SAP SE ($SAP)

Good morning. At this time, we would kindly ask that you please take your seats and silence all devices as our program is about to begin. Thank you. Please welcome to the stage SAP Global Head of Investor Relations Alexandra Steiger.

That was too fast. Good morning, everyone, and thank you for joining us at our annual financial conference. We hope you're enjoying Sapphire so far and had a chance to walk the floor and explore all the exciting innovation here on display in Orlando. A warm welcome as well to those joining us virtually from around the world. Today's agenda offers a great chance to hear directly from our executive team, take a closer look at some of the key developments across our product portfolio and see how our technology and strategy are coming together here at SAP. As AI continues to reshape the technology landscape, and the way our customers operate their business, this year's conference is an opportunity to reflect on that change and also the progress we are making against our own vision. Last year, we shared how we laid the groundwork in enabling AI and data capabilities across our existing product portfolio. This year, we're building on that momentum by going all in on AI. We have a strong agenda for you today with our executive sharing updates on our strategy, product road map, execution, our workforce transformation and our financials. So let's get started. First of all, welcome our CEO, Christian Klein, on to the stage to share his perspective on our vision and how our strategy differentiates us in the age of AI. Next, Muhammad Alam, who leads SAP's product and engineering of the board area, will share an update on our technology portfolio and how we're continuing to innovate across the business. Following this, Thomas Saueressig, our Chief Product Customer Officer, will share his view on the progress we are making on our cloud migration journey while helping customers realize value in the business. Afterwards, well, welcome Gina Vargiu-Breuer on stage, Chief People Officer and Labor Director, to discuss our people strategy and how we're transforming our workforce to support SAP's next phase of growth. We'll then hear from Sebastian Steinhaeuser, our Chief Operating Officer; and how we're accelerating strategy execution and simplifying operations internally. Last but not least, our CFO, Dominik Asam will provide an update on our financials and the key drivers of our top and bottom line growth. Together, these sessions will hopefully offer you a broad view of SAP's strategy and execution across our business. After Dominik's update, we'll take a short lunch break and then afterwards the entire SAP executive team will come back on stage for an interactive Q&A session. Before we begin, it's always a great pleasure to reach you a disclaimer. So let's get out of the way. In this presentation, we will make forward-looking statements, which are predictions, projections or other statements about future events. These statements are based on current expectations and assumptions that are subject to risks and uncertainties that could cause actual results and outcomes to differ materially. Additional information regarding this risk and uncertainties may be found in our filings with the SEC, including, but not limited to, the Risk Factors section of our annual report on Form 20-F for 2025. Unless otherwise stated, all numbers in this presentation are non-IFRS and growth rates and percentage point changes are non-IFRS year-over-year at constant currencies. The non-IFRS financial measures we provide should not be considered a substitute for or superior to the measures of financial performance prepared in accordance with IFRS. That out of the way, I'd like to ask Christian on to this stage.

Hello, everyone. Welcome to all of you here joining us on site in sunny, sunny Orlando and of course, also welcome to all of you joining us virtually. What a difference 12 months can make? I was here on stage where we talked about the acceleration of our cloud transformation. I guess we can all agree, a very successful cloud transformation. We talked about Joule, and now we are talking about the future of the software industry in the age of AI. And after yesterday's keynote, I would like to share a little bit more about what is actually the right to win for SAP in AI. Why will be -- why SAP will be a winner. And then second, of course, what does it take to become a winner in AI. And to start, maybe a quick look back into our cloud transformation because some of the things we did, which you see behind those financial numbers are now very, very important also for the success in AI. Now first, when you remember, we acquired a bunch of cloud companies starting 2012. And then when we took over we said, hey, we want to harmonize our portfolio. And harmonization means we want to actually build a best of suite. We want to harmonize data. We want to harmonize processes, and I will come later to that, why this is so important that customers and our stack are not sitting on a bunch of data silos and not on a bunch of pro business processes, but that we are now really building AI on a harmonized foundation. And second, I mean, what was equally big, I would say, is actually the transformation we did on the go-to-market side. I mean in 2019, there were not many people in SAP know how to manage subscription consumption, not how to adopt actually drive adoption of consumption-related business models. There was a lot of change happening inside SAP to not only equip our sales force for that, but then, of course, also to change the whole operating model. And then third, no transformation actually just will happen bottom up. I mean it needs leadership, it needs strong people, it needs strong expertise. It needs a culture when you turn around a company which is big 110,000 employees, I mean it means something. And that needs to happen at fast speed at fast pace. And we also changed our culture so that we are really also having our employees understanding that this change is actually needed and that there is a clear plan in place, which we all have to execute now. And then you, of course, see it in the numbers. I mean, Q earnings in my eyes was really great. I mean, we are outperforming all of our competitors. And of course, even in the cloud world, the ERPs, the SaaS apps, they will not go away. I mean there's a lot of potential still for -- to grow our business in the cloud, but of course, now much more with the value equation happening on the edge and AI. So we transformed SAP once. And yes, I can tell you, we do it a second time. Now what does it take from a CEO perspective to transform and make a transformation successful. In my [indiscernible], there are 4 pillars, 4 levers to make this a success. First product who [ I met ] Philip and I showed yesterday in the keynote, what does it really mean? What kind of value will the autonomous enterprise deliver to our customers today. We want to share a bit more insight, what is really differentiating SAP from the West. And also then, of course, how do we going to make it happen? How do we develop it? And then second, of course, go to market. The way how we sell, the way how we deploy it, the way how we drive consumption going forward, again, we'll also need some transformation on the go-to-market side. Thomas will share more details about that. But obviously, there was a certain reason why we also did certain reorganizations. Why we did also why we are now doing also certain re-skilling on how to vibe code in presales and show the value and then later on, of course, drive the consumption and drive the adoption of our agents. And third, I mean, super important is, of course, the people there. I mean, going back into the cloud transformation, I can't remember where we were. I looked around in SAP and did we have enough people to know how to actually operate the cloud at scale. Do we actually have enough people who develop a multi-tenancy enabled architecture for our core products? No, we brought new people in, but we also did a massive re-skilling inside SAP and the same needs to happen now all over again. And then, of course, with Sebastian on the operations side, it's also now very important that we become on our own and autonomous enterprise. First, I mean, you are looking at us saying, okay, first, how will SAP deliver AI at scale, at speed. And then, of course, also, how can we also deliver the efficiencies you're going to -- you need to see coming out of, of course, deploying our AI inside SAP. For us, I also want to be very transparent on that. There will be also, of course, major investments. You have seen some of our latest acquisitions. But of course, we need to bring top talent in. And obviously, this will also then require certain investments, targeted investments in certain areas to make sure we have the best workforce to win in AI. Now coming to our why to win. We won ServiceNow. We did won a few best-of-breed applications in the past because some of our acquisitions made the mistake to not choose SAP, don't ask me why. But every time when we then migrate it away from these best of breed or no, you're wanting a ticketing system, I mean there's not a lot of domain know-how in the data feels, I mean, you can actually now with AI, you can migrate those over. The switching costs are getting lower and lower. Talking about SAP, it's a little bit different. I can remember well here with Thomas when we started 5 years ago to harmonize our data model. I was actually overwhelmed by -- to understand, oh, there are 1 million of correlations between logistics and finance. And there are another 2 million between the payroll and the commission system and this -- and then you're adding this all up, and you're counting over 7.5 million data fields. And now think about it, at that point, it was about harmonization. Now the agents need a graph where they can correlate this data. So it's actually 7.5 million multiplied by a factor, which is not imaginable where we now need to make sure that we are also bringing this semantically rich data contact sitting in our ERP into the new platform I will talk about in a second. And then obviously, we are running thousands of business processes in these companies. And I talked last earnings about the learning curve we had. I mean clearly, 1 learning curve with tool was also that these agents need process context. You can talk about agendic AI use cases all day long, but if they don't understand your process logic and you cannot also flexibly extend that because every customer has a different way of doing sourcing has a slightly different way of running a payroll. Obviously, it's not going to work. But all of that sits in the brain of a company, and this brain is actually the ERP. So I'm very confident that our ERP, our apps, they will stay. And of course, we now need to build the autonomous enterprise and actually enrich it with world-class AI. How do we do that? Yesterday in the keynote for purpose, we didn't start with the business side. We started with the platform. Why? Talking about lessons learned, we know that Joule is not perfect. And when you are talking to some of our partners and customers, probably also here, the results are not really accurate. Is it really compliant? Is it really governed in the right way? Or does it really require a lot of handholding. And I can tell you, why do we create examples? We can deliver world-class AI, but also, I have to admit, there's probably a lot of handholding behind to let the agents connect to the wide data fields and making sure they understand the process context. And then Muhammad, Philip and I were sitting together and said, "Hey, this can't be", because our Asian buildup was on BDP. The data layer was on BDC. The process context sits in [indiscernible] and in Erland in some other places. So then we said, hey, it doesn't make any sense because when we are the plane of every company and you are developing agents, I mean, obviously, the context needs to come right away with that. And I hope you have seen with the new better version of the platform, and in one month, this platform is GA, we are going to deliver now an agent builder where you can bring your own tools. And on the building side, you can use Anthropic, you can use OpenAI. Next week, we talk about Mistral, because of sovereignty in Europe. The differentiation on coding and agent is 0. That's why you don't have to come to us. But then when you start developing it in Joule Studio 2.0, you're going to see that half, I'm developing now a pricing agent. I need access to the material data in S4. My pricing data, it's sits either in Salesforce or in SAP. I come in the second to non-SAP data. Okay, this is how you quote. This is how you price. This is -- okay, I need to understand how to follow this process steps. I understand the approval steps. And then finally, I come to the governance part in a second. So the community will -- we'll see in a month from now, together with our own developers already building agents with that now, they're going to see Aha! now I have the context. Now I have access to the point of every company. But here, we don't stop. I know there are certain concerns around BDC, what data do we share? What data we don't share. We actually share more data. We do -- we do [indiscernible] copy share and also that people don't need to move the data around, that's good for TCO, that's good for cyber reasons. So we do that. But then actually, what happens on BDC and only on BDC, we joined data. We -- a data product alone, it's a raw data. But then when you join data, when you build a semantical module, suddenly, you can say, Aha! my pricing agent actually needs data from SAP and maybe from Salesforce or maybe from another CPQ solution out there in the market. And then you can do these data joins and the way how Muhammad and Philip now engineer that is you can actually say, okay, I joined data, customer data between SAP and Salesforce, and now I link it into the [indiscernible] because then suddenly, the agents also realize Aha! this is how I should understand the semantics of my customer data in the company. The same we do for material, et cetera, et cetera. We build this really, really which semantical data layer between SAP and non-SAP data. You have seen our acquisition of [indiscernible] so that we also then can take care about the master data quality, again, because agents need high-quality data. They should not compensate for a potent data module. And then last but not least, of course, with [indiscernible], we have a Lake House to really also develop own agentic AI scenarios with our own lake house built on Apache Iceberg. So that is actually the context layer. And that is the heart and then we are also now launching SAP domain modules. So we are training these models with our code to even better understand the specifics of our customers' business on how they want certain business processes, what approval steps do they have, et cetera, et cetera. And then last but not least, comes the governance layer. And that is actually really everything what I showed yesterday which sits under the iceberg. You can build, of course, a lot of fancy agents with LLMs, they will miss the context, but especially, you're going to deal with a ton of complexity in the governance part. I actually had in the commerce area, a customer of us telling me yesterday, Christian, we built over 50 agents. And now I have a problem. These agents actually don't understand really the pricing logic. They actually share pricing data with consumers, they should never ever share. They are, for sure, also not actually adhering to certain regulations with our data privacy standards in Korea or actually with the data privacy standards in Germany. I mean this is completely out of control. I can double my IT organization to really make sure that the governance layer is actually working. I said, yes, but hey, SAP is investing over EUR 600 million each and every year for the localization of our software for the certification. I mean here is the logic. It sits in our software. So that's why we said, "Hey, when we are talking about the business [indiscernible], here's the build layer, here's the context there, and here is the governance layer, very, very important. And then other taxes are debating or who has now the orchestration layer and who owns the agent layer. I actually -- we give this away for free. Why? Because we want to monetize the success, the value of our agents. We want to have the best HR agent. We want to have the best source to pay assistant. We want to have the best record to report the system. We want to have the best planning assistant. But the orchestration, if customers want to really also block in third-party agents, of course, this is for free. And I know there was some talk about this API policy. The only thing that we actually manage now with the API policy so that we manage actually the access to our APIs, to our MCP servers so that we really can guarantee high governance standards. And second, obviously, that we also can still guarantee with millions of API calls coming in that we can manage the system performance. But everything that you're going to build on the SAP business AI platform. You can use any model you want, no differentiation, but then comes to context and then comes to governance. And that will be world-class delivered by SAP and some of that is actually already there. Now the autonomous suite, when we are now having the platform, I mean, yesterday, we presented at, okay, what is actually then our vision of wanting our autonomous enterprise will one. So first, you're going to have your air systems, design to the personas in your company. The agents will actually take over a lot of task, will enable you to do more with less or actually enable you to do things which were not even possible before, especially when it comes to prediction, you saw RPT 1.5, you saw prior labs. I mean there will be a lot of business scenarios we are now going to enable, which were just not possible before. Everything on on our AI platform. And then next to whole center, obviously, it's very important that it's outcome-based. So I just had a U.S. public sector customer, 4 rooms away. Actually, they ask me, Christian. I now build in custom agent and okay, understands he -- the agent understands, listen, of course, but actually now to change something in the system of record and to do -- and to wide back into the system of [indiscernible] it's not possible. And actually, that is not good because I want to want this autonomous. This was about a payroll. I said, he see. This is actually the power of the SAP platform together with our autonomous domains because you can actually tell us, does this agent need to read, change or wide back even into an SAP system. So we are going not only from listening, understanding, we are getting all the way down to execution. And there's always a human in the loop, but the customer can decide, okay, how far do I want to go with regard to the level of autonomy, I want to give these agents, very important, audit-ready. [indiscernible] was smart enough, super smart as always to say, okay, hey, we have so many certifications for our software. I mean, at the end, the same what we need to have for our software now needs to happen for our agents. So SOC certifications. I mean we are really going down now, not only giving you the full traceability of the actions what every agent is performing now. We're going even 1 step further. We are actually telling you when you won your financial close with our assistant, "hey, this is actually SOC-certified." This has all the certifications you were used of running your financial close with S/4HANA, with our software, extensible by design. When I talk about learning of tools, my god, maybe we should have thought about that before. But again, everyone is still learner these days. extensibility. I mean, when I look at our policies inside SAP, still Sebastian is fighting it a lot to make it simpler, simpler, simpler, but we have different policies. And sometimes, we have new ideas about new policies, new pricing policies, et cetera. So extensibility is super important. There will be not this 1 standard agent for pricing, not this 1 standard agent for sourcing and customers will also have different policies. So that's why extensibility is key. But what is also very key, and I hope you saw that yesterday with Joule Studio 2.0 that you can extend also your autonomous sweet layer now with new agents and with new skills on the fly. We just had -- actually, we showed the new platform to a customer right now, and they are building actually a personalized AI agent for commerce. And actually, we said, "Hey, okay, you actually have this one more step." You want to have this agent to do. You go in and say, okay, as a business user, you can say when you have the authorization, while you can just say, okay, with [indiscernible]. Here is 1 more step the personalized AI agent. And you should consider when you are making this recommendation to commit consumer. Think about that. Today, we are thinking about in ERP on-prem, we sold about 8-year long upgrade cycles. In the cloud, we moved to quarterly, then to monthly and now you can add agents and skills where we are, and we deploy it and we govern it. So the speed, the agility and the extensibility is very, very important. And think about that. Now you could build all of this with Anthropic. Let's assume that. Your business is changing so fast every day, not only the tech world is changing. I mean how do you want to actually make sure that these agents are always extensible everything when everything is custom, have fun with your IT organization to make sure they understand all the business model changes, all the business process changes is the business wants tomorrow and not in 3 months. So industry AI. Yes, some customers told me a question yesterday in the keynote, so, so happy that we heard this term industry, again, from SAP. I can tell you that when we sometimes say SAP is so sticky because we are the point of every company. But actually, I take this as a huge credit credit to SAP is that we delivered incredible value. I mean why would someone run all of these industry applications with SAP because it's sticky now, because it has value. And especially in these domains here, what's the best [indiscernible] and the team picked, I mean look at that, when you do asset management, when you do commodity management, yesterday, we showed unified commerce. There is no scenario, not 1 single 1 who doesn't need a tight integration into the ERP of SAP, not single -- not 1 single one. And in many, many cases, again, you need to even have a wide bag because otherwise, forget about autonomous, and forget about running autonomous asset management, when you cannot maintain or change certain assets or trigger actions in the SAP system to actually trigger a predictive maintenance or get one worker from place A to place B, in order to fix the asset. How is this possible? You can always say it's autonomous. But what you're missing is the last mile with the deep integration into the ERP. And that's what we are going to deliver. And then yes, Palantir has the copy wide for the FTE. But [indiscernible] actually told me just 4 weeks ago, I said Christian, you know what, with this autonomous suite, it's great. I like the agents. Over 200 agents are already here now, but you need to completely reinvent, tell your product management, forget everything about what they learned in the past and just think greenfield and think about how do we deliver autonomous asset management in the future. When we talk to Coca-Cola now and Kona about last-mile delivery, we talk to the truck driver, talk to the people in the warehouse to understand not about how to incrementally apply AI on top of our transactional application. No, no, no. Just to completely reinvent this process because now truck optimization happens with AI agents without anyone sitting in front of Tableau or Power BI and trying to somehow manage it on top of a transactional system. No. That is now done via autonomous industry AI use cases. We're pulling now together our IBU, our industry experts with our industry developers. You're going to see in that space that we will also build here a huge ontology layer for each industry. We are now started with -- you saw H&M yesterday on commerce, on retail, oil and gas, everything around related manufacturing because this is high value. This is -- these are complex scenarios, but they are high, high value for all of our customers. And actually, by the end of the year, we want to actually scale this business to over 200 projects. More to come. I can tell you after I told everyone yesterday call, Sebastian,, if you want to do something. His inbox is exploding and the pipeline looks actually anyway really, really good because again, what is the why to win? The why to win is it is so natural that you need to not only collaborate with the brain, you need to also wide it back and the agents need to understand the full logic of our suite. Now when we are now building the autonomous enterprise, when the platform gives everyone a great reason why to build agents with SAP. Why to an autonomous with SAP. Now 1 lesson I learned during my whole career is technology means nothing if you don't bring it to adoption. And every great technology needs redesign, needs business process change. you can plug in the best agent into your sourcing. But if your procurement department actually is not really leveraging it, not really thinking how can I shortcut the process now? How can I actually use this agent now to also do more intelligence sourcing, it's useless. And that's why we said, "Hey, we really want to also deliver at Sapphire a new [indiscernible] with SAP offering." And what does this mean? Two things. When we decided to give Thomas sales and consulting, it was not that I want to have more time for my family, maybe that as well. But at the end of the day, we also said, hey, in order to do that, we are not now inventing new roles. Now what we need to do is actually right from the beginning is to start prototyping is actually start to show value. And we can't have our customers waiting for 3 years until they finish the modernization of the system to actually use AI. And to underpin that, we said, hey, we are not only saying this in some nice words. I say, we contractually commit to that, and we come on site. And we're going to take all of these great AI assistance and agents coming from for Muhammad and the team now and bring it to adoption, not only activate it, bring it to adoption. And now, of course, I heard, "Oh, Christian, you changed your strategy because now you're also allowing it for on-premise systems." No, no, no, it's also not a defensive move. What we are now doing is after a ton of feedback, I mean think about that, there are customers here who are actually shifting now and harmonizing over 100 ERP systems over the next years. I come to the acceleration for that. But now, of course, these customers are saying, Christian, my CEO, my CFO is telling me, "Hey, I need to deliver AI tomorrow. I can't wait here until I have finished all the modernization". And even for some of the on-premise systems, I mean, they are mission-critical systems in the company, I see great value AI use cases. If you -- if I'm not doing it with SAP, I do it with someone else, custom, but I would love to do it with SAP. And that's why we build this on-premise connector to really now also then connect our assistance and our agents to these on-premise systems. But of course, what we would like to see is for us the AI foundation, the AI platform needs to be there, and there must be a commitment there to also then go with us on the modernization journey because needless to say, when I talk about extensibility, if you have a heavily customized landscape, obviously, there is more handholding, more extensibility required than when we are talking about the [indiscernible] a very clean system with a lot of standardized business processes and a standardized data module underneath. Then ERP migration, acceleration. I can still remember when Alex Karp called me and said Christian, I would love to switch to SAP, but my god, my CIO tells me just takes that long. That needs to be done faster. And I said, Alex, we are on it. We have tool for consultants, for developer. But please feel free to join us. I mean I'm welcome every partner to shrink this time line for getting our customers from place A to place B. And when I'm now talking to Accenture to Deloitte to PwC, everyone. Everyone is investing into that. Somehow we are disrupting someone's business model here -- but that is overdue. And so that's why we are now also today, in Thomas keynote, we launched our AI-led ERP migration platform, where we are SAP on its own is shipping new assistants for data migration, for business reconfiguration. And we are very confident by the end of the year when we are testing this now with our customers, that we can actually cut the time and the effort and the expenses by sometimes up to 50% when we are deploying all of these assistants now available for the ERP migration. And then we are pulling in twice enters for test automation, AI-based. We are pulling in Palantir. We are pulling in Accenture. And there are many, many more now to join. So that journey will be accelerated without any doubt. And then the customers have both. They have actually a very harmonized data module. They have a very clean system of record where you then can plug in the AI agents. You don't have to wait for that anymore 3 years, even if you have an on-premise system, we can start tomorrow while we are modernizing your landscape. And that actually resonates extremely well with our customers, and I feel we can combine here really the 2 primary objectives of our customers, meaning modernization and of course, AI value. Now talking about the go-to-market model. Thomas will deep dive on that, so I want to keep that short. Look, at the end of the day, what we are going to do is you can show a lot to customers on PowerPoint. What is more convincing is that 4,000 of our presales experts can already wipe code prototypes. That is what we need to show. In industry really also even with more on-site presence to really build that while we are still defining the value, showing the customer the value and then come to sign a value-based transformation outcome based on industry AI, where we then, okay, say, okay, let's go and really bring those agents now fully into production. And here, I mentioned already before, we didn't want to add new roles to our go-to-market model, we just simplify that. But then with the consultants we have, the data consultants, the LOB consultants, the industry consultants, I mean, they can hear help. So that we don't need to onboard new people. We don't need to build an AI deployment engine in the SAP. We have it. We need to do some reskilling, but we have it. And when I see the amount of consultants who can today code already with our new platform with Joule Studio 2.0 or also understand how to optimize an LLM module and how to actually fine-tune it. I mean it's remarkable and we will double down on that and come in a second tool at the people section. And of course, that we need to use our own agent led tool chain is, of course, a mandatory task to all of our people in the post sales area. And then yes, when you actually then see when they have adopted a suite when they have adopted BDC. And now with the data catalog and you can then build these data [indiscernible] and you can add it into the KG, we, of course, also see that the AI consumption is already going up because now finally, we see, okay, in ERP, a best of suite, best of suite will win, not best of fleet. And then finally, actually, we also then see much higher consumption and much higher penetration not only of our LOB solutions but also with AI in our installed base. The people side quickly. First, again, we are investing. We are investing into top, top experts, but do we always need to deliver certain AI agent or a feature, less and less features, more and more agents. Do we still always need a team of 10? No. Of course, you're going to see productivity gains. But again, I also want to highlight to get the best ontologists, the best data scientists to get the best full stack developers. I mean, these are all the roles at AI architects. These are all the roles where we also then hire from the top, top universities in the world and bring those in, but even more important is, of course, the rescaling and here, you see, I mean, we call it mandatory, I actually call it, it's actually an offer to our employees to have a quite career within SAP to enjoy all of these learnings. And the managers are always in the lead. It's not about that we have -- here is IT and here's your AI tool. No, no. We really want to have cross SAPs, the managers and some frontrunners on AI showing about what is possible with AI in the one or the other job and we are doing this [indiscernible] SAP. Obviously, with more intensity in engineering. And as I mentioned, the vibe coding and also then the LLM trainings we are doing with our consultants. Sebastian is responsible not only to simplify SAP and make SAP AI-ready to really deliver a consumption business model from the beginning to the end at scale, but of course, also to make SAP more efficient. Here, you see by function, again, with the business and the lead how we are now applying AI internally. We already have realized efficiency gains triple digit. I'm not allowed to share the exact number, but it's actually quite substantial. And of course, we see a huge potential to make this even a bigger number than 2 billion. And again, don't only take this as efficiency. I mean we will take some of that money, of course, also to reinvest. But again, it's not about the quantity of people in development. It's about having the best people in certain areas. The market, I mean, believe it or not, I'm 100% confident that our SaaS past business will not go away. AI agents don't work without a brain. The brain is SAP and having a best of suite versus the best of breed, I see definitely scenarios where customers are replacing SaaS apps, but the domain expertise is not so deep where the data models are not so mission-critical. But this is why I actually see a unique chance now with the suite. We always said the suite will win. I guess, in the age of AI, there is one more reason to believe that. And then, of course, when we are growing our market share, and there is no doubt that we are going to grow our market share even further, then of course, we rather see this $5 trillion market here as an opportunity because if our platform now delivers what we here presented and it will, and when we then deliver these high value-add use cases for the different domains plus industry, there is no doubt that we can accelerate now also our growth towards 2030. Here, 1 last point on the commercial modules. I said this as earnings. So over 2/3 of our cloud revenue is already today see [indiscernible] meaning. It's nonuser based. So we have all kinds of value metrics. So no one needs to be scared that, oh, efficiencies, users, not anymore needed. And suddenly, the revenue goes down. No, no, no. We price a lot of our apps already today value-based. And then, of course, you see now with AI with the platform, with our migration tools with the autonomous domains that, of course, we see that at least we're going to see a 30% consumption-related revenue share in our cloud revenue in 2030. Again, it's there's some time to go, but we see that we are on the right path. We have the right strategy and the execution is there. Now look, that's how I see our growth journey. There was, first, an enabled AI. Maybe we can also a learning AI journey, where we delivered SAP Business Data Cloud. Let's not forget. This offer is only one year old. We actually fixed a lot in our AI foundation over the last 2 years. We delivered the first assistance and tool agents. And then, of course, we worked further on the harmonization of our suite. Now we are definitely in the scaling phase. Now I say, hey, with this platform, there is no reason why in the SAP context, you are not building agents with SAP. The autonomous wheat layer. By the way, not only that we are delivering 200 agents for 60-[indiscernible] systems this year. This morning, the customers who are the partners who are better testing the platform already have developed over 600 agents. And that will be over 1,000 attacked, and there is more to come. So when the platform now starts to scale and the autonomous suite will become bigger and more mature and with higher value, I mean, that will, of course, help the adoption of AI massively. And then last but not least, I mean we also then have our migration tools, and we definitely want to leverage them, and we will monetize them because again, this is a big, big market, customer spending big, big money on that, and we will hit it right there where we say, no, you don't have to spend that money, spend what are your money on our AI migration tool chain. And then, of course, for me, it's actually now very important to build the community on the platform, creating the belief in the ecosystem with the new platform being in better and 1 month productive. And then actually, we build this. And then we build the graphs for the different industries. We build the graph and we will get more and more mature for the horizontal process layer in the industry. The domain modules will get better and better. [indiscernible], you can feed in a lot of more additional process logic. We have [indiscernible] with our AI government hub, where you can free of charge actually also then see the transparency and govern your AI layer. And that's actually what I believe will happen within a year from now. And I guess this year was a very, very important sapphire for us to show that we are now really accelerating both on product, go-to-market, people, operations. And when this all comes together, there is no doubt that this will be a second successful transformation for SAP. Many thanks.

Good morning. It's kind of hard to follow that act. Thank you. I think Christian already covered most of the product strategy. I think what I'd like to do here in a few minutes is maybe go through 3 things: one in not as much detail. But thing number one is I want to at least share with you that as we think about our product strategy, what were the shifts that we saw both in the tech and in the market that guided the strategy that you saw yesterday and what Christian just walked you through. And how internally are we really mobilizing that strategy to build a product that we believe our customers need. So that's one. The shifts that we see and hopefully, in an effort to see if those are the shifts that you see in the industry as well. Thh second is the product strategy. A lot you saw yesterday, Christian covered a lot in detail as well. But related to the product strategy, what I also want to attempt to do is share a little bit more of the conviction that we have as to why this particular strategy is both differentiated and unique then the strategy that you're hearing from some of our other tech peers as well. Because at the category level, things are probably starting to sound very similar, right? Everybody has a set of agents, everybody has an agentic platform. They think that it's the world's best and people want to control the orchestration layer as well as the engagement layer as well. But I want to sort of build a little bit more conviction in you at least share the conviction we have as to why what we're doing and what Christian just walked you through is different than the rest of the tech space. So let's start with the shift. The basic shift that we see is happening in the industry is the tech stack that hasn't changed in a few decades is going through a fundamental shift. It has a new layer that's getting added on top of it. And that layer, the top most layer, historically has what's commanded the most value, both in terms of what it creates for the customer and then the value it generates for the provider. And that has been the SaaS applications now for the recent past, if you will. But what's happening now is AI instead of being in the SaaS applications, you can put on top of the SaaS application. So there's a new layer on top, and that's what everybody is trying to buy for. So what that means, though, is two things if you think about. One, as SAP, we clearly need to make sure that this is a new layer that's coming on top of it of the agentic experiences, we can take the experiences and the value and the products that we have to shine in it, hence, the autonomous suite and the industry AI, but the second thing is the layers underneath become more commoditized or the better way to look at them is to become more of a platform layer. Now what that means for SaaS applications is particularly in the category of SaaS applications that we play in is that they're not going away, they effectively become a platform layer that provides and feeds the agentic layer on top of it. Now the thesis is not that all SaaS applications become platform, there is for sure, a class of SaaS applications that are going to be disrupted, that will go away that can probably entirely be subsumed and consumed by the agentic layer. But as you think about finance, as you think about GL, as you think about supply chain, as you think about core employee management, that layer isn't going away because it has a level of specialty compliance laws, regulations, capabilities, logic that needs to be executed. Now can it be executed better with intelligence and generative AI on top of it and predictive models? Absolutely. Can it be executed in a decoupled manner with the apps? Absolutely. And that, hence, comes the new layer on top of it. But what that means is the more this new layer takes foothold in our customers' landscape for us, two things would happen: One, if we can provide this new layer of compelling agentic experiences for the customers to consume out of the box as much as possible with the right extensibility, we will get a share of the value that we're creating for our customers. Hence, the autonomous suite. Two, even if the customers then say, choose another agentic platform to build that agentic layer on top for the class of business process domains that we live in, the platform underneath will still continue to grow as well, the more you interact with it. Hence, our openness that from an a to a perspective, from an orchestration layer perspective, can we be the top Layer? Absolutely. Do are we buying to be the top layer? Absolutely. Do we need to only be for a customer landscape, do we have to be the only top player? The answer is no. They can choose what they need to and what they've already bet on, but we're still going to win because the SaaS layer that we are underneath that needs to exist for it to consume. So there's going to be a level of growth that we're going to see in both, if you will. But this, to me, as you think about the shift in the tech landscape, right, and the stack that hasn't happened in multiple decades, there's -- we sort of whittle that down to 4 different shifts: One, the fact that AI on apps era is gone, it's now AI on apps. And that now allows everybody to be a player in this new agentic layer. Now some SaaS apps may go away. But as we talked about, the class of apps we belong to will now become a platform layer by definition because it sits underneath that highest layer up down. And there are going to be a pull-through on that. But of course, we want to also play in that new agent layer up top that by definition, is loosely decoupled to the layers underneath. The second thing is, is now how we think about our product strategy and what you just saw Christian talk about is this app boundary versus landscape boundary. Now as we think about, for instance, source to pay, historically, what we've shipped our product in Ariba, right? There's sourcing, there's contracting, there's buying and there's invoicing. And any features we ship sort of stayed within the boundary lines of whatever the scope that we addressed in those applications, that could be S4, success factors and anything. The mindset shift now is this agentic layer at the customer level that they're going to go build is going to be one agentic layer for a source to pay, if they're using us for Ariba and they've got 2 other tools completing that process, we, by definition, whatever a genetic layer we provide has to factor in the additional tools that they have because the customer will only have 1 layer. If we only provide agents that work on our app, they're going to have to find another layer on top to rule at all that can connect to those other applications because the customer is not going to stitch together that new layer up top by multiple agentic platform. So that's why, as you now think about the core properties of our agentic platform, the new business AI platform extensibility and our partnerships with N8N is so core because there's hundreds of connectors that N8N brings that's embedded in our business AI platform, the extensibility that says, hey, because we generally are the core of that process, right, as for finance or even a rebind others and the other tools are sort of secondary and subsidiary to it, that we have more of a right to be that agentic layer. And with the open extensibility we have connect and really create that landscape view. So again, if you think about and step back and believe in that thesis, ones that have a smaller share of that app landscape will have a harder time becoming the agentic layer. But because in all of these core processes record to report, hire to retire, make to deliver we are the core application. We have a lot more of a right to say, listen, majority of this is what comes out of the box with SAP and will extend with N8N or the vibe coding experience that you see with business AI platform and Versal and others to expand. So we do believe that in that SaaS landscape, we do belong generally in a category of 1 as we believe it in our customer base. I think generally, the category is a category of 2 because there's another player that has the breadth that we do, but we don't really compete head-to-head with them from that perspective. But in the SAP customer base that are running these processes, chances are that if you get 60%, 80% up to 90% of the process that you run on the core application with that new agentic layer out of the box with our autonomous suite, you'll go cover the rest of it with us as well. And as you do that, the stickiness of let me go do a few more things would come into play as well. That's the app boundary versus landscape boundary that's so core to the product strategy that you saw. The third one, and we've been -- that's been received very positively is, listen, historically, we've said -- you won't get the value that you're going to get from our innovation is when you get to our latest products. But that's shifting now, right? Because this layer up top is loosely coupled with the platform layer. So we have to shift our product strategy as well to say, listen, we're going to build this agentic layer, the autonomous suite that is loosely decoupled with the application here because it has its own ship cycle. I'm not shipping agents on S/4 every 6 months like I'm shipping features on S/4 every 6 months. Those agents are coming monthly and soon in a weekly basis, working with S/4 as well because we're building them in a loosely decoupled manner as well. And hence, it allows us to now make the commitment to the market that says, hey, as long as you're in the modernization journey, which is valuable in and of itself because there's components that are going out of support, you need the compliance, the regulations and things like that, that the modern solutions have. We will, as SAP, allow you to take that agentic layer and connect to your ECC and S/4 environment. So your value can start on day 1, not on day 700 and however many years it takes you to sort of get to the modern solutions, and that's been received very positively as well. And the last 1 is, of course, because modernization is still important and so core to our sort of business model as well as the compliance and regulations for the customers and the value, we want to make the cost of getting to the modern solutions much faster as well. And that's what Christian talked about with our modernization assistant as well. But these 4 shifts, I believe in the class of applications we live in are fundamentally sort of changing how customers think and are reshaping our portfolio. So as you think about SAP now in this autonomous suite, it's not what we've been shipping for the last 4 decades is fundamentally a new product line that we're going in with that a much bigger TAM, not just the new cloud solutions, but as S/4 on-prem and ECC. And not just that, it has also those additional tools and maybe non-SAP applications that sit adjacent to us because we have the right being one of the largest tech partners for most customers, right? It's either us or a hyperscaler. Those are the top 2 if you ask some of our larger customers to who your largest 2 tech partners are. Now how this shapes and leads to a strategy is the picture that you've seen. And this largely has 3 layers, right? And that's what Christian walked you through. The layers on the right, unfortunately, don't map the layers on the left. But if you would allow me to the bottom most as a business, AI platform, built on that is this decoupled genetic experiences layer that can work on any application landscape and you can connect it to non-SAP landscape, too. Now that's the agentic layer, right? Agents can run headless, but agents need a place for the user to interact with them when there's an exception and hence, comes due. So those are the 3 core things. Now you can argue, listen, you can go to any of the leading tech conferences and you'll probably find these 3 layers there, at least the bottom 1 from a platform and the top 1 from an orchestration layer perspective. Nobody else largely plays in saying, listen, we're just going to build an end-to-end autonomous suite for you. Nobody else can do that besides just anyway because they don't have the right and the applications and the need to run it. Now there could be small start-ups just saying, oh, I've got the finance agents that can really run close for you, but they're not doing supply chain, right? And they're not doing HCM. And a customer doesn't want 22 different agents running in them, right, turning to different platforms, agents on different platforms because that proliferation, the security nightmare is going to be pretty massive. So we're one of the only ones, again, in our installed base that has the breadth of that autonomous suite, and that is what we talked about yesterday to say, from this process to the end process and not just that, with industry AI, we're going to go to your core processes last mile distribution, distributed energy unified commerce adaptive manufacturing going to go build those as well to complete your story on it. So that's what these 3 layers are. But I'm not going to spend -- because I think Christian did an amazing job, makes me proud sitting there. on what these platforms are and our differentiation. But what I do want to spend a few minutes in each 1 of those is talking about what are our differentiators and uniqueness is, right? And starting with the autonomous suite, right, the one in the middle. We're, of course, building 200 agents that we talked about. It's going to be more than double by the end of the year. There's a set of characteristics that these agents have that are unique. One, because we run the underlying application, we can be far more committed in terms of the outcomes we think that these agents can generate. And not just in aspiration, but in reality because we know what that data model is we can track those outcomes, and we can prove that in the AI agent that, that's there because that's the biggest issue that some of the customers have. Second, they are extensible. And that needs to be there, like I said, because it needs to be that landscape view. Third, the audit-ability part of it and the traceability is unique in what we can offer because we have one of the world's leading process mining platform in Signavio, and we're using the same tech to have agent mining logs for customers to see how these processes work. And second, from an audit-ability perspective, we already made a significant portion of our portfolio through audit validation, audit readiness for those audit relevant places, and that's what we're working with external partners on. That's just a natural thing for us that we do, and that's what we provide to agents, that's what people need. Works where you are, we've talked about in the openness from an [indiscernible] perspective. These assistants, of course, come together in each 1 of those domains. And hence, there's only 1 organization in our customer base that can canvas out of the box set of patients that allow you to get to an autonomous enterprise stage, and that's us. And we've talked about the numbers. the stat on the right, from an engineering perspective, just to give you some appreciation for it, again, is the uniqueness of the platform layer that we're going to get into it, right? The ability to take an idea on what agent you need to go build and take it to GA at an enterprise level is what this platform enables us to be able to do by the end of Q2 in under 10 days, and we're going to get to under 5 days. And GA is a high bar, right? You can go use Perplexity or any tool to find us listen, and what does it take to really GA an agent? And what you're going to find this timelines that's from 3 to 6 months or even further because there's a lot of complexity at an enterprise level, you have to account for the -- under the waterline iceberg that Christian talked about, that we have platform made as a platform in our agent governance and business AI platform. So this ability to innovate at a pace that's going to be very hard for anybody else to be able to go do is what's the differentiator for our platform. Moving on, we've talked about this. I'm going to skip this. The fact that we've really expanded the TAM without risking our modernization story to say, hey, the AI consumption can now expand massively to the customers' ECC estate and the SFC on prem state alongside the cloud state is a big, big plus for us with not just, hey, you can go Custom Connect to ECC, but there is a connector that we've built that is part of our agent gateway that truly understands the ECC data model and the patterns because we're, of course, the writers and the publishers of that software as well. Moving on to the platform layer. Again, I'm not going to spend too much time here, but I want to sort of give you the highest level of the strategy here, right? We're partnering with things that we believe are going to be commoditized in the AI tech landscape, too, meaning the public LLMs, we believe is a commodity. Of course, that's where a lot of the early money and revenue is going, but the race between an Anthropic and Gemini and OpenAI and who else to come with public models is going to be a race that's going to see new winners and new laggards probably every quarter, if not every month, soon enough, right? So we want to make sure that we bring the best of that to our Agentic platform. Second, the design experience of the agent is also going to be a commodity. Like do we want to go build a design experience that's better than any -- and of course, we want it, but is that sort of really what our secret sauce is not. It's not. So we want to go partner with an [indiscernible] to say, "Hey, if customers are choosing an experience to go build something, we just want to make that part of the business AI platform a way that we can add another N9 or N10 and whenever it comes up in the future to make sure the platform itself is complete. Same thing we do on the hyperscaler side, on the connector side. The thing that differentiates us is this, and I want to spend 30 seconds that I don't have on this slide still, which is listen, the public LLM part is what people can generally go build an agent on, and they're getting smarter every day, right? The OPUS 4.6 and the new models are going to continue to get very smart. But the things that we do that again, as you sit in another tech conference that you should think about, like, does this really exist when they say they're going to be the best agentic platform is a knowledge graph of our canonical model, right? That's already over 7 million fields and the relationships that we need to go maintain because that's very hard to go do. The process graph because we cover end-to-end process, these 120 top level but thousands below as to where things fit. We also have domain models, over 2 billion lines of code that is not in the public domain that we train, pretrain and then make it available with the public LLM to say, if you're building an extension or an app or an agent and SuccessFactors, ours is always going to come out to be closer to the pin on the first iteration than just using the public model. I mean, that's just common sense, but we'll prove that with e-valve as well. And the fourth one is predictive models. This is where we shine because we don't just have the shape of the data as our IP, which is the knowledge graph. We have the data itself with tens of thousands of consent an aggregated anonymized manner, that we're training this model on to say, "hey, we can give you predictions on the fly and really make machine learning science a thing that could be disrupted next with a model business, right?" These are the tabular model. So we've done that for SAP data, which is Rapid One. But then we said it's not going to be enough just for SAP data because customers always live in a world where they have a lot of non-SAP data and hence, prior labs because we want to bring the best of both to be able to bring into our context. These 4 things, I would ask, and you can humble me if you have an answer that I don't have, like who else can give on top of the best public models because it's not an or, it's an in. And then finally, that's the SAP context, it's the customer context that we already know that we can embed in the platform, which is every customer has extensions. So the knowledge graph is not just what we canonically ship, but the thousands of customizations and extensions that customers have, we know that. We bring that in, in a dynamic way. Company memory, which is the customer context that's process mining, process insights, process models, e-mails, chats and others, we announced that yesterday as well as part of our Agentic platform. And finally, the agentic run time which has the governance built in, which has the sovereignty built in because it's natively on BTP. It can run anywhere you want. Like you add all those 3 things, and it gives our Agentic platform a differentiation that's going to be very hard for a hyperscaler to go do because that's a bit hard from a sovereignty perspective. They don't have access to the shape of the data or the data at the scale or the predictive models and so forth. But anyhow, this is what leads to -- you can see the evals here, right? There's about 5 agents that we tested multiple times with just a different kind of public model and then a model that has that goodness in it. And you can see we beat each 1 of them every time. And that's only going to get better. Now the public models are going to get better. But of course, the context that we have and the company context that we build every time a customer runs on it, is going to get even better and better. So both will continue to show some difference in movement, if you will. Now finally, I'll close with because we've talked about the data. We've got the [indiscernible] on the [indiscernible]. Again, on the [indiscernible] part, you should think of that we have MDD, but we knew because we have to go from an apps app boundary to landscape boundary that we have to provide a solution for non-SAP master data management, hence, Reltio that comes together with our MDG. Because we have to, again, go to the landscape view, we need to be able to be able to access that data that's not SAP on the fly as well and hence [indiscernible] on top of BDC to be able to go to. So you can see that story is now building up from a clear product strategy perspective. And finally, I'll just stop here. governance, we've talked. Actually, the reason why -- because you also hear a lot of control towers, you'll hear a lot of agent 365. Like our right again to win on this is the platform that this cycle is built on in a seamless way is a set of platforms that we're largely leading in already. So LeanIX is one of the leading enterprise architecture platforms, which already understands your landscape beyond SAP as well as the best place to understand where all your agents are. And that's why we already have 55,000 agents registered on them, and they're not all SAP agents, most of them actually are not SAP agents, but [indiscernible] already know them because they are the enterprise architecture solution in our customers' landscape. Signavio already does mining. So adding agent mining was just in the world's best mining platform, another capability to add. Cloud ALM was already a leading platform for observability, and we continue to active. It's our right to win in agent governance. And then finally, sort of tackling it all the way to the end to success factors to say, listen, the total workforce discussion for our customers is going to be a pretty important one as it is for us, as Sebastian is going to talk about that, your total workforce is not humans, if not FTEs and contingent, but FTE's contingents and agents and how we brought that together with SuccessFactors is pretty unique. But finally, I'll stop at this and then hand over to the next presenter here is the Joule, the engagement layer. And what's our right to be in this. Now there's 2 points here that I want to talk about, right? One is there's a lot of feedback out there in the public domain on Joule. And I think some of it is actually fair. Now there are some good successes as well. But we know, as Christian talked about earlier that we had a lot of learnings in Joule. So if you look at what Joule is today and what we're now launching and already we have with some early customers is a cloud-based harness that completely reimagines Joule, both from a deployment experience perspective as well as the value perspective. Internally, honestly, we call it V10 because we thought the difference between what we have today in the market, B1 this was so massive and so redone that it's not even V2 or V3 or V4. Externally, we didn't brand it like that. Our product marketing folks prevailed on us, but this is a massive uplift. So you will hear feedback if you call a customer and say, "Hey, what do you think about Joule?" And I think some of it is actually fair. There's obviously success stories like Ericsson out there, too, but this is what we're completely reimagining. And then finally, we announced something called Joule work. This is that engagement layer, right? And the reason why we believe this is something we again have a right to have a significant market share in as we have over 300 million end users, right, that engage with our applications, so they're just in the cloud space. There's another few hundred depending on how you estimate in our on-premises estates in ECC. So for us to provide an interaction layer that understands those applications to say, hey, now we're actually solving our evils of the past because we have a lot of bad rep on user experience as well. You can argue in our earlier core ERP apps, is something that we believe that will resonate and is resonating with a lot of customers as well. Now I'll stop with this and hand over to, I think, Thomas, because I was having this conversation earlier. Listen, if you go out to the market now, and you pick any random pie customers to say, "Hey, I want to get your feedback or maybe any random [indiscernible] partners, right?" I think what you're going to find is a couple of things: One, the strategy that we outlined yesterday is a strategy that is not a vision. It's not Figma, it's not vaporware, right? We have a lot of early customers that through FTEs we've been working with the logos that you see here, the examples that you saw all day yesterday and what you're going to see today are people -- customers that we've been working with through this FTE program that we've had now for about a year to be able to go work with them. They have the experience on the new platform. The others, and there's a lot of them are still working on our previous platform because we're now just publicly announcing our new stock and mid-June is when we're going to scale out the rollout of it to any customer that wants it is part of our early adopters. So you're going to find 2 classes of feedback. And you need to be able to ask the question, "Hey, now, do you have the experience and the new one that was just announced at Sapphire? Or is it the old one?" And you need to be able to discriminate in your assessment to say, "Hey, what strategy are you outlining here?" And as as Christian pointed out, I think the industry AI part -- I didn't touch on it. Christian did, but it's a big, big focus for us. And this is where we're setting up an organization that have now thousands of FTEs from SAP with already deep understanding of industry and our products that are going to engage with customers at scale a majority of them in engagements to activate these domains and this industry AI as well. So now is when we're ramping up, hey, we've got the platform that we feel is now in some cases, better than best-in-class because it's using the best-in-class and adding our context that slide, if you remember on top of it, and you want to go unlock value if you hopefully, this makes sense. Now I'll hand it over to Thomas. Thank you.

Hello and also welcome from my side. And Dominic challenged me a little bit. If we can accelerate migrations by up to 50%, if I can accelerate my presentation up to 50% as well in light of the time. So let's give it a try. No, but I'm really looking forward to talk with you about how we evolve our go-to-market model in the company. And in order to do so, I think it's always good to basically remind ourselves which world and environment our customers are living because that's basically the expectations we want to not only meet but actually exceed. And when you look at the world, I think we all see the volatility, we all see uncertainties. We see that the world is changing faster than ever before. Geopolitical fragmentation, economic uncertainties, supply chain disruptions, and this is just compounding. And for sure, this is a pressure on all of our customers. In parallel, we see this little disruption called AI, but we also talk today a lot about that. So on the one hand side, there's cost pressure. On the other hand side, is how to get value of AI. And that's coming together. So the customer expectations are raising up. And for sure, they want to have faster outcomes because the pressure is high. The world is changing more quickly. But also here, it's clear that resiliency is becoming more important for them as well. Resiliency is one of the highest value driver they see. And then for sure, a huge pressure in the market about return on AI because everybody is piloting and POC-ing some of the AI capabilities, but also if you see the various research in the enterprise context, for sure, it's clear that it's a little more complex. And that's what we discussed the last 2 days here at Sapphire how we, as a company, want to help our customers to overcome it. But it also means for us, we need to adjust ourselves that we need to serve our customers in that world even better. And that's what we do and what we already prepared. So for sure, we focus on adoption services. So our services transformation really driving to get into an AI engine, a deployment engine for our customers, how we evolve our business model with consumption-based, outcome-based pricing in that sense. Also thinking about the consumption of value realization, which we see with all of the agents, all of the AI capabilities what we drive and also our responsibility to help our customer in doing so. And that's why we basically also elevated our services and support portfolio to the next level and included the AI capabilities and deployments as part of our success plans with our customers. That's also the reason why we brought together all the respective customer functions from presales, sales, post sales, service and support and cloud operations to really have a delivery engine for our customers to make that happen. And we said also, we simplify the entire customer engagement throughout the customer value journey. And this is, for sure, something where the customer experience is improving dramatically. It gives us also the opportunity to inject the relevant resources in the mix, and I will come to that as well as Christian [indiscernible] to it, how we leverage our technical skills on the services side, also in presales and sales to really drive these AI conversations with real prototypes, with POCs on the spot when we are at the customer because that's one of what [indiscernible]. But we should also ground ourselves where we are. I mean we talk about 440,000 customers. If we zoom in now to the Global Fortune 500, 91% of them are SAP customers. And 68% of them are already using SAP Business AI. That's the scale what we have. And scale for us is a critical component, which we for sure want to enable with our workforce. And if you think about this customer value group, which we brought together, touching the entire customer life cycle, as I mentioned, the seamless journey is something what we want to enable, but also think differently how we work. So also embracing AI and the best team we talk about how we internally embrace AI to serve our customers. For sure, we changed the entire motion about consumption-led models. And that's also, by the way, part of the bonus plans of all of the people in this organization. So harmonized KPI, which is fully consumed ACV on our customer side. also leveraging the deeper expertise and especially with AI, it's clear that we need to think about how can we embrace AI in a complex enterprise landscape. And that means we use our architects to make that happen. So for the deployment of AI, we leverage our colleagues as part of the success plans to really activate all of these cases for our customers. And that's, I think, a critical aspect of what we want to see. And then we talked for sure about also how we leverage AI itself to proactively guide our customers to proactively deflect tickets and support and the like to really put that on steroids. Now if you think about the growth lever, I think there's a common misunderstanding and believe that we only grow by basically migrating our on-premise customers to the cloud. This is wrong. And I will show you a little bit where we are here because actually, our growth is extremely diversified from new customers where we land and expand our existing Forshaw customers with our proud installed base, which we have, but also thinking about the cross and upsell based on the new portfolio based on the new innovation is what we see with data, but also the huge opportunity we have in sovereign cloud. And I also want to touch on that one a little bit in a second. Now if you think about purely the cloud growth from 2020 to 2025 and think about the new customers, the new logos, which we acquired, daily up to close to EUR 3 billion in cloud revenue, which but you also clearly see already here the indication how much cross and upsell is happening there. So our new cohorts, which are coming since 2020 are significantly accelerate with cross and upsell the revenue as well. What we also see, because our mid-market engine is really now at steroids where we also use the indirect channel partner territories. And here, you see some of the numbers, I mean, 1,000 partner territories more than 3,000 sales partners, which we have. And they also -- when you see the cloud revenue growth comparison of the indirect and the direction are really at speed. And that's what we see on aggregate, which means our grow with SAP offering for small and medium-sized enterprises, 4 start-ups, 4 scale-ups is extremely important for us. And also here, I mean, if you think about companies like Snowflake, Databricks, I mean for sure, they want to grow infinitively and which is the single only 1 ERP system on this planet, which you cannot outgrow, which scales infinite, it's SAP. Once they sign up with growth SAP, they never need to worry about any local market, 160 local markets, all legal regulations, all taxation system. You don't just do what you just can grow. When you even come to customers like NVIDIA, which shows how amazingly these systems can scale when Jensen said that he wants to do $1 trillion in revenue next year. This is 1 ERP system. This is just unlimited scale. And we saw the other customers on stage today, which clearly shows that this is a big advantage. So basically, you already see the contribution of 20% of our cloud revenue growth in the last 5 years coming from new customers. And we see with the SME share that there's even a huge potential for us based on our new portfolio to really accelerate the growth also of our net new customers. Now if you think about our installed base, if you think about a like-for-like comparison from the support to the cloud, then you see it is 2x multiplier. But actually, with rise with SAP, we're in the moment of the landing of the offering already do up and cross-sell in that motion. That leads up to the 3x potential what we see. But also here, you see -- if you think about the numbers, again, how much cross and upsell we do in our entire installed base. So it's a nice mix of new customers about customers migrating to the cloud. But even more aggressively the cross and upsell what we see across our installed base, which I believe is a huge opportunity. And you see some of the facts here on the right side as well with our customers use our cloud ERP and our BDC as a beta platform, then you see that 9 out of these 10 customers have more than 4 cloud solution in place. So you clearly see the drive, but also are starting this flywheel from AI data and applications. And with that also to cross-sell across the portfolio because to Muhammad's point, the more you in aggregate in this agentic AI world bring together, the more value it adds. So 1 plus 1 does not equal 2, but actually 3 in the world of AI and what we do here. Now how does it look like in our usual customer cases? For sure, we start with cloud ERP. We start with [indiscernible] with SAP conversation. And for sure, business transformation management components are already vehicle because for sure, to support the transformation and accelerate the migration. They need these tools like Signavio, Linux, which we directly also package together for our customers. But also in a rise migration, what is important for you to know, it's not just that we do on steel, and that's basically the flat line. Actually, we have customers like Bosch in Germany, they have more than 350 productive ERP systems. Now we can multiply that by 5 or 6 from a system landscape perspective. So we see thousands of ERP systems which we migrate into the cloud, which means you see the ramp and the journey over time, which means that the revenue is increasing over time even further. In the meantime, to give you some context, we operate more than 190,000 system [indiscernible] for [indiscernible] with SAP customer at scale. So it's the largest operating scale in the market and growing. And here, for sure, on top, we use already the business technology platform for all the customer extension, for the customer bill, the differentiation, the integration capabilities, which is part of that and further compounding where we then also see the business data cloud as the flywheel to really add more and more of our cloud LoB solutions on top of that. For sure, in order to help our customers, we started already our services transformation 2 years ago to really shift away from a traditional services business, but really going to an adoption engine to basically really focus on adoption and consumption for our customers. And that led also to the establishment of the new success plans, which we have in place. And here, we've embedded the activations. We've embedded it. We help our customers building up AI COEs. So it's all natural part. And also, we evolved our engagement model that with rises SAP, with our enterprise architects, we actually help our customers to get to that point. And for sure, with our agent-led migration and tool chain, we not only support the migration in modernization, but also the continuous innovation, the continuous transformation our customers do, and that's a huge advantage. Now when we talk about a reduction of the migration time and effort for 35% to 50%, that also means that we can also shift this capacity in order to support our go-to-market capabilities exactly as Christian mentioned, from the presales capabilities to the adoption capabilities, and we can handle that within this workforce transformation in itself. What is for sure, super important for us as well to handle that volume is that already 85% basically of tickets from our customers are reflected by self-service, by AI capabilities that the tickets are even not getting created. And that is with the scale of the growth we have with new logos is super critical for us. For the remaining tickets, which actually get opened, already 20% get actually fully solved by agentic AI. And actually, customer efforts so the customer satisfaction is go for our customers is even better as it would be a human so we see even a further customer experience improvement with AI. So AI is not only for the productivity, but also for the customer experience in itself. Now we talked about these migrations. And I think I don't want to go into details in light of the time, but think about this market of EUR 100 billion plus for SAP migrations. And for sure, with our [indiscernible] migration, which we just launched today and showed in the keynote how that looks like with the reduction potential which we have. We not only on the 1 hand side, get the benefit of additional revenue streams based on that, but also for sure, the customers are freeing up their budget. And that means they can also invest more into innovation results, which is a good thing. And another good thing is there was a lot of worry in the market about, oh, what is happening with the maintenance and yes, ECC maintenance is 2027. The extended maintenance is 2030. Now with AI, we can help our customers to accelerate these transformations into the new world. And that's also a benefit for our customers, which we leverage here big time. Another growth lever is Sorin cloud. And here, just to remind ourselves, we talk about 60,000 critical infrastructure customers. 23 NATO armies are running fully on SAP, just to give you some context where we are also in the defense industry. You've seen customers like Lockheed Martin on stage RTX, [indiscernible] across the world are betting on SAP, 9 out of 10 defense manufacturers here in the U.S. rely fully 100% on SAP. That's where we are. And because of our architecture because how we engineer the serenity, we can serve those markets. And I think this is a huge opportunity what we have. What is important is that if you think about serenity, what does it really mean? What's the definition of serenity. And what we offer, which is unique to us is on the one hand side, the data serenity, an operational sovereignty, a legal sovereignty, especially if you think about also Europe and Germany and the likes, and also technical sovereignty which is absolutely essential. We already delivered it in various forms of shapes. In the U.S., we have NS2. In Germany, we have a vehicle called Delos Cloud, but also we have capabilities like sovereign cloud on-site options, where even for really critical customers in highly defense activities, bring our Sorin cloud on-site on the customer's place with the physical security of this defense organization in itself. And this flexibility, what we have is based on our platform, which is on the one hand side, agnostic from an infrastructure, so can run on our own SAP infrastructure can run on all the hyperscalers, can run on partners in Germany like [indiscernible] or [indiscernible] Systems because this platform is super flexible and agnostic. On the other hand side, in this platform, based on our partnerships, we natively embed all the AI capabilities from here, Mr. [indiscernible] OpenAI in this platform to be able to only deliver our entire cloud portfolio, but also the agentic AI world, which our customers need. And that's differentiating us in the sovereign cloud. And that's for sure, a huge growth driver, which we see, especially based on the geopolitical evolution, what we see in this world. At the end of the day, I think the key aspect is always when we talk about customer and go-to-market that we actually talk with customers and get some insights from our customers first time about how they work with SAP, what they do with us in the cloud with AI specifically and here, it's actually my pleasure to welcome on stage the CIO of Ericsson, Marlin [indiscernible]. Good to have you here with us. And perhaps we start with the -- for the audience a little bit to set us a little bit up with kind of what triggered and started the Ericsson transformation and the challenges we were addressing on the outset results.

I think that your starting slide was actually spot on, right? We are running a quite traditional company. and our core business being hardware being network technology, fueling the global connectivity is quite cumbersome. There's very few customers, and they are struggling over their margins. So in order for us to be relevant, we needed to transform. So we started that kind of journey. And the same thing goes when you're having that kind of landscape of customers that you really need to accommodate, right, it also drives complexity internally. So the way we're operating was really hampering us from both safeguarding bottom lines, but of course, also be relevant towards our customer base.

Yes. No, absolutely. -- nothing about where you are today because I already mentioned earlier about what we achieved together. Can you describe a little bit the path how we got there. And also even more important, the status today, the outcomes you have and some of the learnings based on the journey which we have together.

No, great. As we started out, of course, it was more about the clean core and what can be utilized on that actually be a catalyst for necessary simplification and transformation. But as we have progressed of course, it's evident the trajectory of the development of AI is just massive. And of course, when we're talking about Gen AI and Agentic, there's a lot of things that need to be done. And I think that we have truly showcased how we are parking up on that. And I think the communication you did on the BDC last year, that was a missing piece of the puzzle because that was really the foundational element that kind of allowed us to fix the fragmentation that we had. So when we're looking into where we're at at the time being, starting point, I would say. There's still a lot of things that we are exploring and looking into, and we have high ambitions for this year together. But what we have done and what is in live practice is for instance, some really good agents within the HR domain. They are highly appreciated by all our employees and me being a manager also highly appreciated [indiscernible] it says a huge amount of time then there are more perhaps complex ones being implemented in the supply chain, where, of course, we can get substantial tangible values.

You mentioned already the AI and how you use it in supply chain in the employ space. Can you give us also a little bit more data about kind of what kind of numbers do we talk about here with the adoption of AI in your business. And with that, for sure, the employ experience and how the people in the daily work fundamentally start changing to work based on AI.

Of course, it's starting to change. And I think we are beyond that phase when it's more exploration. So all of our employees actually have access through SuccessFactors and other thing, which of course, is simplifying data. So personal productivity is obvious, right? That's already there. And then we have the agent build of different sorts, which is getting much more in the hands of the democratization of quite a few uses.

No, absolutely. So we've talked about more than 80,000 users who use it actually on a daily basis, and that's real today. And that's what you heard at appreciation, how people will start working differently. That's exactly what we want to do. And that's exactly where with our customers, we bring that not only to our portfolio, but as Muhammad and Christian shared, really also with these new capabilities now on steroids, which is certainly amazing. Now if you think about a little bit your churn, also a little bit ahead, what do you see in the future? Where do you see us playing also? You mentioned already data as well. So where do you see us in our joint journey also for the next couple of quarters to com.

I think, to be honest with you, we actually changed our data strategy. That was a necessary thing when we're looking into agent trade flows, right? But when you communicated the BDC last year, we actually fit things around. So we are depending heavily on your trajectory and road maps within the full domain of the autonomous enterprise, right? We are moving quickly. We have already quite a few things in line running in first quarter, but the ambition levels for '26 is that we're going to have more than 100 in live running. p

Well you heard that 100, but also, I think it's a good example because you mentioned, I mean, RISE with SAP, BDC, the flywheel with AI and how that's coming together in real life on a customer. And I think that is for me the perfect example how to drive that change. And just perhaps as a last question because I truly believe in the world we are living is also a little bit about -- it's about leadership, and we talked also about that. What do you think in such transformation now embracing AI in the enterprise and some customers for sure, struggle and you fundamentally changed this upside down in Ericsson to really be AI first and do all of that. So give us some context about also from a leadership perspective, what you see in the age of AI?

I think, firstly, it needs to be a commitment top down. So when you're talking about AI first, that can easily be something that you see as the [indiscernible] for it and then you're facing master resistant entire organization. So it's everything about how you're changing your metrics, your incentive models to actually drive a new set of behaviors and also allowing for a safe zone for people to explore and learn because if you're just saying that is for productivity to reduce workforce, of course, you're going to get that resistance. So I think that leadership in this never been more important. I think that is 1 of the key things that we need to add.

No, absolutely. No, first of all, thanks so much for sharing on stage here with us.

Thank you so much.

So I think what you have seen is here, AI is there is here now. driving value for our customers already now. And I think Ericsson is a perfect example. More than 80,000 people experience SAP's business AI every single day. I think it's a great testimony of the strategy, which also is again proving basically the topic about these various components, which we now bring together enable us to take that to the next level, which for sure from a go-to-market perspective is exciting because the new platform, the new autonomous suite will help us to even more simply scale through the customer base with the capabilities which we have. It's certainly an exciting time. And I'm certainly looking forward to work with all of our customers to bring our autonomous enterprise to life with them. We are ready to do that. We have the right organization in place. So I'm super excited about what's coming. And with that, thank you so much for your attention. I want to hand it over to Gina. Thank you.

Thank you, Thomas, and also good afternoon, I can say already also from my side. So actually, you have heard Christian presenting the strategies. So he was setting the strategy for the autonomous enterprise and my job is now to focus on what we are caring about next. And this is actually how to do a repeatable execution because we need to have the talent. We need to have the skills. But we also need to have the operating model in order to enable delivery at scale. So it all goes hand in hand because AI won't be a growth driver because it's powerful. AI will be a growth driver because we have to enable organizations, only then we are able to produce value. And autonomous enterprise means actually that we have no agents in place that do the work proactively end to end. And then you have humans in the loop that is making this the decisions and also the judgment because it's important that agents are operating in boundaries with clear accountabilities and also a clear governance around that. And this is actually why values created only when technology operating model, but also the workforce capabilities move in lockstep. And we have looked at that very holistically and I will also go into the pillars in a second with very concrete examples as well. Because to translate also AI investments into consistent results, we understand that we have to fundamentally rewire also how we as SAP run. It's an operating model shift. And it's not just putting AI as a layer -- on top as a layer. Christian already shared our growth formula when we talked about product times go-to-market times people times operations, and this is leading to AI led growth. And the CHRO, of course, I have to underscore the importance of our people because this is actually how we can then also unlock capacity speed but also the operating leverage. In order to achieve that, we have now built the transformation backbone, how we call it with 4 integrated pillars and this is all supported with our skilled foundation. Because with that, we can drive actually scalable and repeatable execution, but we can also expand execution efficiency, which is also extremely important. So let me quickly walk you through the 4 pillars. So the first pillar is very much about enablement and adoption, extremely important 1 because we are investing heavily in our employees through let's work the Away -- we are combining structure change management, together with hands-on enablement, which is important, that is not theoretical and abstract and also real tool usage in daily workflows because our employees have to test that have to experiment that have to use it in order to get adjusted and learn about that. And this is, of course, also reinforced and this targeted app and reskilling. We have put out a role-based and skill-based learning journeys and a lot of learning recommendations. And I will also speak about that in a second to make it even more concrete. The second pillar is extremely important as well. So it's all about structure and process redesign because organizational development is so important but when we are moving towards an autonomous enterprise because as humans and also agents work together hand in hand, we have to redesign the roads because now you have an overlap of tasks, agents will take over task. So we have to make sure that we know, okay, what is in the role and who is doing what, that you have a clear task it. But we also have to change organizational run models and also processes. So -- and when you are also redesigning everything that also requires that we have to look and reduce hierarchical complexities. This could also mean that we have -- it also includes actually that we have smaller teams, more agile teams with less decision makers. And this is also helping us to drive faster results and also have go-to-market speed. The third pillar, which is also important, is now strategic workforce planning. As I just said before, because now we have tartan accountabilities together between split between agents and also humans. And that's why we also have to make delivered by [indiscernible], build and also automate decisions. And then we have to say, okay, where do we invest now also in strategic AI skills. And [indiscernible] also includes simulations, actually, of workforce compositions, the capacity but we also have to look where are skill shifts happening and what is the right role and also location mix. So those are the questions we are also answering with the execution backbone. And this translates, of course, them into the right decision to say how do we do also talent shifts because we can't be combining here very clearly skills led hiring, but also we are buying skills with selective M&A activities. And we are also investing, as I said before, in target up and reskilling for our 110,000 employees that we are securing also critical AI talent and critical AI skills. So this goes hand-in-hand. It's all 3 that are important. And because skills are changing fast, we are always talking about a so-called skill flux. So because the duration of the shelf level of skills is only 6 to 18 months. We always need to have a system in place that gives us a current view of our capabilities. We were building now a 700 skill taxonomy. This skilled taxonomy, we have translated now into an updated drop in kill profile is extremely important. And we will also have that up and running in our SuccessFactors growth portfolio by middle of the year because we have to measure proficiency and we also have to see, okay, what is our scale inventory. You need the transparency. Otherwise, it's we are unable to drive the workforce transformation. In this -- and this data is also informing of the game, okay, where do we have to hire and where do we have to also improve our learning portfolio. So bottom line is we are reviving the organization to enable AI, but to also -- and building also the execution engine that makes AI outcomes also repeatable, extremely important. But now let's go and deep dive a little bit also into the pillars. So the first one is, as I said, AI transformation really fails on technology. It normally fails on the lack of adoption. And that's why we also drive enablement and adoption through our unified approach, how we call let's work the way campaign. So this transformation program is actually combining different formats of learning, different communication formats but also different change management formats. Because you have to drive the transformation at scale. And we don't -- we cannot stay actually with only localized or very fragmented usage of AI. So we have to make sure that we scale across the organization. And for that, everything starts also with communications. We have designed a global narrative that is also saying, okay, why is it so important for everyone at SAP -- for every employee SAP to use right, and to make sure that we are freeing up time also of our employees for higher value tasks. And we are also reinforcing it with our growth culture. Also, Christian talked about that, and I think we have proven we have a very strong culture that has proven that we were able to adjust for over 52 years meanwhile. But it's important to keep the culture strong and to always make sure that we are putting it into new context for our employees. And we are also doing that to apply performance management. So in 2025, and I think I spoke about it also last year on stage, we introduced the growth culture. And meanwhile, we have driven transformation journeys for more than 2,000 leaders, and we have also reached more than 15,000 employees in growth summits around the world in order to strengthen the innovation, the customer focus and also the impact. And one of the flagship promotes is also what we call grow with AI sessions alone this year, we have already reached more than 13,000 employees with these kind of formats. Those are formats where people come together and where we can also share experiences with AI usage with tools, but we are also using very focused change agents to speak about, okay, what are the barriers today in order to use AI? And what are the misperceptions also? Because it's a holistic approach. It's not just a tool usage and to understand, okay, what is possible. I think it's also important to engage the employees in these kind of conversations. And then from there, we say, no, we are scaling actually also the enablement with 3 levers. So the first lever is actually that we say is skilled learning. Skilled learning is, I said it before, that we have now AI-based and role-based skills and learning journeys. So we're investing heavily also in trainings. We have one example I would like to share this is the -- our AI developer program with 2 learning tracks. So the first 1 is tool developer. It's for developers who are then building and building extensions and integrations with Joule Studio. And then the second track is actually intelligent agents. This is for developers who are building autonomous agents or multi-agent systems. But we are also complementing formal learning with practical learning out of the projects we are running with our customers. And we are bringing that back together with our centralized AI customer research team in order to have practical learning and then bring it back into the project. Additionally, we also have launched just in January this year, our so-called Learning Navigator. This is a single point of entry for our AI training. We have meanwhile more than 49 learning journeys for the selected profiles, and we have more than skills who are directly linked to trainings. And we keep that also current actually this quarterly learning priorities. We also set out a target last year to say 15% of the learning of the working time should be also invested for learning. And we are complementing that on the one hand side, with quarterly learning priorities. We have also more than 3,000 AI courses internally and externally. And we will also make that adoption sustainable even further also after Sapphire because we are putting out 2 hours of protected learning time per week for our employees. Because this is the feedback we're receiving also in the employee services that give us more time to learn. So this is what we're doing because AI capability is a must. It's extremely important for everyone to learn and embrace how to use AI. The second pillar or the second lever, how we are also scaling is actually experimentation. So we are running code camps across SAP, codes camps enhanced on build sprints, where cross-functional teams are working actually on real business AI problems for our customers. And this is also a very practical way how we can transfer learning directly into impact for our customers. In addition, and I also said it before, people have to use it. So we have also released more than 200 tools for our employees. It's clearly linked to roles, but also to the workflows because it has to be relevant also for our employees. And those are tools like dual work or also Claude Code. The third lever is how we scale is actually through our multipliers. So this is also when it comes to change mine trend, when it also comes to large-scale adoption, it's a peer-to-peer influence. We have meanwhile an ambassador network with more than 9,000 ambassadors. We have, since this year, beginning of this year, increased that network by 60% alone. And this is also how we try to remove the adoption bottleneck. Let's come to the second pillar. And this is something where we really have to look on how can we wire SAP. We are experimenting, and I also say that very clearly. So this is something where we have pilots, and I will also speak about it in a second, but it's important that we have a very clear [indiscernible] star of how does an agentic company looks like. And how can we also design the operating model that is actually integrating humans at agents in the operating model. Because it's very clear that humans have to stay at the top and always have to stay in control. I think this is extremely clear. But when you look at the current models we are having, we have verticals, we have old boxes. And now we have to shift into layers because you also see -- saw before on the slides, how it works. So we have, on the one hand side, you have the so-called strategic decision layer. This is where the human is always in control and is also making the decisions and also decides, okay, what are the problems, what do the agents have to do and then we also have the second layer. And the second layer is how we call it the orchestration layer. So this is where Joule sits, and this is where the Joule is orchestrating the agents, and then you have the execution layer underneath. And the questions we are asking at the moment is, okay, how do we bring these 3 layers in swing. What do we have to change, especially on the strategic decision layer because this is where our team sits. And then we also have to ensure how do we integrate actually agents together with our teams. So the separation is, of course, deliberate because humans have to decide actually what and why we are doing things, Joule orchestrates and the how and then the agents actually execute at scale. But now the question is how do we actually transfer that now into our own organization. We have started with a pilot in Muhammad's organization. It's the pilot we are driving because in the P&E area, you can see that this is where work is changing extremely fast at the moment. And this is where we can also see where execution discipline is also most visible to our customers. So we have introduced last year, the so-called AI native harmonized product operating model in [indiscernible], which brings products, engineering and UX under 1 shared accountability. The point is here that we have fewer handoffs that we have faster decisions and also faster time to market. And that was the foundation actually to say, okay, how can we now evolve the system end to end? And how can we now integrate all the questions I have asked before. So how do all look like? How do skills look like? What do we have to infuse. How do we have to make sure that our people learn how do we put change formats in place and how do we also hire. And we have tightly connected everything in order to start shifting also the operating model in P&E. So first of all, of course, we had to look at the role of responsibility. So we have a deep pay design of the most critical engineering roles, engineering and technology roles with very explicit AI responsibilities and oversight and also here, we are already considering actually the impact of the tool usage but also the integration of agents. And then when you look at skills, we have now infused more than 30 AI skills into our skilled taxonomy because it is cleared taxonomy, what I mentioned before, the 700 is not static. So this is changing all the time. We are adding every quarter, we are adding skills, we are removing skills again because of the relevance. So just recently, we added 30 new AI skills also into the taxonomy. Those are skills like contact engineering, rapid field prototyping or also AIS is development. And everything is directly embedded into the role architecture. Year-to-date, when you look at learning and the P&E organization, we have more than 7,000 AI learning completions already recorded. We have run more than 65 AI experiments with real AI business problems. And we also have tested more than 73 tools that informs also the SAP wide adoption. And in June, we will also start rolling out a code camp's in Muhammad's organization in more than 26 locations in order to make sure that we're bringing our teams together and that we have hands on learning again. And then we also shifted the hiring, I'll also say a few words about that in a second. So we hire for AI critical skills through very targeted and very active, proactive sourcing AI hubs around the world. So we are hiring in Munich, Berlin, Singapore, Palo Alto, but also in Bangalore. And then change management. Also here, we have used existing formats already because it's important that the contact is relevant for the employees that we are not putting artificial change formats on top. So we have used existing formats in order to drive change. So actually, P&E is, for us, the foundation. It's true point for us. We are now seeing, okay, how is this evolving? Also with the Norstar I have explained before in mind. And then we will also roll that out across all other board areas, of course, with adjustments because for corporate functions, it looks differently than for the P&E organization, but there are some organizational design principles in common, and this is what we do. So let's look next also how does it look now for the overall workforce investment and mix. So strategic workforce planning is extremely important because we are now continuously analyzing actually how workforce implications look like. And we also have to make sure that we have the right role and skill mix in place because our internal agentic AI road map is now in place, and now we have to translate that how does it look like? How does it impact the workforce composition? How does that impact also the role compositions, which task is automated, which tasks are stay and how do we then have to redesign the roads. So first of all, I also want to share, okay, where are we now leaning in because we have to make strategic investments in certain profiles and just as an example, so we're investing here very clearly in machine learning, engineering. We're investing in data science, enterprise architects, but also business in data platform consultants. And then you also have roles that needs to be reshaped very clearly. So we have 1 example because autonomy increases, we have to see, okay, how is that role impacted and where do we have to invest to make sure that we are that we are redesigning the role, but that we also have the right learnings and the right training in place for our employees. So roads such as quality management and user assistance developers, supporting roads and shared service roads, those are roads that need to be reshaped when you also have the vision at the new North Star in mind. And then we also try to make that, of course, actionable. So how do we make that actionable. So we have defined now, on the one hand side, core AI skills that go across all roads, context engineering, AI assistive prototyping, iTrust and verification. You can see that on the right side of the slide. But we also have come up, as I said, with the AI skills that are now embedded in the role architecture of selected profiles. So here you see machine learning engineer, the skills we have embedded are, for example, genetic engineering, cemented with people or evaluation and benchmarking engineering. So this is important because we say, okay, how again, we translate that into learning, but we also translate that very clearly into hiring plans depending on how do we have to infuse the skills. And depending on the strategic workforce planning, do we have to also buy or build the capability of the market. So this is actually how how we create structural operating leverage. So -- and we are allocating capability to way AI I can scale execution. So now let me also share how are we now investing also in these profiles and how do we also approach the hiring. So we understand, of course, that we -- that AI execution requires also different talent in very specific roles. And this is just an example from the roads I showed before on the previous slide. And here, we have a twofold approach. We have 85% at the moment where we say we are doing up and reskilling. And 15% is where we invest in hiring of senior experts in the market in an expert talent I have spoken about up and reskilling, but I would love to speak about now how hiring has changed using the 4 profiles as an example. So first, what's very different in our hiring approach, and you saw it already on Christian's slide because we say we have a 3:1 ratio. That means that we are instead of hiring the developers or standard developers with standard skills, we are now investing also in top caliber AI talent because, on the 1 hand side, the productivity impact is meaningful and especially in development where we see roughly a 30% uplift already. But that doesn't mean that the cost base is going down because we have to invest in these care talent. And [indiscernible] talent is quite in high demand in the market. So we also have to make sure that we have premium salary packages in place. And that's why we are also redesigning also the com package at the moment in order to be competitive out there. But we also have shifted now the way we are recruiting our talent because we are coming from optimized cost location mix, and we are shifting towards a top AI talent market to secure these expert profiles. And we are also scaling seen an expert hiring where most directly drives build velocity, but also customer value organization. And you can see it also in the numbers already, how we are shifting and how we are changing our hiring. So you can see the numbers that our AI relevant senior hires have nearly tripled from '23 to 2026. And since 2025, we have made already 6,000-plus relevant hires in engineering and technology alone. And in data science and also machine learning engineering, we have seen a 200% increase in hiring just about the last 6 months. But we are also bringing in exceptional key AI executives because from the market because the caliber of talent is necessary to accelerate also our AI ambitions. And what is also important is that we are also building our own internal pipeline because even though we have -- we are more selective also in the volumes we are hiring, we absolutely firmly commit that we are also getting AI native talent into SAP through our academies and our vocational trainings. We have meanwhile in every vocational training academy program, AI installed also as a curricular as a strong pillar. And 80% of occasional trainings are already in IBD programs, especially for machine learning, engineering or data engineering. And what is also maintaining strong that we are also -- also this year saw in Christian slides that we are also collaborating with top-tier universities like Stanford, [indiscernible] or UC Irvine. And altogether, this is actually how we are also removing the talent constraint and to make sure that we're able to execute our autonomous enterprise. So with that, I would love to hand over to Sebastian. Thank you very much.

Thank you, Gina. By the way, I love what Gina showed not just because it's super important, but also because all of that is powered by our autonomous HCM solutions, and that's just 1 domain. But now, look, my job as a COO typically is to keep us on time and in budget. I have some good news to share on the letter, and I will make up some minutes on the former. So hello, everyone, right to be back. Now my real job is actually to turn SAP into an autonomous enterprise. And that means both in terms of making our vision for SAP and at SAP. So both in terms of what we commercialize for our customers, as well as how we run SAP itself. So let's first dive into the commercial side. You already heard from Christian about the gradual mix shift, incremental consumption growth on top of a very resilient subscription model. Let's click a bit deeper into these different models, starting with the subscription side. Now here, we see significant resilience already today. But you can see the majority of our subscription base already, so 55% is non-seat-based that's even excluding the consumption share. And there are some great examples here, like BRIM, billing and revenue innovation management measured on revenue spend. By the way, a great reminder that we are the company bank rolling the software industries, commercial infrastructure. So if anyone can be flexible for commercial metrics, that's definitely us. There are many more examples here. What I like to call out is success factors. Now you might say, oh, total employees, that's a disruption risk. I would say no, think of -- I mean, Starbucks presented down there with hundreds of thousands of employees at the front line. So even there, I would say, this is super resilient. And then on the cloud ERP side, by the way, we have a battle proven model that has been driving automation for decades and decades and decades, but the dominant share is also coming from nonseat-based components already. So let's move on to the consumption side. Christian and Thomas already talked about accelerating consumption growth. You will see that both on the autonomous suite layer that Muhammad's described, so through consumption of Joule's systems and agents as well as on the business AI platform layer where we already see significant growth in things like AI and BC. Now how do we do that? That's not just a product strategy thing. It's actually we are turning the entire company already since years frankly, in our field incentives and our ecosystem investment in simplified commercial models and so many, many more levers to ensure we see this accelerating consumption growth, and we are on a great track here already. Now third, that's an area I'm personally very passionate about there's a lot of talk about outcome or value-based pricing. And that's where we will see where we will actually monetize our most premium offerings. And to give 2 examples here, Thomas talked about our migration assistance. This is where we are pricing based on the proven outcome that we show our customers how we can actually reduce the migration cost. And think about the trillions in services TAM we address with that. Second example is industry AI. Here's where we deliver autonomous outcomes. Think of an autonomous batch release for a pharma company. We talked about yesterday, an autonomous maintenance ticket closed for an oil and gas company. So in a nutshell, here, we will be monetizing on a value basis. We need the outcomes and savings that we prove to deliver to our customers. So that's it on the commercial model. Now let's turn into how we actually turn SAP itself into an autonomous enterprise. And let me start with a very clear statement here. You heard us talk about investments but our commitment remains absolutely clear. We are continuing to drive increased operating leverage with expenses growing 80% to 90% as a percentage of revenue growth with continued improvements across all key KPIs, decoupling the expense growth from the revenue growth, and that's despite significant AI investment. Now how are we going to do this? First of basically by demonstrating that our AI is how we scale SAP profitably by actually also simplifying SAP tech, for example, the operations function since I took over, we drove for more than 30% productivity in that function, but really then by turning SAP into an autonomous enterprise itself. My team is fully focused on making this vision real, transforming our internal processes and driving AI adoption, acting as our own customer 0. Our ambition here is clearly that a lot of simple tasks and executional tasks are going to be executed autonomously for SAP so that our employees can focus on the highest value work. We already see a triple high triple-digit amount of value realized in budgets today, and we are committed to deliver over 2 billion in productivity by 2028. And that's a value that has already measurably increased since we first talked about it. I think it was in Q4 2025 based on basically what we showed you yesterday, the significant jump we've made in the AI, we are shipping to our customers as well. And that means delivering productivity gains in every single function, north of and sometimes significantly north of 20%. Now let's take a closer look at some of these domains starting, well, how could it be different for SAP with finance and spend where we see a clear head start, I guess, that's no surprise. You know Dominik. From planning to risk and compliance to invoicing and reporting. Our AI assistance and agents are already taking over strictly governed process execution. And the operational impact we do already see is material. We see 45% productivity gain in Contract Management, over 40% time reduction from offer to collected cash. And not to mention there, the significant jump in intelligence, we can arm our people with when planning by making every finance professional, a business data cloud user. Let's look into 1 example, the financial planning assistant, which supports budgeting and forecasting, detect margin compression risks early identifies actions to protect profitability or increase top line, collaborating agent surface here, actionable financial insights, uncover cost and margin drivers and enable data-driven recovery scenarios driving 35% or more productivity gain in planning, which leads to more planning cycles, which leads to much better decisions and much more accurate and real-time planning. Now let's look into the development side of the house. So here, of course, we are deploying AI tooling jewel agents and our own business AI platform as well as best-in-class third-party tools. And Muhammad talked about it in a nutshell, what we see is a compression from starting to build an agent to GA from month and month to a few days. How does that work? Well, our own team builds our agents with Joule Studio already with the agent buildup for low code and pro code development. Every developer SAP has access, for example, to Claude Code. Claude code via our own agent hub on top of -- sorry, AI Hub on top of BTB, using our Joule SDK for Pro code development. And then, of course, our other developers have tool for developers as well. Then we contextualize these agents with easy access to business data Claude more than 300 data products available to all of our developers and our knowledge graph. So it's a super fast process and honestly pretty full proof. So it really accelerates how we build and ship our own agents like it is accelerating how our customers and partners are going to extend and ship new agents. And then on the governance side, that's basically typically in the development process, the hardest work, and Muhammad already talked about it, it's basically taken care of in the majority for agent development for our teams based on the AI agent hub, which takes over the majority of life cycle tasks, so you don't have to build it. So the intent is clear. It's basically not to reduce our R&D ambitions or R&D investments but to unlock more innovation for every euro we invest into the R&D function. Now Gina talked about the workforce side of the house. We are already operating here a highly, highly efficient shared service center set up, by the way, saying true for finance and many other areas. Now in autonomous HCM, the objective is really to elevate the employee experience as while taking away burdening administrative tasks. Our AI assistant support the full employee life cycle. And the impact is adding up quickly. So around 15 hours saved for simple onboarding per year, 25% productivity gains for performance preparation tasks, 80,000 hours saved an early rollout across as many, many operational HR workflows already within SAP. And with that, we are really turning Genus function into a truly experienced lab high-productivity HR function. A good example here is our career and talent developments. By the way, all of that is either live or in rollout within SAP. It supports creation of development plans. It automates talent discovery. You now talked about how critical that is for us as it is critical for many others and proactively build succession plans and the impact is tangible. We already saved more than 45,000 hours per year across SAP, and that's in the initial rollout phase. So we don't even yet see the full productivity impact. That results not only in productivity, but in a much more personalized employee experience that gives additional productivity gains and enables us to do better talent planning. Now last but not least, that's my favorite area of productivity and AI, autonomous customer experience. So -- our focus here is really on optimizing the end-to-end customer journey from demand creation to adoption and expansion. The journey Tom has talked about. Juul becomes really our single engagement layer already for our customer-facing colleagues save more than 15% for their preparation and coordination of customer meetings. Our consultants save 1.5 hours per day roughly with AI guidance on implementations and consulting activities, account executives gain more than 20% productivity in lead qualification. Ultimately, really, what we do is we free up time of our customer-facing teams to do what they should do, spending time customer. But way it's also a great example here how this AI layer and the underlying architecture layer, fit together because we are continuously also modernizing our internal stack here. We just went live with a new version of CPQ, our own product for quoting, which actually led to reduction in quoting time and time to produce a quote within now AI agents coming on top to further reduce the time spent on quoting, which is a TDS activity, as you can imagine, especially for large and complex customer. Take one example. Thomas already touched on it, something I'm very proud of, which my team in SAP's IT build on top of SAP AI platform with really the golden part we provide for Pro code-based development through the Joule SDK in this case. And basically, what you see here is how we build a fully autonomous software support multi-agent system. It has, I think, 26 agents knowledge sources, all of that connected through Business Data Cloud, all of that built on BTP. It has human in the loop capabilities. And what we achieved with that basically of the 2 million cases that we don't reflect, we get more than 10 million cases a year. And you can imagine that stack was already highly, highly optimized. By now 100% of these remaining 2 million tickets get a very strong recommendation from Joule already for our technicians. And actually, 20% of these hardest to solve tickets are now solved fully autonomously. And that's early rollout. So you can imagine the productivity gains as well as and Thomas mentioned that the improvements actually we are seeing even in customer experience goes on support at the same time. So one thing to close off is also very clear for us, you cannot have autonomy without governance. That's why we have established a strengthen and holistic AI life cycle and value management within SAP with our business transformation portfolio. We manage more than 300 AI use cases cutting across more than 2,000 processes within SAP with an average time to value 3 months. And that comes with rigorous value management, so I can commit more than $2 billion to Dominik in productivity, but also with rigorous change management in partnership with Gina in how we roll out these capabilities, using all of our own tools to do this. And these productivity gains with that are not just claimed but realized measured and continuously improved across hundreds of use cases. So I hope you take away from this short presentation, we are firmly on the journey of running SAP itself as an autonomous enterprise. The very same vision we provide to our customers is what we are driving internally at SAP as well. Thank you. And with that, over to Dominik.

So also a warm welcome from my side. It's great to have so many of you here. It has been a pretty wild ride over last year. So let me just kicking off now with some numbers. And then I go to a more conceptual part about the autonomous enterprise for finance, my board area. And last but not least, and I want to also go back to the numbers part. So the first point I want to make is, while there has been a roller coaster on some metrics like our share price, there has been actually a great stability in what at least you, the sell side sees as the free cash flow estimate for next year. It's actually slightly up from last year here and at a weaker U.S. dollar exchange rate. And there is one key number that I just calculated this morning, our free cash flow yield on 2027 free cash flow estimates is at about 7% now. That's up from 3% 1 year ago. And that's interesting because when I then -- Chris, the Investor Relations department, what is actually our weighted average cost of capital because I want to back solve what's the growth assumption SAP. I hear it's between 7%, 11% and the median and the mean is pretty exactly 9%. So if I deduct that 7% free cash flow yield, from the 9% weighted cost of capital, I think go back to business school, Corporate Finance 101. I see the implied pet growth rate is 2% nominal and that's a negative absolute growth rate in the perpetuity. So basically, we get a very clear message from the market. You're going to be kind of disappearing in the world economy, while the economy is growing at 2.5% -- real, your real 0 or shrinking. And now what I want to do is share with you why I'm not convinced about this hypothesis from my point of view. So I want to sneak first in the shoes of somebody who has been a CFO role for a large group for now in my 16th year and not only at SAP but also before and share with you how we view the world and what the component can do to us. First of all, I think we need to conceptualize 2 hemispheres in what is a finance department. And I want to characterize 1 as deterministic. And what is that kind of deterministic world. It is a world of very clear rules, standards, transparency, compliance, auditability, internal controls IT general controls, ensuring that you have a high degree of repeatability that when there is a certain input exactly the same output will come. And you would not be surprised that this deterministic world is where CFOs like myself, really feel at ease where audit committees feel at ease because this gives you the assurance that critical processes are run properly. Think about producing the financial statements, you are all looking to the tax returns. And by the way, in the Executive Board in Germany, all of us signed with our blood that these documents are correct. And in U.S., the CEO and the CFO are signing that. So there is just no tolerance for risk in these processes. And this is really what is the absolute key priority of any finance department. I always say various kind of jokingly staying out of jail. We want to just make sure that the assurance levels are really met. And by the way, sometimes we have some errors. These errors tend to be always related to the more probabilistic world. I come to later, maybe humans doing stuff. And I can tell you when the small errors occur, luckily, so far, nothing material. The Audit Committee is reminding us in a very stringent fashion about the necessity to have proper internal controls in place throughout this enterprise. And I think it's not only the audit committees that should care about it, but investors should care too because after [indiscernible], I think it showed that these internal controls have a certain meaning. Now comes the fun part for CFO is the probabilistic world. There's no fixed rules. This is kind of the exception handling thing. And there's a lot of creativity in here, tribal knowledge, test acknowledge you require subtle reasoning. Inputs are unstructured. So what do I mean unstructured inputs. Think about us quoting to a customer. When we do that, then suddenly exceptions are popping up. So the nice kind of regular process that has been described before is busted because some deviations occur, what could be these deviations. Let me start with one extremely unstructured example. It could be as little as a rumor from somebody that this is what Christian client has promised to some customer at some point in time or even a Board member who is not even there anymore or this is the pricing we had whispered into the years, 5 years ago to somebody in the supply chain or some professional that was in another company leading to another company and now that kind of knowledge about pricing has migrated to another company. So I think you can all agree that it's very hard to put that kind of fluffy stuff into a deterministic process. And this is why we have a significant amount of work. I would actually venture the lion's share of the work in a well-organized finance department like an SAP on these exceptions. These are what I call the exotic animals that are put in our Zoo which are really hard to deal with. So -- and by the way, this is a compliance risk. It's extremely difficult to put controls around it, proper authorizations. And yes, that's where AI plays such a huge role suddenly, there is a recipe how we can even deal with these much more complicated topics in finance, but it requires a certain framework of guard rates. The key point I want to make here is that it's not about either classical software deterministic stuff or AI. It's really the fusion of the two, which I would call is really the solution to squaring the circle of a flood of assurance requirements, new regulations, the scrutiny we are under and the efficiency requirements that we are all subjected to because of course, productivity is absolutely paramount. So we have to combine that kind of neural part of the world which the more algorithmic deterministic symbolic part of the world. And in order to come to that autonomous enterprise, we have to really make sure that the software is not a tool you work with anymore, but it's really becoming a tool that works for you. So the systems that will achieve that are the ones which connect that neural intelligence I just described in the kind of probabilistic world with the hard reality of what we require in finance in the deterministic world. And I can assure you I'd say, if I think about my mission as a CFO, it's kind of chasing everything that's fluffy and probabilistic and moving it to the deterministic world to have no issues to make it cheaper and all these type of things. So let me share some deep convictions we have on that front. The A models are not substitute for traditional deterministic systems, and I give you 3 reasons for that. First of all, I mentioned all the shortcomings of probabilistic in what trouble they kind of into. Secondly, simply economics. Once you've cracked the nut of putting something into a workflow in a deterministic way, the marginal cost of getting the job done is fat. In contrast to a kind of prompting process, which is regarding a lot of GPUs, a lot of kind of compute power and who knows where the cost of the token will need to go at some point in time to amortize the kind of trillions of investment that is currently bold into that machinery. Third point, very trivial, but important. This stuff preexists. Our customers are really busy. They want to prioritize on what's adding value on what's differentiating the enterprise. They don't want to reinvent the wheel on something that's already working quite properly at almost no cost. So it's also that this kind of preexisting knowledge what has embarked in our systems is not publicly available. So yes, of course, you can run pretraining on data. But in this part of the world, it's kind of hidden in every single customer, and we can then aggregate, of course, the findings of this data in these systems. So that's, again, the reason why the deterministic world will not disappear and will continue to use the software. Next point is the quality of the agents really depends on the quality of the underlying systems. And I would venture to say that because of the data gravity in our systems and because of the fact that we are triggering so many hard monetary or other transactions in the company. The SAP system is like the Alpha and Omega of many of the transactions of the lion's share of transactions that enterprises are actually going through. So not surprisingly, we have already the data gravity now we extend it with BDC. On the other hand, we have the transaction point on many of these transactions, and now it's about inserting that probabilistic model in that flow to also create that very autonomous user experience that has been highlighted by the colleagues here in such a powerful way. So it's all about having the quality of the data, context which having a golden record and [indiscernible] also, of course, is exactly in that spirit, it was mentioned before. It's also about the governance of the system access. I mean who can see what, who is governing the whole discussion and the guardrails we need, and that's a very important point that is often overlooked. If you look at the lion's share of [indiscernible] at [indiscernible] today, these are often 1 shot type of transaction. So it's a customer calling a call center and saying, what do I need? And then it's over. If you make a mistake here, you've spoiled that 1 transaction. In these processes, we run in finance, we have often multistep transactions. So there's many things from a quote all the way to the cash in all the way to the financial disclosure where when you only make 1 mistake in 1 step of this huge chain, it's over, you have the wrong number in the disclosure. So what that all means is that the probability of failure in 1 single step is compounding through the processes like in automotive manufacturing, when you have 1 part that is wrong, the whole car is broken. And that's very different from this 1 shop processes, vibe coding is a one-shot process. You vibe code, you get a product. It's not a black box. You can test it. That's very different in the type of world in this kind of transactional long chain world that we are exposed to in financing. Third one is on the probabilistic side, yes, it's absolutely super useful to use these large language models. There are good ones out there, we can use. But we also have now this Rapid 1 model on large tabular data, which is, again, proprietary SAP data in SAP systems, and now we extend that with the third-party data by virtue of the acquisition we've recently done. I don't need to go in the details. I think Muhammad has really explained that extremely well. And last but not least, and maybe most importantly, I think it's a huge mistake to think that wipe coding is automatically the same thing as creating enterprise-grade ERP system engineering. We benefit a lot, as has been shown before. We gained huge productivity with that. But the new long pole in the tent is really to making it enterprise great to put what I call a trust trip around it to satisfy all the club compliance requirements to govern it properly. It's almost like an insurance policy for the customer. And by the way, we see very little customers, especially in this highly sensitive area where they come to us and say, "Look, I would love to do all that stuff myself to the contrary." They want to put everything they can under that SAP trust wrapper to say, this is the thing, this is my insurance policy. These processes are all auditable, that they can be traced that we have documentation around them that when law is changing in a certain country, we know that it has been updated accordingly. And also, it's a floor to think that the cost of development is purely the functional code development. It's then that whole hardening in an enterprise context, plus the maintenance is a moving target. A lot of the parameters that make it hard are changing over time, like legislation, cyber attacks and so forth and the higher share of the total cost of ownership of the software is actually in that maintenance cycle. So here, I summarize some topics where you can see how we think about it in finance. I don't want to go through this because we are already far beyond time, but I hope I made the point clear that actually what we have to achieve in I also wanted to be modern and use an AI agent to coin something is that kind of symbiosis that neuro-symbolic system of action to become an autonomous enterprise. Now back to numbers. We keep that very short because sorry to say, not much new stuff. It's pretty much the same thing we have told you last year. Here, just a summary, 1 year added, the CAGR on cloud revenues over that time frame has been 23%. And if you focus on what is really the center of gravity of our activity, which is the fast-growing SaaS pass layer. You know that Infrastructure as a Service is phasing out. The only specialty, so to speak, we're going to push is the infrastructure for sovereign applications. And also now looking a little bit in the forward indicators that give us a glimpse into the future I mean the CCB is growing healthy, 24% and the TCB is growing even faster at 34%. That means the ratio between TCB and CCB is actually expanding and this is not only driven by longer deal duration. It's also driven by steeper ramps. I think you heard us talk a lot about that in the last quarterly calls. So we think that actually the numbers that we can produce are giving a lot of substance to the growth story, the TCB growth inflection, so to speak, is good for a couple of percentage points acceleration and helping us, so to speak, to offset that kind of expanding base. We have to compare ourselves against them. It's, of course, more difficult to generate that ultra-high growth when the cloud revenue base is growing massively. Let me quickly double-click on our support revenues -- these support revenues have come down by around about $1 billion over the year 2025. And you see that we continue to believe that over the time frame through 2030, we should see that maintenance base be cut in half, and this is predominantly related to the fact that, of course, as mentioned before, we continue to see the end of maintenance for ECC and older versions of SAP, the mainstream maintenance, '27 and then the extended maintenance 2030. There's 1 thing I want to highlight on top, which is that actually the ratio of conversion today is about 2/3 coming from ECC and 1/3 coming from S/4. So we're not only converting legacy directly to rise, but also the S/4 is going more and more to rise and when customers start the journey, they're not taking the detour through S/4 anymore. They're really going straight to the rise offering on S/4. In most cases, why? Because of what had been depicted before the processes themselves are massively reengineered with AI. So if you do the blueprinting of an S/4 transformation and you go on-prem, you're basically barking up a little bit the wrong tree because you have to design on processes that one of the ideal processes once you're in cloud, and it's not very efficient to make that big detour and kind of try to cross the Canyon into steps. So that is why you see also an acceleration there, which I think bodes well for the sustainability of our conversion story because it's actually also tracking well on S/4. Margin-wise, I think the most important message here is that this 80% to 90% operating leverage, as we call it, I see, i.e., the total expense growth versus the revenue growth is still intact. And Sebastian mentioned the more than 2 billion AI efficiencies will embark over the next years through 2028. And if you think about that and you then correlate it with that ratio you can roughly estimate that there is about a kind of mid-single-digit billion increment on OpEx. If you assume on top of the gross margin is more or less stable, if you say that's the operating leverage will not come much from the gross margin but comes more from operating expenses, but that's a fair assumption to take. So we have actually a lot of incremental firepower to drive our cloud to AI transformation in a very rapid way. And also to do tuck-ins and you would not be surprised, and you will see the numbers in the not-too-distant future, that if you -- the last 3 tuck-ins, they were kind of depending on which 1 you look at more or less early stage. We are talking about transactions that are dilutive that have a J-curve, but this will not bring us to de-commit from these numbers, but we create the efficiencies to fund that aggressive acceleration of our road map on cloud. So last but not least, cash conversion, always near to my heart. As you know, we don't see any deviations in the model. Yes, there is a little bit of investment from the sovereign cloud topics we talked about, yes, there is some headwind from higher hardware prices. But given the size of this operation, again, we will offset that in other parts of the business. We are still working on cash conversion as we have done in the past, and we will continue to grind that to make sure that we can adhere to these rules. If you depollute for the restructuring we had in 2024, there is like a 80%-ish cash conversion, which is the free cash flow divided by the non-IFRS operating profit. And the way you have to think about it is that we basically slam the tax rate on our EBIT and then add back the delta between stock-based comp and what we accrued in the P&L for stock-based compensation. cash out versus P&L, and this gets you to these numbers. And this is also the reason why we think actually we are going to grind up further on the famous Rule of 40 target that we have set a while ago. Capital allocation, no change on the policy, a very prudent allocation in terms of M&A. And I say we are not buying growth per se in M&A. That's not the idea. It's really about buying technologies, complementary capabilities that are really accelerating the time to market. You can often then just debate, is it a kind of make or buy an opportunity here, but time is really of the essence. So I hope you also understand that this is extremely useful to complement our capabilities to really become credible and execute on the autonomous enterprise. No change on our policy in terms of rating. We like that super conservative balance sheet we have. especially with some big risks looming. I mean, who knows how long this [indiscernible] will still be shut and what's happening if it's shut for too long. It's good to have a strong balance sheet because really, we want to execute on our strategy and don't want to be bogged down by leverage topics or anything of that nature in case something really bad happens. Let's all hope it doesn't. But SAP is fully prepared for that, too. Share buyback, we have announced EUR 1 billion after the smaller program. We had in the past about EUR 5 billion. We have stepped it up to EUR 10 billion. We've done 1/4 of that already. And now the remain to do, which is about 3/4 of the EUR 10 billion will become quite linear. We don't speculate on the share price here. We just do that in a very disciplined, rigorous fashion over the next 1.5 years. So to conclude and bring it all back together, you see a CFO here who is a little bit desperate to see that market is telling me this company is going to decline in real terms because it's not at all what we plan. I have been always pushing back on giving long-term guidance on growth. Some people said, "Dominik, why can't you say it's kind of mid-teens is the new norm?" now we are in a completely different environment where everybody says, well, will this business still be there? I hope the entire Executive Board has been able to give you some feeling why we have a deep conviction that we have the ability to grow significantly in real terms for long. This is a sustainable enterprise. And now I don't want to be any longer between you and your lunch. We will do the lunch very quickly because we have been spending more time with all the passion we have about the topic here. And then we will open up for Q&A after you grab your lunch boxes and maybe we can reconvene here to then continue the session. Thank you so much. [Break]

Please welcome back to the stage the SAP Executive Board.

Welcome back, everyone. I hope you had a good quick lunch. Also, thank you for the Executive Board to be with me on stage for an interactive Q&A session. Muhammad is actually joining us in 1 second. So we're now going to open it up to questions from you in the audience. So if you would like -- you would like to ask a question, please raise your hand. And we have mic runners here to bring you the mic. Okay, let's get started with Adam, please.

It's Adam Wood from Morgan Stanley. Maybe if I could dig in a little bit around how SAP is using its own technology. And I wanted to start the big discussion we have with investors is we're seeing the Frontier labs massively monetize what they're doing, seeing kind of unprecedented acceleration. And we're not seeing that happen within a lot of the enterprise software companies at least today. Could you talk about your use of your own technology? What's your experience been in terms of frontier lab usage, your own software. If you were paying for your own software, how far away would you be from accelerating your spend with SAP? And then could you talk a little bit around how you think about cost and return on investment, both from your own software internally, but also what tokens are costing and how you monitor and manage that whole usage of technology internally and whether you feel you're getting a return right? It feels like you're being judged a little bit differently in terms of purchasing decisions than maybe some of the other players in the market.

Maybe I start. Thanks, Adam. So first of all, I mean, I showed some of the cases we have. We have over 300 cases. We see significant productivity and that productivity of north of EUR 2 billion we committed to you and is committed to our budget. And we see that actually across every function, I mean, we see a significant -- the north of 20% productivity lift we expect from go-to-market to corporate functions to development. And then if you compare usual productivity tools then the cloud code of this world and what we see, actually, it's a good healthy mix development. I think all of our people have C code. I know all of our people have C code available through BTP, by the way, which takes care of a lot of the governance, IP concerns and so on that we have. So that's a very good model. But the real productivity but what we found is actually when our developers worked on enterprise-grade code, this is now by coding, that actually the productivity gain was still there, but the real uplift came when we now infuse things like the domain models Muhammad talked about, made our KG available, the agent life cycle management. Actually, in many cases, now the productivity bottleneck becomes not so much the coding itself. I would argue in most cases, that has never really been the bottleneck. It's actually all the approval processes that come after data protection, security and so on. But with what we are shipping in the -- with through Studio 2.0 and the SDK that goes with it, take care of a lot of that for our internal development teams. And with that, what we've seen is actually a significant uplift on top of frontier led AI model usage. So based on measured productivity. And we see right now, I think a good measure, I can say, on data pull request. I'm not saying that's the natural yardstick. We see a significantly north of 30% acceleration in terms of development velocity that we are seeing. But then I always like to say the amount of unwritten software in the world is infinite. So I think this unlocks great opportunities for us to provide more solutions like in the industry AI space. And then we have a full road map. So every function of SAP is co-innovating with Muhammad's team, Dominik's team, Gina's team to make sure the jewel assistance and agents we are shipping actually solve real problems for us and deliver real and measurable productivity.

I just want to add, let's not forget that the autonomous enterprise is, of course, a very, very ambitious goal because we have these complex processes I described where the compliance and assurance and quality requirements are just excruciating and you don't have the same easy ability for giving ability to mark up code that you have generated by one of these coding tools because that's a human being can easily do that. So I think it's a little bit like the autonomous car where once you get to these high stakes, of course, it takes longer to activate it. And I would venture to say that today's Frontier labs, none of them has any meaningful big revenues on these more complex food chains we discussed here. But the ones we also massively use like coding, like writing text. So the things that human beings can easily control as opposed to a big well-oiled machine where you really have to make sure that it's kind of end-to-end, high-quality, high fidelity, and that's the focus. And we're going for that more ambitious goal, and that's why it takes a little bit.

So I mean, we looked at that. I cannot disclose the numbers, but we are a right customer. We, of course, use our full suite. And if you go across AI and BDC, it would be a measurable, measurable uplift we would see on top of that. If we were a paying customer luckily, I don't have to pay usually for our own software. But yes, absolutely. And I think that's also one of the sessions where we had a session on SAP runs SAP. I'm not sure if you joined. Those were some of the best booked sessions where we actually show what we are using within SAP and what's working for us and how do we make it work. So we would pay a significant uplift on our [indiscernible] and AI assistance already today.

Okay. Let's go to Toby, please.

It's Toby from JPMorgan. Maybe just on the cloud revenue evolution chart. I think you showed that the consumption mix is about 10% today, and you sort of expect that to ramp up to over 30% by 2030. I think if we look at that on absolute numbers, that could imply quite a significant scaling in terms of absolute revenue. So could you perhaps talk through specifically what are you -- what gives you the confidence sort of in that consumption ramp? And any more detail you can share around the big kind of product components that would drive that sort of ramp in consumption revenue?

Yes. First, a small comment on the financial part. The key message we want to get across on the financial part is that this is not a massive impact like we had on the cloud transition where suddenly a lot of revenues are cannibalized and then we shift all the revenues to the right because we move from license. This is a very gradual movement where we have the growth in the market and on top of that, a replacement of seat-based revenues or other types of revenues by more consumption because indeed, it doesn't make sense to have a seat-based model when you are reducing the number of seats by virtue of the tools. I honestly think that the concern is a little bit overblown because what really matters is can you deliver some differentiated capabilities to the customer that create value at the customer. And if you have that leverage with the customer because they want to have access to that value, then you can discuss a new monetization model with the customer, and you will be able to get your fair pound of flesh. If you don't have that product, it's a little bit theoretical to think about seat-based, consumption-based, whatever. But if you have that leverage and have a differentiated product, and if the customer cannot do it more cheaply himself or herself, that's the floor, so to speak, then I think we have definitely the ability to reconfigure our monetization model. Now on the growth trajectory, maybe Thomas, do you want to?

I mean, absolutely, what you see, what I've showed in the slide. First and foremost, we see with the business AI platform for sure, the consumption-based scale in that sense. So the cross and upsell within. And that's something if you think about the more data, which is processed, the more agents which are running, this is a compounding effect. And as you see the cross and upsell potential in this business AI platform capabilities, this is for sure a huge opportunity for us for our customers. So basically get the majority of the consumption-based revenue. For sure, on top of that, we also talk about some of the large-scale RISE transformation, which is also in a consumption-based model as well, which for sure will continue to continue. And that gives us, quite frankly, a lot of confidence that this is exactly in that direction. And we see, I mean also talking here on the floor, I mean, there's huge excitement about the potential by bringing all of these things together in this flywheel, what we described earlier.

Yes. And maybe one last word, what was actually, for me, a little bit of a reminder about our cloud transformation 5 years ago. I can still remember when we announced RISE with SAP, many partners came and customers and said, "Oh, now it's for the first time, also understand how can I leverage better my contracts, my consumption commits with the hyperscalers." I met here a lot of partners who said, oh, I signed this consumption commit with [indiscernible] with Entropic. And now I see, oh, I can use this on your platform. I can actually drive consumption on your platform. Plus, of course, now I see how I get the context and the governance part into the agents. And so let's not forget that also to Adam's question that there is always what we are selling is the end-to-end. We sell the LLM plus the governance and the context part.

Jackson Ader at KeyBanc Capital Markets. The one that I had was, if I do some envelope math on the cloud starting point for revenue in 2020, take out the migrations, new customers, whatever, to the EUR 21 billion that you ended for 2025, it's, call it, like a doubling, right, of that like EUR 7.6 billion. So if we think -- I'm not asking like -- I'm not going to like keep you to it. But if we think about like the new base, right, '21 in 2025, is there -- understanding there's law of large numbers, like should AI be an accelerant? Should we expect a shorter -- like a compressed time to doubling that cloud revenue because these AI products in a consumption model? Just curious how we're thinking about like this new base in the next 5 years.

You know my notorious reluctance to do a guidance on revenues because if you think back, we committed to say we see some acceleration in '26, '27, then we had 3 impacts. We had the trade dispute, then we had the Iran situation. And we also had this shift of saying we are going to, in '26, reduce the hours we bill in services and use more hours to adopt. So that teaches you that you have to be always very careful to say that this is what it is. The very strong point we can make and it's trivial is that we can't see a scenario where this company would shrink in real terms. But to the contrary, it's more a question about how do we modulate the growth rates we are currently seeing to the upside how much to the upside? Are there some risks of macro economy? Is there some headwind indeed from some [indiscernible] stories? So that's the modulation. But in general, we feel that the underlying growth trajectory is -- the puts and takes are very favorable in total for SAP. So that's why we said it more an opportunity than a threat from our perspective with all the reasons we try to give in this session.

Maybe my answer to you would be -- I mean I talked last earnings about the learning curve. And I feel this also speaks for the honesty to say, hey, I don't want to sell you here now a shiny world on AI while we are still learning. And we learned about the context. We learned about the governance. I mean you do your channel checks, we, of course, got a lot of feedback. And I guess -- but now when I look at the feedback what we are getting on the better version, which now will become GA on the platform, this time, I can say we are coming out of this learning curve. And also with RISE, I mean, when we launched this offering, there's so much learning which we then said, Signavio and then LeanIX, and we need to govern and support the customers even more with the architects. And the same is true here. But I see also now a phase coming where we say, wow, now there is this point where we see, oh, now we can start scaling because now we see the accuracy. Now I see in the beta test things, oh, the accuracy, the compliance, the output is getting better. We measure that. And so that's why I have the confidence to say we are over the learning phase and now we are in the scaling phase. And then to Thomas' point, what is now very important that also our ecosystem is embracing the platform because similar to the ERP world, now we need partners, ecosystem customers building on the platform, building the expansions, building new industry AI use cases. And when that is happening, then you really then go into the accelerate phase, what we also outlined on the slide. But of course, Dominik is right. You never know what happens next in the world. But just looking at our AI journey, we are definitely now entering the next phase.

Let's do Charlie, and then you can do Ben afterwards.

Yes, Muhammad, I like some of your slides, particularly the one where you showed the fourth Agentic layer and the current battle to see who wins that opportunity. How important is it for SAP to be the dominant player in that part of the market? And if customers decide to build their AI away from SAP, are you still comfortable that you'll monetize that through API access that in either scenario, it creates an attractive financial outcome for SAP? And then if it is important for you to be a dominant player there, did you think about being a little bit bolder in some of your investments? I think we heard EUR 100 million co-investment with your partners to drive AI adoption. Did you consider bigger numbers to make sure that you're the dominant player?

Yes, I can start, and then I think we can also maybe reflect a little bit on the investments. To me, I think it's absolutely important for us to be able to sort of go win in that new layer of top, if you will. And I do believe -- I think the thesis around our growth, going back to a couple of questions as well. there's a belief in the thesis that, hey, the token consumption in the world is going to grow, and it's going to grow exponentially in some ways, and that's sort of what we've been seeing. Then you can sort of separate that growth into 2 buckets. Certainly, there's going to be one on the consumer side and then there's one going to be on the enterprise and the commercial side, right? On the commercial side, there's going to be some maybe on the SMB side and the other is going to be on the enterprise side. On the enterprise side, further, there's unstructured world, like the office, the productivity stuff and then there's enterprise applications. On the enterprise application part of that token growth, the thesis, the proposition that we have is whomever in that value chain of ultimately Agentic experiences creating value for those enterprises, there's going to be a few things, right? And we think uniquely to whatever is -- whoever is the best LLM out there on top of what we can add creates the most value. And then I think you have to sort of think through, we believe we're sitting on a pretty unique position that we can actually, for a good set of our customers, be that layer up top because to the best LLM, and that might change quarterly, right? That might change monthly, that might change every half year because the ability to shift from one LLM to the next LLM while the stuff on top continues to run is very simple. It's going to get even simpler. And the science around what LLM should you use for what workload is also going to get more sophisticated because you don't have to use the most expensive one for the simplest tax, right? So to me, where the value ultimately our customers are seeing and will see is the thing on top that can do the smart determination of which LLM to pick to drive that token consumption to create them and that's where we shine, right? So to me, I feel like we've got the unique value proposition to be the layer of top to benefit from the thesis of token consumption explosion by providing the value to the customer, and that same. Now let's say that doesn't happen for a percentage of our customers because it might not, right? I think there's different reasons why customers might select others. For that purposes, the way I look at it, our frame B is, again, if you go back to that stack, our application layer becomes, by definition, a platform layer, right? Because you're not rewriting GL, you're not rewriting supply chain. So there's going to be, even if it's somebody else's agentic layer, the consumption will pass through us ideally through our orchestration layer through A2A or other means, if you will. So we will obviously monetize that as well, not as much as the scenario A, but we're going to be in that cycle one way or the other, if you will. So to me, However, way you guys want to sort of lay out the thesis for token consumption, there is a material portion that we add value would come through us. And the LLMs, I feel like would be the layer that there should be a lot more skepticism on that is the durability of one LLM provider in the fullness of time really that strong because that's the interchangeable layer. Not the stuff that we add on top of it, that's SAP context, as the customer context, the company memory, but the ability to switch out that is super simple. So anyhow, it's a bit longer and complicated answer, but I hope that makes sense. Now in terms of investments for us to be able to go win big in that agentic layer, one of the things Christian announced, 2 things Christian announced yesterday, if you remember, is not just this $100 million for our partners to rewrite and replatform on this new platform that we have, but the fact that we are giving design time of this agentic layer free of charge to the customers, design time, if you think about this is where Anthropic is making all their money right now, right? It's when you go engage an LLM to say, hey, build this or build that because that's still design time. It's not run time, right? Run time happens later. That's where N80 also makes a lot of money. And we're saying to our customers that, listen, we're going to give you the design time that's not just the best of what's out in the public, but with our context layer. And not just that for a period of time, we're going to give you run time free as well. So to us, that's significant investment to be able to say, hey, let's get the foothold, the stickiness with the higher value stuff with our customers on scenario A, which is we really want to be that agentic layer for you. So hopefully, that makes sense. But Dominik and Christian, you want to add something?

That's from a go-to-market perspective. I think the partner incentive is one of the aspects. But also for partners, we have many means of various fundings and investments what we have. But also we are more prescriptive how do they need to work. in order to reach the quality levels, but also the acceleration in all of what we want to achieve. To give you an example, I mean, all of our RISE with SAP validated program partners commit on the methodology and the tool chain. So they all use Signavio, they all use [indiscernible] and tool for consultants and tool for developers, which is super critical to reduce the cost for our customers. And that's also something where we basically ensure that also here, we see the AI acceleration through partners. If you talk to KPMG, PwC, Accenture, the like, they talk about thousands of users for tool for consultant and tool for developers, which they now include into the project work, and we will for sure. We also have, to your point on incentives for this world, also customer incentives, which we also associate with some of the RISE with SAP transformation where we for sure on purpose, want to also invest into the AI adoption as we discussed. So basically, partner and customer incentives in that sense.

I would like to add one very common sense, maybe trivial observation on this question. First of all, what is important is really when does the customer need to get a license for our dual capabilities, whether he put something in between or not. I mean that is really triggering a lot of value creation for us. And then dominant is a super strong word. We are not and we have not been dominant. We have been always in fierce competition. So I don't want to hear that word from an antitrust point of view anyhow. So we have been kind of attacked by SaaS companies left right and center. So I don't see that it's now so completely different with the Agentic thing. And then last point I want to make is I think it very much depends on the persona, how big our market share will be. The shared service center guys who are working exclusively on SAP system, why the hell should they use anything but [indiscernible], obviously, at some point in time. But if you are kind of higher-level executives in a media company, maybe you have different needs and then you kind of make that jewel capability connected with Copilot, a kind of invisible slave below, so to speak. But as long as we get the license for that customer, too, commercially, it also gives us a lot of value.

And maybe just on the internal side because the question. I mean, I would say actually that engagement players is our to win and not to lose. What I've seen with the initial employees that have access to the new Jewel experience is exploding. And I think there's this misconception I hope you took away. I mean a lot of our application base is already in a way, lights out, and I couldn't tell us if it's a user or an agent that's triggering a an invoice that's triggering a purchasing order. Our commercial model is immune to that, and I'm seeing that within SAP, too. But actually on the engagement layer, I mean, let's face it. I mean, in many cases, we've been abstracted for a decade plus away from the end users who ultimately produce a report. And what I'm seeing now is actually for the first time that our own team as customer, as user is going absolutely crazy about the experience that we are providing, and that's something that makes me incredibly proud of what this team has achieved over the last couple of months.

It's Ben Castillo from BNP Paribas. Maybe one for Sebastian and Dominik. It's reassuring to hear your level of comfort in that cost to revenue growth ratio. But as you alluded to, that's a net number. And Sebastian, you're extremely focused on delivering that at least EUR 2 billion of AI efficiency. So when we're looking at that gross investment wallet over the next 3 or 4 years, mid-single-digit billion euros, it seems a very large number, particularly if I compare it to the last invest cycle that you made when you did this cloud transformation wave. So I guess my question is that 80% to 90% feels quite conservative. Why could that not be maybe slightly more aggressive and maybe be below 80%? And then the follow-up question might be, well, if it is in that range, could you just help us understand where that sort of quantum of spend is going, headcount, talent acquisition product, just to help us get a sense of why it's needed to be that high?

Maybe I'll start with some ideas on that. So it's true. It's a big amount of money and a lot of firepower. And we don't want to change that target. We don't think it's the right time because it's all about speed. It's executing our plan as quickly as possible. And I did mention that behind some tuck-ins we did recently was also very much the idea of time to market. So the make option might have been available, but it might take too much time for us to then benefit from that capability in our offering. So we would err right now also because we see that, frankly, from a valuation point of view, the growth is more favored by investors, we would err on really keeping an aggressive investment. And if there were opportunities to get even more productivity, then we would redeploy that into growth initiatives. And we've done that in the past. I mean we didn't even think about BDC 3 years ago, and then we had the J-curve from BDC. We absorbed that in our model because we created the room by better than targeted productivity. honestly, these acquisitions we do, there are actually, to some degree, dilutive in the coming years, but we don't have to change the model because we are also seeing great positive surprises on the productivity gains, leveraging AI internally. So we feel we have actually reached a pretty natural balance between how we can aggressively grow and sustain the top line and how we can see a grinding up on the margin. And I don't see any reason why we should deviate from that model for the time being at least.

And maybe to add, first of all, you will see Dominik and me being relentless in ensuring these productivity gains are met, the 80% to 90%. Because for me, it's also showing that this autonomous enterprise vision is real. It creates the space we need to invest in areas like the EUR 100 million fund for AI adoption that we can commit to you without changing -- touching that guidance. But what's even more, I would say, encouraging to me is we announced EUR 2 billion, approximately EUR 2 billion in Q4. We've actually now worked over the last month incredibly close our internal functions with our development team. And yesterday, I saw the Head of our finance shared service, and you can trust this is a highly optimized operation that we are running, decades of automation that went into that, proudly presenting how she co-innovated on the financial close assistant where she expects significant productivity increases that gives me a lot of confidence not only in that productivity number, it gives me a lot of confidence in what we are shipping now to our customers in terms of the productivity and with that also the growth that can unlock for us.

Johannes Schaller from Deutsche Bank. A lot of your customers are in kind of different stages in their AI innovation journey. And I think for the ones in the early innings, it's very easy to see how your offering is very compelling. Also, Christian, you mentioned some that have built agents and they're probably not working that well, also very easy to see where the value add is. But then there is this kind of small set that are very advanced in the AI journey. Some of them have even been a bit vocal kind of how this has enabled them to cut back maybe even an SAP spend to a certain extent. Can you maybe zoom in a bit on the pitch specifically to this customer group and how you can win them back? And then just a quick question for Gina. You talked about the evolution kind of the skills mix investing versus reshaping. Maybe can you give us a bit of a time line for that and also what you think that will do to the overall headcount of the company?

Let me start and I give my pitch I just given to a customer from Indonesia here, a large conglomerate. And then Thomas, you can give your pitch. Look at this customer in Indonesia, they are running around about 40 ERP system, even 4 or 5 non-SAP acquisitions. And now they asked me that question said, "Oh, Christian, you said in the keynote, we can use AI also for S/4 on-prem or even ECC, what should we now do? Should we just build now the agentic AI layer? Or should we start modernizing?" And I said, hey, there is AI tooling now to speed up the migration because when I look now in your architecture, and there was actually with LeanIX an architecture which we built for the customer and said, hey, the handholding to get all of these policies now what you have in the different systems, which are absolutely not harmonized. This will create a lot of work to get the agents now up and running because there's still a lot of customization in which we now need to reflect also in the agenda. We can do that, Muhammad build the connector. So we can do that. And you should pick these scenarios where you see the highest ROI, but let us also start working now with our architects on the modernization because then you will see that we are reaching a completely different scale. And also the TCO then of running those agents will also come and I guess this is not -- and also for many customers, it's not really an either/or. It's really about both. And this on-premise option now is by no means a defensive move. It's just about giving our customers this option because, of course, we have seen there's a lot of pressure on them as well, similar to Dominik and Sebastian here to deliver these efficiencies, what we are all shooting for.

And I think what is super important actually also for these critical customers what you mentioned, also they, for sure, explore what is the right way to go. What they now see is actually exactly the benefit with this business context, business data, business processes and governance and the governance as should not be underestimated how much simplification that brings because, again, in the end of the day, systems need to be audible, auditors need to testify, Otherwise, people go to jail. And that is certainly an aspect where nobody is joking around. I'll give you first one very concrete customer in that sense, always good to talk about concrete customers. They are in Germany, one a very advanced technology-wise customer, which we have. And basically, with the new platform went to Bayer and said, look, let's go with an FDA approach. Now basically, actually this weekend, which is coming, they will have with 8 agents from SAP seeing the new power, and they basically leverage the platform. They also fully embraced tool for consultant as one of the early customers. In the beginning, for sure, also here, feedback was, yes, it's nice, but we have so much amazing SAP consultants in Bayer. So it's not yet that we would roll it out to many people. Fast forward 2 months ago with all the adjustments and evolutions because what Muhammad said, innovation speed is unparalleled actually unbelievable how quickly we add more content, more context into that. Now Bayer is rolling out tool for consultants for all SAP professionals in Bayer. And actually, even more so, they now ask all the system implementers to do the same because they expect the productivity gains from tool for consultants now for the implementation projects. These are the examples where I clearly see the proof that the business context, what we deliver as part of our offering is the differentiation, what we have. And I think it's a great example where the new world, what we've shown today, which again, the customers already can see and use is a totally different world. And I'm absolutely convinced, quite frankly, also after the [indiscernible], this will be a big boost in AI adoption, by the way, around the world. And I hope that when we then see the statistics from some of the research institutions end of the year that the statistics for enterprise AI adoption will be totally different end of the year than today.

I think the other thing I would add, just specifically on the -- taking it from the perspective of they bet on an Agentic AI platform, and they don't want to go change that because they've already deployed a bunch of agents on top of it. There's a few customers in that category, right? Or either they've done that or they built their own because they believe nobody else, they can't use anybody -- anything that's off the shelf with you. I think that I haven't run into a customer that has done that, but still doesn't see value from the autonomous suite. And this is why I think the characteristic of our strategy that says, hey, it's open. What the question they ask is not that, hey, I want to now get rid of my Agentic platform. The question they ask is, hey, can I call your autonomous suite from my orchestration layer and get the value of what you're doing without having to replace. So this and part really excites them to say, "Hey, for my corporate functions and all the things I'm doing, I don't want to go rebuild that because I bought the application from you, and I'd rather just get the agent, but don't make me change my orchestration where I can just..." and the A2A allows us to have an and story as opposed to always [indiscernible] or story if you already bet on it. But now as you do the end, this is where the beauty of the context comes is that now as I continue to add SAP agents, I can build on this platform, but have orchestration on whatever I built internally. So this and to me is the answer to those class of customers, if you will. It's not an [indiscernible] discussion or are we going to try to go compete and say, get rid of that completely now. We're in the game and you should entirely use us.

So you asked on the time line. I think it's difficult to put a time line out because this is an ongoing effort. So we are trying to build a depth of organization that we can -- that we are able to say, okay, how do we up and reskill. So this is the first lever. As I also said, 85% at the moment in the most important profiles or to build up the AI skills is that we are saying we do up and reskilling and 15% for the roles we are getting from the outside market. But this is ongoing. This is the cycle of strategic workforce management. You always have to ask yourself, okay, what do you buy, what do you borrow, what do you build and what do you automate. This is a constant cycle. And that's why I think we cannot say what is the time line. We have to make sure that we have the right skills on board in order to deliver on our customers and to our customers and to our products. So that's why we have an ongoing effort in order to do that. And from a headcount point of view, it's very much that we are keeping that flattish.

Okay. Let's go to Mohammed and Michael.

Mohammed Moawalla from Goldman Sachs. There's obviously been various discussions from -- in the marketplace around the momentum around the migration of the transformation cycle. In your opinion, to what extent is the sort of perhaps the lack of a road map in the past or the high cost of kind of implementation being a kind of factor because still a small percentage of the base has moved to the cloud. And now post the announcement of the road map, stuff around dual consulting, how big of a step change independent of the macro can this drive in your opinion? Because you've obviously talked about in the next couple of months, some of these products coming. So when you measure this versus, say, the time of kind of cloud, how big of a step change in terms of that adoption do you see going forward? And what's been the feedback also on the kind of the road map from customers here?

I mean I can get started, and please team share your feedback. I mean, especially after that week, I mean, of course, we have customers who didn't yet move with us to the RISE journey. This was less of, okay, we don't see the value. I mean many customers in this camp also have seen their own transformation. They divested, they invested, they changed their portfolio. They said, "Oh, the organization will look differently in 1 or 2 years." And these customers said, SAP, we are going to make a move, but please give us the time, give us a year until we have figured out how we want to run this company. And there were quite a few. The way how I now see it going forward is, I mean, first, these AI migration tools will help a lot because the much they like that, oh, after that, there is no ERP upgrade anymore. Of course, it costs a hell lot of money to migrate an ERP system. And so the AI migration tools get incredibly good feedback, and they will definitely help. The second part, what will also help is the on-premise connector because now we are saying, okay, go with us on the journey, and we actually help you to drive AI adoption from day 1 on while we are together modernizing your landscape. So all 2 announcements, I would say, make a ton of sense and will help us also to get now the remaining customers over the line. By the way, don't -- because you're looking at the maintenance and you say, oh, there are many, many customers still left. I mean, of course, there are customers left. But oftentimes, it's also a customer has already started the journey, but then there is still started with 10 ERPs, but they are 50 out, gives these customers time, we are touching the most mission-critical system in the company. That's why you see also in our total cloud backlog always this WA because exactly that customers want to time that also in the right way.

Yes. I think that's an important perspective. If you look about the support revenue base, and already a portion of the support revenue base is already a customer who signed up to RISE. And based on, as I mentioned earlier, customers with 350 productive ERP systems need 1 or 2 years to move all of them to RISE. And then for sure, you see the shift in the support revenue. So that certainly is something we clearly see an acceleration as an expectation on acceleration based on the migration and the agent-led migration, which we've put out. And I think that's certainly something what is helping our customers also in light now what we discussed for the maintenance and which we see with ECC. So we are quite positive.

And let's also think about the commercial decision you have to make when you're sitting on ECC and think about what am I going to do? And like you do, they do a risk return analysis. They just say, okay, how much would it cost me if they even think about that, to rip out my entire system, to pay all the maintenance for that versus -- and then the risk associated with that not delivering to the excruciating governance and compliance standards that you need in enterprises. By the way, I have been just checking in most companies, the recurring kind of SAP fees are lower than what they spend for important insurances. We talk about from middle of revenue type of tickets. So why would a CFO say, I take that risk, that my job to do all that myself. And it might not even be cheaper because they will also crunch a lot of tokens. The need to maintain that and they can get all that hassle that and sometimes it's a necessary evil for them. They can get that from SAP with a super high degree assurance level. We see exactly the contrary that they think how could I sneak more of the kind of critical things under that kind of trust SAP umbrella to make sure that I can use AI but can use it safely.

And I would say, I mean, I meet many customers on a peer-to-peer basis. Most -- actually almost all customers, I mean, they know that clustering AI on top of something that's broken 3 levels down. The only thing it gets you to is with Lightspeed into an RPA 2.0 disaster and the only thing that will go through the roof is token consumption. So actually, I don't meet customers really that really question the modernization itself. Often, it's a question of timing, when to do it. Sometimes there are constraints around data centers. They still have to retire I can emphasize with that with running our own IT operations. So I don't sense there is hesitancy always stay on-premise. It's timing. And then, of course, they love everything we can do to reduce the cost and time to get there.

Go to Michael, please.

Michael Briest, UBS. So a question for, I guess, Christian and Thomas. Thomas, I think you talked about leaving no customer behind. Christian, you've been very passionate about public cloud ERP. And my takeaway from the last couple of days is it's a hell of a lot easier to run AI on public cloud. Where are you on -- or what are you doing to drive more customers down that path? Dominik, I think at our conference 18 months ago, you said maybe it was an order of magnitude smaller than private cloud. Could you maybe sort of give an update on that? And then just a very quick one for you, Dominik, just a yes, no answer, but do you think you can get to the rule of 40 before the end of the decade?

Should I start with public cloud. I mean, for sure, I mean, with public cloud and SaaS for sure, by definition, all the integrations with AI go out of the box. So basically, what you will see is that our customer can simply go to SAP for me, press the button, agents will be connected because that's the beauty of a SaaS public cloud ERP. We see a significant actually what I shared, acceleration increase of the public cloud share what we have. It's the mainstream model for all net new names, actually, which go to market. We are very prescriptive on that one. So basically, all the net new names and logos you will see going on will be, by definition, grow with SAP customers. And for sure, we leverage significantly here on the one hand side, the scale with our indirect channel, what we have in the partner ecosystem for the mid-market, but also we leveraged opportunity with private equity companies with all the portfolio companies that for sure also they have an interest to use and we have a dedicated program called Grow Fast, actually with a dedicated fixed price associated frame contract for them that their portfolio companies quickly can jump to modern cloud ERP with AI embedded. And with that, again, they can grow infinitively. So for me, this is absolutely clear that this is the predominant net new name engine for the company.

We talked last year about partner-driven territories. We see phenomenal growth in that area.

The way I would react to your question on Rule of 40 is the following. We have a deep conviction that no matter whether it's normal, but of course, also in real terms, we will show significant growth going forward. You know where we're growing currently, and that growth will translate into margin expansion and therefore, also better cash conversion of the revenues down to free cash flow. So there will be a trend of grinding up the Rule of 40 performance. Now how fast that really goes depends on so many factors. And again, I want to kind of be cautious on that. But it's, of course, it's a journey that we want to consistently pursue. We had some bumps in the road now with the topics I mentioned before. And honestly, with whatever might happen straight of a moth and some commodities running out of steam and being not available and there might be kind of meltdown scenarios I can't talk about. So that's why I would also refrain from giving you a specific time line because then I would be proven wrong potentially by one of these shocks like we had with COVID or what might happen with escalation scenarios that I don't want to be hung up with.

Okay. Let's do one final question and go to Fred.

Fred Boulan, Bank of America. If I can stay on the AI topic. Can you discuss your AI ambitions? So I think in the past, you talked about that EUR 1 billion-plus opportunity. Can you be more specific about your ambitions, time line, et cetera? I think last year, you had this kind of 5x multiplier with product innovation. So keen to hear your thoughts on that. And then secondly, coming back on the AI commercial model and economics, there's a lot of announcements around AI capabilities for free, pushing FTEs. So can you discuss the economics of building versus running agents? What kind of margins can you make on that revenue stream?

I mean look, let me start and please chime in team. I mean, first, what can we monetize with AI? And I hope this really came across today. First, we have, of course, the platform. Then on the new AI platform, we are building assistance and agents. Plus, we have our AI migration tools where we can deliver a ton of value given the size of this market, which currently is, of course, all going predominantly to SIs. Now if you add this all up together, it's a sizable potential. But think about that, when now Thomas comes with his AI architect to the customer and says, okay, here's the platform. Now we can extend it. Oh, there is a root cause. The master data quality is not so good. Okay, in the platform, let's pull in [indiscernible]. This is -- there's still too much machine learning in. You are actually applying a ton of data scientists. Okay, let's use Prior Labs and RPT 1.5. So I see a lot of cross-sell potential then also in the AI platform together with BDC, where we then can really further optimize how the agents are running our customers' business. And now that the platform is coming to life, that is, of course, for us giving us an enormous scale. And so -- and when you take those 3 components together, given where the solutions are, where the assistance are, I can now say with way higher confidence than 2 years ago that the stuff is mature and it's ready to be consumed and the adoption will go up without any doubt.

Maybe on the commercial model, we have, of course, a very dynamic development on the cost of token. I mean in the prior periods, it was actually a very strong productivity boost, really the bang for the buck you got was tremendous. Now the question is how sustainable is that, given that at some point in time, all the data center investment needs to be amortized. So it's a little hard to speculate on where exactly might the cost of token go. Can you extrapolate from the fast decline in the past? Is the sustainable and of course. Now the good news is we are not as dependent as others on the development of the token cost because on all the stuff that is not RPT-1 and Prior Labs, we don't do pretraining. So we do inference. So it's a relatively efficient compute, not really spinning a lot of tokens. So I think it also will fit in our margin model on the gross margin. And RPT-1 and prior labs on this tabular model, it is a much more structured finite data set than boiling the ocean of the world's languages and mathematical formulas and ingesting all the books that have been written on mathematics on the planet. So I think that gives me the confidence that it shouldn't be a major variance on the gross margin development, which we said is probably the part in our margin profile, which is the most stable. And recall, we said we don't want to focus on expanding margin on cloud in percentage terms, but what we want to really achieve is the incremental euros of gross margin from the cloud that we want to maximize.

Great. And this wraps up our Q&A session. Thank you to the Board. Thank you to everyone here in the room, and also thank you to those who are watching online. Looking forward to speaking to you again with our -- at our Q2 earnings in July. And for those in the room, we also have our management reception. So we're looking forward to seeing you there. Thank you all.

Thank you.

Thank you.