Now that this 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. Welcome to Big Technology Podcast, a show for coolheaded 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.
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 off of it. We just talk about it 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 is going to 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 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, happy to answer that 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. I said, look, we climbed the cloud mountain. We did a cloud transformation, and then now we have to climb the AI mountain. And on this journey there will be obstacles we have to overcome. AI has to overcome. And you know, 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 now 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 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 optimal.
Okay. But Christian, this is 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 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. 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, a 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 when, and I would say, look, I mean, you know, we, for example, code financial closing agents here, 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% too high. And they say, what? I mean, you know, you need audits, 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 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 fatwam. So that, you know, which data can I access, what data can I share, where's 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 running 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 run 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, you know, here we are maybe there in 8 to 9 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 Juul, works, put me together a customer brief. Juul is our coworker. And when it's 90% accurate, it's totally fine. 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 a 95% accuracy is totally fine. But I don't need a bunch of people in my office preparing me for these customer meetings and writing all of these preps. 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. You could check the work, but you don't know where the problems are. Can you ask another chat about where are the problems? If it finds 90% of those problems, you now have a compounding issue. 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.
It depends on the 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 hanging somewhere around in this company, some are non-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 there.
Okay, but I thought smarter and smarter AI was supposed to solve that problem, right? That basically, like, one of the big reasons that enterprises 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 to try to figure it out. But why is the AI sort of not able to do that on its own?
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 or 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 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, 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 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 a mess. It moves fast despite the fact that culture can be a problem. So is basically the net of this that just business, you know, I guess like we've heard stories of companies that used to be on this annual plan of planning. You plan, you 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 grow? Is everything going to get faster?
The speed of execution is definitely getting faster, and that's also, I mean, you need to also then redesign your own planning cycles inside the company to react to that. And 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 the agents and here we plan, you know, do our employee plan and so on. 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 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 many customers 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 ride this wave, otherwise it could be too late.
Okay. So I have a lot of culture and hiring questions, talent questions to ask you throughout our discussion today, but I'm just going to start with the first one. Are you noticing people getting exhausted? Because if you, you know, there was a story in the Wall Street Journal I've referenced a couple 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 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 handles all this rote work, so all you're doing is like very highly intense cognitive work. And everything is so fast. 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, other players. What's going to get automated? The controls, the compliance checks, everything. So also the typical stuff. What many people are doing in their business life. And then of course, that's a full-time job for thousands of people. And now you're even in planning and steering a company and putting the pricing list 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 to spend on, we would say, the value-adding tasks, 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 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, 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.
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 won't say on my own because I have a lot of help, but like, 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 is doing my invoicing, Claude coworker, 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. I love it, but I can't tell if I'm more tired because of it, where 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 that's kind of, whether it's what I want. So you're, obviously your company's 100,000 people in the neighborhood. What do you hear from people about this?
You know, I see this also in my daily life. You know, all the prep work, you know, is suddenly getting automated, the customer brief 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 right track or do we need to course 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 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 talked 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 these compliance checks and controls, and then let me focus on the stuff what really matters. So yeah.
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.
Software takes what I'm of course getting in all hands is, okay, is my job still secure? Yeah. And what about restructuring? What about that? And so I get that. Yeah. So not about necessarily I'm getting exhausted or it's more because AI can do so much of the work they might be afraid to tell you. 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. So and the workload is not getting lower for our people. Yeah. 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 or other parts of the company.
Well, that's why like when 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? My answer is always like I don't think so. It used to be I'm sure no. Now it's I don't think so. And the reason 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 with people and tech.
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. We will hire people, data scientists, full-stack developers, etc. 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, new employees to come in with new skills, with a new mindset and pair them with the experienced colleagues we have as well because you need the domain know-how, you need to, they understand the software which is underneath the AI. So I would say 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.
Yeah. People often hear the term reskilling 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. Yeah. 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 run stable. So it's really a different way of coding and 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 that is so absolutely it's possible. Or taking out when we are rolling out our coworker to work, 80,000 people are using it, but at the beginning it was also there certain 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 correct certain things what Juul is producing for me. But once you, you know, you train the people, you coach the people and you see, hey, you can get your 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 then you see, yeah, that people are, you know, able to adapt to that and learn and also acquire new skills.
You know, pre-ChatGPT, I wrote a book talking about how the tech giants were already using AI to like minimize rote 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 data, 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 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 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, 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, yeah, 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 business 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, 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 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 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. 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. I'm not saying that 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 SaaS apocalypse because, you know, 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 everything. There are features you're going to want, features you're never going to click on. And this idea, once this idea became popularized that once people could build software on their own, yes, you know, because you could prompt it and they can show up for you, then the old way of building software, the old way of building software wasn't going to work anymore and we would see new software, VC-coded replace, you know, the old software. And that narrative has plagued software companies until recently for, you know, about the better part of a year. At this point. Let's just high level, you know, what was your reaction to that? What was your feeling as that narrative became popular?
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 development productivity is going up like hell. Why can't people just reproduce what we built 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 ERPs. Yeah. 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 of data correlations what you need to understand to run a business. But there's 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 are maybe, you know, one or two solutions, small solutions where there's not so much process and data context inside which you might, you know, just can replicate. But the rest, 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 the 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 agenda 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. 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, I'll sit on the side of the SaaS podcast and you can be software for this example. SaaS podcast people would say, man, the AI is getting good enough that those hurdles, domain knowledge, process knowledge, you know, being able to put data together, it's going to do it. And therefore the moat, while it still exists today because the AI wasn't good enough, when the AI gets good enough, that moat's gone. What do you think?
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 an 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, semantical ontology layers and so on. 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., etc. But it's an LLM plus our AI foundation plus the governance. Yeah, 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's 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 a living since 50 years.
Yeah. Okay. Well, I am 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. 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 like AI is going to get better and people can vibe code, you know, 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 — which currently make their best models available via API, will close those models off? So, for instance, let's say GPT-6, Astra right now, it's pretty smart, but it has those limitations.
But we've seen, like, unreleased versions of OpenAI'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, and 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 AI models aren't going to commoditize, they have to go upstream, which means take that knowledge and do a chatbot version of your business?
Yeah. 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're just talking about. So, you know, but what you also find then on top of it is that we connect, you know, those models, you know, to the business process and the data, knowledge, the context of what our ERP actually, you know, knows about your business, and we are reproducing that in our ontology, in our data layer. So, you know, you have all of these models.
Sorry, what's ontology? Explain that.
The ontology is actually when you have, you know, you have SAP data, but you also have non-SAP data. What the agents need to understand when you do asset management — yeah, 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, 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's maybe not working anymore, where can I find the worker who is fixing it, etc., and everything on time, so that there is no downtime to the machine. And that is the ontology. Yeah, that is the semantic layer. And again, why would you go to, you know, 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.
