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SAP: Bringing the ‘Operating System’ of a Company into the AI Era with CTO Philipp Herzig
00:45:44
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- [00:00]
Sarah
Foreigners. Welcome back to no Priors. Today I'm here with Philip 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 Strait of Hormuz. Welcome, Philip. Philip, thanks so much for being with us.
- [00:40]
Philip Herzig
Yeah, it's a pleasure to be here. Thank you.
- [00:41]
Sarah
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 definitely describe what you guys do for customers?
- [00:59]
Philip Herzig
Oh, absolutely. I mean, look, SAP is the market leader, right? In enterprise of software, applications and platforms, right? 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 of a company essentially in order to get from everything from order to cash or source to pay, end to end managed for companies around the entire globe. Mm.
- [01:57]
Sarah
I definitely want to talk about AI, LLM, some of the stuff that you guys are doing internally and then around predictive models as well. But just because the, the macro backdrop is on everyone's mind, both from a technology and an economic perspective. Oh, sure. I want to talk about like SAP's position in the market a little bit. SAP has, has stood the test of time through multiple technology and market cycles. I, as a early stage venture capitalist and I'm 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, even today is the, I believe the largest market cap, enterprise software vendor versus sort of the last generation of the new guard, like the sales forces of the world. How has that happened? Like, how did you do it and what makes it so durable?
- [03:04]
Philip Herzig
Well, what makes it so durable? Right. At the end of the day I mean, if you think about this, and it's happening a little bit the same way also when we talk about the SaaS stat narrative or the SaaS apocalypse, right? I mean, anyway, I have the feeling like in this market, last year AI was in a big bubble and everybody was kind of doing, no, it's not, and not this year, SaaS is that. And so on and so forth. Look, the reality is now, of course, with the costs of building being so low, right? With specifically agent decoding and all these latest powerful models, I mean, something has always prevailed over the years. Because even when SAP was founded in 1972, a long time ago, I mean, why was it started? Because actually in the 70s, 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 he 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 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 end to end. And that also has transformed throughout the years. You've mentioned that, right? First from the mainframe to client server, 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. But what hasn't changed is what customers are seeking for, which is outcomes, 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, 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 working in. And we believe that still will continue, right? Because this is exactly what we are 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 narrow. And that is what we are really focused on, that customers get the outcome from AI to achieve more. Given of course the foundation we have, but simultaneously of course, the system. We are re engineering the entire system with the help of AI in a totally new way.
- [06:17]
Sarah
You now as CTO of SAP, have 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?
- [06:39]
Philip Herzig
No, look, I mean we, we, we are in the meantime all in on AI, right? So I mean everybody in the company is using agenda 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 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 Juul for consulting, which is phenomenal because it's one of our fastest growing AI products. Because what this actually helps is to build the, to help the consultants, right, 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 Drufor consultant, they can reduce like 30% 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 get to the latest software. And we've seen this of course with Conqueror, for example, right? Where now our travel booking agents, our expense agents are alive. And so, so there's many of these outcomes that we are designing. But when you look from a CTO perspective, really 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 move 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 a certain period of time, people realized, oh, what does actually CI CD mean, right? You can deploy daily or multiple times per day. And then you realize, oh, 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 to really build real SaaS software. With all the concepts in cloud computing and with AI, the same is happening and it's happening on three levels. It happens of course, on the UI side. The time is clearly over where you design software, 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 designed a user interface, trying to, hopefully you did some user research, try to figure out in the easiest way or the most intuitive way 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. So the UIs get dynamically generated, right? 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 of formal is 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 we only dreamed of in the last, I don't know, 20 years at least, since I'm a developer, where now 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 because now if you're an oil and gas customer, obviously you want to know what are my options, you need to replan, right? 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, like the standard operating procedure of a company. But now of course with these agents, right, we can blend the structured and the unstructured world 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 in order to kind of build this one harmonized semantical view. Because only we always say AI is only as powerful as the data is, right? 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.
- [11:38]
Sarah
What is the biggest engineering or technical challenge for, for, for you guys when you look at, you know, these three bodies of work or anything else that you're doing at SAP?
- [11:48]
Philip Herzig
Well, the biggest challenge, quite frankly is of course, when you, 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 POC, blew off everybody's, the CEO's socks and they're 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, I do now with 100 documents becomes a little harder. Thousand documents becomes a deeper engineering challenge. And now if you go into Joule, you are, Sarah, you're maybe an SAP US employee. Of course, if you ask a question, of course, for a travel policy, for example, of course you expect a very different answer that me as a German employee would get. So you now need to connect this actually with your master data. Like, where are you located, in which country are you 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, amazing things with it. But that's becomes like for 10 APIs, not an issue hundred because you get already context, bloat and all these challenges. But we have 20,000 APIs. So it becomes this like, because it's so huge, right? 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, you can ask your HR question, your supply chain, you can correlate that like this is the biggest challenge to bring that so to speak together and design it then really for the right outcome. And you said this also. It was interesting. And another interesting thing is, from my perspective is you had recently this other podcast, I think with Andre, you had it on. The most important thing from a development perspective is actually that people start writing their evals. That is like I was on this tour for a very long time. Because the problem. Why does agent decoding work so well, Sarah, is of course you can verify the outcome, right? You can either say, hey, is the program compiling or are you unit tests, right? Does it work? Et cetera. And of course, combined with a little bit of taste 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 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, the harness. 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 day two and day three and day four, not just 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 variety of ways. Do you still, and you sometimes need to laugh because do you still remember when I was a computer science student where the Google guys came in, in a lecture and they said like, hey, I can go home at 5pm because I wrote my tests. And of course this was non. You still remember that test first or Test driven development.
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Sarah
Yeah, it's coming back,
- [15:38]
Philip Herzig
it's coming back. 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 write the test actually first. So while you wrote the code, you kind of iteratively discovered how the system would actually behave. Right now the behavior and writing the code is so much automated, right? Because now you can write almost software completely on its own. But of course now you need to describe the right outcome, what you want from this thing. 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.
- [16:30]
Sarah
It's really hard or I think it's not obvious how to picture if there's 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 is much, I don't know if I would say it's more diverse than code, but it's not obviously verifiable, as you pointed out. Do you think it can be?
- [17:12]
Philip Herzig
That's exactly the point. That is where the starting condition is. Great, right? I think in terms of two lanes, the first lane is of course you have the system of record today, you know exactly in the system, hey, given this or that instruction, right? What is the outcome? Because you can see it in the database and then you can construct, hey, if the order to cash process runs like this, then you need to expect that the cash, like the accounts receivable needs to come in this way 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, 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 cloud 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, 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, maybe in slack channels, maybe in teams channels. Maybe it was just a discussion on the phone, right? 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 call this process mining. Now we call it agent mining because you record all these decision traces, these contexts of 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. Because now you learn something, how the organization behaves. Because it can go two ways. It could be either 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. 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.
- [20:05]
Sarah
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?
- [20:28]
Philip Herzig
It's an interesting question. I have not a finite answer yet to this. So I think 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. And gaming is still a little bit slow, right? And so on and so forth. But I still believe for the most part it will, that 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, you 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 think that will be the preferred method but then of course there will be always kind of things where an API is maybe not available or you have a legacy system for 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.
- [21:35]
Sarah
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?
- [21:50]
Philip Herzig
Well, I mean, you 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 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 in, and maybe sales, right? And then of course in anything related to knowledge work, right. That deals a lot with documents. Of course this is where we see like just jewel 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 is a very overloaded term. It's very, very ambiguitive. And that of course this orchestration logic, that is a hard thing to build. And so I think 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 80% of the cases that might be a good enough dashboard. But for all the other 20% 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 with until you have that point of view of the data that you want to have. And then you just pin it and say like okay, that's actually my problem now. I want to manage that problem for the next, I don't know, two, three weeks until the problem maybe has disappeared. And then of course you move on maybe then you Delete that tile and so on and so forth. So this kind of combination of the structured unstructured world, which is required, right. If you want to go into the tabular world, right. Because lots of data in finances stored in tables and sales and in the supply chain and so on and so forth. Right. Unlocking that took a little bit of time, but now it's actually, we are seeing through, for example, the knowledge graph, the SAP knowledge graph that we've built, which is kind of the glue between natural language and the structured data in the system to really bring this together.
- [24:22]
Sarah
That actually leads to one of your, I think, like, I don't know if it's unconventional, but it's certainly not the dominant narrative in AI right now, which is your interest in predictive and tabular models. Can you talk about 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?
- [24:57]
Philip Herzig
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 want to plan forward, right. If you want to make good decisions in a company, you need predictions. You need predictions in terms of, oh, what's my demand for? Oh, is this depending on the seasonality effects and so on. What's my demand forecast? Maybe for my products in the retail store or what's my demand? Right. For my product so I can plan accordingly. My manufacturing. Right. If I'm a manufacturing customer or you want to predict your cash flow, right. You want to pre. And that has a bunch of input variables like, oh, what are actually my days? Outstanding. Right. And that is determined based on our customers paying. Yes or no. That's a classification question. And if you then say, okay, if a customer is not paying 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 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. The way how they generate just one token after another essentially in a sequence to sequence modeling. I mean, they're language models and they do this phenomenally well. But if you still want to do these predictors, when you have to go back to These classical machine learning approaches, right, you use XGBOOST or auto gluon and many of these automl approaches that might be, that are still out there. The problem is just it doesn't scale. So we haven't seen in the predictive space the same level of democratization. You still need to hire a very good talent, a data scientist. And then if you, for example, if you're 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. 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, you need to train the models, figure out what the right model is. Feature engineering, like the classical machine learning kind of approach that was used in the past. And what we said all the time is okay, look, we have all this data stored in these tables, thousands of tables 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. And that led. Actually this was two years of research. We published it also at NeurIPS and a bunch of other conferences. We call this RPT1, so Rapid1 stands 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 meanwhile some very, very good results from that in various domains where 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 make these predictions. Which large language models have a really hard time with.
- [28:30]
Sarah
When you think about the gap in. I don't know, I think you described it as like hype versus adoption within the enterprise customers like the innovation.
- [28:44]
Philip Herzig
Innovation race versus the outcome race.
- [28:47]
Sarah
Yes, innovation race versus outcome race. It's a good framing like the change is happening very quickly. That's hard for companies to absorb. 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?
- [29:08]
Philip Herzig
Yeah, that's a good question, right? I mean usually I say the primary problem, as I said, is the problem of a Data, Right. Because most of the time the data is of course very disaggregated in a company for a variety of reasons. Either because you made certain decisions how you purchased solutions in the past or you did an M and A. 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 to segregate that 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 it's not a new topic. We're discussing this for 10, 15, maybe more years. The ones that did their homework, they of course have a much easier life to then also reap the benefits. For example of AI. 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. And 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 what this has brought to the world in terms of further ideas. And of course, I mean from a security perspective that's a problem. You don't want to run this just like it is there on GitHub and deploy this in your organization, do something nobody would ever do this. So then of course you need to bring this, make it secure. I mean we have seen with light LLM. 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 and you don't want, right. If you're the chief information and security officer in the company, you don't have a job anymore, right? And that's of course another big challenge from an adoption perspective as well.
- [31:17]
Sarah
What do you think the 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?
- [31:45]
Philip Herzig
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 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 cloud. 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 codecs or with code, but they actually then start supervising the code, give feedback and capture of course the essence of what the code should look like and then do much more review and then rather think about what to build next, think about the next requirement and 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 work that's being done today will be pushed down to, will be pushed down to these agents. Right. And therefore I think, I believe in general what we will see is that people will just achieve so much more because there is a lot of intelligence baked into the system that gets rid of many of the things that we're doing today and that are actually, well, at least in many cases, not a lot of fun.
- [33:27]
Sarah
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?
- [33:51]
Philip Herzig
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 seed 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, 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. 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 saying they are not yet ready in many cases for a purely consumptive model because they need one predictability. And then of course they are not yet fully also everywhere trusting the outcome or node. And 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 at the end of the day, what we have designed is a hybrid that is basically ready for this consumptive world, but actually meets the customers where they are today, knowing that they demand still a lot of predictability in the enterprise space in order to cost control the whole thing for themselves as well.
- [35:28]
Sarah
That makes sense. It's unclear how. I also believe that transition is going to happen. It's unclear how quickly it will.
- [35:35]
Philip Herzig
Exactly. No, absolutely, I agree. I mean nobody knows this and at first you see customers that are more. You have a wide range right there also of opinions, right. And of course some customers are a bit more forward leaning, already ready and then others are more still asking or demanding a classical model, so to speak. And so therefore it's a journey.
- [35:59]
Sarah
What do you. Let me rephrase that for a second. 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 the challenge is real and yet it will affect the incumbent software companies very differently. There will be winners and losers versus universally everybody's market cap should come down. What do you think is going to be characteristic of a winner? Or why does SAP get to endure
- [36:47]
Philip Herzig
Again, 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. 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 of course not just the value itself. Of course you'll also be able to produce and price it in a way. So it's a win win situation for the customer. Right. And of course the vendor Right. At the end of the day. So what we are really trying to do, and this is also why we from an architecture we are so flexible, we said we don't over index on a specific partnerships with all of them and really 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 if you take a lot of time to reap the value, then your return on investment is essentially gone. Right. Or the business case becomes harder and therefore what we are really focusing on is to, to deliver these outcomes to the customers 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.
- [38:16]
Sarah
As we wrap up, I want to ask you a few quick fire questions, including a little bit more personal one. Our listeners always want to know what do you do all day as a CTO of SAP? Can you just describe how you spend your time?
- [38:34]
Philip Herzig
Well, I spent most of the time reviewing the progress with the teams and we're thinking along from all the layers with the teams, from the database to the models to the ui, review the progress, give guidance, feedback, learn something new, study of course what happens outside, do a lot of prototypes where we speak here. I have a bunch of command line interface instances running here, prototyping a bunch of things, trying things out, see what works, what doesn't work and then give this also as kind of inspiration back to the team. And then of course work a lot with the teams, think about of course 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 give you, they keep you honest in terms of what you can do already and where you can still get better.
- [39:32]
Sarah
Can you give us a flavor of just because you have such an amazing global customer base, something challenging or interesting that a customer's asking you to do right now?
- [39:43]
Philip Herzig
Oh well, there are plenty of things, right. As I said, the predictive one just immediately comes to my mind, right? Like if you can improve the accuracy of the demand forecast by 3, 4%, right. This is huge for them. Right. This is multimillion 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 are now a jewelry customer, right? You build jewelries, right? You have a challenge, right? You have a challenge because now your entire business model, 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? Because now they need to change your manufacturing, right. Processes and come up with new products. And then the question is like, how can you help me research the right products, find product market fit and then of course out of that then determine how to actually source new suppliers. Because maybe I need to source different materials from different suppliers. These challenges are real in this ever changing world. And then for some people it's other things because they are challenged. On others, the world is very, very diverse in terms of business challenges that customers have out there. Yeah.
- [41:03]
Sarah
Yes. I mean to your. So something you mentioned earlier, you know, being the partner that folks come to and say, you know, how does my business change given what's happening in these straight of Hormuz is a hard question, right? Make it better, make it, make it easier for me to navigate around it.
- [41:24]
Philip Herzig
Yeah, but that's an interesting one, right? I also, look, I go into every, even if I'm the cto, you know, and usually people then 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. 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 your current challenges? Right? And then work backwards to the technology, right. And I always found that this is the most useful approach.
- [42:00]
Sarah
And then last question for you personally, outside of everything that you're doing at SAP, just as a technologist, what else are you interested in and paying attention to in tech or AI or a belief you have about something that's going to happen?
- [42:16]
Philip Herzig
Well, I think, I mean obviously AI is the dominating thing because it's so pervasive and ubiquitous. But no, I think what we also am very excited about the work we are doing finding in the quantum computing space, trying to find new algorithms also there because programming quantum computer is very, very different compared to everything else that's in a very early research stage. But that's super exciting as well.
- [42:45]
Sarah
And why is that commercially relevant to you? To your point of solving backwards from the business problem.
- [42:53]
Philip Herzig
Let me put it this way. The hypothesis is that of course, once the hardware matures in a 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 think logistics, traveling salesman problems, knapsack problems, 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 different kind of computing paradigm to solve for. And we try to be learning, so to speak. And we will be very hardware agnostic in that sense. Right. Like SAP always ran on different computers in this world. But what is important to us is that we find early on already and intellectually, but then also find these new algorithms that then can propel that forward. Because if you can obviously load your trucks and do the route planning even more, the outcome is the emissions go down 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 today, of course, with the limitations you have, depending on how large the problem size is, you can only approximate and then need to live with the best solution you can get in any finite amount of time.
- [44:18]
Sarah
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 might assume that you're like, you know, like, building a CRUD application is pretty easy and at minimal scale in 2026, it's actually remarkably easy with coding agents.
- [44:50]
Philip Herzig
It is. Oh, absolutely.
- [44:52]
Sarah
And yet there are problems where we are limited by algorithms and computation everywhere. Right. And it takes some imagination to just go attack them.
- [45:01]
Philip Herzig
And it is also to your point, right? Yes. A CRUD application is like, 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 software is a little bit more complicated than that usually.
- [45:20]
Sarah
Thank you so much for the time, Philip. This is great.
- [45:22]
Philip Herzig
Yeah, thank you for having me.
- [45:27]
Sarah
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