PRIORITIZE OUTCOMES

“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. 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.”

“With AI, the same is happening. It is happening on three levels. It happens, of course, on the UI side. The time is clearly over where you design software, where the dump software that requires the intelligence to sit in front of the computer. Then the second one is, of course, the business processes... a rather rigid process, like the standard operating procedure of a company. But now, of course, with these agents, we can blend the structured and unstructured world more seemingly. And then, of course, below that, you have the whole data layer.”

“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.”

“LLMs, unstructured world, that's all good, right? But most of the time, if you want to plan forward, if you want to make good decisions in a company, you need predictions. Now, the problem is, of course, still today, if we look at these predictive questions... 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.”

“With AI, the same is happening. It is happening on three levels. It happens, of course, on the UI side. The time is clearly over where you design software, where the dump software that requires the intelligence to sit in front of the computer. Then the second one is, of course, the business processes... a rather rigid process, like the standard operating procedure of a company. But now, of course, with these agents, we can blend the structured and unstructured world more seemingly. And then, of course, below that, you have the whole data layer.”

“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.”

“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. Last year, everybody could build an MCP server. It was so super simple to hook up your MCP server and do amazing things with it. But that becomes like for 10 APIs, not an issue, 100 because you'll get already context bloat and all these challenges. But we have 20,000 APIs, right? So it becomes just like because it's so huge.”

“Now what we do in the past, we call this process mining. 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... 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 or more countries to run your company more efficient because now you'll learn something, how the organization behaves.”

“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.”

“Now what we do in the past, we call this process mining. 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... 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 or more countries to run your company more efficient because now you'll learn something, how the organization behaves.”

“Now what we do in the past, we call this process mining. 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... 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 or more countries to run your company more efficient because now you'll learn something, how the organization behaves.”

“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.”

“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.”

“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. 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.”

“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.”

“LLMs, unstructured world, that's all good, right? But most of the time, if you want to plan forward, if you want to make good decisions in a company, you need predictions. Now, the problem is, of course, still today, if we look at these predictive questions... 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.”

“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. 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.”

“LLMs, unstructured world, that's all good, right? But most of the time, if you want to plan forward, if you want to make good decisions in a company, you need predictions. Now, the problem is, of course, still today, if we look at these predictive questions... 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.”

“With AI, the same is happening. It is happening on three levels. It happens, of course, on the UI side. The time is clearly over where you design software, where the dump software that requires the intelligence to sit in front of the computer. Then the second one is, of course, the business processes... a rather rigid process, like the standard operating procedure of a company. But now, of course, with these agents, we can blend the structured and unstructured world more seemingly. And then, of course, below that, you have the whole data layer.”

“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. Last year, everybody could build an MCP server. It was so super simple to hook up your MCP server and do amazing things with it. But that becomes like for 10 APIs, not an issue, 100 because you'll get already context bloat and all these challenges. But we have 20,000 APIs, right? So it becomes just like because it's so huge.”

“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.”

“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. Last year, everybody could build an MCP server. It was so super simple to hook up your MCP server and do amazing things with it. But that becomes like for 10 APIs, not an issue, 100 because you'll get already context bloat and all these challenges. But we have 20,000 APIs, right? So it becomes just like because it's so huge.”