SAP: Bringing the ‘Operating System’ of a Company into the AI Era with CTO Philipp Herzig

More than fifty years ago, the modern idea of the standard enterprise software was birthed at SAP. Now, after managing companies through technological shifts from the mainframe to mobile, SAP is at the forefront of closing the AI adoption gap for their customers. SAP Chief Technology Officer Philipp Herzig joins Sarah Guo to talk about how SAP has remained a durable end-to-end “operating system” for its more than 400,000 customers from finance to supply chain. Philipp argues that the AI transition in businesses should focus on customer outcomes, UI changes, business processes, and the data layer. He also explains the challenges in enterprise AI adoption, including security, scaling, and data fragmentation, as well as the importance of evals and verifiability. They also discuss SAP’s suite of AI products, limitations of predictive tabular models, how SAP is shifting its pricing models in the AI era, and Philipp’s interest in quantum computing optimization. Sign up for new podcasts every week. Email feedback to [email protected] Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @pheartig | @SAP Chapters: 00:00 – Cold Open 00:42 – Philipp Herzig Introduction 01:18 – What SAP Does 02:51 – Why SAP Endures 06:53 – CTO Priorities and AI Push 12:14 – Scaling AI in Enterprise 17:06 – Verifiability and Agent Mining 20:42 – Tool Calling vs. Computer Use 22:11 – Domains Where Agents Deliver Value 24:58 – Limitations of Predictive Tabular Models 29:07 – Barriers to Enterprise Adoption 31:54 – How AI Will ‘Uplevels’ Work 34:03 – How AI Changes SAP’s Pricing Model 36:41 – What Makes a Winner in the AI Era 38:53 – Day in the Life of a CTO 40:08 – Customer Challenges 42:36 – Business Problem of Quantum Computing 46:21 – Conclusion

• 0:05Speaker 00

  Hey listeners, welcome back to No Priors. Today, I'm here with Philipp Herzig, the CTO of SAP, the enterprise juggernaut. We talk about their AI strategy, why SAP has endured and thrived through several technology transitions, why entrepreneurs are underestimating the challenges of scale, why AI is a business model transition, not just a technology transition, why he thinks that LLMs are not enough for predictive analytics, and even about the traveling salesman problem in the real world and the straight of her moves. Welcome, Philip. Philip, thanks so much for being with us.

• 0:39Speaker 01

  Yeah, it's a pleasure to be here. Thank you.

• 0:41Speaker 00

  Everybody knows the name SAP, but I do think that for lots of engineers or people who aren't close to the system in a larger enterprise, they don't really know like the breadth and function of the platform. Like, can you just describe what you guys do for customers?

• 0:59Speaker 01

  Oh, absolutely. I mean, look, SAP is the market leader, right, in enterprise software applications and platforms, right? There's 400,000 enterprise customers. And usually I just running their finance, HR, and you know, supply chain, manufacturing, execution, logistics, warehouse management, and then of course, everything on the customer side, sales, services, commerce, procurement, you name it, right? End to end, right? 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, right? Giving real time insight. And usually I really describe this as it's not just software in itself, it's kind of the operating system, right?

• 1:43Speaker 01

  Of a company essentially in order to get, you know, from everything from order to cash and source to pay, right? End to end managed for companies around the entire globe.

• 1:56Speaker 00

  I definitely want to talk about AI, LLM, some of those stuff that you guys are doing internally and then around predictive models as well. But just because the macro backdrop is on everyone's mind, both from a technology and an economic perspective.

• 2:13Speaker 01

  Oh, sure.

• 2:14Speaker 00

  I want to talk about SAP's position in the market a little bit. SAP has stood the test of time through multiple technology and market cycles. I, as an early-stage venture capitalist, am kind of on the other side of this where the narrative is like, well, when you have internet and cloud and mobile and AI and social, like you have an opportunity for new players. What do you like SAP, you know, even today is the, I believe, the largest like market cap enterprise software vendor versus sort of the last generation of the new guard, like the sales forces of the world.

• 2:59Speaker 00

  How has that happened? Like, how did you, how did you do it? And what is make, what makes it so durable?

• 3:04Speaker 01

  Well, what makes it so durable, right? At the end of the day, I mean, if you think about this, and it's happening is a little bit the same way also when we talk about the SARS is dead narrative or the SARS-Pak ellipse. I mean, anyway, we're, I have the feeling like in this market last year, AI was in a big bubble and everybody was kind of doing it. No, it's not. And this year, SARS is dead and so on and so forth. Look, the reality is now, of course, with the costs of building being so low, right? With specifically agentic coating and all these latest powerful models. I mean, something has always prevailed over the years because even when SAP was founded, right, in 1972, a long time ago. I mean, why was it started?

• 3:46Speaker 01

  Because actually in the seventies, when the founders of SAP worked still at IBM, what did they do? They went to each customer, right? And they implemented the finance system again and again and again and again. And then they said like, hey, this makes no sense, right? Because the economics, it doesn't scale, right? Because of course you can do this, right? But you can only add so much value, right? In any given time. And by the way, we are basically programming the system. Very similar. Of course, there's always a little bit that is specific then to the customer. And this was the idea where standard, the notion of a standard software was born essentially. Right. And then, of course, that stood the test of time, right? Because there is simply things and companies that need to get managed, right?

• 4:30Speaker 01

  End to end. And that also has transformed throughout the years. You've mentioned that, right? First, from the mainframe to client server, right? Then to the internet, then mobile, and now, of course, AI. So, of course, the software has changed and evolved all along with these technologies. 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? Towards, 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,

• 5:12Speaker 01

  is, of course, to help customers to achieve more by deeply embedding AI, AI agents, and of course, transforming now the user interface and so on and so forth, to help them get more, right, done in whichever industry that they are, that they are working in. And, and, and, 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? And the gap almost increases, right, versus getting, getting narrow.

• 5:55Speaker 01

  And, and, and, and that is what, what we are really focused on, right? That customers get the outcome from, from AI to, to achieve more, given, of course, the foundation we have. But simultaneously, of course, the system, we are, we are kind of re-engineering the entire system, right? With the help of AI in a totally new way.

• 6:16Speaker 00

  You now as CTO of SAP have like a very broad purview. It also includes the AI strategy piece of it, internal and for your customers. Like, what do you think of as your, your own top priorities for the organization? And where is SAP on this re-engineering or reimagination journey?

• 6:39Speaker 01

  No, look, I mean, we, we are, we are in the meantime, all in on AI, right? So, I mean, everybody in the company is using agentic coding, right? Like, because that's, of course, an amazing productivity boost, right? That our developers have, no matter in which programming language they are building the, the software for the customers. But of course, it's also really, again, focusing on customer outcomes, right? And we've seen this, for example, early, in the early days now with consulting, for example, right? We built this thing called Jewel for Consulting, which is phenomenal, because it was one of our fastest growing AI products. Because what this actually helps is to build the, to, to help the consultants, right?

• 7:21Speaker 01

  In the, in an SAP project or in a complex landscape, if they're, I mean, again, right? We are serving some of the largest customers. They have a lot of heritage, they have a lot of complex landscape. To help them actually to move into the cloud, to adopt the latest AI capabilities and so on. And with Jewel for Consulting, they can reduce like thirty percent of their efforts, right? To get to the outcome faster, which of course then directly reduces the cost, not just the time, but also the costs that are necessary in order to get, to, to, to get to the, to, to the latest software. And we've seen this, of course, with Conqueror, for example, right? Where now our travel booking agents are expense agents are alive. And so, so there's many of these outcomes that we are designing.

• 8:01Speaker 01

  But when you look from a CTO perspective, really, it, it, in my mind, it's three things that are really getting, not, not disrupted, but are massively changing. Yeah, to me, the metaphor is a little bit like when we moved from on-prem to the cloud, right? Originally, everybody thought, hey, we just take the on-prem software we already built, we put it on the internet, call it cloud, right? But then, only over, over a certain period of time, people realized, oh, what does actually C I C D mean, right? You can deploy daily or multiple times per day. And then you realize, oh, hmm, we always had multi-tenancy in the on-prem software already. But then, of course, in the cloud, you have to learn how to scale it up and down, right? And like, all of a sudden, the software kind of got re-engineered, right?

• 8:45Speaker 01

  To really, you know, build real SaaS software that, that, that.

• 8:50Speaker 01

  With all the concepts, right, in cloud computing and with AI, the same is happening, right? And then it's happening on three levels. It happens, of course, on the UI side, right? The time is clearly over where you design software, where the dump software, where the that requires the intelligence to sit in front of the computer, right? So, I mean, if you look at classical software, what did you do? You decided user interface, trying to, hopefully you did some user research, try to figure out in the easiest way or the most intuitive way to figure to to teach a human how they get their tasks done by clicking through the UI essentially. This is over, right? It's now, we call this generative UI, right? So the UIs get dynamically generated, right? If you have analytical questions, for

• 9:32Speaker 01

  example, or if you want to do your deep research, not just the deep research you find on perplexity or the the usual chatbots, but deeply rooted, let's say tariffs are being introduced or new taxes or the straight of foremost, what does this mean for my supply chain? And then you can analyze this in conjunction with your SAP data. So there's a lot of exciting opportunities and new things you can build that were almost impossible, that that we only dreamed of in the last, I don't know, twenty years, at least since I'm a developer.

• 9:58Speaker 00

  Mm hmm.

• 9:59Speaker 01

  Where now, you know, the system becomes much more multimodal, much more proactive, right? Because it can run overnight and then only if you wake up in the morning tell you, hey, Sarah, have you looked at this, right? Here's a problem on the sales side. Maybe the order entry is going down. You should do something here. And here are some recommendations already for this. Or here, there's a problem in the supply chain, right? Because now if you're an oil and gas customer, obviously you want to know what are my options you need to replan, right? Like, so all these things become super important for customers and that changes the UI. Then the second one is, of course, the business processes, like in order to cash in the past, of course, it has variances and so on and so forth. But it was a rather rigid process, but not like the standard operating procedure of a company. But now, of course, with these

• 10:42Speaker 01

  agents, right, we can blend the structured and the unstructured world, yeah, more seemingly so to get actually more work done. So this whole move from software as a service to some call it service as a software outcome as a service, that is, of course, what these agents are building for us. 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, right? Like all your general ledger and your invoices and your warehouse and inventory information, et cetera. But of course, you now want to combine this right with the plethora of other data, right? In order to kind of build this one harmonized semantic view. Because only, we always say AI is only as powerful as the data is, right?

• 11:26Speaker 01

  So, and that is exactly what we are then also doing and transforming on the data side. To help our customers to benefit from a globally harmonized data model to fuel the AI.

• 11:37Speaker 00

  What is the biggest engineering or technical challenge for you guys? When you look at, you know, these three bodies of work or anything else that you're doing in SAP?

• 11:48Speaker 01

  Well, the biggest challenge, quite frankly, is, of course, when you look at this is how do you? It's actually not the AI so much, right? But it's actually teaching the AI to do the right thing at scale, right? Because I mean, you can, look, you can build, two years ago, right? Everybody built a rack service, right? And you could easily with a P O C blew off everybody's, you know, the CEO's socks and like, look, how easy it is to build a chatbot on 10 documents, right? But SAP and these large customers, right? They always have a problem of scale. Okay, well, do you now with 100 documents, it becomes a little harder. 1000 documents becomes a deeper engineering challenge. And now if you go into jewel, you are Sarah, you're maybe an SAP US employee, right?

• 12:32Speaker 01

  Of course, if you ask a question, of course, for a travel policy, for example, of course, you expect a very different answer than me as a German employee, right? Would get, so you then now need to connect this actually with your master data. Like where are you located and which country are you on? And under which payroll are you actually, which taxes apply to you and so on? So all of a sudden, it becomes a very, very tricky problem. Same with MCP, like last year, oh, everybody could build an MCP server. It was so super simple, right? To hook up your MCP server and do amazing things with it. But that's becomes like for 10 APIs, not an issue, 100, because you'll get already context load and all these challenges. But we have. Twenty thousand APIs, right?

• 13:15Speaker 01

  So it becomes this like, because it's so huge, right? There's so much things. So it becomes this problem of scale, right? And doing this really end to end for the customer. Because what we also build is really an integrated experience across. So you can ask your finance question, because your HR question, your supply chain, you can correlate that. Like, this is, this is the biggest challenge to bring that, so to speak, to, to, to, together. Yeah, and design it then really for the, for the, for the right outcome. Yeah. And you said this also, it was interesting. And another interesting thing is, um, from my perspective, is

• 13:50Speaker 01

  you had recently this other podcast, I think with Andre, you had it, uh, on the, the most important thing from a development perspective is actually that people start writing their evals. That is, was like, I was on this tour for a very long time, um, because and the, the problem, why does agentic coding work so well, Sarah? Is, um, of course you can verify the outcome, right? Uh, you can either say, hey, is the program compiling? Or are your unit tests, right? Does it work, et cetera? And of course, combined with a little bit of taste, uh, and a lot of hard engineering work. Anthropic and OpenAI built these phenomenal code generation models. The problem is, if you now want to build a reliable outcome in finance and so on, you need the data

• 14:34Speaker 01

  that say, hey, with this input, that's the output, right? In order so that the coding agent can validate that and assert that against the reliable outcome. And that's something where there's a mindset shift in terms of how you describe the right boundary conditions to your coding agent, right? The harness, like, like all the boundary conditions need to be true from a security perspective and from the data privacy perspective, all the code qualities, because you also still want to maintain that code on, on day two and day three and day four, not just get, get the first version vibe coded. And then, of course, these evals, right? That then tell you, hey, this agent is actually doing what it's supposed to be doing in a, in a, in a, in a variety of ways.

• 15:17Speaker 01

  Do you still, and you sometimes need to laugh, because do you still remember in the, when I was a computer science student, where the Google guys came in, in a, in a lecture? And they said like, hey, I can go home at 5:00 PM because I wrote my tests. And of course, this was non, you still remember that test first or test driven development?

• 15:34Speaker 00

  Of course, yeah, it's coming back.

• 15:37Speaker 01

  It's coming back. It's like the the the reality is nobody did it. At least I never did it because A it was so much more fun. It was not very popular at the end. Why was it? Because A it was A so much more fun to write the code first, right? And then B, of course, usually the product manager gave you a very messy requirement. It was very hard for you to to write the test actually first. So while you wrote the code, you kind of iteratively discovered what the system, how the system would actually behave, right? Now the behavior and the writing the code is so much automated, right? Because now you can write almost software like completely on its own. But of course, now you need to describe the right outcome, what you want from this thing.

• 16:19Speaker 01

  And so that changes very much how the developers, of course, also now need to work, specifically now that the models have the steps changed since December last year.

• 16:29Speaker 00

  It's really hard, or I think it's not obvious how to picture if there's a a version of agents and models powering those agents in enterprise systems like SAP getting better in a compounding way, the way they have in generic code generation. Do you think it's possible in terms of verifiability or the ability to go understand and evaluate against that intent? Because it is much, I don't know if I would say it's more diverse than code, but it is, it's not, it's not obviously verifiable, as you pointed out. Like, do you think it can be?

• 17:11Speaker 01

  That's exactly the point. That is where the the starting condition is great, right? I think in terms of like two lanes. The first lane is, of course, you have the system of record today, right? You know exactly in the system, hey, given this or that instruction, right? What is the outcome, right? Because you can see it in the database, right? And then you can construct, hey, if the order to cash process runs like this, then you need to expect, right, that the that the the cash, like the accounts receivable, needs to come in this way, right? With the following taxes and so on and so forth. So that gives you verifiability. Now, the challenge, of course, is rather, this is never enough. Right, because if you just look into the system of record today,

• 17:53Speaker 01

  that data is insufficient for this grant vision that everybody has, that it becomes this autonomous enterprise, or like the agency of these agents is increasing, right, over time. So at the beginning, the agents, of course, are coming back to you. Some people call this human in the loop or whatever, right? So they need to come back to you, like also still with Claude Code or Codex, and still ask you some clarifying questions. Hey, I have now, I could now go this way, I could do that way. And with that, what you want to design for is that you start to capture more of that context, right? I always call this the tribal knowledge, the stuff that is not in the system, stored somewhere, that just lives in people's heads, or maybe in Slack channels, maybe in Teams channels, maybe it was just a discussion on the phone, right?

• 18:38Speaker 01

  So it's not stored anywhere. So how can you drive a decision from that? And then, so the question is, the agent needs to come back, ask you for input. Now you want to store that. And now what we do, in the past, we called this process mining. Now we call it agent mining, because you 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 or more countries to run your company more efficient,

• 19:23Speaker 01

  because now you learn something, how the organization behaves, because it can go two ways, right? It could be the good, or it could be a bad thing. And then you maybe want to streamline the process, how people then actually conduct the process in a different way. And that then leads to this kind of, I call this then this data flywheel, so to speak. So because with every trace, every input a user gives you, with all the observability that an agent writes you, you have new data sources that can then lead to new evals, where somebody says, yes, that's a verified output, so to speak, that I want. And then, of course, you can optimize the system more towards that outcome, depending on which data you gathered.

• 20:05Speaker 00

  Do you have a strong point of view today as to whether agents operating against these business processes within SAP or otherwise in enterprise software, do you think it's going to be computer use? Do you think it is all code and tool use on APIs?

• 20:28Speaker 01

  It's an interesting question. I have not a finite answer yet to this. So I think,

• 20:38Speaker 01

  given of course also how clunky UIs are and so on and so forth, and knowing the challenges also from UI automation from the past, I mean, it's phenomenal what they can do already today, quite frankly. I mean, it's still a little bit slow, right? And so on and so forth. But I still believe for the most part, it will, the majority will live with tool calling, right? And agents running in the background and so on, right? Because you also don't, you know, maybe want to have the browser open all the time. Okay, we can do this with headless browsers and so on. But I mean, if you can do this right with a more structured approach from an integration point of view, I think that will be the preferred method. But then, of course, there will be always

• 21:20Speaker 01

  kind of things where an API is maybe not available, or you have a legacy system for a time being and so on. And then, of course, these computer use approaches and so on will nicely tie in, so to speak, as well.

• 21:34Speaker 00

  If we zoom out a little bit and just think about agents and automated business processes, in what domains do you hope customers will see that be most effective first?

• 21:50Speaker 01

  Well, I mean, we need to be clear, right? I mean, it has been, for the most part, very productive in what I call the unstructured world, right? Because let's face it, I mean, large language models are very good in the unstructured world, right? Text and the images and stuff like that. And so, of course, everything where unstructured data is concerned for the most part, like in services and in support and maybe sales, right? And then, of course,

• 22:15Speaker 01

  Anything related to knowledge work, right, that deals a lot with documents. Of course, this is where we see like just do it for consultant product. I've mentioned, right, this is a lot of unstructured information. This is, of course, where, you know, it was the easiest to get quickly to the, to the, to the, to the return on investment. It was harder now to kind of combine this also you mentioned tool use, for example, right? I mean, the models had to learn, of course, first of all, and got, needed, got better, right, on how to use the tools. And then you need to build orchestrators, right, and disambiguate. Oh, what does an order actually mean? You mean a maintenance order, sales order, purchase order? Many, what, what order is a very overloaded term. It's very, uh, uh, very ambiguous. And, um, so, and that's, of course, this orchestration logic.

• 23:00Speaker 01

  That's what is a hard thing to build, yeah? And, um, and so I think, uh, overall, but now that it's become gotten better, right? Now you can do things like chat with your data, right? And instead of going to their data analyst, business analyst that curates you some dashboards and in eighty percent of the cases, that might be a good enough dashboard. But for all the other twenty percent of the question, you always need to go back to your IT department. No, now you can just converse in natural language with the system. It pulls the data, right, natural language to SQL or whatever have you. Pulls that data, you converse it until you have that point of view of the data that you wanna have. And then you just pin it and it's like, okay, that's actually my problem.

• 23:40Speaker 01

  Now I wanna manage that problem for the next, I don't know, two, three weeks until the problem maybe has disappeared. And then, of course, you, you, you, you move on, maybe then you delete that tile, uh, and so on and so forth. Like, so this, this kind of, um, combination of the structured, unstructured world, which is required, right? If you wanna go into the tabular world, right? Because lots of data in finances stored in tables and sales and the supply chain and so on and so forth, right? Um, unlocking that took a little bit of time, but, uh, now it's actually, we are seeing through, for example, the knowledge graph, the SCP knowledge graph that we've built, which is kind of the glue between natural language and the structured data and the system to, to, to really bring this together.

• 24:22Speaker 00

  That actually leads to one of your, um, I, I think, like, I know it's unconventional, but it's certainly not the, the dominant narrative in AI right now, which is your interest in like predictive and tabular, um, models. Uh, can you, can you talk about like? You know, why LLMs aren't the be all, end all here? Why we can't just use tools and calculation external to the model in combination with LLMs to achieve what you want to achieve?

• 24:56Speaker 01

  Yeah, now, first of all, from a business motivation, it's a great question, right? Sarah, I mean, first, from the business motivation point of view, right? Again, LLMs, unstructured world, that's all good, right? But most of the time, if you, if you want to plan forward, right? If you want to make good decisions in a company, you need predictions, right? You need predictions in terms of, oh, what's my demand, right? For, oh, is this depending on the seasonality effects and so on? What's my demand forecast maybe, right? For my products in the retail store? Or what's my demand, right? Uh, for my products, so I can plan accordingly, my manufacturing, right? If I'm a manufacturing customer? Or you want to predict your cash flow, right? Uh, you want to predict, and that has a bunch of input variables like, oh, what I actually,

• 25:41Speaker 01

  my data says outstanding, right? And that is determined based on, are customers paying yes or no? That's a classification question. And if you then say, okay, if a customer is not paying in the, within the payment terms, what's the payment delay? A classical regression question and so on and so forth. Now, the problem is, of course, still today, if we look at these predictive questions, right? And then you want to maybe do a what-if analysis from it, right? Now, if you want to do these predictions, quite frankly, then the challenge is, large language models are not made for this, right? The way how they, you know, generate just one token after another, essentially, in a sequence to sequence modeling, I mean, they're language models, right? So that, then they do this phenomenally well.

• 26:22Speaker 01

  But if you still want to do these predictors, well, you have to go back to these classical machine learning approaches, right? You use XGBoost or AutoGlue on and then many of these AutoML approaches, right? That, that might be, that, that are still out there. The problem is just it doesn't scale, right? So we haven't seen in the predictive space, the same level of democratization, right? You still need to hire a very good talent, a data scientist, right? And then if you, for example, if you are a large company, we did this, for example, at a pharmaceutical company, if you just want to solve the payment delay prediction problem I've mentioned, right? You have, they are, they are running in 90 countries around the world and they need these two models, so you end up with 180 models you need to train. You need to curate the data.

• 27:08Speaker 01

  And to train the models, figure out, right, what the right model is feature engineering, like the classical machine learning kind of approach, right, that that was used in the in the past. And what we said all the time is, okay, look, we have all this data in stored in these tables, right, hundred 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. So you can just basically provide a little bit of context, a small amount of data, not a large amount of data, because that was always a problem, small amount of data, and then starting making high accurate predictions, so to speak, in that domain.

• 27:50Speaker 01

  And that led, actually, this was two years of research. We published it also in Europe's and a bunch of other conferences. We call this RPT1, so rapid one, sent for relational pre-trained transformers. It's still based on the transformer architecture, but with a very different architecture. We released this and we see some, meanwhile, some very, very good results from that in in various domains where, as I said, classification and regression, sometimes time series and so on, are concerned. And we believe this will be huge, because it obviously will allow way more people from a business impact to to to make these predictions, which large language models have a really hard time with.

• 28:28Speaker 00

  Mm hmm. Mm hmm. When you think about the gap in, I think you described it as like hype versus adoption within the enterprise customers, like.

• 28:43Speaker 01

  The innovation race versus the outcome race.

• 28:45Speaker 00

  Yes, innovation race versus outcome race. It's a it's a good framing, like, the change is happening very quickly. That's hard for companies to absorb. Where where do you see challenges for the enterprise and adoption today? And where are our customers making the most progress with with you? Or were they most excited?

• 29:07Speaker 01

  Yeah, it's a good question, right? I mean, usually, I say.

• 29:13Speaker 01

  The the the primary problem, as I said, is is is the problem of A data, right? Because most of the time, the data is, of course, very disaggregated in a in a company, right? I mean, for a variety of reasons, right? Either because you made certain decisions, uh, how you purchased uh, solutions in the past, or you did an M and A, right? So you acquired a company naturally. Of course, they bring a very different IT system landscape as well, and so on and so forth, right? So you have disaggregated information. And the problem is, of course, that limits the potential of what you can actually do with AI, right? And then the question next is, how do you integrate this safely? And what I see is clearly customers who did that kind of homework, right? Now, of course, I it's not a new topic.

• 29:58Speaker 01

  We're discussing this for ten, fifteen, maybe more years, right? The ones that did their their homework, they, of course, have a much easier life, right? To then also reap the benefits, uh, for example, of AI, right? The second one, as I mentioned, is already is the problem of scale, right? The bigger, the more complex the landscape and so on, right? Then, of course, also then bringing this together in a unified experience is a challenge. And then finally, of course, everything around then security and so on and so forth, right? Because then there's always then this gap between, oh, there's an amazing innovation. Take OpenClaw, for example, right? I mean, amazing, uh, what what what this has brought to the world in terms of further ideas. And of course, I mean, from a security perspective, that's a that's a problem.

• 30:40Speaker 01

  You don't want to run this like, just like it is there and get up and deploy this in your organization too. So maybe this is so so so nobody would ever do this, right? So then, of course, you need to bring this, make it secure. I mean, we have seen with Light L L M, how long is this now ago? Two weeks or something, you probably saw it, right? Like with this vulnerability that still all of a sudden steals all your keys and credentials and so on and so forth, like? And that you don't want, right? If you were the chief information and security, the the security officer in the company, right? You don't have a job anymore, right? And that's and that's, of course, another big challenge from an adoption perspective as well.

• 31:17Speaker 00

  What do you uh, think the um?

• 31:21Speaker 00

  Function of a finance or an HR or a supply chain team that would have been operating out of SAP in their day to day work, you know, a year ago, what do you think that looks like a few years from now if they're successful, if you and your customers are successful with the AI transformation?

• 31:44Speaker 01

  Yeah, I mean, first of all, it's very simple. They will get rid of a lot of the mundane work, right, like collecting information and preparing PowerPoints for decision making and so on. So what we're going to see is a much, much faster way of making decisions, making better decisions, right? And then, of course, automating the mundane work. So what the people will do is they will run more scenarios, they will run, get better, deeper insights in a much faster way in order to then really think about, we always call it this more strategic thinking, right? And kind of in a way, Sarah, if you will, for me, this is the same way, like, like everybody who works today, maybe in the finance shared service center, right? It's for me, the equivalent of a junior developer today with Claude.

• 32:26Speaker 01

  So now they actually become, they get one level higher, right? They're now not so much anymore tasked with then writing a lot of the code, right, with, with, with Codex or with Claude. But they actually then start supervising the code, give feedback, right? And capture, of course, the essence of what the code should look like and then, you know, do much more review and then rather think about what to build next, right? Think about the next requirement and how is that actually differentiating? So it will, like every, every role, every level will kind of get up leveled, so to speak, right? Because the, the, the work that's being done today will be pushed down to, will be pushed down to these agents, right? And thereby, therefore, I think, I believe in general, what we will see is that

• 33:11Speaker 01

  people will just achieve so much more because there is a lot of intelligence baked into the system that gets rid of, of many of the things that we're, that we're doing today and that are actually, well, at least in many cases, not a lot of fun.

• 33:26Speaker 00

  I must admit my ignorance here. I don't, I'm thinking about this and I want to talk a little bit about the impact on the business, if you're right as well. I don't actually know how SAP prices broadly today, but the question would be like, how do you price? And if you are, you know, delivering more outcomes for customers or serving them, you know, services, software in a different way. Do you think that changes the business model for SAP?

• 33:50Speaker 01

  It does, absolutely. I mean, there's no question and we have prepared for this already. So 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 Conquer or Fieldglass, for example, or the Business Network. But, you know, very clearly with AI, it was very clear for us that, you know, step by step, it will go towards this consumptive world. Right? At first consumptive and then maybe in the next step, uh, once we have more verifiability in the system. Then also towards maybe an outcome-based, uh, license model, uh, to, for example, what Sierra is doing and so on and so forth.

• 34:34Speaker 01

  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 and so on and so forth. Because also it's a joint journey with the customer, because the customer is saying they are not yet ready. In many cases, uh, for a purely consumptive model, right? Because they need one predictability, right? And then of course, they are not yet fully also everywhere trusting the outcome, right? And well, no, then also of course, is the value already there? But then they are afraid of that the costs may explode from a consumptive perspective, et cetera, et cetera. So what we, so what, at the end of the day, what we have designed is a hybrid that is basically ready for this consumptive world.

• 35:15Speaker 01

  But actually meets the customers where they are today and knowing that they demand still a lot of predictability. In the enterprise space in order to cost control, uh, the, the whole thing for themselves as well.

• 35:28Speaker 00

  That makes sense. It's unclear how I also believe that transition is going to happen. It's unclear how quickly it will.

• 35:34Speaker 01

  Exactly. No, absolutely. I agree. I mean, nobody knows this. And at first you see customers that are more, you have have a wide range, right? They're also of opinions, right? And of course, some customers are a bit more forward leaning already. And then others are more still asking or demanding a classical model, so to speak. And so therefore, it's a it's a it's a journey.

• 35:58Speaker 00

  What do you, let me, let me rephrase that for a second. When, when you look forward and think about SAP's position five years from now, and you compare it to the broad market pivot away from SaaS and software in terms of just how investors are valuing these businesses and their enthusiasm about their durability. My own opinion is like, the challenge is real, and yet it will affect these, the incumbent software companies like very differently, right? There will be winners and losers versus like universally, like everybody's market capital come down.

• 36:41Speaker 00

  What do you think is going to be characteristic of a winner? Or why does SAP get to endure again?

• 36:48Speaker 01

  I think at the end of the day, it's all about adoption and the outcome you bring to the customer. Right? I mean, the technology, look, the reality is for most companies, the technology doesn't matter. Right? I always tell to my developers all the time, our job at SAP is to make the technology disappear. Right? We need to get the outcome in front of the customer. And of course, not just the value itself, of course, you also be able to produce and price it in a way. So it's a win-win situation for the customer, right? And, and, and, and of course, the vendor, right? At the end of the day. So what, what we are really trying to do, and this is also why we, you know, from an architecture, we have so flexible, right? We said like, we don't over index on a specific, we have partnerships with all of them.

• 37:29Speaker 01

  Right? And, and, and really also only invest in the things that are actually differentiating for our customers versus the things that anyway will. Likely get commoditized in the tech stack 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 in order to benefit from it. Why is this important? Because in order, because if you take a lot of time to reap the value, then your return on investment is essentially gone, right? And or it becomes the business case becomes harder, right? And therefore, what we are really focusing on is to to deliver these outcomes to the customers.

• 38:08Speaker 01

  And I think that will differentiate the winners from the losers at the end of the day to really focus on the business outcomes for the customer at the end of the day.

• 38:16Speaker 00

  As we wrap up, I want to ask you a few quickfire questions, including a little bit more personal one. Uh, our listeners always want to know, like, what do you do all day as like a CTO of SAP? Like, can you just describe how you spend your time?

• 38:33Speaker 01

  Well, I spent most of the time reviewing the progress with the teams, right? And we're thinking along, you know, from all the layers with the teams, from the database to the models, right? To the UI, review the progress, give guidance, feedback, learn something new, study, of course, what happens outside. Do a lot of prototypes, right? While we speak here, I have a bunch of command line interface instances running here, prototyping a bunch of things, right? Trying things out, see what works, what doesn't work, right? And then give this also as as kind of inspiration back to the team. And then, of course, you know, um, yeah, work a lot with the teams, think about, of course,

• 39:15Speaker 01

  how we connect the vision then to the execution and of course, work a lot with customers as well. So I love working with customers because they always, you know, give you, they keep you honest in terms of what what you can do already and where you can still get better.

• 39:31Speaker 00

  Can you give us a flavor of just because you have such an amazing like global customer base? Like something challenging or interesting that a customer is asking you to do right now?

• 39:42Speaker 01

  Um. Oh, well, there are plenty of things, right? As I said, right, the predictive one is just immediately comes to my mind, right? Like, if you can improve the accuracy of the demand forecast by three, four percent, right, that is huge for them, right? This is this is multi-million value immediately that you can deliver to the customers. Or I have many customers, of course, that come and say, like, oh, Philip, can you actually help me? Look, the commodity prices are all through the roof, right? Oil and gold and silver, right? Like, if you're now a jewelry customer, right? You have you have you have jewelries, right? So you you have a challenge, right? You have a challenge because now your entire business model, you you need to think about this. And then you discuss with the customers coming from the business challenge, hey, how can we actually help you to address this?

• 40:27Speaker 01

  Because now they need to change your manufacturing, right? Processes and come up with new products. And then the question was like, how can you help me research the right product? Find product market fit? And then, of course, out of that, then determine uh how to actually, you know, source new suppliers, right? Because uh because maybe I need to source different materials uh from from different. So like, these challenges are real in this ever-changing world, right? And then for some people, it's it's it's it's other things because they are challenged uh on on others. So the the world is very, very diverse in terms of business challenges that customers have out there.

• 41:03Speaker 00

  Yes, and I mean, to your uh uh so something you mentioned earlier, uh, you know, being the partner that folks come to and say, you know, uh, how does my business change, given what's happening in the street of Hormuz? Is a hard question, right? And make it better, make it make it easier for me to navigate around it.

• 41:22Speaker 01

  Yes. Um. Yeah. Now, but you but that's an interesting one, right? I also look, I go into every, even if I'm the CTO, you know, uh, and usually people then to I expect from me, I tell them a lot of technical things and so on and so forth. What I learned, actually, and I did this mistake probably more than anybody else in this world, is to kind of pitch the technology.

• 41:42Speaker 00

  Uh huh.

• 41:42Speaker 01

  And this is completely wrong. When I sit together with CFO or a CIO, the first question is like, hey, what's top of mind for your business? What are what are current? What are your current challenges, right? And then work backwards to the technology, right? And that always, I always found that this is the most useful approach.

• 41:59Speaker 00

  And then, uh, last question for, for you personally, outside of everything that you're doing at SAP, uh, just as a technologist, like what else are you interested in and paying attention to or in, in tech or AI or like a belief you have about something that's gonna happen?

• 42:15Speaker 01

  Well, I think, I mean, obviously AI is the dominating thing because it's so pervasive and ubiquitous, but, um, no, I think what we also are very, uh, very excited about the work we are doing, um, finding in, in, in the quantum computing space. I'm trying to like to find new algorithms also on there because, you know, programming quantum computer is very, very different, uh, compared to everything else. Uh, that's in a very early research stage, but that's also, that's super exciting as well.

• 42:44Speaker 00

  And, and why is that like, uh, commercially relevant to you? Like what's the, you know, to your point of solving backwards from the, the business problem?

• 42:52Speaker 01

  Let me put it this way. The hypothesis is that of course, once the hardware matures, right? In the quantum space, there are certain problems that you can address that are hard to, to address today. And what we are focusing on is the optimization domains, obviously. And then if you go into, uh, things like logistics, traveling salesman problems, Knopssack problems, like all these kind of usual, uh, hard problems in computer science, these are interesting problems where we, where some people, where we believe that could be interesting for the future, uh, for maybe different kind of computing paradigm to solve for. And we try to be, uh, a learning, so to speak. And we will be very hardware agnostic in that sense, right? Uh, like, like S E P always, you know, always ran on different

• 43:37Speaker 01

  computers, uh, in this world. Um, but what is important to us is that we find early on already, uh, and intellectually, but then also finds, uh, these, uh, these new algorithms that then can propel that forward. Because if you can obviously load your trucks, right? And the, and do the route planning even more, and the outcome is safe, the emissions go down, right? And you save a lot of money and so on and so forth. So there's a lot of things you can still opt that, that, that today, of course, with the limitations you have, you, depending on how large the problem size is, you can only, you can only approximate, uh, and then need, need to live with the, with the best solution you can get in any finite amount of time.

• 44:17Speaker 00

  Yeah, I think that's a great note to end on just because it reminds everyone, including me, that actually there are like interesting computer science problems everywhere in the enterprise where I think a lot of people, if they haven't worked on these problems of scale with real customers, they they might assume that you're like, you know, like building a CRUD application is pretty easy at minimal scale in 2026. It's actually remarkably easy with coding agents.

• 44:49Speaker 01

  It is. Oh, absolutely.

• 44:51Speaker 00

  And yet, there are there are problems where we are limited by algorithms and computation everywhere, right? And it takes some imagination to just go attack them.

• 45:01Speaker 01

  And it is also to your point, right? Yes, a CRUD application is like, that's that's that's a solved problem, if you will, right? But most of the time, it's not just building a little CRUD application for a data object in the database. I mean, this is a software is a little bit more complicated than that usually.

• 45:20Speaker 00

  Thank you so much for the time, Philip. This is great.

• 45:22Speaker 01

  Yeah, thank you. Have fun having me.

• 45:26Speaker 00

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