The Data Storytellers Podcast

Enterprise AI Governance in 2026 | Engin Bozdag & Stefano Bennati

Enterprise AI adoption is accelerating, but most organizations are still unprepared for the security, governance, and operational challenges that come with deploying generative AI at scale. In this episode of The Data Storytellers Podcast, Laszlo sits down with Engin Bozdag and Stefano Bennati, authors of the book AI Governance, to explore why many GenAI initiatives fail once they move beyond the demo phase. They break down the biggest risks organizations face today, from prompt injection and data leakage to vendor over-reliance and weak governance processes. The conversation also introduces their six-level AI governance framework and explains how leaders can move from high-level principles to practical controls that actually protect enterprise systems and data. AI Governance book: https://www.manning.com/books/ai-governance 45% off the book with no exp date or format restrictions: TDSTbozdag Engin's affiliate tracking link (specifically for this book): https://hubs.la/Q03_5wmG0 Stefano's affiliate tracking link (specifically for this book): https://hubs.la/Q03_5vLc0 Connect with us: Website: https://thedatastorytellers.com/ LinkedIn: https://www.linkedin.com/company/the-data-storytellers Apple Podcast: https://podcasts.apple.com/gb/podcast/the-data-storytellers-podcast/id1493766476 Spotify: https://open.spotify.com/show/2N0vZtHZHgod4Tll2LX2xa YouTube: https://www.youtube.com/channel/UCz9e56lhYUfORiOHMiLlPmA Chapters: 00:00 – Introductions and the enterprise AI adoption moment 05:00 – Why GenAI deployments fail in production 12:00 – The “demo in production” problem 20:20 – The gap between AI governance principles and implementation 27:20 – AI adoption models and governance trade-offs 36:30 – Build vs buy in enterprise AI systems 40:40 – The six-level GenAI governance framework 52:20 – Why traditional security is not enough for AI 1:00:30 – Privacy risks in generative AI systems 1:10:00 – What trustworthy AI actually means 1:15:30 – A practical checklist for AI leaders deploying GenAI

What "Data-Driven" Actually Means | Anthony Jackel (Ferrara)

What separates analytics teams that get a seat at the table from those stuck in the reporting queue? Anthony Jackel, Senior Director of Business Intelligence & Analytics at Ferrara, has spent his career answering that question, from Kraft to one of America's largest candy companies. In this conversation, we explore: * What "data-driven enterprise" actually means * The shift from data providers to decision enablers * Why user-centered design matters as much for dashboards as it does for iPhones * How to build analytics products that drive action, not just curiosity * The mindset shift that earns analytics a seat at the table Connect with us: Website: https://thedatastorytellers.com/ LinkedIn: https://www.linkedin.com/company/the-data-storytellers Apple Podcast: https://podcasts.apple.com/gb/podcast/the-data-storytellers-podcast/id1493766476 Spotify: https://open.spotify.com/show/2N0vZtHZHgod4Tll2LX2xa YouTube: https://www.youtube.com/channel/UCz9e56lhYUfORiOHMiLlPmA Chapters: 00:00 – Introduction to Anthony Jackel and his role at Ferrara 03:35 – Building Ferrara’s analytics team from scratch 07:42 – The problem with dashboards and low-value data work 11:50 – Connecting analytics to business decisions and revenue 16:25 – The importance of commercial empathy for data leaders 20:18 – Getting out of the service provider mindset 25:04 – Translating technical insights for non-technical audiences 28:45 – Why building trust is more important than being right 33:29 – Lessons from finance that shaped his analytics approach 37:12 – Coaching and developing high-performing analytics talent 41:05 – The challenge of balancing curiosity with execution 45:18 – Internal marketing and the power of repeatable wins 49:56 – Final advice for future analytics leaders 📍 Chapter Timestamps (Finalized for 50:13 runtime)

AI Optimism vs. Evidence in Late 2025 | Ylan Kazi (Blue Cross Blue Shield North Dakota)

In this episode of The Data Storytellers Podcast, we sit down with Ylan Kazi, Chief Data & AI Officer at Blue Cross Blue Shield of North Dakota, to cut through the noise around enterprise AI. Ylan explains why early machine learning hype never matched operational reality, how LLMs changed the game once they became accessible to anyone, and why most AI failures stem from cultural friction rather than technical limitations. He breaks down the gap between expectations and actual ROI, the overlooked complexity behind agentic AI, and the fundamental difference between inserting AI into old workflows and redesigning processes to be AI native. We also explore the economic and societal contours of the current AI cycle, including energy constraints, the cultural backlash against AI generated content, and why exponential progress looks slow until it doesn’t. Ylan shares what the coming micro bubbles might look like, how labor markets are shifting, and why new technology forces each of us to examine the meaning of human work. We end with a look at his AI Edge newsletter and what he is tracking as this transformation accelerates. Chapters: 00:00 Introductions and Ylan’s early AI skepticism 04:30 Traditional AI vs generative AI and why prompting skill matters 08:00 Why enterprise ROI lags and why most failures are cultural 12:00 The economics of LLMs, energy constraints, and infrastructure realities 18:00 Agentic AI, undocumented processes, and why value requires redesign 25:00 Automation, talent pipelines, and shifts in labor markets 34:00 Cultural reactions to AI content and the value of human creation 45:00 The case for micro bubbles and faster boom bust cycles 57:00 Ylan’s newsletter, what he is writing about, and closing thoughts

Dispelling The Myths of The AI Bubble | Ron Hetrick (Lightcast)

In this episode of The Data Storytellers Podcast, we sit down with Ron Hetrick, Principal Economist at Lightcast, to unpack the deep structural issues driving the U.S. labor shortage. Ron explains why this is not a short-term blip but a long-term demographic shift that will redefine how companies hire, grow, and operate. From fertility rate collapse and immigration policy to flawed assumptions about productivity, he helps us understand the real economic forces reshaping the talent landscape. We explore why AI and automation are not the silver bullets many executives hope for, and what leaders should focus on instead. Ron also shares what economists missed about remote work, the dangers of oversimplified labor data, and how companies can rethink workforce strategy in an age of persistent scarcity. Chapters: 00:00 – Introductions and Ron’s career path from TV to economics 03:12 – What’s really driving the U.S. labor shortage 07:21 – Why this isn’t a temporary workforce dip 11:44 – The demographic math no one is talking about 15:32 – Immigration, fertility, and the myth of the "lazy worker" 20:10 – Why unemployment stats don’t tell the full story 25:33 – The silent shift from job scarcity to talent scarcity 30:48 – What economists missed about remote work and productivity 36:15 – The false hope of automation as a labor solution 41:09 – How companies are overestimating what AI can replace 46:27 – The hard ceiling of productivity without people 51:12 – Forecasting the long-term consequences of talent shortages 56:33 – What future-ready organizations are doing differently 1:01:44 – Redesigning workforce strategy for a shrinking labor pool 1:06:28 – Advice for leaders on adapting to the new labor reality 1:10:13 – Final thoughts on demographic truth vs tech hype

Making Sense of the AI Narrative Chaos in 2025 | Rich Heimann (Author, Director of AI @ State of South Carolina)

On this episode of The Data Storytellers Podcast, Rich Heimann, author of Doing AI, GenAI Revealed, and the newly released Sutskever's List, joins us for a deep and provocative conversation on why most enterprise AI initiatives fail. Rich unpacks how internal power structures, belief systems, and corporate incentives shape what AI gets funded, how it's framed, and why so many efforts fall short. We explore the gap between organizational theater and technical progress, and why understanding human systems matters more than mastering the technical stack. If you're an AI or data leader struggling with implementation, this episode offers a fresh lens that might explain why the problem isn't your model. Want more? Rich’s new book Sutskever’s List: Foundational Ideas of Modern AI is now available — and you can get it HERE [https://hubs.la/Q03Pqb5w0] with 45% off. Chapters: 00:00 – Introductions and Rich’s background 02:44 – The real reasons AI adoption fails in enterprise settings 07:12 – Power, status, and the illusion of AI progress 12:25 – Why many AI initiatives are more about performance than impact 17:31 – Data science vs business theater: who’s really calling the shots? 21:50 – Cultural friction: how belief systems shape technical outcomes 26:43 – Rich on the danger of over-rationalizing human systems 32:17 – Lessons from behavioral economics and AI ethics 37:09 – What leaders pretend to want vs what they actually support 42:03 – Redefining AI success: who gets to decide what matters? 47:58 – Stories vs truth: how narratives become strategy 53:11 – The objectivity myth and why some orgs cling to it 59:04 – LLMs, RAGs, and the current moment of hype 1:04:50 – Why technologists need to understand organizational design 1:09:44 – Advice for AI leaders trying to make change stick 1:14:02 – Final reflections on trust, transformation, and learning