00:09:38 · Neil C. Hughes, Steffen Hoellinger
[00:09:38] S0: And again, I'm curious, when you're looking at real world examples, which industries across Europe are are making the most progress with real time data? Is there a customer story where having access to information as an event happens delivers that real measurable business outcome? Anything stand out there? Any particular industries leading the way? [00:09:58] S1: Yeah. I think it's it's across different industries, which also makes it, I think, very flexible and very, yeah, open to to different business problems across different industries. Of course, you have like the traditional use cases, for example, in banking, financial industries like fraud detection, but also like improvement of customer service or personal advice for customers that might be universal to several industries. But then again, manufacturing is an important category where we see different manufacturing customers implementing these new AI capabilities based on real time data and AI ready data on the factory floor to do things like predictive maintenance where you actually predict which machines might fail next so you can actually maintain them before the factory floor stops to work. So this is incredibly relevant for these kind of companies and you see that happening across different different industries and different customers in the manufacturing space, for example.
00:09:58 · Steffen Hoellinger, Neil C. Hughes
[00:09:58] S1: Yeah. I think it's it's across different industries, which also makes it, I think, very flexible and very, yeah, open to to different business problems across different industries. Of course, you have like the traditional use cases, for example, in banking, financial industries like fraud detection, but also like improvement of customer service or personal advice for customers that might be universal to several industries. But then again, manufacturing is an important category where we see different manufacturing customers implementing these new AI capabilities based on real time data and AI ready data on the factory floor to do things like predictive maintenance where you actually predict which machines might fail next so you can actually maintain them before the factory floor stops to work. So this is incredibly relevant for these kind of companies and you see that happening across different different industries and different customers in the manufacturing space, for example. [00:10:54] S0: And if we've got anybody listening in any of those organizations across those kind of industries, if they aspire to be exactly what you've just said there, what did those organizations have to change behind the scenes to achieve that result? And how long did it take before the business saw a return? Because I think that's something that so many people watching and listening are trying to get to at the moment.
00:10:54 · Neil C. Hughes, Steffen Hoellinger
[00:10:54] S0: And if we've got anybody listening in any of those organizations across those kind of industries, if they aspire to be exactly what you've just said there, what did those organizations have to change behind the scenes to achieve that result? And how long did it take before the business saw a return? Because I think that's something that so many people watching and listening are trying to get to at the moment. [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.
00:11:13 · 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.