Big Technology Podcast - SAP CEO: AI Won't Kill Software, But It Will Change Your Job — With Christian Klein

Episode Date: September 30, 2026

Christian Klein is CEO of SAP. Klein joins Big Technology to discuss whether the Saaspocalypse is over and how AI is reshaping the future of enterprise software. Tune in to hear why he believes AI could soon become reliable enough to handle mission-critical business tasks and why SAP still has a moat as models get smarter. We also cover job displacement, reskilling, token spending, cheaper AI models, cybersecurity, and Europe’s tech regulation. Hit play for a wide-ranging conversation about what happens to software when AI gets good enough to run more of the business itself. --- Enjoying Big Technology Podcast? Please rate us five stars ⭐⭐⭐⭐⭐ in your podcast app of choice. Want a discount for Big Technology on Substack + Discord? Here’s 25% off for the first year: https://www.bigtechnology.com/subscribe?coupon=0843016b Learn more about your ad choices. Visit megaphone.fm/adchoices

Now that this has Poclips is over or paused, software is booming. What's happening? Let's talk about it with the CEO of Europe's largest software company right after this. This episode is brought to you by Genesis. What does it actually take to put Agentic AI to work across an entire enterprise? I recently spoke with Genesis chairman and CEO Tony Bates about why orchestration is emerging as a competitive advantage in customer experience and what companies need to get right in the AI era. Then I sat down with Adam Mitchell, head of Enterprise Business Solutions at Voya Financial,

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BetMGM operates pursuant to an operating agreement with Eye Gaming Ontario. Welcome to Big Technology Podcast, a show for Cool-Eadded, and nuanced conversation of the tech world and beyond. We have a great show for you today. Today we're joined by the CEO of SAP Christian Klein, who is here to be. to talk with us about the state of software amid all these SaaS pop clips worries, and along with some other big decisions that software companies are faced with. Today, everything from hiring to the integration of artificial intelligence and more. Great to see you. Welcome to the show.

What a great introduction. Thanks for having me, Alex. Okay. So, you know, we talk a lot about software on the show and AI on the show. And we don't really have to, you know, when we say we think AI is going to do this or that, you know, we don't have to make any planning decisions based of it. We just talk about on the next show. You on the other hand, you do need to make planning decisions. You have to basically decide, and this is why I think this could be such an interesting conversation, you have to basically decide what you think the trajectory of AI's progress is going to be, and then you plan based on top of it, because if AI stops now, it's one set of decisions, if AI keeps going a little bit, but then hits a wall, it's another set of decisions, and if we go

into recursive self-improvement. It's a third set. Each one of these impacts your business dramatically. So actually, you know, you make the choice and then you run a company based off of it. I think that AI has to hit a wall at some point. We've seen great progress, progress that's gone through many walls that people put up or, you know, presumed walls. And it's continued to build. But it can't keep going on this like this forever.

What do you think? And do you think we're about to hit like recursive self-improvement? Or do you think that what I'm saying makes a little sense? Yeah. I'm happy to answer that question. And let me maybe just describe it, how I describe it to my employees. When we do in all hands, I always show them a mountain. I said, look, we climbed the cloud mountain.

We did a cloud transformation. And then now we have to climb the AI moment. And on this journey, there will be obstacles. We have to overcome. AI has to overcome. And when all of this hype started with Generative AI, I mean, of course, everyone was using all these LLM models and start testing it, experimenting with it.

And now it's really about, okay, but it's nice to experiment with it. But all of our customers are saying, okay, but what is in it? Show me the value. And why does AI understand so much, you know, all the unstructured documents? I can summarize a mail. I can summarize a document and so on.

But what about the business? Why does this? there are always certain limits with the accuracy. So that is the first obstacle we have to overcome. And the third one, I guess, the world is talking about that. Do we need to regulate AI? Do we need to govern AI? Because obviously, when you're running the world's most mission-critical businesses,

I mean, obviously governance plays a huge role. And this is where I would say these are the kinds of obstacles we have to overcome to further, you know, climb up the moment. Okay, but Christians, this is kind of the core question here. Yeah, all right. AI doesn't understand structured data that well right now, I think. But your job is basically, a big part of your job, is assessing whether the technology will be making those leaps in the future. So we could talk a lot about what the current state of AI is, and we will.

But for you to run the company, right, let's say AI, for instance, gets really good at, like, going into ERP and being able to, like, make serious calculations. about things, which it's getting better at. And SAP's business, I should say. Yes, yes, yes. It's enterprise resource planning. That means that it brings together systems like finance, HR, manufacturing, supply chain, and sales into a single system, single soft forces. So your job is basically to figure out, big part of your job is to figure out whether

AI will be able to overcome those obstacles, what time frame AI is going to overcome those obstacles, and then how you adjust. So do you think it's going to get over the hump is basically the question I'm asking, 100%. And I would say, look, I mean, you know, we, for example, code financial closing agents, and we are sitting here at the WallSuite.

And of course, they work today. I mean, AI understands business, but, you know, maybe it understands today with a 93% accuracy. But your auditors, when you release financial results, they say, okay, so this time my numbers were 7% you know, too high. And they say, what? I mean, you know, you need audits. You need to be certified and so on.

So it's 100% accuracy is needed. And that's where I guess every tech company is now working on. And every software player is now using, especially SAP, as we have so much knowledge about industries, processes, and data. I mean, we are working this on a constant basis. And yes, of course, AI understands business, but sometimes you need an accuracy of 100%. 90% accuracy is not enough.

And then when it comes to governance, I mean, here in the US, when we are running the US government, when we are running, you know, many public sector customers, you know, agents need to understand, what is FedWRM? Yeah, so that, you know, which data can I access? What data can I share? Where is the data going to be stored?

And so that is also the governance part and we are working on that. So for me, Alex, this is more a question of now months, until we are really reaching a level where you can really trust AI one in your business. And then you can still decide, okay, the human is in the loop for us. That's a rule. But then you can really decide on the autonomous level of the agents, on how autonomous can the agents want the business? Okay, so this is important.

So your perspective is within months, AI can go from basically working within software and giving your customers like 90-something percent accuracy. You anticipate 100% accuracy within months. I would say that's absolutely possible. So it's for certain tasks. And obviously, we will then develop the next agents for supply chain optimization. And then here we are maybe there in eight to nine months. But it's not, we are now talking not anymore about years.

So business AI is real. Business AI is coming. And also 100% accuracy is not always needed. I mean, for example, when I do my customer visits here in New York, I'm asking Jewel works, put me to get a customer briefing. This is your AI. My, exactly, Jewel is our, you know, co-worker. And when it's 90% accurate, it's totally fine.

Yeah, maybe, you know, when I ask for the latest earnings and it's not exactly the latest earnings, maybe it's the earnings before. okay, I mean, who cares? But, you know, and there, 95% accuracy is totally fine. But I don't need a bunch of people in my office preparing me for this customer meetings and writing all of these briefings here because I get the data out of SAP. I mean, the public content is anyway understood by the LLM. So, you know, here we go.

So it really also depends a little bit on the AI use case. Yeah, okay, so 100%. That's really interesting because I think the big thing that has been holding back a lot of enterprise rollout and the ability of enterprises to integrate. This technology has been the fact that, yeah, if you're doing something like tax supply chain, projecting your sales pipeline, and you're at like 92%.

Might as well not use it. And you could check the work, but you don't know where the problems are. Exactly. And you ask another chap about where are the problems? If it finds 90% of those problems, you now have a compounding issue. Yes.

But things change when it gets 200. So I think the way that you're framing it is, we're about to see a real explosion of enterprise. It depends on the AI use case. It also depends just an hour ago. I visited a big customer here in New York. And they said, oh, Christian, you know, I love your financial closing assistant, but it's only 93% accurate.

But, okay, there are 100 finance systems hanging somewhere around in this company, someone on SAP. So, okay, we need to clean up the house. I mean, you know, your data is a mess. So it's also not only the agent per se. So it's also the data, the data quality, the data silos in a company where we then need to match data so that the agent can really understand, okay, where does all, you know, the financial data sit, how do I depreciate a certain asset, you know, and so on. And the more systems you have and the more you lack data quality, I mean, of course, you know, this also then reduces the accuracy of your AI agent. Okay, but I thought smarter and smarter AI was supposed to solve that. problem, right? That basically, like, the big, one of the big reasons that an enterprise point at for

like not being able to roll out AI, like, there are all these stats about like one in 20 pilots or one in 10 pilots actually make it to production. Yes. Is because the systems are working on sort of faulty data foundations? But, you know, is the AI at the point where it's getting good enough to figure that out on its own? Like companies are putting teams and teams on this, try to figure it out. But why is the AI is sort of not able to do that on its own. I mean, AI will also solve that challenge. I mean, when I look at, when I look at our data platform, I mean, we are partnering, you know, with other data like data breaks, a snowflake, a big query, and so on.

But obviously, I mean, it's easy to do zero copy so that, you know, we can share data without moving the data. But then, of course, the semantics, you know, matching data, fixing data quality issues. Of course, AI now, you know, in our data platform, AI will help us to match data from a SAP to a sales force system, from a workday system to an SAP system. So that also that the cleanup of the house is getting easier and easier. And also that the agents can really access a semantical data layer, which is also coming out of the box so that you don't need an army of data scientists anymore.

Always, you know, from every month's end, you need to clean up your financial data and you match it to your HR data and to your employee data and to your payroll data. I mean, you know, all of this work will also get automated because AI is very good in matching those data points together. And that is of course also part of what is happening underneath the agents in the data. Business today moves very fast. But it moves fast despite, you know, it moves fast despite the fact that processes are amassed. It moves fast despite the fact that culture can be a problem.

So it's basically the net of this that's just business. You know, I guess like we've heard stories of companies that used to be on this annual plan. of planning. Your plan, you release at your big event, and then you plan again. Now they're on quarterly or monthly plans. Yes. So is that what we're going to see is basically a speed up of the ability of businesses to release products, to serve customers, to grow?

Is everything going to get faster? The speed of execution is definitely getting faster. And that's also, I mean, you know, you need to also then redesign your own planning cycles inside a company to react to that. No matter if you do supply chain planning, financial planning, workforce, I mean, we at SAP, we look at our virtual agents and then we look at our employees. And I said, hey, this now needs to be looked at together. We can't plan, you know, here in the agents and here we plan, you know, and do our employee

plan. And so, and we need to do this faster, because the software, the AI is moving now much faster. So absolutely. And then the second point, what I would say is you mentioned one important point and that's culture. Because, you know, when I look at some of the ACP projects, I mean, obviously when you technically migrate to a new software, to a system. But you don't change anything on the business side because also the business says,

why to change and so on. It just creates uncertainty. I mean, obviously, you are not seeing the business benefit. Now with AI, the mindset in many customs I'm seeing has completely changed. They say, wow, if we miss that boat, I mean, it's really disruptive, you know, for our company. So we have to change. So the openness for change is also now very, very different.

So speed of execution getting much faster, planning cycles getting shorter. But also the business is now very clear, hey, we have to be on. We have to write this wave. Otherwise, it could be too late. Okay. So I have a lot of culture and hiring questions and talent questions to ask you throughout our discussion out today. But I'm just going to start with the first one. Yes.

Are you noticing people getting exhausted? Because if you, you know, there was a story in the Welsh Reacher Live referenced a couple of time on this show that I initially hated and now I'm like starting to see the wisdom where it was like bosses were mourning the loss of busy work because you know even though you know the the example is doing expenses people are like I'd rather my expenses be automated so I can focus on the bigger thing and that makes sense but like if AI handles let's say it you know it handles all this rote work so all you're doing is like very highly intense cognitive work and everything is so fast it's seems, I don't know if it's a recipe for burnout or not. What do you think?

I actually, it could be actually. I mean, you have to see. I mean, many jobs will change really dramatically. I mean, you know, to look at all the controls in finance, in HR and so on, they will get all automated now with the AI agents. We are delivering out of ways. What's going to get automated? The controls, the compliance checks, everything. Yeah. So also the typical stuff, yeah, what many people are doing in their business life. And then, of course, it's a full-time job for thousands of people. That's a full-time job for thousands of people. And now you're even in planning, steering a company and putting the pricing lists together and so on.

I mean, AI will, you know, infuse a lot of intelligence into that. And then, of course, people have more time, you know, to spend on, we would say, the value-adding task, yeah, to spend more on, okay, when the earnings is already prepared, including my remarks. And they even, you know, AI even tells me how to, when to. to waste my voice or lower my voice. I mean, obviously, you know, it's such a different preparation. It's also done the way on, okay, now I have more time to spend on really on delivering, you know, my earning speech. And so I guess that is really a different change of working and definitely needs a lot

of change management, absolutely. And how about the part about people getting tired? I mean, I'll give you an example for my life. I'm blessed. I mean, I really am. You know, it's nice to be able to run. you know, this company, I won't say on my own because I have a lot of help, but like the, you know, running, you know, a media company with one person mostly wouldn't have been possible to the level that we're doing it. But like, I'll have Claude Code. Cloud Code is doing my invoicing, Claude Co-work. You know, soon it will do these projections. It will keep on top of my inbox. It's going to do, you know, all these other things like expenses and things like that. And so,

My time is like really spent on interview prep interviewing, like doing these higher, higher low, cognitive load tasks. Yes. I love it, but I can't tell if I'm more tired because of it. We're like rarely do I have a moment to like downshift. And that's, it's great. It's great, but it's also like I'm starting to questioning whether it's kind of, whether it's what I want. So you're obvious, your company's 100,000 people in the group? Yes, yes, yes.

You know, what do you hear from people about this? You know, I see this also in my daily life, you know, all the, all the prep work, you know, is suddenly getting automated, the customer briefings and all of that. And so I can really focus more on the content, but do what, you know, and then it's actually much better time, you know, where you have with your team talking about the strategy. You're discussing, you know, certain things on are we here on the white track or do we need to cost correct about what about this leadership decisions I have to take or what are. about other things. So I would say, yes, there is a change of the type of work what you're doing, but I would say, will it get more boring or more exhausting? I mean, I don't believe so, but you have to structure indeed your day to day in a different way, absolutely. Yeah. Okay, so no increase in fatigue so far. No, no. Okay. That's good. And I also, honestly,

to the customers, I talk to many customers and also end users of AI and so on. I didn't hear that now. They are all happy if, okay, let's get rid of all of these workflow approvals. Let's get rid of all this compliance checks and controls. And then let me focus on the stuff what really matters. Yeah, I mean, I'm framing it in the most negative way possible. But I also think that, like, yeah, I would never go back to like wanting to do all this, all the invoicing and expenses on my own. Glad software takes care. What I'm, of course, getting an abbey employee all hands is, okay, is my job still secure? And what about restructuring? What about that? And so I get that. Yeah, so not about so necessarily I'm getting exhausted.

Because AI can do so much of the work that might be afraid to tell you. Yeah, yeah, yeah. But also, honestly, I really appreciate. I mean, you know, we are running at full speed these days. The transformation happens at full speed. And the workload is not getting lower for our people. So AI helps them to get more productive. But it's not like that we are running out of work in development or in finance or in HR and other parts of the company.

Well, that's why like what I think, I think I spoke about this with Muhammad Alam, one of your colleagues. Like, when I think about, at least with today's technology, like, is it going to create mass job loss? Yeah. My answer is always like, I don't think so. Yeah. It used to be, I'm sure no. Now it's, I don't think so.

And the reason is, is because companies, they have a massive roadmap. They have so many things they want to do. They have things their competitors are doing. And so if your choice is, you know, do the same of what you're doing with fewer people, but with technology. Or keep people. but do even more because they have this technological boost the companies that survive

will do more with people and tech yeah and I believe that and I tell this my people honestly I said hey I mean our workforce the way all our skills and you know the people working here at SEP

I don't believe that in 12 months from now you know we will you know there will be a mix a different mix of job profiles yeah we will need other you know jobs will we will hire people data scientists, full stack developers, etc. But, you know, we will need less people in other jobs.

So now it's a question about how much can you reskill, how much can you, you know, train people on a new job. But also sometimes, you know, in these moments, a company also needs, you know, new employees to come in with new skills, with a new mindset and pair them with the experience colleagues we have as well because you need to domain know-how. You need to understand the software, which is underneath the AI. So I would say it's, it's, there will be a work for.

force transformation, but it's not necessarily that it's only about restructuring. It's really about, you know, at the end of the day, you need to have the right skills and the wide mix of people. Yeah. People often hear the term reskilling. Yeah. And they don't believe in it. They think it's a nice thing that academics think of.

But when you go to somebody and say, we're going to reskill you, you've been doing job A forever. You're going to do job B. It doesn't work in practice. What's your experience been? Like, have you seen successful reskilling programs? Oh, yes. I mean, inside our company. So talk about it.

I mean, for example, we had a lot of developers coding on premise software. And now coding cloud software is different. It's more DevOps, you know, what you code, need to be tested automatically. You need to then ship it and it's in production and it's then on us, yeah, that it's bug-free and that we deliver it and that the systems were unstable. So it's really a different way of coding and it's a different mindset. And then the question is, also it's not only about a re-skilling from skills, functional skills, it's also about the mindset.

Does someone really need to go into this new field, into this new world? And that is, so absolutely it's possible. Or take now, when we are rolling out our co-worker to work, 80,000 people are using it. But at the beginning, it was also there's a certain resistance. You know, what does it do to my job? How can I practically use it? Going across this barrier of, you know, does it really help me? Do I need to still cost correct certain things what Schulberg is producing for me?

But once you, you know, you train the people, you coach the people and you see, hey, you can get, you know, stuff done faster and you can focus on higher value adding task, that is the moment where they say, okay, let's use this tool in legal, in HR, in finance, in sales. And then you see, yeah, that people are, you know, able to adapt to that and learn and also acquire new skills. You know, pre-chatchatship UTIA, I wrote a book talking about how the tech giants were already using AI to minimize work and make room for more inventive work. And, you know, it came out in April 2020. Not a great time to release a book, but I'm glad I did it. And the main pushback that I got was, or basically what I said was about work was that you take people off of these rote tasks and you put them on more inventive tasks. And it's more fulfilling. and it helps a company grow and move faster and be more inventive.

And the main pushback I got was when AI automates like back office work, like moving data from one place to another. Or like, you know, one example that we gave in the book was like AI is going to eventually be able to write like new hire letters and, you know, benefits letters and stuff like that. Which like felt crazy at the time, but it's been doing it her years now. The pushback was people who are used to doing this like data, let's say moving data from one to another, are not going to want to do the more inventive work. It's a different type of thinking.

It requires different culture, different set of permissions, and they're not going to be able to adapt to the new type of working. What's your read on that? Is that a fair criticism? Yeah, it also depends on each individual. I give you an example in our, world, you know, for 50 years we coded software. And for 50 years, we were never running out of customer requirements on features, new features for our software. But over time, obviously,

you know the business, you know the business which you are running. But the product managers didn't need to go out always to the customer and to completely reinvent how businesses is running because it was one feature more and the software was running the business in a very stable way. Now with AI, we tell our product management, no, no, no, no. Don't see. too much in-house. Go to the customer and reinvent how supply chain works, how we do inventory optimization, how payroll will work in the future

so that not an army of people need to make sure that the payroll is working correctly and people are getting paid in a correct way. And now it's really about this excitement of, oh, now I'm going there and with this technology, I can

really completely reinvent how businesses, how companies will work in the future. I really reinvent the future of the future of work. And I would say with the vast majority of our people, that creates excitement. Are there maybe a few people who say, ah, I'm not sure

if I'm into that. I'm used to a certain type of working. Yeah, it could be, but this is really about how do you bring your people with you and how you do the change management. I'm not saying that every, every single employee will make that move. But I guess a lot of people get it.

Oh my God, this is exciting. This is new. And I love to learn something new in my working life. Okay. And you've segued perfectly to the cesspocalypse because there was this belief, and remains a belief among many, that software

like yours was developed for you develop it for the masses, right? So you build software like SAP. People have to be able to use it in different functions, different companies. And so when you get a seat to it, you're going to

get this software that's kind of built for everywhere. They're going to be features you're going to want to use, features you're never going to click on. Yes. And this idea, once this idea came through became popularized that once people could build software on their own, you know, because you could prompt it and they can show up for you, then the old way of building software, old way of building software, wasn't going to work

anymore. And we would see new software, vibe coded, replace, you know, the old software. And that narrative has plagued software companies until recently for about the better part of a year at this point. Let's just high level. What's your reaction to that? What was your feeling as that narrative became popular? Okay. Let's go back in time, especially when this narrative came up.

I mean, of course, the first moment you think to yourself, of okay, I see development productivity is going up like hell. Why can't people just reproduce what we build over 50 years? And then I ask my product managers and my portfolio team and go through our portfolio. And which solution do you believe can you wipe code easily and just reproduce it and replace it maybe? And the answer was very quickly that they said, oh my God, I mean, you know, we are building earpiece. So we are building supply chain, payroll, finance. It's not only that you need, you know, you have 7 million data fields in such an ERP,

which you need to correlate. So there are hundreds of millions data correlations, what you need to understand to run a business. But there is also, of course, deep process knowledge by industry, you know, by country, local requirements. I mean, we are investing hundreds of millions every year to keep our software compliant. And then, you know, the answer was, okay, there may be, you know, one or two solutions, small solutions, where there is not so much process and data context inside, which you might, you know, just can replicate. But the West, I mean, definitely, you know, you need to have a lot of domain knowledge.

It's definitely not easily, you can definitely not easily wipe code it. And so, yeah, and that gave me a good feeling. But still, Alex, I mean, obviously there's not a time to lean back. Because clearly now, I guess in the next phase, you know, the value creation is now moving up from the system of record into the agentic AI layer. So absolutely our AI needs to be world-class to still also defend our existence in the system of record. Okay, but now let's go back to something you said earlier.

Yes. Where you said that AI is getting good enough to be like 100% accurate on some of these tasks. You said that AI is getting good enough to sometimes reconcile data. Just to all sit on the side of the SaaS podcast. and you can be software for the sixth and yes yes okay as paccos people would say man uh the a i is getting good enough that that those hurdles yeah domain knowledge yeah process knowledge

you know being able to put data together yeah it's gonna do it yeah and therefore the mode while it still exists today because the a i wasn't good enough yeah when the ai gets good enough that mode's gone yeah what do you think yeah i mean well when we talked about the the beginning of about all of these accuracy. I mean, that was the accuracy on our AI platform. I mean, we tag an LLM and then we put, you know, our AI foundation next to it, where we train business data, business process knowledge, where we built knowledge graphs, semantical ontology layers.

And that's why we are giving the LLMs now the understanding to deliver, you know, agents who can actually live up to all of these accuracy targets we talked earlier on about it. If you just use an LLM alone, there's no way that you can run a warehouse with it. that you can do a financial close with it, etc, et cetera, but it's an LLM plus our AI foundation, plus the governance. It's not only the business data and the business process. It's also that you need to understand all the legal requirements.

I mean, the agents cannot just go wild and do a financial close without understanding the tax requirements in a certain country. So there is more to it. And that's why, you know, I'm so confident that, you know, these LLMs are super powerful without any doubt, and they're getting better. But they need the process context, the data context and the governance.

And this is, you know, I would say where SAP, I mean, this is what we are doing for living since 50 years. Yeah. Okay. Well, I have not done with my questions here. And I have some other things to speak with you about regarding this conversation. So let's do that when we come back right to this. There are a lot of things AI can replace. Teamwork isn't one of them.

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And we're back here on Big Technology Podcast with SAP CEO, Christian Klein. Great to see you. Thank you for coming. I said before the break, I wasn't done with my questions, and I'm sure not. All right. So, okay, this idea that, like, AI is going to get better and people can vibe code, you know, their own SAP. It's going to be hard because even if the AI gets better, all this domain-specific knowledge is tough to include. I have this theory that what you might see instead of people vibe coding software is that they're

AI companies, which currently make, like the foundational labs, Open AI and Anthropic, which currently make their best models available via API, we'll close those models off. So, for instance, let's say GPC, GPT6 Astra right now, it's pretty smart, but it has those limitations. But we've seen like unreleased versions of Open AI's technology. Be able to do things like team up in a swarm. work for 88 hours and solve like, you know, the hardest math problems ever. So is there a world where they say, we're not going to keep, we're not going to release these models, maybe for safety. But we are going to put them on some of these problems that SAP has been working on for a long time and then decide that in order to make money because A.A. models are going to commoditize. They have to go upstream, which means take that knowledge and do a.

chatbot version of your business. Yeah. Fair question. And look, the truth is when you come to our business AI platform, as a citizen developer, as a business user, or, you know, as an IT guy, a developer,

you can actually develop agents on top of our platform. So we are delivering hundreds of agents, standard agents for finance, supply chain, HR, but also the business and the pro code, the developers can also build agents on our platform. And what you find is all these models where you just

talking about. But what you also find then on top of it is that we connect those models to the business process and the data knowledge, the context of what our ERP actually knows about your business. And we are reproducing

that in our ontology, in our data layer. So, you have all of these models. What's ontology? Explain that. The ontology is actually when you have, you know, you have SAP data, but you also have non-SEP data, what the agents need to understand when you do asset management. the agent needs to understand the sensor data,

the signals of a machine to understand when does the machine need maintenance. And then it needs to pair this with the SAP data about, okay, let's create a maintenance order, who is the worker who can fix this machine, etc., etc. So it's the logic. And that is the ontology, what we are building now, what we are bringing together.

And we also tell the agent, okay, machine has a problem, where do I find the spare part for this machine who is maybe not working anymore? where can I find the worker who is fixing it, et cetera, and everything on time so that there is no downtime to the machine. And that is the ontology, that is the semantical layer. And again, why would you go to a third party platform

when you find all of these great models on our platform, plus you get all of the semantics, the ontology, plus we are going to run these agents to make sure they adhere to the governance of your company, of a country, of an industry. So you're skeptical that there's a, even if they hold those. So right now,

Open AI and Anthropics are making their models available in your platform. But you're skeptical that if they were to withhold their latest models from your platform and try to do it themselves, they pay to do it. I'm, you know, the same question I got years ago about the hypers. I mean,

we are. This is a different technology. Yeah, that's a different technology. But we are partnering. And, you know, they are,

of course, they are super powerful. I mean, they are wanting, you know, the most, you know, the most systems of the world.

They are owning, you know, a lot of our systems. And they could also say, yeah, we are not providing this infrastructure anymore. So, and this is, you know, and this is similar. But I see, you know, there are so many models now coming to the market that I would say there is a healthy competition also going on. And we are not seeing this one model who, you know, solves all of the problems. Actually, we are seeing a lot of open source, a lot of own weight models who are also doing a very, very good job when it comes to also looking at the price outcome ratio of an agent.

It's not only important to it, that the agent does a great job, but it's also at what costs us the agent does a good job. And so I feel there is so much healthy competition that I'm not so much worried about, okay, there is now a provider saying, oh, we are not offering you this model anymore. Because, again, there is competition. There are many models, and there is not this one model who actually will solve all of the problems. Let me ask you this. Would you accept, or I don't know if you do this already,

would you accept Open AI or Anthropic as like a front end to your software? And by that I mean like you have all this great data. Could, let's say, a user of SAP who's used to like locking in and going through the graphical user interface or using your agents. Are you open to them like having a connector through chat GPT for instance and being able to query that data and do that through chat GPT for business? I mean, the way how we solve for that is there is tool work, you know, our co-work. It's our interface. But you won't integrate. we actually embed

those models into true work so you can also switch the models you can do a certain task with Anthropic you can also

do certain tasks if you're in Europe and soft fronties in porn you can also use mistwell you can use their latest model

and then but then it's really about SAP's interface that's our tool work it's our co-worker

and then of course there will be also scenarios where you will build an agent let's say if Salesforce

Adobe and so on. And they need to also then access, you know, SAP data. And that's totally fine as long as people are going through our API gateway and the agent gateway. And so it, but I would say predominantly, you know, what we are seeing now with our customers that users working with SAP software, they will use tool work. But there will be definitely also third party agents accessing SAP.

Right. We will have access to SAP systems via our agent gateway. Including opening eye and Anthropic? Including. So there will be a world world where like... Yes, yes, yes.

But it needs to be controlled. And the customers will also rely on that that it goes through our agent gateway. Because again, governance is important. Whiteback is important. You don't want to go having an AI agent doing uncontrolled things in your ERP system. So that's why I'm very, very confident

that first of all, a lot of the SAP users who do work in finance, in HR and supply chain will come via true work. Because that is the place to be. You can do everything what you do with an LLM model, plus you get the SAP data, the business process, the context. We also have a data platform who infuses non-SAP data. And if you build a third party agent, for example, for your next marketing campaign and you want to weed out some order data, I mean, yes, you can do it. But it will happen via our agent gateway.

And the marketing users are anyway, is not, you know, our prime users of the SAP software. Okay. Let me ask the question one more time in a maybe a different way because I don't know if I'm. getting the right answer or fully understanding the answer or getting the question not the right way. All right. There's an example recently where META has this MUSE, personal assistant. And you could get Mews to shop for you. Yes.

There's two approaches to this. Yes. There's Amazon's approach where META is like, we want Mews to be able to use Amazon, and people can say, buy me a leaf blower, and then the muse will go into Amazon and buy them a leaf blower. Yeah. And you never have to touch Amazon. Yes. And then there's the...

Okay, so Amazon blocked that. Yes. Shopify said that's fine. So Shopify said we want a third party agent to come in and use our technology. And Amazon said, no. If you want to buy on Amazon, you can use Amazon.com or use our agents. Yes. What is your philosophy?

The Amazon or the Shopify? Actually, we offer both. So that's... And the ideal world, the idea world is that, you know, because our AI blood, blood, from true work is so great. In my eyes, customers, end users will understand. Why would I use an LLM model standalone if I'm not getting the same business context and not the same governance?

Yeah, but the API connection is not giving you all the business context, is not giving you the semantics, it's not giving you this deep process mode. So it limits some of the stuff that. Yeah, yeah, of your case. A pure API connection, pure MCP server, is not the same like the ontology, the context we are having on our AI platform. And the governance, when it's SAP managed, we also make sure that when you're doing

a financial analysis, that when we govern it via true work, it is ensure that only the people who are allowed to see the numbers are also getting access to this report. So the agents understand that. So why would you then go into a third party platform where you have, yes, API access via our API gateway?

That's possible. You can access from a third party platform, you know, into the ACP system via the API gateway. But there is so many reasons for SAP users to use toolwork because better context, better governance that we believe, yes, the main route will go through, will go via toolwork. Yours, yeah. It is interesting.

Like when we sit down with someone like yourself and hear it, it's like, oh, this is actually not as simple as a one-sentence tweet. Yeah. And that's what the market seems to have been responding to. I mean, SAP this year is, it's down 21%, but it's been up 43% over the past two months. It's a roller coaster for you, isn't it? Oh, yes. Oh, yes.

But I'm used to it. I mean, when I became CEO, seven years, when I'm used, you know, I did the cloud transformation. Right. The market was, say, oh, are they going make it or not? And, you know, the share price was down. Then we reached the all-time high. We proved that we can transform the company.

And that's another transformation. And it's a similar pattern. Now, the good piece this time, we are not alone in this, the whole software industry, you know, went down. But I guess more and more, and investors, analysts and of course also customers realize, hey, an LLM alone will not cut it. So we will need the software, the app, the Wobrites, the context, the governance. And then the LLMs are, of course, the key, you know, to unlock this value. But it needs to be really brought together with the business process context and the governance. And I guess that is something what in the last two months, many people realized in the money.

I can't. Yeah, software has made a dramatic comeback. Oh, yes. Oh, yes, yes. I want to hear a little bit about, you mentioned open source. I want to hear a little bit about the way that you think about spending with these models and which models you want to use. Yes. So we recently on the show cited some data from Ramp.

They have an economics lab, which obviously is like they're getting credit card data from lots of startups. Yes. But they think it's indicative of what the rest of the economy is going to do. Yes. In August, 53% of spend on AI was on the frontier models. Models like Opus and Fable and some. In September, that number was 40, or at the end of August, the number was 45%, which is quite a decline, some, for 53% to 45% decline in terms of the spend on the frontier.

Yes. What that suggests is that companies are discovering they don't need to use the best AI to succeed. Yes. Have you found that at SAP? Similar patterns, absolutely. And I mean, again, that's also to the discussion, you know, to the point you brought up earlier on, would customers go via a set party platform directly to the ACP system or will they use, you know, our true work, our platform?

I mean, in our platform, you're also not locked into one model because you see, yeah, with every model release, you know, it can change again. The one model gets better than there's a new open source model who is really producing great results and we also see agent by agent

not every model performs the same so this multi-model being agnostic is really also a key differentiator what customers are loving they're saying

okay I'm not locked into any frontier model and SAP is even doing the switching from one model to another to always optimize the token spend with regard to the outcome

in comparison to the outcome and now to your question yes indeed I mean, we of course also started, you know, coding with a lot of frontier models. Still, coding is something what we do a lot with the frontier models, also for soft frontier reasons, et cetera, et cetera. But then, you know, for many agents we are running, you know, also for task like, okay, create me a headcount report for my manager or do me a financial report.

We actually see that some of the open source model perform so good that it, and then compare it to the cost of those models, we are going to switch. And many agents we are building, in the meantime, have. seen the fifth model because we are always, you know, optimizing, yeah, outcome versus the tokens and the cost we pay for such a model. Yeah. And has that emphasis on cost grown recently? Oh, yes. Of yes, yeah, because the overall token spend is up, which is good. But, you know, it doesn't help you if some of your employees is getting 20% more productive. If at the same time, 30% more up, yeah, and of course. And by the way, this is also how our product manager,

a cultural change. We told them, test every agent which we are releasing to the market needs to be tested with different models. And they need to do this all the time. And because we see new model comes, oh, maybe we can switch the model, because it's a better price outcome ratio

at the end of the day. And so, yeah, that will continue. And we see this also as a clear differentiation that there's a software company like SAP who does this for you so that your AI tokens are not running away while you are celebrating maybe your productivity gains. and then at the end you look at your Piano says,

oh, shoot, you know, my profit is actually not hitting the mark anymore. Yeah. It's interesting, right? It says that it says a couple things, actually. First of all, it says the AI has gotten good enough that the frontiers is only going to be applicable to certain use cases. Yes. Which is wild. Yes.

It also says the standard models are, you know, are really working. And an interesting thing about the standard models is they don't tend to be, like, like standard versus frontier. They don't tend to be like 80% of the price for 80% of the performance. Yes. Right? They tend to be like 10% of the price for 80% of the performance. Yes.

And look, I mean, we also acquired an AI model, one, a tabular AI model who does predictions. And I mean, it's remarkable. We took all the data of retailer, SAP, non-SEP, and without a data scientist touching it and building data pipelines, doing the semantics, matching the data so that it makes sense. The tabler AI model actually did it with the same accuracy for the customer than what a team of 10 data scientists did before. So you see, you know, it's not only the frontier models, it's also the Table AI models. There are other models coming up for the structure data, all the models we are building, you know, for the business data. So I would say there is not this frontier versus, you know, old source standard models.

There's also no tablet AI models coming because for predictions, these models are becoming back. and better as well. As a business person, what do you think it says about the business of these frontier AI labs if the frontier is, you know, it's harder to charge a premium for the frontier?

Yeah, keep on inuating, keep on, you know, making the model better, better. No, they are making the model better and better, but people don't need the cutting edge. But others are on it as well, yeah? So that's why I'm saying. I'm not afraid of all that, you know, one model is,

one provider says, oh, you are not going to allow to use our model anymore. There are so many models in the meantime, and they are making all good progress. But again, it's the same like SAP.

When Hasso Blatner founded this company, there was not many competitors. Now we have hundreds of competitors. I mean, that's the name of the game. We have seen that, others have seen that. The only way is keep on in rating to justify the price.

And if you don't, if you are not much better than the rest, of course, at a certain point, it's then hard to justify the price. But this is a game we all playing. I'm going to answer my own question. I think that if you're spending billions and billions on training frontier models and the standard models are doing just as good or good enough job for many of your customers to the point where we're seeing things like 8% declined, not year over year, but month over month, that might be a problem for your business. All right.

I won't ask you to agree with me on that one or disagree, but I have to put that in for the record. Yeah, I mean, I mean, I guess. this is also the question what everyone at the frontier model is also asking you know and it's also interesting maybe one addition to that I mean you know

there's of course a financial close there is inventory this is when you ship when you bill accuracy needs to be record high we have a rule 95% of better otherwise it's not worse to ship the agent customers will just not use it

but then there are many other agendia use cases take you know a customer briefing a headcount report what I mentioned I mean this is not, doesn't need to always be 98% yeah, it's also okay if it's 94% because I'm still, you know, all the people who needed to prepare that stuff

are, you know, we need less time on that. We can focus more time on the value adding stuff, prepare myself for the meeting, et cetera, et cetera. So, and that's why I would also say all of these frontier models and, you know, we don't need to always use the best, best model from an outcome perspective. You always need it to match it to the price

and to the nature of the model. What is the accuracy, what I need, to have the acceptance of the business. Yeah, very interesting. We have IPO, so we'll learn a little bit more about this. How about, you know, you've sort of talked a little bit about this token maxing reckoning or the token reckoning. Ramp also has interesting data about the spend for the top 1% of employees. Oh, yes.

Right? So top 1% of employees, they're not just consuming a little bit more tokens, they're consuming way more. a knowing smile for those on audio. So we're going to ask this, all right. So in a month, the spend from the top 1% of users fell from $7,976, according to Ramp Economics Lab, to $7,205, which is a 9.7% decline. With the users, the top token gobblers within SAP, have you encouraged a similar pullback? have you seen a similar pullback?

What's that knowing smile all about? Yeah, we also have this 1%. And my CFO says, hey, where is this going to? And honestly, it took some time until we actually introduced certain token limits in SAP because I told my CFO and my CEO, I said, hey, I mean, now we tell everyone use AI to reskill yourself, to change and become more productive. And when I'm now the first one who actually introduces this token limits, it's also the message what I don't like. But at a certain point, we are really so, oh my God, now the token spend is really coming to a point where we definitely need to set limits for certain jobs.

But, you know, for the top 1%, I mean, the ones who are doing the model training and so on, we didn't really set a limit. Let them run because we see this is a very important task, very important job to make the accuracy of the agents better. So I said, hey, we can't compromise on that because all, you know, the success of our AI really depends on the accuracy of our agents. So let them do the job. But then, of course, you know, for, you know, all the developers, the product managers, to design us. I mean, there we also introduce limits, also for people in finance and HR. And of course, there are certain limits where we can't say, hey, you can't just experiment around spend the tokens when we are not seeing, you know, the respective productivity outcome. But we said very healthy and still, I would say fair thresholds where everyone says, okay, I can definitely do my job with AI and I'm not reaching this limit, you know, every month.

And then, of course, people are allowed to say, but I have this certain task where I feel AI can really help me. This time it's very special. So we need a higher token spend. And then we have a approval process where a manager can also say, okay, for this month or for this task or for this project, we give you more token spend. And I feel the people accepted it well. and it was also the right message. Do not send a message of, we forbid you now the use of AI

or you're reaching the limit so fast that it's hard to really optimize your day job. But vice versa, also making sure that token spend, stays under control. I guess what was even more important than this token limits is the model switching. I mean, yeah, of course.

I mean, we are testing, as I said, different models. And when you switch from one model to another, that can cut your cost by 10, by a factor 10. And that is, of course, even more effective than introducing all of these

token limits for the certain job profiles in the company. When did this all go into place? I would say the token limits, the budget by job profile we did four months ago three months ago. And the model switching, we built

this into the platform from day one on, but in all fairness, and we were just building the agents, getting them out, celebrating success, getting references. And now, I would say in the last three months, we also intensive Our product mentioned, intensifying the work on, switching the models, testing the models, and, you know, optimizing them. And we also look into the TCO of a model because, you know, at the end, it's not only about delivering great agents.

I mean, at the end, it also needs to be, from an economic standpoint, it needs to be reasonable. How long does it take between when we see some crazy frontier AI behavior and when it gets diffused into business? Let me just give you one example. Like the hugging face attack from Open AI. Hundreds of bots, you know, attacking a problem. Well, I mean, that's like well known. But there's also the meth, the attacking the Millennium Problem and succeeding. That seems to me to be, you know, these sort of swarms of agents working to solve business problems.

I don't know. It's, it hasn't, I haven't really heard about it. much from businesses. Like even those that have said, you know, we have like a multi-agent system. It's like six or seven, not hundreds or thousands working together. Yes.

How long does it take from, you know, from when we hear about something like that at the frontier to when you get it and where do you think we are on this agent's form thing? I would say, look, on cybersecurity, obviously it's something, yeah, where you always have to be ahead of the game. Yeah, so we are testing different models all the time.

But it's not cyber. It's like these type of uses anywhere. Yeah, okay. On the users everywhere, I mean, whenever a new model comes, we are testing it right away. A model switch can be done in one week, two weeks. We need to change, you know, when there's a new model, we need to change APIs. We need to, you know, adjust the MCP service. But we can do this very fast.

And so it doesn't take a long time to really test out a new model and how does it perform on the business side. But like swarms of agents for business use cases, are you testing that now? Yeah, yeah, absolutely. Yeah, we are doing this. And when we are building all of these A-to-A use cases, I mean, obviously, we need to test it. We also need to test, you know, the collaboration of these agents. How do these models also perform? And so, yeah, we are doing all of that.

Yeah. I will say, if I was sitting in your seat, the thing I'd be most afraid of is what's going on with cybersecurity right now. Because when you think about your customers, they've got finance, supply chain sales, HR, these are the most sensitive records of business could have. Yes. And if we see agents going zero day and people being able to direct them, that would scare me. Exactly. And that's why guess what we are doing. In our deaf operating model inside the company, we are always now working on how can we use AI to detect vulnerabilities earlier?

How can we put more proactive measures in play to test the firewalls of the company and the product? And what do we do on patching the system and so on? So that's definitely now something, what I would say, for every technology company, this needs to be at the very top of the agenda. Your cybersecurity budget's going up substantially? Actually, I mean, on the one hand side, it's going up because you're investing into AI. But, you know, finding the abilities, patching. I mean, of course, that also gives you productivity gains. So it's going up, but it's going up in a reasonable way.

Okay. You know, we're a podcast where we don't like to do the theatrics or the we try to read. really understand. So I'm really going to resist being like what's wrong with Europe. Okay. I'm going to resist that question. I actually want to ask it to you this way. Europe has put so many regulations on its tech companies and people would say business in general. To the point where if you speak with people here in the U.S., they'll they will be like, it's not even worth doing business there. we have a lot of European listeners. As I told you before, I'm from a mixed marriage year.

I'm American. My wife is European. I think that it would be good if Europe became a good place to do business. Yes. So this is, I'm not going to harangue. I want to know from your perspective, because you're running the largest software company in Europe. What is the thought process that the European regulators are going through? to get to them where they are.

What are their intentions? And do you believe that those are good intentions? Yeah. I mean, first, I really believe they have all good attentions. I mean, who in Brussels does something to disadvantage Europe? But of course, what people sometimes do, especially the ones who are not so close to the technology, they tend to regulate the technology itself.

I guess what we are now learning, And we talked about also the concerns and the risk we are seeing coming with AI. I guess you need a regulation, but you should regulate more the business outcome, the impact on the society and not the technology per se, not the use of data. Because otherwise, you know, it's so hard to do business and get a startup going in Europe. And also for us, I mean, we are a global company. And we are doing our research and our development also in Palo Alto. We're doing it in Bangalore. We're doing it in, you know, everywhere in our labs.

But for a startup, it's really hard. And I guess, you know, Brussels and Europe, I mean, it's definitely they realize that probably they stretch it too much. That's why they call it omnibus. This is where you can, where they now collect all the simplification measures, the deregulation measures. And I know they're dealing with it right now. And I hope there is stuff coming, which definitely helps us to put the regulation to the right level. And again, I can really emphasize not enough that the need to. make sure that you regulate the impact on societies, but not regulate the technology per se,

because how can you be competitive if you are the only part of the world who is really regulating the technology? Yeah. So you think there's going to be some form of rollback? I'm confident. I mean, the message is loud and clear, not only by SAP, I mean, many startups. They've heard it. There are other companies like Siemens and so on.

I mean, we are building a lot of industry AI in Europe. So there is AI. I mean, more applied AI. industry AI and all of these companies, you know, say message to Europe, we have over-regulation. Let's scale it back. Yeah. And it's mostly GDP. Sorry, I come from like more of like the ad perspective.

I mean, GDPR is like speaking of the intentions. Like I get it. You want to protect people's data. Yes. Say mostly that or it's just. No, it's actually not so much about GDPR. I mean, what Europe does is the AI Act, the Data Act, which then puts another layer of regulation on top. And, you know, it's not only another layer. Sometimes, you know,

these layers are also overlapping so that even the legal people or your data protection officer is not even knowing anymore, okay, there's so many quasons, so many overlaps. So it also takes forever until you understand how can I use no data to build AI and to do research and to apply AI in the customer's business. And so that is, you know, all of these layers of regulations. And then, you know, the recent ones with the EU data act and the AI act. All right. Christian, feeling thank. Yeah.

Thanks for coming. Thanks for coming. Thanks for saying. Thanks a lot for your time. Should be to say. Very good chairman, actually, yeah. I'm learning. All right.

Well, thank you for being here. Thanks to the New York Stock Exchange for hosting us today. Good to be here. Great to speak with you. Thanks everybody for listening and watching. And we'll see you next time on Big Technology Podcast. My rec league goalie says he plays for the love of the game. You gave up six.

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