00:06:46 · Steffen Hoellinger
[00:06:46] S1: Yes. Of course. I mean, it's it's definitely a big subject, particularly in regulated industries Yeah. And banking, insurance, but also some retailers that actually had to learn it the hard way over the last few years moving everything into the cloud, and then you had some disruptions because certain cloud region was, for example, offline. So we had a lot of these use cases where people rely on the resilience of Kafka, have used Kafka for mission critical use cases and they need to be able to, yeah, compute and to transact all the time. So they have local Kafka clusters running in the factory floor and running Kafka cluster in the retail store. And these are fully independent solutions where you even have compute where you have local models running locally and, yeah, making sure that retail store is always able to sell products to their customers and the factory is always able to function even if the link to the cloud is not working for a part of the time. And then, of course, what you can do is you can kind of catch up once the cloud comes back up or the the region becomes available again. You can kind of cluster link that with other regions and you can catch up on that. So never... You never lose data and you're always there. You're always able to process data and to keep keep doing your business.
00:07:57 · Neil C. Hughes
[00:07:57] S0: Love it. And I've got to ask from the conversations you're having, what tends to be the moment when maybe a promising AI project might run into trouble? Is it the technology, the quality of the data, or the way that the business operates? What... Where does it... Those problems typically begin?
00:08:12 · Steffen Hoellinger
[00:08:12] S1: Yeah. Lots of AI projects still fail at this point because also sometimes people have unusual expectations. Yeah? And I think you really need to be smart about how you implement AI for the relevant use cases and have realistic goals in in that regard and also proper cost assumptions and proper ways to implement that. So I think we really don't believe that you should use LLMs for example to to resolve everything, but you need to be smart about how you combine the relevant technologies that you have. So traditional ML has a lot of advantages. It's often faster and cheaper to run. And it can be combined with LLMs and it also can be combined for example with state computation at Flink. So you can also do something like complex event processing in Flink without any ML and LLM technology at all and create a lot of value from that. So particularly when you look at use cases like observability, it's just so much data that is continuously being generated that you cannot possibly analyze all of this data with LLM. You need to be smart about that combining for example filtering based on complex event processing or traditional ML to detect anomalies from the data and only filter out the patterns you want to then analyze further with LLM. You need to be smart about which models you use and also which, yeah, combinations of agentic AI use cases you you kind of implement in order to get the relevant value out of that for your relevant organization.
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.