Philipp Herzig

“I mean, for the most part, SAP software is seed-based, licensed today, with a few exceptions. But very clearly, with AI, it was very clear for us that step by step, it will go towards this consumptive world, first consumptive, and then maybe in the next step, once we have more verifiability in the system, then also towards maybe an outcome-based license model. But the reality is also, it is today for us, it's a hybrid model. It's consumptive, but it still has a certain element of seats in there.”

“The hypothesis is that, of course, once the hardware matures in the quantum space, there are certain problems that you can address that are hard to address today. What we are focusing on is the optimization domains, obviously, and then if you go into things like logistics, traveling salesman problems, knapsack problems, like all these kind of usual hard problems in computer science, these are interesting problems where we believe that could be interesting for the future, for maybe a different kind of computing paradigm to solve for.”

“I think at the end of the day, it's all about adoption and the outcome you bring to the customer. I mean, the technology, look, the reality is for most companies, the technology doesn't matter. I always tell to my developers all the time, our job at SAP is to make the technology disappear. We need to get the outcome in front of the customer. And then try to make sure that we of course, bake the enterprise qualities in and the integration is there. And the customers can turn these capabilities on almost instantaneously.”

“SAP is the market leader in enterprise, software applications and platforms. It has 400,000 enterprise customers. Usually, I just running their finance, HR, and supply chain, manufacturing, execution, logistics, warehouse management, and then of course everything on the customer side, sales services, commerce, procurement, you name it. End-to-end, like SAP, we always say we have the broadest portfolio in terms of end-to-end running the business end-to-end. This is where SAP started with, giving real-time insight. Usually, I really describe this as it's not just software in itself, it's kind of the operating system of a company essentially.”

“What hasn't changed is what customers are seeking for, which is outcomes, right? Outcomes and return on their investment in order to get the things done, right? And of course, now AI is an amazing technology that again helps to get more things done in the enterprise, right? And then that is actually what SAP is standing for, right? And so what we are really doing is in given, of course, also the breadth of the portfolio and the customers is, of course, to help customers to achieve more by deeply embedding AI, AI agents, and of course, transforming now the user interface.”

“SAP and these large customers, right? They always have a problem of scale. Okay, what do you know with 100 documents? Well, it becomes a little harder. A thousand documents becomes a deeper engineering challenge. And now if you go into Julia or Sarah, you're maybe an SAP US employee, right? Of course, if you ask a question, of course, for travel policy, for example, of course you expect a very different answer than me as a German employee would get. So you now need to connect this actually with your master data.”

“The time is clearly over where you design software, where the dump software that requires the intelligence to sit in front of the computer. If you look at classical software, what did you do? You decide to use an interface. This is over. It's now, we call this Generative UI. The UIs get dynamically generated. If you have analytical questions, for example, or if you want to do your deep research, not just the deep research you find on perplexity or the usual chatbots, but deeply rooted, let's say tariffs are being introduced or new taxes or the straight-o-formals, what does this mean for my supply chain?”

“Now we call it agent mining because we record all these decision traces, these contexts, what the users are entering into the system. And then you can either use it to say like, hey, wait a minute, this is actually an anomaly. The folks in, I don't know, in UK from our company or the folks in Australia shouldn't do this because the standard operating procedure is this. Or you say like, oh, that's actually a very good improvement. And then you can elevate this to be the new standard operating procedure, maybe not just for Australia, but maybe for the rest of the world.”

“Large language models are not made for this, right? In a way, how they generate just one token after another essentially in a sequence to sequence modeling, I mean, they're language models, right? And they do this phenomenally well. But if you still want to do these predictors where you have to go back to these classical machine learning approaches... What we said all the time is, okay, look, we have all this data stored in these tables, right? Thousands of tables, right? Where all this information is stored. Can we not apply the same idea that large language models or multimodal models did for the unstructured world, actually for the structured in order to start predicting things?”

“For me, it was always very clear. I mean, for the most part, SAP software is seat-based, licensed today, with a few exceptions, like Concur or Fieldglass, for example, or the business network. But very clearly, with AI, it was very clear for us that step by step, it will go towards this consumptive world, first consumptive, and then maybe in the next step, once we have more verifiability in the system, then also towards maybe an outcome-based license model to, for example, what Sierra is doing and so on and so forth.”

“The hypothesis is that, of course, once the hardware matures in the quantum space, there are certain problems that you can address that are hard to address today. What we are focusing on is the optimization domains, obviously, and then if you go into things like logistics, traveling salesman problems, knapsack problems, like all these kind of usual hard problems in computer science, these are interesting problems where we believe that could be interesting for the future, for maybe a different kind of computing paradigm to solve for.”

“What hasn't changed is what customers are seeking for, which is outcomes, right? Outcomes and return on their investment in order to get the things done, right? And of course, now AI is an amazing technology that again helps to get more things done in the enterprise, right? And then that is actually what SAP is standing for, right?”

“And we believe that still will continue, right? Because this is exactly what we're also seeing right now with, of course, there's still, of course, there's tremendous progress, but we also see that the AI adoption in the enterprise is still not where we want to see it, right? Like there's this Gardner curve, right? Where say like there's this AI innovation race, and then there's this AI outcome race, right? Then the gap almost increases, right? Versus getting narrow.”

“With AI, the same is happening. It is happening on three levels. It happens, of course, on the UI side. ... Then the second one is, of course, the business processes like an order to cash in the past. ... And then, of course, below that, you have the whole data layer, right? The whole data layer of bringing, of course, SAP has a lot of super valuable data for a company.”

“But SAP and these large customers, right? They always have a problem of scale. Okay, what do you know with 100 documents? Well, it becomes a little harder. A thousand documents becomes a deeper engineering challenge. ... we have 20,000 APIs, right? So it becomes just like because it's so huge, right? There's so much things. So it becomes this problem of scale, right?”