00:11:13 · Steffen Hoellinger, Neil C. Hughes
[00:11:13] S1: Yeah. Yeah. I think you can actually get a lot of value by starting to invest into the underlying infrastructure in a way that you should have schemas everywhere. You should actually have metadata describing where your data comes from and how your data is composed to have just a generally good understanding of the lineage of your data and you have to process the data close to the source. I think this is incredibly important not to dump the data somewhere and process it later which is just not good enough anymore in the age of AI. You kind of just have to, yeah, start investing in the underlying infrastructure. This way by, yeah, really being able to have a good understanding of your data and to effectively build an ontology of your of your data. Yeah. I think this is incredibly important as a as a measure and all the companies that I've seen investing into into this category and into this effort have had returns, it paid off on the way because they were faster in adopting the the Genetic AI use cases on top of that. [00:12:12] S0: And, of course, you also previously founded Airy, which Confluent acquired, I think it was in 2025. So having that experience both the startup world and now the challenges facing large enterprises, what have you learned about that gap between demonstrating an impressive AI application and successfully deploying it across a complex organization? It's probably a a podcast episode on its own map. What the big lesson you've learned?
00:12:36 · Steffen Hoellinger
[00:12:36] S1: It's it's obviously very different, like, to operate as a startup as compared to obviously operating as a larger organization. Also, Confluent also was already, working with lots of very relevant large companies across different industries. But now with Confluent being acquired by IBM, it's even more important, I think, to provide that reliability, that resilience that, of course, these large organizations expect from a provider like IBM and Confluent. And of course, in that sense, sometimes things are moving a bit slower, but at the same time, I think it's incredibly important to take that contribution and that that value also into consideration because IBM is really strong in the category of AI governance, for example, which is of course something that if you think about large enterprise adopting AI, it's not only about how do you build a large amount of agents. So there is a Gartner study, for example, that was released in April this year predicting that the average Fortune Global 500 enterprise will have a 150,000 agents on average by 2028, which is... Might seem a large number, but I think actually it can be even higher down the road. Let's see about that. But then again, it's not only about how you build these agents, but also how do you orchestrate them? How do you govern them? How do you make sure that they're regulatory compliant, which is incredibly important for an enterprise as well. It's not only about building them, which is incredibly easy these days to kind of build agents with Claude Code or with any other kind of coding tool. But it's also equally important to take that regulatory aspect into account and to help these organizations build agents that are fully governed and that are, yeah, fully able to comply to the regulatory requirements of certain industries like the banking industry. And of course also if you're looking at European requirements that are compliant to the EU AI Act and further regulation that doesn't work at the moment.
00:14:29 · Neil C. Hughes
[00:14:29] S0: And when we're talking about accountability and governance of hundreds of thousands of agents, how do businesses give AI systems access to current information while still making people remain accountable for the decisions that those agents or systems make? Anything you're seeing here, again, must be a big a big topic and you get lots of questions on this.
00:14:48 · Steffen Hoellinger
[00:14:48] S1: Yep. I think it's it's maybe the biggest challenge that we're currently observing and that very few people are actually aware of in terms of, yeah, it's a business process transformation challenge in terms of every large organization that currently runs a process has to redefine that process that maybe previously was run by 30 people on some software to be run-in the future by maybe 10 people and lots of AI agents supporting these people and making these decisions. But ultimately, you wanna have people being accountable for whatever has happened and being able to take responsibility for the decisions that these agents recommended to be implemented. You rarely wanna have agents just decide on their own what is gonna happen in the relevant business. But I think the technologies we have and also within Flink, for example, are incredibly important to be able to control these processes to make sure that everything is running smoothly and to be able to bring together a deterministic perspective on how these processes should be run and implemented with the probabilistic capabilities of AI agents in order to kind of make recommendations, make additional value on top of of that deterministic action. But in the end, don't wanna have arbitrary decisions. You wanna have predictable outcomes as an organization. And ideally, you wanna end up in the same place all the time. You don't wanna have ambiguity in regards to how your processes are being implemented and executed as a large organization. I think the technologies we develop and we implement together with these organizations can have tremendous value for these companies in that sense.
00:16:20 · Neil C. Hughes
[00:16:20] S0: And I suspect 2026 will be remembered as the year that AgenTek AI and agents all completely dominated our newsfeeds. But when we look to 2027 and AI agents become capable of taking actions across business systems and it becomes the norm, what should European CIOs and tech leaders be prioritizing next year, about the next twelve months? What should they be doing to start to prepare their data infrastructure without spending money on tech that they might not need? Any advice you'd give to people listening there?
00:16:50 · Steffen Hoellinger
[00:16:50] S1: Yeah. Yeah. I would I would say it's what I said before in terms of the models are rarely something we control as organizations but the data is really what we are in control of and the data will not change tomorrow. So the model that might be the model of the day and the best model, it will constantly change and the AI labs are really competing for whoever has the best model. And we shouldn't overly obsess about that. We should just use the models that are best suited for our particular use cases, but we should really be in control of the data. And, yeah, the... Lots of the problems we currently see in the space as AI problems are actually just data problems from my perspective. So having a strong foundation for the underlying data, good understanding of where the data originated from, what is the schema of the relevant data, having schemas everywhere in place. This is kind of, I would say, the no regret move that every CIO should invest in. And then also, of course, like in in governance solutions. So ontologies and governance solutions like enabling these agentic AI use cases to be built on top are incredibly important. And then I would say apart from that, just being flexible, not settling too soon on one particular platform where you think all your agents will live in the future, but leveraging capabilities that also Kafka, for example, gives you to distribute the data to all systems that you can actually create value from your data. So I think in the end you will have agents living in all kinds of different systems in the future and just not settling too soon to one particular systems where you think all your agents will live for your organizations. I think this is this is incredibly important. So staying flexible about the future would be my recommendation.