure of ‘what is the impact if we don't do this?’”
Bernard Marr
“What we are seeing is we will see an augmentation of pretty much every single job. I can't think of many jobs that will not be augmented by GenAI. We need to really expect change as individuals.”
“We must create a world where education is seen as something that never stops, that carries on. I believe that we are currently entering a hyper evolution cycle, with artificial intelligence right at the center of it.”
Me…
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How Mark Cuban Wins with Data for Affordable Drugs, Sports, and Developing AI Talent
4 september 2024 · 40 min
Key Moments: Lees meer
The road to entrepreneurship (1:00)
Bringing transparency to the prescription drugs industry (4:30)
Analyzing in-game sports data to shoot for new heights (20:00)
Changing the world by making data and AI accessible to everyone (25:00)
How LLMs can build curiosity for the next generation of tech talent (33:00)
Key Quotes:
“As with all things technology, everybody has access to the information, but few people take the time. But those who do tend to have an edge. If you're curious, if you love to learn, you're going to do pretty well. But how do you find those people when they're kids and how do you try to just capture their imagination and get them excited about the technology? That's why we started the bootcamp.”
“I wanted to make technology accessible to people who otherwise couldn't get it. It doesn't matter what you look like, who you are, what your ethnicity is, what your background is. There are just going to be people who don't have access. I wanted to open that door for them. I'm a big believer in diversity, and that when you look at places where other people aren't, that's when you find brilliance that can change the world.”
“The path to least resistance to learning AI is simple. All you’ve got to do is use it. You can use it in ways that you can’t possibly imagine. You can learn how to use large language models to start your own programming and teach yourself how to do it. The sky's the limit. Better way to put it, there is literally next to nothing you can't teach yourself using a large language model. You can even train and educate the model. It’s a virtuous cycle. It can surge curiosity with kids.”
Mentions:
Cost Plus Drugs
The Master Algorithm: How the Quest for the Ultimate Learning Machine will Remake our World
Mathletics: How Gamblers, Managers, and Sports Enthusiasts Use Mathematics in Baseball, Basketball, and Football
Mark Cuban AI Bootcamps
Mark Cuban Bio:
Mark Cuban has been a natural businessman since age 12, when h…