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