Using AI at Work

The episode contains substantive ideas about the AI-business gap, cultural enablement vs. technology adoption, and distributed AI governance models grounded in prior data management experience. However, it suffers from repetition (the data warehouse comparison is circled back to multiple times), some soft advice ("educate your people"), and padding with Junto AI promotional content that dilutes focus on the core governance argument.

“There is a gap between what we promise around AI and where we create business value out of it.”

“It's not a technology topic, it's a cultural enablement topic.”

The contrarian position on Chief AI Officers (arguing against rather than for the role) is notable and generates genuine tension with the host's business model. The framework drawing parallels to the data-business gap and the caution about AI as distraction from core business (low AI adoption creating more sustainable companies) are fresh takes not commonly heard. However, the in-residence/field deployment model and broader decentralization argument are adaptations of existing data mesh and organizational change management patterns.

“We don't need a chief AI officer. It's a wrong decision to hire one.”

“I'm really wondering if a low AI adoption at the moment creates more sustainable companies in the next two to three years than the ones who are opening up AI to everyone and do every crazy thing with AI.”

Dr. Schmidberger brings 15+ years of genuine data leadership (led 40-person teams at major companies like Scout 24 and AWS), has built operational frameworks in practice, and currently operates as CTO of a startup. He is a practitioner and operator, not a consultant-for-hire or talking-head. However, his direct operational experience appears concentrated in European tech/financial services rather than broad enterprise sector coverage, and some claims lack visible track record of transformation outcomes at scale.

“With 15 years in data leadership, including stints at AWS Scout 24 where he led a 40 person data organization”

“I was working at Scout 24 we had an AI evangelist. Uh, she was a very talented product manager and she also had a small team”

The episode includes some concrete examples (Scout 24's two-tier approach with evangelists and proof-of-concepts, the travel booking agent case study, the AI board meeting cadence of 6-8 weeks) but relies heavily on anecdotal observations ("several product leaders," "most companies I see") without named references, quantitative data, or published case studies. The Gartner observation about keynotes is cited but not verified. Token usage trends are discussed but no specific numbers are provided.

“I have one company which improved the, the travel experience for their employees. So before they had to go to a portal, search for the hotel, uh, search for the flight. Now they enable the booking agent”

“when I was working at Scout 24 we had an AI evangelist. Uh, she was a very talented product manager and she also had a small team and before an FTE came into the unit, she went into that unit”

The host asks strong follow-up questions that probe the guest's thinking (the ROI measurement tangent, the phased vs. all-at-once rollout question, and the challenge of limited FTE availability). However, the host frequently validates and affirms the guest's positions rather than push back or test assumptions. The host does not challenge the sustainability claim about low-adoption companies, the statistical accuracy of the random number anecdote, or the generalizability of European data examples to other markets. The conversation is collaborative but lacks productive tension.

“So let me address that because you know, uh, we talk to business owners regularly and the three main impediments”

“Is that an impact? Sure. Can I measure it? I don't know. Do you have, and this is kind of a little, uh, of a jag from our conversation, but on that topic of ROI measurement”