SAP on Why the Autonomous Enterprise Needs a New Kind of Leadership,

Neil C. Hughes (0:00)

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What if years of AI investment are only saving each employee just about enough time to fetch a cup of coffee? Well, my guest today calls this the coffee cup level of productivity. I will argue that moving beyond it requires companies to redesign their work instead of attaching AI to yesterday's processes. So, today here on AI at Work, I want to be joined by the head of business AI for SAP, Emia. His name is Jesper, and we're going to discuss why business outcomes should come before models and where bounded AI agents can work reliably today and why context turns plausible answers into incredibly useful decisions.

And we'll cover everything from the autonomous enterprise, human accountability across the every loop, and the leadership changes needed to move from pilots towards trusted execution. And Jesper will also tell us a little about his forthcoming book. I think he's going to leave us a few teasers today on The Intent-Driven Leader. So, if your AI program has plenty of tools, well, limited business value right here in 2026, today's conversation could explain exactly what you've been missing. Intrigued? I was hoping you'd say that. Let me introduce you to Jesper right now.

So, thank you for joining me on the podcast today. Can you tell everyone listening a little about who you are and what you do?

Jesper Schleimann (2:10)

Well, thank you so much for having me, Neil. My name is Jesper Schleimann. I am the head of what we call Business AI in SAP in the EMEA region, which covers everything from Swalbot in Northern Norway to South Africa, and in all across Europe from the UK, where you are, an island, or to the other side of Europe.

Neil C. Hughes (2:32)

Well, it's a pleasure to have you join me today. We've heard a huge amount of noise around AI transformation over the past few years, but a few question marks about return on investment in some quarters. But I'm curious, from everything that you're seeing there right across the world, how much genuine progress are you actually seeing inside large enterprises? What's happening now?

Jesper Schleimann (2:55)

I think if you zoom back a little bit, you're going to see that what we would call adoption, not traditional adoption, is actually accelerating again. But the problem is, in my view, that the true transformation of organization is moving much, much lower than the technology, right? So the progress is real, but it's really also very uneven, especially the last enterprises in certain industries like high tech, pharma, to some parts of retail and service, I would say have moved quite far in strong adoption and also in areas like specific areas like employee productivity, obviously, customer service, the front end, but also the IT office, obviously.

And now the middle office is where we are still to see the bigger revolution, in my view, yeah.

Neil C. Hughes (3:51)

And I'm curious, from what you're seeing there, what is it that is separating the organizations that are successfully generating value from AI, from those that are still struggling to get beyond pilots and experimentation? What is it different about those organizations?

Jesper Schleimann (4:09)

Look, I think it's not about who has the best models or even who has the most models. It's about who can return the intelligence into trusted execution.

The ones that are really successful in my mind are the ones who start with valuable business outcomes and focus on that, and then walk backwards, and not with a focus of technology. What we've seen in a lot of the early deployments is the IT departments get hands of amazing technology that for sure has capabilities we have never seen before. But it becomes technology in search of a problem. It's actually one of the things I uncovered in writing my book, that is a common pattern that has been deployed across many IT departments. And the realization is now in that we really have to, of course, flip this on its head and start with a business problem at hand, and then work backwards towards the technology. And then of course comes the challenges of then scaling this, which is really the second phase of the journey, which is, well, I would say we're in the middle of now, where it's about moving away from just building single-state projects or silo projects and begin evaluating and evaluating the pretty value to reuse of projects, of technology, and building projects that lay the foundation for the next problem, so we can get to that exponential scale. And that really requires another level of true executive ownership, and therefore also understanding of the technology. And that, I think, unfortunately, is still lacking a bit.