SAP SE (SAP) Earnings Call Transcript & Summary

Great. Thanks, everyone, for joining us for the next session. We're delighted to have SAP back at the conference and particularly Sean Kask, who's the Chief AI Strategy Officer. Sean, I think this is probably third year in a row coming back, but we live in interesting times in the -- on the kind of AI front, there seems to be almost an exponential curve in terms of the development. So I wanted to sort of kick off and get your perspective on -- in the past 12 months, what have been the really big step changes that you're seeing. And obviously, the discussion around the application layer has been obviously the most significant. So first of all, just set the stage of what is kind of exciting, what have been the biggest advances and how we see that sort of evolving moving forward?

Yes. Great. Great to be back. So thank you for having me here. Also, given the volume of requests we have coming in from the investor community right now towards me around AI and our team, hopefully, we can address a lot of questions here on math. So that's a great opportunity. Yes, and it's also a good opportunity to reflect. So I think when I was here in 2024, first time generative AI was still kind of new, right? I think we had released around 10 generative AI use cases in production. We had announced Joule. We had announced AI Core, generative AI hub for building extensions. Fast forward to last year, we had around 130 generative AI features that we had released. Joule had around 1,400 skills in it. So skill is basically mapping a user intent to something that Joule can do, right? Like show me open purchase orders, these kind of things, right? So we actually have to map that in the system to the APIs and all the data tables and things like this. And I had spoken about some of the innovations that we were working on then, right, that were exciting, right? And we had 3 buckets. So one was agents. So we're just starting to talk about agents. This year, we've now released agents, right? So we have 30 agents released. We have AI Agent Hub that we've released as well for governing agents, right? We have Joule agent builder to extend agents. So within a span of a year, I mean, it kind of went for where we -- I mean, the whole community was kind of defined what an agent even is and is this feasible to actually have it in like productive agents, which moved very quickly. The other area that I talked about was Knowledge Graph. So this is this neuro symbolic AI, right, where you codify and you represent knowledge and then you provide that to the AI so that it can do its job basically. So we had built a knowledge graph internally at SAP, which is massive. So just in the ERP system, just so you have a feeling for how complex these things are, it's 452,000 tables and 7.3 million fields in the table and 80,000 analytics views and the Knowledge Graph basically links all of these things together, right? So if I'm talking about a sales order, I know which field and table to look in and that's linked to which business process and all this kind of stuff. That is now also being used and leveraged by Joule. So that is now productive. You can extend it on the business data cloud so that's live. And the other innovation that I had spoken about back then were let's say, alternative foundation models to large language models. And the one that we're building on and we're building back then, it was still somewhat experimental. Back then, we called it the SAP Foundation model. And basically, what it was doing is a foundation model built on tabular data to do predictions. So regressions, classifications like numerical calculations, which large language models cannot do. We didn't really know if it worked back then. Now it's productive. So we've released that. We called it RPT-1, so relational pretrain transformer, Rapid 1. So that's productive. We have one big chemicals company who is -- Christian disclosed this in the earnings call. They have around 180 narrow machine learning models that they have in production. So things like auto filling a sales order, predicting delivery dates, these kind of things. They're going to replace all of those with this one model because you don't need to create a data pipeline and feature engineering and train all these individual machine learning models anymore and they get higher accuracy. So I'd also just one last plug. It won a -- or was honored with a spotlight award at the NeurIPS Conference last fall, actually. It's kind of like the top A-Star data science conference. So showing we can play up there where we need to with the big AI providers. That was the stuff certainly, I think, over the last year, right. Looking ahead, yes. I mean, look, it accelerates. I mean there's much stuff going on. I'll plug the article that we published a couple of weeks ago. So we always make like predictions like top 5 themes. You look that up, it's AI in 2026, top 5 themes. But yes, again, certainly, these foundation models that are not large language models. So things like world models and robotics. So we have some partnerships that we've announced there. We work with NVIDIA on these kind of things, the tabular foundation model. As well as agentic governance is going to become key. So you're releasing all of these agents into the organization now. They need to borrow from HR, they need a hire to retire life cycle. You need to discover them, onboard them, monitor them, govern them, offboard them, right, have observability, give them access rights, all this kind of stuff. So we've made some announcements around that as well. Sovereign AI, I think, is an increasingly important topic, and we could see the discussion with Anthropic and the U.S. Department of War, I guess they're called now, right? So these kind of things. So yes, I mean, there's just a lot going on. It never slows down. I kind of hope it slows down a little bit sometimes because I am just exhausted. So I'm trying to keep up with it all as you are as well, but it's also really, really exciting and full of opportunities.

So hitting on that point in terms of the pace of it, there has been an accelerated debate around like how Agentic AI might disrupt application software. So what are your thoughts on the current state of play? Like who, in your view, would be like emerging as the relative winners or losers?

Yes. So the elephant in the room, right? So is SaaS dead, and that's more or less the question, right. So a big debate right now. So no, I mean, look, I think there's 2 things. And from SAP's perspective, I mean, one is we acknowledge that this is a disruptive technology, and we're leaning into that, and we're going on the offense in certain areas, right? So AI will certainly disrupt the user interface, right, how you interact with computers. So you can have intent-driven ERP systems where you ask the system to do something, right? You're giving the intent, the prompt in and then it's going to go and execute and do something, right? So you may not need to click in UI anymore, right? Also, user interface will increasingly contain elements that are generated. So we announced this, we call this Gen UI, generative UI. And we already have elements of that in Joule actually, right? So if you're pulling up a chart or something like that, it's actually like generated on the fly by the application. And in the future, we also may have, instead of humans talking to SaaS, you may have agents talking to SaaS as well, right, where the human is merely notified. So the UI will be disrupted to some extent. I do think UIs are still somewhat sticky, though. People -- my wife's friend works in a trading company in Zurich, and she was complaining to me because they've moved off their ECC system that she's been working in 10 years, and she just knew where to click and just loved it, right? I mean these things will always coexist, right? So you always have a UI. And again, the logic of that can still be accessed by agents and generative UI and all those kind of things. So we're leaning into that. We have Joule already rolled out, right, so we have a solid base for that. Secondly, this AI will disrupt how software is built and maintained, right, certainly. We see how quickly vibe coding and AI pair programming is progressing, right? That opens up a few opportunities. One is -- I mean -- and I think that's driving some of the fears of disruption as well, right? Now for us, we also use vibe coding, right? We've rolled that out to all the developers in the company, and we've seen significant magnificent improvements in productivity to using that. Now for us to vibe code something, it goes through our 388 product standards and testing before it goes into production, right, okay? So it's like enterprise-grade software. But we can vibe code, and we can also offer that to our customers to quickly build extensions, right? So we have SAP build, for example. We've released a Joule Agent Builder. As a user, you can go in and actually vibe code little extensions to SAP systems, which in our point of view, just increases the value of our systems. And the third area, I think, is that AI can also disrupt the commercial model somewhat, right. There's this big debate like around what's going to happen with seat-based subscriptions in the future and licensing. So a couple of points there from SAP's perspective. One is the current commercial model that we have for artificial intelligence is actually based on consumption. So we talked about that a couple of years ago. There was a lot of heavy lifting internally. We built out a capability to actually meter every single one of those agents or use cases that is being run in the system. So if someone goes in and processes an invoice using AI or a delivery note in the shipping yard or something like this, that's metered and charged against these AI units, right? So -- and we basically have a commit-to-consume type model for that. So you subscribe to a certain number of AI units, and then that's pooled and then you consume that, right? And every AI unit is tied to a business outcome. Somewhere around like messages and things like this, but like where we can do it, we try and tie that to like a business outcome. So we actually have that already there, right? That's actually already in place for us. And the other thing, too, is that I think it's less than 50%. I'm not sure exactly what we disclosed, 50% or 40% of our it was seat-based. So the rest of that is actually based on different business metrics like spend under control, for example, business documents in the system, these kind of things. So there, we're going on offense. And then I also think there's defensive moats that we have, right? And one is certainly the data that we have. And that's a bit of a truism, right? Like if you own the data, but it's not just the data, it's owning in the data models itself and the contextual meaning around all that data, right? So -- and again, we're exploiting that. So we've built the knowledge graph. We have business data cloud now, right, where we can expose the semantically rich data in SAP and non-SAP systems to be consumed by agents, for example, right? So we have that data. Customers trust us with it. And that's the other defensive moat that we have is the customer trust, right? But -- I mean, probably the biggest argument I've heard against vibe coding is it's Friday night at 8:00 and you have to close the books and your general ledger doesn't add up. Who do you call, right? Is it your SaaS provider to fix that? Or is it the guy who vibe coded something and tries to figure out what's happening there. So there's a lot of customer trust that we have, obviously, with our systems. So I think -- I mean, I'm biased, but obviously, I think we're in a good position to emerge as a winner out of this whole thing.

So there's a sort of view that horizontal software is particularly more exposed to some of this risk than vertical. But even though you could argue we've had instances of vertical software use cases also getting challenged. You guys are interesting because while you sell horizontal software, you also build for specific industries. Obviously, manufacturing is a key part of your end market. I mean, when you look at sort of, say, analogies of what happened at the time of the cloud and what's happening with AI, how should we think of kind of your evolution or your ability to kind of navigate this cycle. What are the kind of -- you talked about the data moat that you have, there's process knowledge. And when you think about the disruption of this application layer that's happening, the modules as we know them, the line of business applications are clearly changing. And so ultimately, from your standpoint, what are the key things we need to look for, for SAP to kind of navigate, is Joule ultimately going to be all end all, but ultimately, for a lot of customers, they may not want to use Joule. They'll say, "Well, I'll use another agent," right? So help us kind of navigate that kind of blurring lines, right, between that sort of application layer in an AI world?

Yes. And I'm hesitant to put us in the box of horizontal player totally, because if you look at what we do in finance, for example, it's quite deep, right? If you want to know how to process an invoice in Brazil and be compliant with Notifiscala and things like that, I mean, we're there. So we are quite deep. And of course, yes, we have a lot of industry solutions as well, like customer activity repository and retail or these kind of solutions. And it's interesting, too. I mean there's some apps you would think might be disrupted, but they have like messaging apps and stuff. I have a policy of not talking about competitors and stuff, but where they have like network effects and they seem to be pretty resilient to disruption. But I think if I look back at SAP's history, right, was it, 2016, I think we had Clayton Christensen on stage at our big Sapphire event, right? So innovator's dilemma before he passed away. We have a strong history of actually like disrupting our own business model, right? So going back to like moving from client server architecture, in-memory database, right, moving to cloud and SaaS, like that was basically disrupting a nice profitable business model where we sold licenses and then collected 20%, whatever maintenance on it, the customer had to do all the installations. And I see that same spirit here, to be honest, right? So we're leaning into the opportunity that Generative AI brings for us.

So just wanted to see from your lens, how do you see the landscape evolving overall in this agentic AI world, like in between the incumbents, the new age players, which would be AI native players as well as these LLM providers and also like even companies in-sourcing to an extent.

Yes. And I'm thinking of like Porter's 5 forces model right here, right? So straight up substitutes and rivalry and whatnot. No, look, I mean, so we see the models commoditizing, right? So -- and that's been a trend for a while now where the price per token is just falling and they're pretty much collapsing, right, converging in terms of capabilities, and it's no surprise to them that they -- at least the model providers try to move up the stack a little bit. And so we see them making their first like PaaS offerings, right? So the OpenAI Frontier, for example, which Gartner, by the way, issued a note 2 weeks ago just telling their customers to be careful of it because they probably can't scale it, but just repeating what Gartner said, but Claude Cowork, these kind of things. So of course, they start to push up the stack a little bit. From our perspective, the hyperscalers and the PaaS vendors have tried for years to creep up the application stack. And I mean, pretty unsuccessfully, to be honest. I think there's a lot of like -- I would not underestimate the knowledge that's needed to build those applications. So from our perspective, we treat the SaaS -- sorry, the model providers, right, and the hyperscalers as we always have, right? We treat them as partners. We actively participate in the A2A protocol, for example, MCP, we're like founding members of MCP protocol. We collaborate with them. Of course, they're going to try and creep up the stack a little bit. We do see, obviously, there's around 600 start-ups, I think, that we look at right now in the AI native space springing up. I think they have probably the challenges that a lot of start-ups have, right? It's like just having this like enterprise readiness to be able to compete with us.

So just a follow-up on that. How do you think like incumbents will look like in terms of strategy. Will they look to buy these AI native players? Or will they try to build all these AI capabilities in-house. Where will the balance be more like heavy?

Yes. I think Salesforce and ServiceNow went on a bit of an acquisition spree the past 2 years from AI companies. I don't think the market necessarily rewarded them for that, to be honest. We have not -- I mean, we made an acquisition of SmartRecruiters last year, right, in the HR space that was just to boost our recruitment module and SuccessFactors. But I mean -- for us, it's tricky right now because the stuff is pretty new to everybody. So we haven't seen a start-up where we say like they have some amazing capabilities that aren't available somewhere else, right? We're all kind of cooking with water and the valuations are insane at the moment on these start-ups. So I mean, from our perspective, we monitor them, right? So we work closely with them. We still partner very broadly with a lot of start-ups, but we're just kind of like watching the market at the moment.

Got it. So I wanted to come back on this sort of comment you made around the kind of data moat. I mean there's a system of record, right, which is sort of the single source of truth, pretty kind of hard to displace. But increasingly, what we've seen from the likes of Claude Cowork and OpenAI's Frontier that they want to kind of sit as a sort of abstraction layer on top of that system of record. In the future, is that kind of a viable option of a direction that we will go into that they just kind of sit on top, but eventually -- because data access is also another key kind of important characteristic here and license agreements, et cetera, play a key role in this. But is there a scenario where the system of record becomes a kind of dumb pipe, sorry to use such a strong word, but -- or will -- because you've got the business logic, the metadata, all that as well. So I'm just curious kind of is this the next big battleground.

Everybody wants to be that abstraction layer, right, and orchestrator sitting on top of other applications, right? I think in reality, the future is going to be the big players are going to have their own agent, their own Copilot, right, that's doing the orchestration in their systems. And that's going to have to collaborate with other orchestrators and other agents, right? So Joule, for example, again, can access data and context and authorizations and metering and logging and all this kind of stuff in the SAP systems that a third-party agent simply cannot access, to be blunt. And I'm not sure I would want like -- I'm not sure our customers want us to allow this unfettered access to the SAP systems from some third-party agents because I mean that could be -- this is going to be like a security and auditing nightmare, right? So again, I see a future where you'll have these assistants, copilots, right, that are good in their domains and through protocols like A2A, they will then collaborate together. And I don't see anyone really being able to take over like as the uber orchestrator. I will say as well, we're quite open. So we were one of the -- I think we were the first company. We have a bidirectional integration with Microsoft Copilot that is now GA and Joule, right? So I can be in Copilot and say, help me book my trip, and that will go over to Joule and Concur and then make an entry in my Outlook calendar and vice versa, right? So we are open in that sense, but I don't see anyone realistically taking over. It sounds funny because -- I mean, like Anthropic and OpenAI, they almost have like dumb intelligence, right? So you can hire your smartest friend to go work in an SAP system, and they have no idea what to do, right? So all that knowledge of how to run a process is like codified in our systems. That's what makes it valuable.

Right. And just sort of following up on that, obviously, there's the BDC, right, which you sort of launched about a year ago. Help us kind of understand how that sort of fits into that sort of broader data model.

Yes, for sure. Yes. So Business Data Cloud, just to be clear, it's a SaaS offering. And it's a little bit different than what's on the market. It is not a data lake, okay? You're not extracting -- I mean, you can, right? So it has components of that. But you're not extracting data into it and then building up a data model and like PaaS, okay, like you would do with other solutions. It sits actually on top of or it integrates with partner solutions like Snowflake and Databricks and BigQuery, right? Because customers have invested time in Snowflake, for example, right? SAP and non-SAP data, external data, right, consolidating that, okay? And what Business Data Cloud, it sits on top and it operates on the principle of what are called data products. So we release around -- I think we have a target around 500 data products from SAP systems. So how do you consistently define a sales order or a purchase order, right, which can have different data models depending if that's coming from an Ariba system or an S/4 system or whatever. And we expose these as data products and then customers can also then expose data from, again, all these other data lakes that they built up as data products. And that's contextually rich, rich data. And so now you can put an agent on top of that. So if I'm doing, I don't know, planning, for example, for I don't know, headcount planning, things like this, like hiring planning, I might need to know what are the sales forecasts, right? Like how many people do I need to hire? And that data might be sitting in a Workday system and an S/4 system. And now we can put Joule on top natively with Business Data Cloud with all of these data products. So it just brings together SAP and non-SAP data, and we monetize it, obviously, in a way that can be easily consumed by agents. So it's an important part of it.

So just you talked about Joule and Joule has like seen number of customers almost growing 9x over 2025. So when we look at Joule, can you talk about any of the interesting new use cases that you see emerging? And how is like -- and anything on the productivity, any tangible productivity gains and how you're basically like looking at it in terms of measuring?

Yes. I think we discussed that last year that the adoption curve is pretty unremarkable in the sense it's just a typical adoption curve. So you release a product and then customers look at it and then they buy it and then they go live with it. And so we see this kind of like a hockey stick basically, right? So 9x number of customers adopting Joule. No, I think the most exciting thing we're releasing now in Joule are the agents. And let me just digress for a second here. So we don't want to get into the number counting game for agents. So we set the bar very, very high for what we call an agent because you could call any like RAG use case, like information search use case, an agent, for example, and I've experienced that in SharePoint, right, where it says I'm your SharePoint agent and the only thing it does was summarize the SharePoint page. And I was like, this is not helpful, right? For us, agents need to have agency, right? They need to plan and iteratively work through several steps and tools for us to call that an agent, okay? And agents work very well right now, like production grade in these like narrowly constrained type use cases. So some of the agents we've released, for example, is like accruals accounting and finance, right? So when do you post your costs and revenues, right? And when you do accruals accounting, that might depend on a PDF document with a policy. It might depend on some e-mail chains, some past decisions, right? And it's hard to automate, but an agent can actually iteratively reason through that, these kind of processes, right? And then it has like an explanation of how it reached that decision for the accountant to look in and say, okay, yes, that's right. And so we think that would save for a medium-sized company that might spend, I don't know, end of a quarter, 12 hours doing accruals posting, they can get that done in 2 hours, for example. Production planning agent, same thing. So you're planning your production, what happens is delivery is late, right? So you don't have the right parts or you get a big order that comes in that's prioritized and then you have to go back and it's like it's optimization problem, right? We have an agent that will iteratively reason through that and optimize your production planning. And that -- and so you can do this production planning much more frequently now, right? So you can do that like a couple of times a day if you need to, and companies can simply sell more, right? They can deliver more. So release these kind of agents. But definitely, the star of last year was Joule for Consulting. So this won award at the World Economic Forum last month, it's called the MINDS Award for like transformative industry use cases. So we did that together with KPMG as a partner/customer in that. And look, so one of our top priorities this year is AI-assisted cloud migration and AI-assisted cloud transformation, getting customers faster with less effort to the cloud and obviously, on the standardized SAP landscapes. And so what Joule for Consultants does, it's grounded in all of the SAP help documentation, internal knowledge-based articles. Customers can extend that with their own like what are called business blueprints in SAP world. And it leverages our proprietary large language model that we train on SAP code, so it's called ABAP LLM. And so that can like explain legacy code, it can write unit tests, it can do a bunch of stuff, right? Long story short, so Siemens is a reference customer there. They said that saves around 10 hours a week per consultant. So if a consultant is working 40-hour a week, that's like a 25% productivity boost. I mean I started in consulting. I think my daily rate was probably 2,000 or 2,500 a day, right, doing SAP transformations. I mean that's like a massive boost in terms of productivity.

Great. So let's open it up to the audience. I'm sure there are questions. So whoever wants to start.

Helena from Millenium. To your last point about Joule for Consultants. Do you see that accelerating or lengthening sales cycles for the SAP core business? And I'm asking because our clients kind of seeing that, oh, I can get the 25% faster, so let's do it or hang on, let's wait, maybe there is a new iteration coming out that will make it even less costly, even faster or something like that.

Yes. So again, the question is the customers wait for AI to basically automate the entire transformation, right, to the cloud. It's not a completely unreasonable question, to be honest, I think customers weigh that with the benefits that they get when they get to the cloud and are able to use more AI. I mean, look -- and frankly, to be honest, what Joule for Consulting does now is kind of like just the tip of what's possible, right? Because we also announced our integrated tool chain, right? So this is using like Signavio, right, with machine learning to map out the process automatically. We're going to release like data duplication and data harmonization now based on AI, which can be like a huge test automation, right? So there's a lot more coming, right, that will help with their cloud migrations. But I don't -- I certainly don't see any customers who are waiting, right, under the impression that, oh, if I wait a year, then migration costs will be 50% less because AI is going to completely automate it. I think it's more the other way. It already offers quite a good benefit for them to get to the cloud, and they have incentives to get to the cloud because there's more AI that's feasible there.

What are your thoughts on quick commerce specifically on...

So there's been a few protocol -- I think there's 2 or 3 protocols, right? So Google just released the UCP, right? There's agentic commerce protocol. So basically allows agents to shop for you, right, and do financial transactions. No. So we are supporting that in the CX portfolio of SAP and SAP Commerce Cloud. And SAP Commerce Cloud, we're actually opening up MCP servers as well that allow -- because that creates more value for our customers, right? So then it allows agents to more easily find products on the websites. And then, yes, we certainly aim to support that protocol. I think it's a great opportunity.

$100 billion of market cap that's built on top of SAP. And if you do believe that AI does make the marginal cost of R&D in development closer to 0 and historically, you haven't been able to target these sort of upsells and modules that have been done by third parties. Do you see that as a sort of a real opportunity now in unlock? Or do you think you're still far from that and a lot of things have to change? An example of that may be that, you guys did BlackLine last year. So is that something now that's easier to build similar quality in-house because of AI or can get there in the next few years? Or you think it's still very far from the...

I mean everything indicates that AI reduces development by like 20% to 50%, let's say, like productivity to build stuff. And to flip it around, there's also that threat like will there be new entrants challenging SAP? Or will, I didn't completely finish your question before, will customers be more incentivized to build things in-house because it's so easy, right, using vibe coding. So -- but I think, again, the assets that -- I mean, again, we also apply that, right, to your point. So now can we now enter more markets, right? And I think, again, building on the capabilities that we have and again, all this like the product standards and the enterprise readiness and the knowledge and all that kind of stuff is like a really important base. You need that to enter these markets. And we are -- we are taking some measures to actually do that. So this has been disclosed as well. We're doing more FTE, it's for deployed engineering. So this is an initiative from the product and engineering Board area, popularized by -- so OpenAI is doing it now and Palantir. But basically, you send engineers to customers, right? In 90-day sprints, you figure out real problems they have and then you try and like build something. And then for us, we want to build repeatable solutions, right? And that's -- it's not a new thing, but with vibe coding, frankly, and AI, it's a lot more feasible to do that in a 90-day sprint, right? So we can enter all of these markets, and we're going to offer a lot more industry solutions moving ahead to enter those kind of areas where we can certainly build stuff, right? Because again, customers and others can build stuff more easy, but so can we, plus we have these like complementary assets where we have all the customer relationships and knowledge and all this kind of stuff in place. So it has a potential to grow our portfolio.

That's a best of breed? Maybe we carry on. So Dominik talked about this incremental $1 billion of revenue opportunity for SAP from a number of sectors or segments. And obviously, AI offerings, particularly the cross-sell, upsell of these new -- would have historically been line of business solutions is now some of these kind of AI solutions. Maybe talk us through some of the traction you're seeing around this? And to what extent has this really taken off or what we're seeing from some of the other LLMs now that the customers are opting to go down the kind of build route there versus, say, buying something from you off the shelf?

Yes. So I think we've disclosed, I mean, the tremendous attach rates, let's say, right? So 2/3 of the order entry volume was -- had AI attached, like 90% of the top biggest deals had AI and BDC in it, right? So we see -- it's a huge part of the sales cycle, right? Honestly, it's incentive. Now we support customers to also build, right? So that's always been one of the attractive things of SAP systems is that customers like to get in there and for better or worse do their customizations and apparently, they need to run sales orders in the nonstandard way or whatever. But they go in and actually build those. And so we actually offer tools now, right, like SAP Build, which has -- we have client integration, which is one of these vibe coding apps that they can actually use to build and extend around SAP systems. So of course, we give them that option, and that's always been a big part of our portfolio. But it still needs to be based, I think, on -- you need the standard core.

Got it. Maybe just moving on -- sorry, go ahead, please.

Some of the startups. When you're offering a similar product, what does it -- pricing conversations...

Good point. Two things. So one, for the embedded AI that we offer, right, not the vibe coding, but the embedded AI, right, the -- and we sell these by AI units. These are not tokens to be clear, right? Tokens are part of that margin, but the business value is also part of that margin. So when we release -- again, everyone is always pitching use case ideas to me, and I've seen like -- I feel like I've seen all of them now, right? We put those in our products. And we can build the embedded stuff, right, once, which includes testing it, running an ethics review, benchmarking, building the UI integration, all those kind of stuff, right? We'll build that once, but then we can release that to 5,000 customers. And that's the margin that we take on top. So the token cost, the model cost is just part of that. And also, again, we have this strategy where we partnered super broadly. We have access to all the Frontier models. We have partnerships with Mistral and Cohere and all these companies, right? We can simply move to the -- we have like some arbitrage, right? We can move to the cheapest, best performing model as we want under the hood, okay? So that -- in that space, we have the margin, right? And there's a benefit for the customer there. When it comes to pure vibe coding, the offerings that we have are let me put it, it's a little more broader than just the vibe coding part of it, right? So if you're looking at SAP Build, right, there's the vibe coding component of that, which they may undercut us or try to like push the price down. But there's all the other components if you're using SAP Build, right? So like you need to ramp up a database, you need to build a CI/CD pipeline and all those kind of components around it that I think is where we make our margin and can like tell above and beyond what they offer.

So talk about how you guys are protecting sort of cross-sell opportunity assuming that customers vibe code themselves on the edge. So touch on that...

Yes, again. So SAP systems are built to be customized and extended, right? We support that. I think where we have a strength, again, with the broad portfolio that we have and things like Business Data Cloud is a lot of like value-added use cases are not just point solutions anymore. It's solutions that -- or extensions, right, that touch several like business processes and business applications. Again, so like the planning scenario, for example, that I mentioned before, right? You might need data from logistics system and finance and HR, right? And so we can bring together that data and Business Data Cloud, right? We monetize that. You can do a vibe coding and then extend an agent on these kind of solutions. So I think that just strengthens the whole suite story. And then in terms of customers building on the edge, I mean, that's something we historically support anyway. That's one of the attractive things about SAP systems.

I guess one of the -- part of what's driving all the consternation to SaaS versus AI. It seems to be that a lot of the innovation seems to be happening in the labs start up level. So even things like, for example -- did exist by Microsoft already Copilot and I'm the one who use Copilot. And I wonder from your perspective, given your seat, do you feel like you have the right team in place to be able to remain relevant and be at the frontier of all of these developers because at the end of the day, switching costs are quite low. And so if someone can use a Claude code versus using SAP's agent builder. Now obviously, there are client's data sets that are more difficult for them, but certainly not a good idea to force your customers to use a product. So I'm just trying to think about why the cadence of innovation has been so slow with the existing AI software, all the software companies seem to be very much behind the curve from a new product and all the headline...

Did anybody install Open Claude in your computer? Oh man, you're brave. Security nightmare. It's impressive though, what's possible with it. Yes. I mean I disagree that the switching costs are that low, to be honest. Again, I do think there are benefits like if you're building SAP extensions, like a lot of our big customers are SAP shops, and they're used to working in the system and they work there because of, again, the enterprise readiness that we have around it, like auditing and all kind of stuff that's needed. I challenge a little bit about the innovation piece, again, like this Rapid 1 model that we released, this tabular model. I mean that is like, again, spotlight paper at the top AI conference in the world, like huge value. This company, I think what they call it Frontier, I think, just came out of stealth mode, right, building tabular AI models with a valuation of over $1 billion. And we have the same thing basically. I mean, to be honest, even better, I would say, right? So I think, again, I would challenge that a little bit. I do think there is some innovation coming out of the big companies. But again, we're mostly focused on making things enterprise-ready and productizing. And so -- and also, again, a big part of our strategy is partnering very broadly with companies. So look, we have partnerships with OpenAI, right? We made this announcement. We're hosting OpenAI models on Sovereign Cloud in Germany, for example, right? We have -- we're investors in Anthropic. We just participated in the last -- we're early investors. We just participated in the last round as well, right? So we do stay close to them and let them experiment, okay, and fail and be successful in certain areas. And then when they're successful there, that's where we partner.

In a world where I guess, ERP transitions have been short term, maybe months rather than years, to your point, actually could be managed by AI completely. But what's your outlook for the systems integrator?

Yes. I was always stuck with the -- I've seen every $1 sold in SAP software generates like $6 to $10 in the ecosystem for migrations, maintenance, all this kind of stuff. No, I mean, honestly, to their credit, they are also actively disrupting themselves, right? So they embrace, Joule for Consultants is used by all the big systems integrators, right? They dive into it, the customers ask for it. Their perspective is they can do more projects in a shorter amount of time, right? There's no shortage of projects and customers that need to be migrated. They have trouble finding talent, right? They are pivoting their business models very quickly. To their credit, I mean, they've really been also actively embracing this and disrupting themselves, and I think seeing a lot of growth and success as a result.

Great. Well, I think we're on time. So thank you very much, Sean, for the great insights, and thanks, everyone, for joining.

Thank you. Looked forward to the session. Thank you.