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

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

• 0:00Speaker 00

  Now that this SaaS apocalypse 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 to go inside a large scale Agentic CX transformation, including the business decisions, trade offs and lessons along the way.

• 0:42Speaker 00

  You can watch both conversations now on my YouTube channel today. It's never been more challenging to get frontier compute. How do you know where to look, who to trust, or how much to pay? Akash is an open cloud that provides easy access, flexibility and peace of mind. That's how companies like Razor were able to cut their inference cost by 15 times. Visit akash.network/alex for a free consultation. That's akash.network/alex for a free consultation.

• 1:14Speaker 00

  Welcome to Big Technology Podcast, a show for cool, edited 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's here to talk with us about the state of software amid all these SaaS apocalypse 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.

• 1:40Speaker 01

  What a great introduction. Thanks for having me, Alex.

• 1:43Speaker 00

  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 off 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.

• 2:26Speaker 00

  So actually, you know, you make the choice and then you run a company based off of it.

• 2:33Speaker 00

  I, 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 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, do you think it's, we're about to hit like recursive self-improvement? Or do you think that what I'm saying makes a little sense?

• 2:59Speaker 01

  Yeah. Happy to answer the question and let me maybe just describe it, how I describe it to my employees. When we do an all hands, I always show them a mountain and said, look, we climbed the cloud mountain. Yeah, we did a cloud transformation and then now we have to climb the AI mountain. And on this journey, there will be obstacles that 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 you know, 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?

• 3:43Speaker 01

  I can summarize a mail, I can summarize a document and so on. But what, what about the business? Why does, is there 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 mountain.

• 4:13Speaker 00

  Okay, well, Christians, this is the kind of the core question here. Yeah, all right, AI doesn't understand structured data that well right now, I think.

• 4:23Speaker 00

  But your, 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.

• 4:52Speaker 01

  Yes, yes, yes.

• 4:52Speaker 00

  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 software system. 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 timeframe 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?

• 5:17Speaker 01

  100%. And when, and I would say, look, I mean, you know, we, for example, code financial closing agents. And we are sitting here at the Wall Street and of course they work today. I mean, AI understands business, but you know, maybe it understands today 90, with a 93% accuracy. But your auditors, when you release financial results, they say, okay, sorry, this time my numbers were 7%, you know, too high. And I say, what? I mean, you know, you need audits, you, you need, it needs 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 you know, 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, on a constant basis.

• 6:02Speaker 01

  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 fat RAM, 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, yeah? Until we are really reaching a level where you can really trust AI running your business. And then you can still decide, okay, the human is in the loop for us. That's, that's, that's a rule.

• 6:41Speaker 01

  But then you can really decide on the autonomous level of the agents on how autonomous can the agents run the business?

• 6:48Speaker 00

  Okay, so this is important. So your perspective is within months, AI can go from basically working within software and giving us, giving your customers like 90 something percent accuracy? You anticipate 100% accuracy within months?

• 7:05Speaker 01

  I, 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, you know, here we are maybe there in eight to nine months. So, 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 Jule works, they put me together a customer briefing.

• 7:34Speaker 00

  This is your AI, SAP?

• 7:35Speaker 01

  My, my, exactly. Jule is our, you know, coworker. 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. Because I get the data out of SAP. I mean, the, 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.

• 8:07Speaker 00

  Yeah, okay, so 100%. That's, 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. Second, you ask another chap about where are the problems? If it finds 90% of those problems, you now have a compounding issue.

• 8:40Speaker 01

  Yes.

• 8:40Speaker 00

  But things change when it gets to 100. So I think the way that you're framing it is we're about to see a real explosion of enterprise AI use.

• 8:49Speaker 01

  It depends on the AI use case. It also depends, just, you know, 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 wanting, 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.

• 9:16Speaker 01

  So it's also the data, yeah, 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? So, 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.

• 9:41Speaker 00

  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 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. 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 sort of not able to do that on its own?

• 10:15Speaker 01

  Yeah, I mean, AI will also solve that challenge. I mean, look at, when I look at our data platform, I mean, we are partnering, you know, with other data lake providers like DataBricks, Snowflake, BigQuery 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 Salesforce 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.

• 10:56Speaker 01

  So that you don't need an army of data scientists anymore, always, you know, from every month and 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, in matching those data points together. And that is of course also part of what is happening underneath the agents in the data platform.

• 11:19Speaker 00

  Business today moves very fast, but it moves fast despite, you know, it moves fast despite the fact that processes are a mess. 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, your release at your big event and then you plan again. Now they're on quarterly or monthly plans. 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 to grow. It's just, is everything going to get faster?

• 12:01Speaker 01

  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 the company to react to that. No matter if you do supply chain planning, financial planning, workforce planning. 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 SAP projects, I mean, obviously when you technically migrate to a new software, to a system,

• 12:44Speaker 01

  but you change, you change, don't change anything on the business side because also the business has why to change and so on. It just creates, you know, uncertainty. I mean, obviously you are not seeing the business benefit. Now with AI, the mindset in 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 we have to be on, we have to ride this wave, otherwise it could be too late.

• 13:21Speaker 00

  Okay, so I have a lot of culture and hiring questions and talent questions to ask you throughout our discussion today, but I'm just going to start with the first one.

• 13:31Speaker 01

  Yes.

• 13:31Speaker 00

  Are you noticing people getting exhausted? Because if you, you know, there was a story in the Wells Reacher, I've referenced a couple of times 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.

• 14:07Speaker 00

  And everything is so fast. It seems, I don't know if it's a recipe for burnout or not. What do you think?

• 14:14Speaker 01

  I I actually, it could be actually. I mean, you you have to see, I mean, many jobs will change really dramatically. I mean, you know, to look at all the the controls in finance, in HR and so on, they will get all automated now with with the AI agents. We are delivering.

• 14:31Speaker 00

  Wait, what's what's going to get automated?

• 14:32Speaker 01

  The the controls, the compliance checks, everything. Yeah. So also the the typical stuff, yeah, what many people are doing in their business life. And then, of course.

• 14:41Speaker 00

  That's a full time job for thousands of people.

• 14:43Speaker 01

  It adds a full time job for thousands of people. And now you're even in in planning, steering a company and putting the pricing list together and so on. I mean, AI will, you know, be infused a lot of intelligence into that. And then, of course, people have more time, you know, to spend on, we would say the the value adding tasks, yeah, to to spend more on, okay, when the earnings is already prepared, including my remarks. And even, you know, AI even tells me how to, when to raise my voice or lower my voice. I mean, obviously, you know, it's such a different preparation. It's also then the way on, okay, now I have more time to spend really on delivering,

• 15:22Speaker 01

  you know, my earnings speech. And so I guess that is really a different change of working and definitely needs a lot of change management. Absolutely.

• 15:30Speaker 00

  And how about the part about people getting tired? I mean, I'll give you an example from my life. I'm blessed. I mean, I really, I really am. You know, it's nice to be able to run, you know, this company. I don't want to 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, Claude Code is doing my invoicing, Claude Co-Work. You know, soon it 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.

• 16:07Speaker 00

  And so my time is like really spent on interview prep, interviewing, like doing these higher, higher cognitive load tasks. 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, it's great. It's great, but it's also like, I'm starting to questioning whether that's kind of, whether it's what I want. So you're obvious, your, your company's hundred thousand people in the neighborhood. You know, what do you hear from people about this?

• 16:40Speaker 01

  You know, I I see this also in my daily life, you know, all the, all the prep work, you know, it's 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 you're discussing, you know, certain things on, are we here on the white rag or need to, do we need to cost correct about what about these leadership decisions I have to take or what about other things? So I would say, yes, there is a change of the the type of work, what you're doing, but I would say, will it get more boring or more exhausting? I mean, I I don't believe so, but you have to structure indeed your day to day in a different way. Absolutely.

• 17:23Speaker 00

  Yeah. Okay. So no increase in fatigue so far?

• 17:26Speaker 01

  No, no, no.

• 17:27Speaker 00

  Okay, that's good.

• 17:28Speaker 01

  And I also honestly, I to the customers, I've talked to many customers and also end users of AI and so on. I I didn't hear that now. They are all happy if, okay, let's get rid of this, all of these workflow approvals, let's get rid of all this compliance checks and controls and and then let me focus on the stuff what really matters. So, yeah.

• 17:47Speaker 00

  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. I'd love for it takes.

• 17:55Speaker 01

  What I'm of course getting in every employee all hands is, okay, is my job still secure? And okay, what about restructuring? What about that? And so I get that, yeah. So not about so necessarily I'm getting exhausted or it's my boy.

• 18:08Speaker 00

  Because I can do so much of the work that might be afraid to tell you.

• 18:10Speaker 01

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

• 18:32Speaker 00

  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?

• 18:42Speaker 01

  Yeah.

• 18:42Speaker 00

  My answer is always like, I don't think so. It used to be, I'm sure, no. Now it's, I'm, 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 with 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.

• 19:09Speaker 01

  Yes.

• 19:10Speaker 00

  With people and tech.

• 19:11Speaker 01

  Yeah. And, you know, I believe that, and I tell this my people honestly, I said, hey, I mean, our workforce, you know, the way all our skills and, you know, the people working here at SAP, 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, et cetera. But, you know, we will need less people in other jobs. 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, you know, new employees to come in with new skills, with a new mindset

• 19:51Speaker 01

  and pair them with the experienced colleagues we have as well, because you needed the domain know how, you need to, they understand the software, which is underneath the AI. So I would say it's, it's, there will be a workforce 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 right mix of people.

• 20:10Speaker 00

  Yeah, people often hear the term reskilling.

• 20:14Speaker 01

  Yeah.

• 20:14Speaker 00

  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?

• 20:33Speaker 01

  Oh, yes, I mean, inside our company.

• 20:35Speaker 00

  So talk about it.

• 20:35Speaker 01

  I mean, for example, we had a lot of developers coding on premise software. And now coding cloud software is different. Yeah, 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 are unstable. So it's really, it's really a different way of coding and it's a, it's a different mindset. Yeah. And then the question is also, it's not only about a reskilling 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 so, and, and that is so absolutely it's possible or taken out. When we are rolling out our coworker to work 80,000 people are using it.

• 21:19Speaker 01

  But at the beginning, it was also there's a resistance. Yeah, 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? Or do I need to still cost correct some things for Chulburg 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 tasks. That is the moment where they say, okay, let's use this tool in legal, in HR, in finance, in sales. And, and then you see here that people are, you know, able to adapt to that and learn and also acquire new skills.

• 21:57Speaker 00

  You know, pre chat GPT, I wrote a book talking about how the tech giants were already using AI to like minimize road work and make room for more inventive work. And, uh, you know, I, it came out in April 2020, not a great time to release a book, but, um, I'm glad I did it. And the main pushback that I got was, or basically what I, what I said was about work was that you take people off of these road 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 data, moving data from one place to another.

• 22:39Speaker 00

  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 was felt crazy at the time, but it's been doing it for 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, uh, 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, is that a fair criticism?

• 23:18Speaker 01

  Yeah, it also depends on, you know, 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're wanting. But the product managers didn't need to go out always to the customer and 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 managers, no, no, no, no, don't sit 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.

• 24:01Speaker 01

  So that not an army of people need to make sure that the payroll is working correctly and people are getting paid in, in, in a, in a, in a correct way. And now it's really about this excitement of, oh, now I'm going there. And with, with this technology, I can really completely reinvent how businesses, how companies will work in the future. I really reinvent 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, could be, yeah, but this is, you know, really about, you know, how do you bring your people with you and how you do the change management?

• 24:42Speaker 01

  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.

• 24:55Speaker 00

  Okay. And you've segued perfectly to the SaaS apocalypse because, you know, there's, there was this belief and remains, remains a belief among many that, um, software like yours was developed for, you, you develop it for the masses, right? So you build software like SAP. People have to be using, 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 everyone. There are going to be features you're going to want to use, features you're never going to click on.

• 25:26Speaker 01

  Yes.

• 25:27Speaker 00

  And this idea, once this idea came through, became popularized that once people could build software on their own.

• 25:35Speaker 01

  Yes.

• 25:35Speaker 00

  You know, because you could prompt it and it can show up for you. Then the old way of building software, old way of building software, uh, wasn't going to work anymore. And we would see new software vibe coded, replace, uh, you know, the old software. And that, that narrative has plagued software companies until recently for, for, you know, about the better part of a year at this point.

• 25:59Speaker 01

  Yeah.

• 26:01Speaker 00

  Let's just high level, you know, what, what was, what's your reaction to that? What was, what's, what was your feeling as that narrative became popular?

• 26:10Speaker 01

  Okay. Yeah, let's go back in time, especially when this narrative came up. I mean, of course, the first moment you think to yourself, okay, I see more development productivity is going up like hell. Why can't people just reproduce what we built over 50 years? And then I asked my product managers, my portfolio team and go through our portfolio and which solution do you believe can, you know, can you vibe 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 EAPs, so we are building supply chain, payroll, finance. It's not only that you need, you know, you have seven million data fields in, in, in such an ERP which you need to correlate.

• 26:54Speaker 01

  So there are hundreds of millions data correlations, what you need to understand to run a business. But there is also of course the process knowledge by industry, um, 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, uh, you know, you need to have a lot of domain knowledge. It's definitely not easily, uh, 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

• 27:37Speaker 01

  not, 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, into the AI layer. So absolutely our AI needs to be world class to still also defend our existence in the system of record.

• 27:54Speaker 00

  Okay. But now, now let's go back to something you said earlier.

• 27:57Speaker 01

  Yes.

• 27:58Speaker 00

  Where you said that AI is getting good enough to be like a hundred percent accurate on some of these tasks. You said that AI is getting good enough to sometimes reconcile data.

• 28:07Speaker 01

  Yeah.

• 28:09Speaker 00

  The, the, just to, I'll sit on the side of the SaaS podcast and you can be a software for this example.

• 28:15Speaker 01

  Yes, yes.

• 28:15Speaker 00

  Okay.

• 28:18Speaker 00

  SaaS podcast people would say, man, uh, the AI is getting good enough that, that those hurdles, domain knowledge, process knowledge, you know, being able to put data together. It's going to do it.

• 28:32Speaker 01

  Yeah.

• 28:32Speaker 00

  And therefore the mode, while it still exists today because the AI wasn't good enough.

• 28:36Speaker 01

  Yeah.

• 28:37Speaker 00

  When the AI gets good enough, that mode's gone.

• 28:39Speaker 01

  Yeah.

• 28:40Speaker 00

  What do you think?

• 28:40Speaker 01

  Yeah, I mean, well, when we talked about the beginning about all of this accuracy, I mean, that was the accuracy on our AI platform. I mean, we tag in LLM and then we put, you know, our AI foundation next to it where we train business data, business process knowledge, where we build knowledge graphs, semantic ontology layers. And, and, and so, 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, et cetera, et cetera. But it's an LLM plus our AI foundation, plus the governance. Yeah, let's, it's not only the business data and the business process. It's also that you need to understand all the legal requirements.

• 29:25Speaker 01

  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, 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, 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 a living since 50 years.

• 29:52Speaker 00

  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 after this. This episode is brought to you by App Point. Everyone's racing to roll out AI right now. Co-pilots, chatbots, agents doing real work. But here's the part nobody loves talking about. All that AI runs on your data and most teams have no single way to see it, secure it and prove it's under control. That's exactly what App Point does. For 25 years, they've been the trusted layer beneath the world's most demanding data, now extended across your entire AI estate. Your data, your cloud and the agents acting on your behalf.

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• 32:32Speaker 00

  And we're back here on Big Technology Podcast with SAP CEO Christian Klein. Great to see you. Thank you for coming in. I said before the break I wasn't done with my questions, and I'm sure not. All right. So, okay, this idea that AI is going to get better and people can vibe code their own SAP is 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 the AI companies, which currently make, like the foundational labs, OpenAI and Anthropic,

• 33:12Speaker 00

  which currently make their best models available via API, will close those models off. So for instance, let's say GPT-6 Astro right now, it's pretty smart, but it has those limitations. But we've seen unreleased versions of OpenAI's technology be able to do things like team up in a swarm, work for 88 hours and solve the hardest math problems ever. So is there a world where they say, "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,

• 33:56Speaker 00

  and then decide that in order to make money, because AI models are going to commoditize, they have to go upstream," which means take that knowledge and do a chatbot version of your business?

• 34:08Speaker 01

  Yeah, fair question. And the truth is, when you come to our business AI platform as a citizen developer, as a business user, or 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 were 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.

• 34:51Speaker 01

  And we are reproducing that in our ontology and our data layer. So you have all of these models.

• 34:57Speaker 00

  What's ontology? Explain that.

• 34:58Speaker 01

  The ontology is actually when you have SAP data, but you also have non-SAP 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 needs 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?" et cetera, et cetera.

• 35:23Speaker 00

  So it's the logic.

• 35:24Speaker 01

  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 semantic 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.

• 36:02Speaker 00

  So you're skeptical that even if they hold those... So right now, OpenAI and Anthropic are making their models available on 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'd be able to do it?

• 36:18Speaker 01

  I'm, you know, the same question I got years ago about the hyperscalers. Yeah, I mean, we are...

• 36:25Speaker 00

  This is a different technology.

• 36:26Speaker 01

  Yeah, that's a different technology. But we are partnering, and of course, they are super powerful. I mean, they are running the most systems of the world. They are owning a lot of our systems. And they could also say, "Yeah, we are not providing this infrastructure anymore." And this is similar. But I see 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 solves all of the problems. Actually, we are seeing a lot of open source, a lot of open rate models, who are also doing a very, very good job when it comes to also looking at the price-outcome ratio of an agent. Yeah, it's not only important that the agent does a great job, but it's also at what cost does the agent does a good job.

• 37:10Speaker 01

  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.

• 37:26Speaker 00

  Let me ask you this. Would you accept, or I don't know if you do this already, would you accept OpenAI or Anthropic as a front end to your software? And by that, I mean, you have all this great data. Could, let's say, a user of SAP who's used to logging in and going through the graphical user interface or using your agents, are you open to them having a connector through ChatGPT, for instance, and being able to query that data and do that through ChatGPT for business?

• 37:55Speaker 01

  I mean, the way how we solve for that is there is tool work, our coworker, it's our interface.

• 38:01Speaker 00

  But you won't integrate?

• 38:02Speaker 01

  We actually embed those models into tool work. So you can also switch the models. You can do certain tasks with Anthropic. You can also do certain tasks, if you're in Europe, and software is important. You can also use Mistwell, you can use their latest model. But then it's really about SAP's interface. That's our tool, tool work, it's our coworker. And then, of course, there will be also scenarios where you will build an agent, let's say Salesforce, Adobe, and so on, and they need to also then access SAP data. And that's totally fine as long as people are going through our API gateway and the agent gateway.

• 38:44Speaker 01

  But I would say predominantly, 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. They will have access to SAP systems via our agent gateway.

• 39:00Speaker 00

  Including OpenAI and Anthropic?

• 39:02Speaker 01

  Including, including.

• 39:03Speaker 00

  So there will be a world where like...

• 39:04Speaker 01

  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 tool 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 read out some order data,

• 39:47Speaker 01

  I mean, yes, you can do it, but it will happen via our agent gateway. And the marketing users anyway is not our prime users of the SAP software.

• 39:57Speaker 00

  Okay, let me ask the question one more time in a maybe different way, because I don't know if I'm getting the right answer or fully understanding the answer or getting the question out the right way. All right, there's an example recently where Meta has this Muse professional assistant. And you could get Muse to shop for you.

• 40:12Speaker 01

  Yes.

• 40:12Speaker 00

  There's two approaches to this.

• 40:14Speaker 01

  Yes.

• 40:15Speaker 00

  There's Amazon's approach where Meta is like, uh, we want Muse 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.

• 40:26Speaker 01

  Yeah.

• 40:26Speaker 00

  And you will never have to touch Amazon.

• 40:28Speaker 01

  Yes.

• 40:29Speaker 00

  And then there's the shot, uh, uh, okay, so Amazon blocked that.

• 40:32Speaker 01

  Mm hmm. Yes.

• 40:33Speaker 00

  Shopify said that's fine.

• 40:34Speaker 01

  Mm hmm.

• 40:35Speaker 00

  So we 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, uh, you can use amazon.com or use our agent.

• 40:46Speaker 01

  Yes.

• 40:47Speaker 00

  What is your philosophy, the Amazon or the Shopify?

• 40:49Speaker 01

  Actually, we offer both.

• 40:51Speaker 00

  Okay.

• 40:51Speaker 01

  So that's, uh, and, uh, and the ideal model, the idea world is that, you know, because our AI platform toolwork is so great. I mean, in my eyes, customers and users will understand, why would I use an L L M model standalone if I'm not getting, you know, the same business context and not the same governance.

• 41:10Speaker 00

  So that we have. Connection.

• 41:11Speaker 01

  We are, yeah, but the API connection is not giving you all the business context, is not giving you the semantics, is not giving you this deep process mode.

• 41:19Speaker 00

  So it limits some of the stuff that.

• 41:21Speaker 01

  Yeah, yeah, of course. A pure API connection, pure M C P server is not the same like the ontology, that's the context we are having on our AI platform. So, and, and the governance, yeah, when it's A C P managed, we also make sure that you're, when you're doing a financial analysis, that, uh, you know, 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, yeah, 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 A C P system via the API gateway. But there is so many reasons, yeah, for, for A C P users to use true work because better context, better governance that we believe.

• 42:05Speaker 01

  Yes, the main route will, will, uh, will, will goes via.

• 42:10Speaker 00

  Yours, yours, yeah.

• 42:13Speaker 00

  It is interesting, like when we sit down with someone like yourself, uh, and you're, it's like, oh, this is actually, you know, not as simple as a one sentence tweet.

• 42:21Speaker 01

  Yeah.

• 42:22Speaker 00

  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?

• 42:32Speaker 01

  Oh, yes. Oh, yes.

• 42:35Speaker 01

  But I'm used to it. I mean, then I became CEO several years, but I'm used to, you know, I, I did the cloud transformation.

• 42:41Speaker 00

  Right.

• 42:42Speaker 01

  The market was, oh, are they going make it or not? And, uh, you know, the share price was down. Then we reached the all time high, we, we proved, uh, that we can transform the company. And that's another transformation and, uh, and it's a similar pattern. Now, the good piece this time, we are not alone in this. Uh, 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 L L M alone will not cut it. So we will need the software, the app, uh, to write the context, the governance. And then the L L M's are of course the key, you know, to unlock this value, but it needs to be really brought together with the, 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 market.

• 43:26Speaker 00

  Yeah, software is made a dramatic comeback.

• 43:29Speaker 01

  Oh, yes. Oh, yes. Yes.

• 43:31Speaker 00

  Uh, 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.

• 43:39Speaker 01

  Yes.

• 43:40Speaker 00

  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.

• 43:50Speaker 01

  Yes.

• 43:50Speaker 00

  But they think it's indicative of what the rest of the economy is going to do.

• 43:53Speaker 01

  Yes.

• 43:55Speaker 00

  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. So from 53% to 45% decline in terms of the spend on the frontier.

• 44:17Speaker 01

  Yes.

• 44:18Speaker 00

  What that suggests is that companies are discovering they don't need to use the best AI to succeed.

• 44:26Speaker 01

  Yes.

• 44:27Speaker 00

  Have you found that at SAP?

• 44:29Speaker 01

  Similar patterns, yeah, absolutely. And, uh, I mean, again, that's also probably to the discussion, you know, to the point you brought up earlier on. Would customers go via third party platform directly to the A C P system? Or will they use, you know, our true work, our platform? I mean, in our platform, you also not logged into one model because you see, yeah, with every model release, you know, it can change again. You know, the one model gets better, then there's a new open source model who is really producing great results. And we also see agent by agent, not every model, you know, performs the same, yeah. So this multi-model being agnostic is really also a key differentiator, what customers are loving. They're saying, okay, I'm not logged into any frontier model.

• 45:11Speaker 01

  I can, and A C P is even doing the switching from one model to another, to always optimize the token spend with regard to the outcome, yeah, 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, and for also for tasks 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 performs so good that it, and then compare it to the cost of those models. We, we are going to switch and many agents we are building in the meantime have seen the, the fifth model,

• 45:53Speaker 01

  because we are always, you know, optimizing, yeah, outcome versus the tones and the cost of them, the, the cost we pay for such a model.

• 46:01Speaker 00

  Yeah. And has that emphasis on cost grown recently?

• 46:04Speaker 01

  Oh, yes. Of yes, yeah, because the overall token spend is up, which is good. But you, you know, it doesn't help you if some of your employees is getting 20% more productive if at the same time, the cost is going 30% more up, yeah. And of course, and by the way, this is also for our product manager, a cultural change. Yeah, we told them, test every agent which we are leasing to the market need to be tested with different models. And they need to do this, yeah, all the time. And because we see new model comes, oh, maybe we can switch the model, yeah, 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 is a software company like A C P

• 46:42Speaker 01

  who does this for you so that your AI tones are not running away while you are celebrating your, maybe your productivity gains. And then at the end you look at your P and L sets, oh, shoot, you know, my, my, my profit is actually not hitting the mark anymore. Yeah.

• 46:57Speaker 00

  Yeah. Um, it's, it's interesting, right? It, it says that, it says a couple of things, actually. First of all, it says the AI has gotten good enough that the frontier is, is only going to be applicable to certain use cases.

• 47:10Speaker 01

  Yes.

• 47:10Speaker 00

  Which is wild.

• 47:11Speaker 01

  Yes.

• 47:11Speaker 00

  Uh, it also says the, the standard models are, are, you know, are really working. And the interesting thing about the standard models is they don't tend to be, I like standard versus frontier. They don't tend to be like, uh, you know, 80% of the price for 80% of the performance.

• 47:26Speaker 01

  Yes.

• 47:26Speaker 00

  Right? They tend to be like 10% of the price for 80% of the performance.

• 47:30Speaker 01

  Yes. And, and look, I mean, we also acquired an AI model, one, a tablet AI model, uh, who does predictions. And I mean, it's remarkable, yeah, we took all the data of retailer, A C P, non A C P. And without a data scientist touching it and building data pipelines, doing the semantics, matching the data so that it makes sense. The tablet AI model actually, uh, did it with the same accuracy than 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 tablet AI models, how there are other models coming up for more, for the structured data, the, all the models we are building, yeah, for the business data. So I would say there is not this frontier versus, you know, open source standard models.

• 48:12Speaker 01

  There's also no tablet AI models coming because for predictions, these models are becoming better and better as well.

• 48:19Speaker 00

  As a business person, um, 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.

• 48:31Speaker 01

  Yeah, keep on innovating, yeah, keep, keep on, you know, making the model better, better.

• 48:35Speaker 00

  No, they are making the model better and better, but they, people don't need the cutting edge.

• 48:38Speaker 01

  But others are on it as well. Yeah. So that's why I'm saying, yeah, I'm not, I'm not afraid of all that, you know, one model is, or 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. And, uh, but again, it's the same like A C P when Hasselbladner founded this company. There was not, 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 innovating 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 blame.

• 49:15Speaker 00

  I'm going to, I'm going to answer my own question. I, I think that if you're spending billions and billions on training frontier models and the standard models are doing just as good or, or good enough job for many of your customers, to the point where we're seeing things like 8% decline, 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, um, I have to put that in for the record.

• 49:42Speaker 01

  Yeah, yeah. I mean, I mean, I guess this is also the question, yeah, what everyone at the frontier models also asking themselves. And, you know, and it's also interesting, maybe, uh, one, uh, addition to that. I mean, you know, there's of course a financial clause, there is inventory, this is when you ship, when you build. Accuracy needs to be record high. Uh, we have a rule 95% of better. Otherwise, it's not worth to ship the agent, the customers will just not use it. But then there are many other agenty AI use cases, yeah, 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.

• 50:25Speaker 01

  We can focus more time on the value adding stuff, prepare myself for the meeting, et cetera, et cetera. So, uh, 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 to match it to the price and to the nature of the model. What is the accuracy, what I need, yeah, to, uh, have the acceptance of the business?

• 50:48Speaker 00

  Yeah. Very interesting. We, we have IPO, so we'll learn a little bit more about this. How about, 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, uh, the spend for the top 1% of employees.

• 51:06Speaker 01

  Oh, yes.

• 51:06Speaker 00

  Right? So top 1% of employees, they're not just consuming a little bit more, uh, tokens, they're consuming way more. So knowing smile for those on audio. So we're going to ask this. All right. So, uh, 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.

• 51:34Speaker 00

  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?

• 51:47Speaker 01

  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, you know, reskill yourself, to change and become more productive. And when I'm now the first one who actually introduces the 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

• 52:25Speaker 01

  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, the designers, I mean, there we also introduce limits also for people in finance and nature. And of course, there are certain limits where we can't say, hey, you cannot just experiment around, spend the tokens when we are not seeing, you know, the respective productivity outcome.

• 53:07Speaker 01

  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, yeah, but I have this task where I feel AI can really help me. It's this time, it's very special, so need a higher token spend. And then we have a brutal 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 the message of we forbid you now the use of AI or you're reaching the limit so fast that it's hard, you know, to really optimize your day job. But vice versa, also making sure that token spend, you know, stays under control.

• 53:51Speaker 01

  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 cost, can cut your cost by 10, by a factor 10. And that is, of course, even more effective than, you know, introducing all of these token limits for the certain job profiles in the company.

• 54:11Speaker 00

  When did this all go into place?

• 54:13Speaker 01

  Well, 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, we were just building the agents, getting them out, celebrating success, getting references. And now, I would say, in the last three months, we are also intensifying our product manager, intensifying the work on switching the models, testing the models and, you know, optimizing them. And really also look into the TCO of a model. Because, you know, at the end, it's not about only about delivering great agents. I mean, at the end, it also needs to be, from an economic standpoint, it needs to be reasonable.

• 54:55Speaker 00

  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 OpenAI.

• 55:11Speaker 00

  Hundreds of bots, you know, attacking a problem. Well, that's, I mean, that's like well-known, but there's also the math, the attacking the millennium problem, 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, 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.

• 55:42Speaker 01

  Yes.

• 55:42Speaker 00

  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 swarm thing?

• 55:51Speaker 01

  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.

• 55:59Speaker 00

  But it's not cyber, it's like these type of uses anywhere.

• 56:02Speaker 01

  Yeah, okay. On the uses everywhere, I mean, you know, 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, on the business side.

• 56:26Speaker 00

  But like swarms of, swarms of agents for business use cases, are you testing that now?

• 56:31Speaker 01

  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, 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.

• 56:45Speaker 00

  Yeah, I will say, if I was sitting in your seat.

• 56:48Speaker 01

  Yes.

• 56:49Speaker 00

  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 a business could have.

• 57:01Speaker 01

  Yes.

• 57:01Speaker 00

  And if we see, you know, agents going zero day.

• 57:06Speaker 01

  Yes.

• 57:07Speaker 00

  And people being able to direct them.

• 57:09Speaker 01

  Yes.

• 57:10Speaker 00

  That is, that would scare me.

• 57:11Speaker 01

  Exactly. And that's why, guess what we are doing, yeah? And in our dev operating model inside the company, we are always now working on, you know, 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? Yeah. 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.

• 57:38Speaker 00

  Your cybersecurity budget's going up?

• 57:40Speaker 01

  Yeah.

• 57:40Speaker 00

  Substantially?

• 57:42Speaker 01

  Actually, I mean, on the one hand side, it's going up because you're investing into AI.

• 57:46Speaker 00

  Right.

• 57:46Speaker 01

  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.

• 57:55Speaker 00

  Okay, you know, we're, we're a podcast where we don't like to do the theatrics or the, we try to 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.

• 58:09Speaker 01

  Yeah.

• 58:10Speaker 00

  Europe has put so many regulations on its tech companies and people would say business in general.

• 58:19Speaker 00

  To the point where if you speak with people here in the US, 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 here. American, my wife is European.

• 58:35Speaker 01

  Yes.

• 58:37Speaker 00

  I, I think that like, it would be good if Europe became a good place to do business.

• 58:41Speaker 01

  Yes.

• 58:42Speaker 00

  What, so, 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 where they are? Like, what is, what are their intentions? And do you believe that those are good intentions?

• 59:03Speaker 01

  Yeah. I mean, first, I really believe they have all good intentions. I mean, who in Brussels does something to disadvantage Europe? Yeah. 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 still, you need a regulation, but you should regulate more the business outcome, the impact on a 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, in, in Europe.

• 59:44Speaker 01

  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 startups, it's really, it's really hard. And I guess, you know, Brussels and Europe, I mean, it's definitely, they realize that holiday 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. So, and I know they're dealing with it right now. And I hope there is stuff coming, which definitely helps us, yeah, to put the regulation to the right level. And again, I can, 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.

• 1:00:29Speaker 01

  Because how can you be competitive if you are the only part of the world who is really regulating the technology?

• 1:00:36Speaker 00

  Yeah. So you think there's going to be some form of rollback?

• 1:00:39Speaker 01

  I'm, I'm, I'm confident. I mean, the message is loud and clear, not only by SAP, I mean, many startups.

• 1:00:45Speaker 00

  They've heard it.

• 1:00:46Speaker 01

  There are other companies like Siemens and so on. I mean, we are building a lot of industry AI in, in Europe. So there is AI, I mean, more applied AI, industry AI. And all of these companies, you know, say message to, to Europe. We have overregulation, let's scale it back.

• 1:01:02Speaker 00

  Yeah. And it's mostly GD, sorry, I come from like more of like the ad perspective. GD, I mean, GDPR is like, yeah, like speaking of the intentions, like I get it, you want to protect people's data.

• 1:01:12Speaker 01

  Yes.

• 1:01:13Speaker 00

  Say mostly that, or it's just?

• 1:01:14Speaker 01

  No, it's actually not so much about GDPR. I mean, what Europe does is the AI Act, the Data Act.

• 1:01:21Speaker 00

  Right.

• 1:01:21Speaker 01

  Which then puts another layer of regulation on top. And, you know, it's, 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 quays and so many overlaps. So it also takes forever until you understand, how can I use now data to build AI? And to do research and to apply AI in the customer's business? And so that is, you know, these, all of these layers of regulations. And then, you know, the recent ones with the EU Data Act and the AI Act.

• 1:01:57Speaker 00

  All right, Christian.

• 1:01:58Speaker 01

  Vielen Dank. Yeah.

• 1:01:59Speaker 00

  Thanks for coming.

• 1:02:01Speaker 01

  Schön hier zu sein. Thanks a lot for your time.

• 1:02:03Speaker 00

  Schön das zu sehen.

• 1:02:04Speaker 01

  Very good timing, actually, yeah.

• 1:02:06Speaker 00

  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.

• 1:02:32Speaker 03

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• 1:03:02Speaker 04

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