The Data Storytellers Podcast · 2025-11-12 · 1h 7m
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.
Semantic Leverage | Albert Mangahas (Roo)63 / 100
Why Most AI Use Cases Miss the Point | Taylor Black (Microsoft)
Why Your Model Gets it Wrong | Dejan Mitkovski (BetMGM)
What "Data-Driven" Actually Means | Anthony Jackel (Ferrara)
The Hard Truths of Enterprise AI Adoption | Jodi Blomberg (Cox Automotive)