Dr. Sebastian Wernicke is a data scientist, a partner at Oxera Consulting, and author of the new book Data Inspired: Building an Organizational Culture of Inquiry for Lasting Transformation.

Sebastian talks with Greg about why he thinks culture, not tools, is the biggest obstacle to effective data/AI adoption. Sebastian critiques the “data deficit” assumption that more data yields obvious answers, arguing data often increases complexity and should drive inquiry, psychological safety, and willingness to be proven wrong. They contrast data-driven optimization with a data-inspired mindset that also asks “what if” questions to enable transformation, noting A/B testing examples like Google’s “50 shades of Blue” experiment don’t generalize out to most business decisions.

Sebastian also explains why initiatives fail when they treat technology as the hard part, while they ignore tacit decision-making, incentives, and workflows, and rely on pilots/lighthouse projects that don’t scale. He outlines strategy using Rumelt’s framework, discusses silos vs “swamps,” leadership behavior, and why CDO/AI roles need explicit transformation mandates, resources, and CEO-level support.

*unSILOed Podcast is produced by University FM.*

Episode Quotes:

Culture not technology, drives transformation

11:36: I think when it comes to data and AI specifically, it's really focusing on the wrong problem as the hard problem. I think whenever you're tackling one of these bigger initiatives, you're sort of trying to figure out in the beginning to say, "Well, what's hard about this?" Right? Because that's what we should focus on initially. And usually, I think what many of these projects focus on is clearly technology. So they say, "Well, what's hard about this? It will be hard to get the data together. It will be hard because there's all these fancy algorithms, and we need the cloud infrastructure, and we need the experts," and so on. And the other elements, you know, we need to act differently. We have to bring 20 different stakeholders together because many of these data and AI projects are interdisciplinary, and they all need to agree, and they need to agree in spirit, not just say that they agree and we're actually going to change the way we work, so we're going to redistribute power, maybe even in our organization, or who makes a decision, that's the hard part.

More data isn’t enough to make better decisions

02:29: I think we have this inherent assumption that when we want to make better decisions, all we need to do is add a bit of more data to our organizations, right? So, as soon as we see the right data, as soon as we see what the data is telling us, we will know what to do, everybody in the room will agree, and we know how to move forward. And of course, it's not at all like that.

Optimization is not transformation

02:31: We have this inherent assumption that when we want to make better decisions, all we need to do is add a bit of more data to our organizations, right? So as soon as we see the right data, as soon as we see what the data is telling us, we will know what to do. Everybody in the room will agree, and we know how to move forward. And, of course, it's not at all like that.

Show Links:

Recommended Resources:

Google’s ’50 shades of Blue’ experiment
Marissa Mayer
Goodhart's Law
Richard Rumelt

Guest Profile:

Guest Work:

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671. The Intersection of Culture and Data with Sebastian Wernicke

unSILOed with Greg LaBlanc

671. The Intersection of Culture and Data with Sebastian Wernicke