That was a little 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're 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 roadmap, execution, our workforce transformation, and our financials. Let's get started. First, we'll welcome our CEO, Christian Klein, onto 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 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 Customer Officer, will share his view on the progress we're making on our cloud migration journey while helping customer realize value in the business. Afterwards, we'll 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 hear from Sebastian Steinhaeuser, our Chief Operating Officer, on 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, 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 read you a disclaimer, let's get out of the way. 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 risk 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 as 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 onto the stage. Yeah. Hello, everyone. Welcome to all of you here joining us on site in sunny Orlando, and of course, also welcome to all of you joining us virtually. What a difference 12 months can make. It was here on stage where we talked about, you know, the acceleration of our cloud transformation. I guess we can all agree, a very successful cloud transformation. We talked about Joule, now we are talking about the future of the software industry in the age of AI. After yesterday's keynote, I would like to share a little bit more about, you know, what is actually the why to win for SAP in AI, why SAP will be a winner, second, of course, what does it take to become a winner in AI? You know, to start, maybe a quick look back into our cloud transformation, because, you know, some of the things we did, which you see behind those financial numbers, are now very, very important also for the success in AI. First, when you remember, we acquired a bunch of, you know, cloud companies starting 2012, and then when we took over, we said, "Hey, we want to harmonize our portfolio." Harmonization means we want to actually build a best of suite. We want to harmonize data. We want to harmonize processes. I will come later to that, why this is so important, that customers in our stack are not sitting on a bunch of data silos and not on a bunch of broken business processes, but that we are now really building AI on a harmonized foundation. 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, you know, manage subscription consumption, not how to, you know, adopt, actually drive adoption of consumption-related business modules. 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. Third, no transformation actually just, you know, 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, you know, big, 110,000 employees, I mean, it means something, yeah? That needs to happen at fast speed, at fast pace. We also changed our culture so that we are really also having, you know, our employees, you know, understanding that this change is actually needed and that there is a clear plan in place which we all have to execute now. 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. Of course, even in the cloud world, you know, 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. Of course now much more with the value equation, you know, happening on the edge and the AI layer. We transformed SAP once, and yes, I can tell you, we do it a second time. What does it take, you know, from a CEO perspective to transform and make a transformation successful? In my eyes, there are four pillars, four levers, to make this a success. First, product. Muhammad, Philipp, and I showed yesterday in the keynote, you know, what does it really mean? What kind of value will the autonomous enterprise, you know, deliver to our customers today? We want to share a bit more insights, you know, what is really differentiating SAP from the rest. Also then, of course, you know, how do we gonna make it happen? How do we develop it? Then second, of course, go to market. You know, the way how we sell, the way how we deploy, 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. Obviously, you know, there was a certain reason why we also did certain reorganizations, why we did also, why we are now doing also certain reskilling on how to write code in pre-sales and show the value and then later on, of course, drive the consumption and drive the adoption of our agents. Third, I mean, super important is, of course, the people layer. I mean, going back into the cloud transformation, I can remember very well, I looked around in SAP, did we have enough people to know how to actually operate a cloud at scale? Do we actually had enough people who, you know, develop a multi-tenancy enabled architecture for our core products? No. We brought new people in, but we also did a massive reskilling inside SAP. The same needs to happen now all over again. Then, of course, with Sebastian on the operation side, it's also then now very important that we become on our own an autonomous enterprise. First, I mean, you are looking at us saying, "Okay, first, how will, you know, SAP deliver AI at scale, at speed?" Then, of course, also, how can we also deliver the efficiencies? You're gonna need to see, you know, coming out of course, deploying our AI inside SAP. Of course, 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. Of course, you know, 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. 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. You know, every time when we then migrated away from these best of breed or now wanting a ticketing system, I mean, that's not a lot of domain know-how in the data fields. You know, 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, yeah. You know, I can remember well here with Thomas when we started five years ago to harmonize our data model. You know, I was actually overwhelmed, yeah, by to understand, oh, there are 1 million correlations between logistics and finance, and there are another 2 million, you know, 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. Now think about it. At that point, it was about harmonization. Now, the agents need a graph where they can correlate this data. 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 context sitting in our ERP into the new platform I will talk about in a second. Obviously, we are running thousands of business processes in these companies. You know, I talked last earnings about the learning curve we had. I mean, clearly one learning curve with Joule was also that these agents need a process context. You can talk about edge GEN 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. All of that sits in the brain of a company, and this brain is actually the ERP. I'm very confident that, you know, 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. When you are talking to some of our partners and customers, probably you also hear, Oh, 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 hand-holding? I can tell you, by the way, great examples. We can deliver world-class AI. Also, I have to admit, there's probably a lot of hand-holding behind to let the agents connect to the right data fields and making sure they understand the process context. Then Muhammad, Philip, and I were sitting together and said, "Hey, this can't be." Because, you know, our agent builder was on BTP. The data layer was on BDC. The process context sits in Signavio and in EARL and in some other places. Then we said, "Hey, it doesn't make any sense." Because when we are the brain of every company, and you are developing agents, I mean, obviously, the context needs to come right away with that. I hope you have seen with the new better version of the platform, in one month, this platform is GA, we are going to deliver now an agent builder where you can bring your own tools. 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 an agent is zero. That's why you don't have to come to us. When you start developing it in Joule Studio 2.0, you're going to see that, aha, I'm developing now a pricing agent. I need access to the material data in S4. My pricing data sits either, you know, in Salesforce or in SAP. I come in a second to non-SAP data. Okay, this is how you quote, this is how you price, this is, you know Okay, I need to understand how to follow these process steps. I understand the approval steps. Finally, you know, I come to the governance part in a second. The community will see in a month from now, together with our own developers already, you know, building agents with that now, they're gonna see, aha, now I have the context. Now I have access to the brain of every company. 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 raw data. We do zero 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. We do that. Actually, what happens on BDC and only on BDC, we join data. We are not A data product alone is raw data. When you join data, when you build a semantic and 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." You can do these data joins, and the way how Muhammad and Philip now engineer that is you can actually say, "Okay, I join data, customer data between SAP and Salesforce, and now I link it into the KG," 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 rich semantical data layer between SAP and non-SAP data. You have seen our acquisition of Reltio so that we also then can take care about the master data quality. Because agents need high-quality data. They should not compensate for a broken data module. Last but not least, of course, with Dremio, we have a lakehouse to really also develop own agentic AI scenarios with our own lakehouse built on Apache Iceberg. That is actually the context layer, and that is the heart. We are also now launching SAP domain modules. We are training these modules 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. Last but not least comes the governance layer. That is actually really everything what I showed yesterday, which sits under the iceberg. You can build, you know, of course, a lot of fancy agents with LLMs. They will miss the context, especially you're gonna 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, 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, 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, "Yeah, 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. That's why we said, "Hey," when we are talking about the SAP Business AI Platform, here's the build layer, here's the context layer, and here is the governance layer. Very, very important. Then, you know, other tech CEOs are debating, oh, who has now the orchestration layer and who owns the agent layer. We give this away for free. Why? Because we wanna monetize, you know, the success, the value of our agents. We wanna have the best HR agent. We wanna have the best source to pay assistant. We wanna have the best record to report assistant. We wanna have the best planning assistant. The orchestration, if customers wanna really also plug in third-party agents, of course, this is for free. I know there was some talk about this API policy. The only thing what we actually manage now with the API policy is so that we manage actually the access to our APIs, to our MCP servers, so that we really can guarantee you know, high governance standards. Second, obviously, that we also can still guarantee with millions of API calls coming in, that we can manage the system performance. Everything what you are going to build on the SAP Business AI Platform, you know, you can use any model you want. No differentiation. Then comes the context, and then comes the governance. That will be world-class, delivered by SAP, and some of that is actually already there. The autonomous suite. We are now having the platform, I mean, yesterday we presented at, okay, what is actually then our vision of wanting how an autonomous enterprise will run? First, you're gonna have your assistants designed to the personas in your company. The agents will actually take over a lot of tasks, will enable you to do more with less, or actually, you know, 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, you know, which were just not possible before. Everything run on our AI platform. Next to role-centric, obviously, it is very important that it is outcome-based. I just had a U.S. public sector customer four rooms away. Actually, they asked me, "Christian, I now build in custom agent and, okay, understands. The agent understands, you know, listen, of course. Actually, you know, now to change something in the system of record and to write back into the system of record is not possible. Actually, that is not good because I wanna run this autonomous." This was about a payroll. I said, "Yeah, see, you know, 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 write back even into an SAP system?" We are going not only from listening, understanding, we are going all the way down to execution. There's always a human in the loop, but the customer can decide, "Okay, how far do I wanna go with regard to the level of autonomy I wanna give these agents?" Very important. Audit-ready. Muhammad 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, you know, 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. We are going even one step further. We are actually telling you, when you run your financial close with our assistant, hey, this is actually SOC certified. This has all the certifications you are used of running your financial close with S/4HANA, with our software. Extensible by design. When I talk about learning of Joule, my God, maybe we should have thought about that before. Again, everyone is a learner these days. Extensibility. I mean, when I look at our policies inside SAP, yeah, still Sebastian is fighting it a lot to make it simpler, simpler, we have different policies. Sometimes, you know, we have new ideas about new policies, new pricing policies, et cetera. Extensibility is super important. There will be not this one standard, you know, agent for pricing, not this one standard agent for sourcing. Customers will also have different policies. That's why extensibility is key. What is also very key, and I hope you saw that yesterday with Joule Studio 2.0, you know, that you can extend also your autonomous suite layer now with new agents and with new skills on the fly. We just had, actually, we showed, you know, the new platform to a customer right now, and they are building actually a personalized AI agent for commerce. Actually, we said, "Hey, okay, you actually have this one more step you wanna have this agent to do." You go in and say, "Okay," as a business user, you can say, when you have the authorization, right, you can just say, "Okay, with end-to-end, here is one more step, dear personalized AI agent, you should consider when you are making this recommendation to my consumer." Think about that. Today, we are thinking about, you know, in ERP on-prem, we thought about eight-year-long upgrade cycles. In the cloud, we moved to quarterly, then to monthly, and now you can add agents and skills every hour, and we deploy it, and we govern it. The speed, the agility, and the extensibility is very, very important. 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 as the business wants tomorrow, and not in three months. Industry AI. You know, some customers told me, "Christian," yesterday in the keynote, "So, so happy that we heard this term industry again from SAP." I can tell you the when we sometimes say SAP is so sticky because we are the brain of every company, actually, I take this as a huge credit. The 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? No, because it has value. Especially in these domains here, what Sebastian and the team picked, I mean, look at that. When you, when you do asset management, when you do commodity management, yesterday we showed unified commerce, there is no scenario, not one single one, who doesn't need a tight integration into the ERP of SAP. Not single, not one single one. In many, many cases, again, you need to even have a write back. Because otherwise forget about autonomous, forget about running autonomous asset management when you cannot maintain or change certain assets or trigger actions in the SAP system to actually, you know, trigger 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 are missing is the last mile with the deep integration into the ERP. That's what we are going to deliver. Then, yes, Palantir has the copyright for the FDE, but you know, Hasso Plattner actually told me just four weeks ago, he said, "Christian, you know what? With this autonomous suite, it's great. I like the agents. You know, over 200 agents are already here now. But you need to completely reinvent. Tell your product managers, 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 CONA about last mile delivery, talk to the truck driver, talk to the people in th--e warehouse to understand how to incrementally apply AI on top of our transactional application. No, no. Just to completely reinvent this process. Because now truck optimization happens with AI agents without anyone, you know, 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 pulling now together our IBUs, our industry experts, with our industry developers. You're gonna 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, you know These are complex scenarios, but they are high value for all of our customers. Actually, by the end of the year, we wanna actually scale this business to over 200 projects. More to come. I can tell you after I told everyone yesterday, "Call Sebastian if you wanna do something," his inbox is exploding, and the pipeline looks actually anyway 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, the agents need to understand the full logic of our Suite. When we are now building the autonomous enterprise, when the platform gives everyone a great reason why to build agents with SAP, why to run autonomous with SAP. One lesson I learned during my whole career is, technology means nothing if you don't bring it to adoption. Every great technology needs redesign, needs business process change. You can plug in the best, you know, agent into your sourcing, but if your procurement department actually is not, you know, 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 intelligent sourcing?" It's useless. That's why we said, "Hey, we really want to also deliver at Sapphire a new RISE with SAP and GROW with SAP offering." What does this mean? two things. When we decided to give Thomas sales and consulting, it was not that, you know, I want to have more time for my family, maybe that as well. You know, at the end of the day, we also said, "Hey, in order to do that, we are not now inventing new roles." No, what we need to do is actually right from the beginning is to start prototyping, is to actually start to show value. We can't have our customers waiting for three years until they finish the modernization of the system to actually use AI. To underpin that, we said, "Hey, we are not only saying this, you know, in some nice words at SAP Sapphire." No, we contractually commit to that, and we come on-site. We're going to take all of these great AI assistants and agents coming from Muhammad and the team now and bring it to adoption. Not only activate it, bring it to adoption. Now, of course, I heard, "Oh, Christian, you changed your strategy because now you're also allowing it for on-premise systems." 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. 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.'" 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 I'm not doing it with SAP, I do it with someone else, custom. I would love to do it with SAP. That's why we built this on-premise connector to really now also then connect, you know, our assistants and our agents to these on-premise systems. Of course, what we would like to see is, first, 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. Needless to say, when I talk about extensibility, if you have a heavily customized landscape, obviously there is more hand-holding, more extensibility required than when we are talking about the clean core, a very, you know, clean system with regard, with a lot of standardized business process and a standardized data module underneath. ERP migration acceleration. I can still remember when Alex Karp called me and said, "Christian, I would love to switch to SAP, but oh my God, my CIO tells me this takes that long." That needs to be done faster. I said, "Alex, yeah, we are on it. We have SAP Joule for Consultants, SAP Joule for Developers, but please feel free to join us." I mean, I welcome every partner to shrink this timeline for getting our customers from place A to place B. When I'm now talking to Accenture, to Deloitte, to PwC, everyone is investing into that. Somehow we are disrupting someone's business model here, but, you know, that is overdue. That's why we are now also, today, in Thomas' keynote, we launched our AI-led ERP migration platform, where 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. We are pulling in Tricentis for test automation, AI-based. We are pulling in Palantir, we are pulling in Accenture. There are many, many more now to join. That journey will be accelerated without any doubt. 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, three years. Even if you have an on-premise system, we can start tomorrow while we are modernizing, you know, your landscape. That actually resonates extremely well with our customers, and I feel, you know, we can combine here really the two primary objectives of our customers, meaning modernization and of course, AI value. Talking about the go-to-market model, Thomas will deep dive on that. 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 pre-sales experts can already vibe code prototypes. That is what we need to show. In Industry AI, it's really also even with more on-site presence to really build that, while we are still, you know, defining the value, showing the customer the value, and then come to a sign a value-based transformation, outcome-based in Industry AI, where we then, okay, say, "Okay, let's go," and really bring those agents now fully into production. Here, I mentioned already before, we didn't want to add new roles to our go-to-market model, we just simplified that. With the consultants, we have the data consultants, the LOB consultants, the industry consultants, I mean, they can here help so that we don't need to onboard new people. We don't need to build an AI deployment engine in SAP. We have it. We need to do some re-skilling, but we have it. 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 I come in a second to the people section. Of course, that we need, 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. Yes, when you actually then see when they have adopted the suite, when they have adopted, you know, BDC, and now with the data catalog, and you can then build this data trends, and you can add it into the KG, we of course also see that the AI consumption is already going up. Finally, we see, okay, as in ERP, a best of suite, best of suite will win, not best of breed. Finally, actually, we, you know, 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 experts. Do we always need to deliver a 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 gonna see productivity gains. Again, I also wanna highlight, you know, to get the best ontologist, the best data scientist, to get the best, you know, full stack developers, I mean, these are all the roles, data The AI architects, these are all the roles where we also then hire from the top universities in the world and bring those in. Even more important is, of course, the re-skilling. Here you see, I mean, you know, we call it mandatory. I actually call it's actually an offer to our employees to have a bright career within SAP to enjoy all of these learnings. The managers are always in the lead. It's not about that we have here's IT and here's your AI tool. No, no. We really wanna have cross SAP, the managers, and some front runners on AI showing about what is possible with AI in the one or the other shop, and we are doing this cross 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 in the lead, you know, how we are now applying AI internally. We already have realized, you know, efficiency gains, triple digit. I'm not allowed to share the exact number, but you know, it's actually quite substantial. Of course, we see a huge potential to make this even a bigger number than EUR 2 billion. Again, don't only take this as, you know, efficiency. I mean, we will take some of that money, of course, also to reinvest. 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 PaaS business will not go away. AI agents don't work without a brain. The brain is SAP. Having a best of suite versus a best of breed, I see definitely scenarios where customers are replacing SaaS apps, where the domain expertise is not so deep, where, you know, the data models are not so mission-critical. 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's one more reason to believe that. 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 EUR 5 trillion market here as an opportunity. If our platform now delivers what we here presented, and it will, and when we then will deliver this high value generative AI use cases for the different domains, plus industry AI, there is no doubt that we can accelerate now also our growth towards 2030. Here, one last point on the commercial modules, I set this as earnings. You know, over two-thirds of our cloud revenue is already today seat independent, meaning it's non-user based. We have all kinds of value metrics. No one needs to be scared that, oh, efficiency is users not anymore needed, and suddenly the revenue goes down. No, no. We price a lot of our apps already today value based. Of course, you see now with AI, with the platform, with our migration tools, with the autonomous domains, that of course, you know, we see that at least we're gonna see a 30% consumption related revenue share in our cloud revenue in 2030. Again, there's some time to go, we see that we are on the right path, we have the right strategy, and the execution is there. Look, that's how I see our growth journey. There was first an enable 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, you know, fixed a lot in our AI foundation over the last two years. We delivered the first assistants and tool agents. Of course, you know, we worked further on the harmonization of our suite. Now we are definitely in the scaling phase. Now I see, hey, with this platform, there is no reason why in the SAP context you are not building agents with SAP. The autonomous suite layer, by the way, not only that we are delivering 200 agents for 60 assistants this year, this morning the customers who are, the partners who are beta testing the platform already have developed over 600 agents, and that will be over 1,000 at SAP TechEd, and there is more to come. 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. 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, customers 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 rather your money on our AI migration tool chain." Of course, for me, it's actually now very important to build a community on the platform, creating the belief in the ecosystem with the new, you know, platform being in beta, in one month productive. Actually we build this, and then we build the graphs for the different industries. You know, we build the graphs, and we will get more and more mature for the horizontal process layer in the industry. The domain modules will get better and better. Scenario, you can feed in a lot of more additional process logic. We have LeanIX with our AI governance hub, where you can free of charge actually also then see the transparency and govern your AI layer. That's actually what I believe will happen within, you know, within a year from now. Yeah. I guess this year was a very, very important SAP Sapphire for us to show that we are now really accelerating both on product, go-to-market, people, operations. When this all comes together, there is no doubt, no doubt that this will be a second successful transformation for SAP. Many thanks. Hi, good morning. It's kind of hard to follow that act. Thank you. I think Christian's already covered most of the product strategy. I think what I'd like to do here in a few minutes is, maybe go through three things. one in not as much detail. Thing number 1 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? How internally are we really mobilizing that strategy to build the products that we believe our customers need? 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. The second is the product strategy. A lot you saw yesterday, Christian covered a lot in detail as well. Related to the product strategy, what I also want to attempt to do is, you know, share a little bit more of the conviction that we have as to why this particular strategy is both differentiated and unique than the strategy that you're hearing from some of our other tech peers as well. 'Cause 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 wanna control the orchestration layer as well as the engagement layer as well. I wanna 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. Let's start with the shift. You know, 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 topmost 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. That has been the SaaS applications now for the recent past, if you will. What's happening now is AI, instead of being in the SaaS applications, you can put on top of the SaaS application. There's a new layer on top, and that's what everybody's trying to vie for. What that means, though, is two things if you think about it. One, as SAP, we clearly need to make sure that this 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. The second thing is the layers underneath become more commoditized, or the better way to look at them is they 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 gonna be disrupted, that will go away, that can probably entirely be subsumed and consumed by the agentic layer. 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 'cause it has a level of statutory 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. That, hence comes the new layer on top of it. 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 A2A perspective, from an orchestration layer perspective, can we be the top layer? Absolutely. Are we vying to be the top layer? Absolutely. Do we need to only be for a customer landscape, do we have to be the only top layer? The answer is no. They can choose what they need to and what they've already bet on, but we're still gonna win because the SaaS layer that we are underneath that needs to exist for it to consume. There's gonna be a level of growth that we're gonna see in both, if you will. This, to me, as you think about the shift in the tech landscape, right, and the stack that hasn't happened in multiple decades, you know, there's, you know, we sort of whittle that down to four 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 'cause it sits underneath that highest layer up top. There are gonna be a pull-through on that, but of course, we wanna also play in that new agentic layer up top that by definition is loosely decoupled to the layers underneath. The second thing is as 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 products in Ariba, right? There's sourcing, there's contracting, there's buying, and then there's invoicing. Any features we ship sort of stayed within the boundary lines of whatever the scope that we addressed in those applications. That could be S/4, SuccessFactors, and anything. The mindset shift now is this agentic layer at the customer level, that they're gonna go build is gonna be one agentic layer. For a source to pay, if they're using us for Ariba, and they've got two other tools completing that process, we, by definition, whatever agentic layer we provide has to factor in the additional tools that they have 'cause a customer will only have one layer. If we only provide agents that work on our app, they're gonna have to find another layer on top to rule it all that can connect to those other applications 'cause the customer is not gonna stitch together that new layer up top by multiple agentic platforms. That's why as you now think about the core properties of our agentic platform, the new business AI platform, extensibility in our partnerships with n8n is so core 'cause 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? S/4 Finance or even Ariba and 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. Again, if you think about and step back and believe in that thesis. You know, ones that have a smaller share of that app landscape will have a harder time becoming the agentic layer. 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 we'll extend with n8n, or the vibe coding experience that you see with, Business AI platform and Vercel and others to expand." We do believe that in that SaaS landscape, we do belong generally in a category of one as we believe it in our customer base. I think generally the category is a category of two 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. 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. 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 that's been received very positively, is listen, historically, we've said you won't get the value that you're gonna get from our innovation is when you get to our latest products. That's shifting now, right? Because this layer up top is loosely coupled with the platform layer. We have to shift our product strategy as well to say, "Listen, we're gonna build this agentic layer, the autonomous suite, that is loosely decoupled with the application layer because it has its own ship cycle." I'm not shipping agents on S/4 every six months like I'm shipping features on S/4 every six 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. 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 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 one, 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. The last one 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 wanna 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. These four shifts, I believe in the class of applications we live in, are fundamentally sort of changing how customers think and are reshaping our portfolio. As you think about SAP now and this autonomous suite, it's not what we've been shipping for the last four decades. It's fundamentally a new product line that we're going in with that has a much bigger TAM, not just the new cloud solutions, but our S/4 On-prem and ECC. 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 two if you ask some of our larger customers as to who your largest two tech partners are. Now, how this shapes and leads to a strategy is the picture that you've seen, and this largely has three layers, right? 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 bottommost is a Business AI platform. Built on that is this decoupled agentic experiences layer that can work on any application landscape, and you can connect it to non-SAP landscape too. 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 Joule. Those are the three core things. You can argue, listen, you can go to any of the leading tech conferences, and you'll probably find these three layers there, at least the bottom one from a platform and the top one from an orchestration layer perspective. Nobody else largely plays in saying, "Listen, we're just gonna build an end-to-end autonomous suite for you." Nobody else can do that besides us anyway because they don't have the right and the applications underneath to run it. Now, there could be small startups that say, "Oh, I've got the finance agents that can really run close for you," but they're not doing supply chain, right? They're not doing HCM. A customer doesn't want 22 different agents running in them, right? 22 different platforms, agents on different platforms, because that proliferation, the security nightmare, is gonna be pretty massive. We're one of the only ones, again, in our install 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. Not just that, with industry AI, we're gonna go to your core processes, last mile distribution, distributed energy, unified commerce, adaptive manufacturing, and we're gonna go build those as well to complete your story on it. That's what these three layers are. I'm not gonna spend, because I think Christian did an amazing job, makes me proud sitting there, on what these platforms are and our differentiation. What I do want to spend a few minutes in each one of those is talking about what are our differentiators and uniquenesses, right? Starting with the autonomous suite, right, the one in the middle, we're of course building 200 agents that we talked about. It's gonna 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 hub that that's there. 'Cause 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 auditability part of it and the traceability is unique in what we can offer, 'cause 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. Second, from an auditability perspective, we already take 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, and the openness from an A2A perspective. These assistants, of course, come together in each one of those domains, and hence, there's only one organization in our customer base that can canvas out-of-the-box set of agents that allow you to get to an autonomous enterprise stage, and that's us. You know, 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 a uniqueness of the platform layer that we're gonna 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 gonna get to under five days. GA is a high bar, right? You can go use Perplexity or any tool to find out, listen, what does it take to really GA an agent? What you're gonna find is timelines that's from three-six months or even further, 'cause 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 SAP Business AI Platform. This ability to innovate at a pace that's gonna be very hard for anybody else to be able to go do is what's a differentiator for our platform. Moving on, we've talked about this. I'm gonna 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 customer's ECC estate and the S/4 on-prem estate alongside the cloud estate," 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. I'm not gonna spend too much time here, but I wanna sort of give you at the highest level the strategy here, right? We're partnering with things that we believe are gonna 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. The race between an Anthropic, a Gemini, an OpenAI, and who else to come with the 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? We wanna make sure that we bring the best of that to our agentic platform. Second, the design experience of the agent is also gonna be a commodity. Like, do we wanna go build a design experience that's better than n8n? Of course, we want to, but is that sort of really what our secret sauce is? Not. It's not. We wanna go partner with an n8n, a Vercel, to say, "Hey, if customers are choosing an experience to go build something, we just wanna make that part of the Business AI Platform in a way that we can add another n9n or n10n 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 wanna 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 gonna continue to get very smart. 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 gonna be the best agentic platform, is a knowledge graph of our canonical model. That's already over 7 million fields in the relationships that we need to go maintain, 'cause that's very hard to go do. The process graph, 'cause we cover end-to-end processes, 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, pre-train, and then make it available with the public LLM to say, if you're building an extension or an app or an agent on SuccessFactors, ours is always gonna 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 evals as well. The fourth one is predictive models. This is where we shine, 'cause 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 in aggregate, 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 models. We've done that for SAP data, which is RPT-1. We said it's not gonna 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, 'cause we wanna bring the best of both to be able to bring into our context. These four things, you know, 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? 'Cause it's not an or, it's an and. 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. The Knowledge Graph is not just what we canonically ship, but the thousands of customizations and extensions the customers have. We know that. We bring that in in a dynamic way. Company memory, which is the customer context that's in process mining, process insights, process models, emails, chats, and others. We announced that yesterday as well. That's part of our agentic platform. Finally, the agentic runtime, which has the governance built in, which has the sovereignty built in, 'cause it's natively on BTP. It can run anywhere you want. Like, you add all those three things, and it gives our agentic platform a differentiation that's gonna be very hard for a hyperscaler to go do, 'cause 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. Anyhow, this is what leads to You can see the evals here, right? There's about five agents that we tested multiple times with just a different kind of public model, and then a model that has that goodness in it. You can see we beat each one of them every time, and that's only gonna get better. 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. Both will continue to show some difference in movement, if you will. Finally, I'll close with 'Cause we've talked about the data. We've got the Reltio and the Dremio. On the Reltio part, you should think of that we have MDG, but we knew because we have to go from an 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 access that data that's non-SAP on the fly as well, and hence Dremio on top of BDC to be able to go. You can see that story is now building up from a clear product strategy perspective. Finally, I'll just stop here. Governance, we've talked. Actually, the reason why, 'cause 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 platform that we're largely leading in already. 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, LeanIX already knows them because they are the enterprise architecture solution in our customer's landscape. Signavio already does mining, 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, we continue to add to it. Our right to win in agent governance. Then finally, sort of tackling it all the way to the end to SuccessFactors to say, "Listen, the total workforce discussion for our customer is gonna be a pretty important one, as it is for us," as Sebastian's gonna talk about, that your total workforce is not humans or it's not FTEs and contingent, but FTEs, contingents, and agents. How we've brought that together with SuccessFactors is pretty unique. Finally, I'll stop at this and then hand over to the next presenter. Here's the Joule, the engagement layer, and what's our right to be in this. There's two 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's actually fair. There's some good successes as well, but we know, as Christian talked about earlier, that we had a lot of learnings in Joule. If you look at what Joule today and what we're now launching and already we have with some early customers, is a cloud-based harness that completely re-imagines Joule, both from a deployment experience perspective as well as the value perspective. Internally, honestly, we call it V10, 'cause we thought the difference between what we have today in the market, V1, to 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, this is a massive uplift. You will hear feedback if you call a customer to say, "Hey, what do you think about Joule?" 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 re-imagining. Finally, you know, we announced something called Joule Work. This is that engagement layer, right? The reason why we believe this is something we again have a right to have a significant market share in is we have over 300 million end users, right, that engage with our applications today just in the cloud space. There's another few hundred, depending on how you estimate, in our on-premises estates in ECC. 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. I'll stop with this and hand over to, I think Thomas. You know, because I was having this conversation earlier. Listen, if you go out to the market now and you pick any random five customers to say, "Hey, I want to get your feedback," or maybe any random five 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 figmas, it's not vaporware, right. We have a lot of early customers that through FDEs, we've been working with. The logos that you see here, the examples that you saw all day yesterday, and what you're gonna see today are people, customers that we've been working with through this FDE 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 stuff, and mid-June is when we're gonna scale out the rollout of it to any customer that wants it as part of our early adopter care. You're going to find two classes of feedback, and you need to be able to ask the question, "Hey, now, do you have the experience in the new 1 that was just announced at SAP Sapphire, or is it the old one?" You need to be able to discriminate in your assessment to say, "Hey, what strategy are you outlining here?" 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 FDEs from SAP with already deep understanding of industry and our products that are going to engage with customers at scale, majority of them, in engagements to activate these domains and this industry AI as well. 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 we want to go unlock value if you will. hopefully this makes sense. Now I'll hand it over to Thomas. Thank you. Hello and also welcome from my side. Dominik challenged me a little bit, if you can accelerate migrations by up to 50%, if I can accelerate my presentation up to 50% as well in light of the time. Let's give it a try. No, I'm really looking forward to talk with you about how we evolve our go-to-market model in the company. In order to do so, I think it's always good to basically remind ourselves in which world and environment our customers are living, because that's basically the expectations we want to not only meet, but actually exceed. 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. For sure, this is a pressure on all of our customers. In parallel, we see this little disruption called AI, what we also talked today a lot about that. On the one hand side, there's cost pressure. On the other hand side, there's, well, how to get value of AI. That's coming together. The customer expectations are raising up. For sure, they want to have faster outcomes because the pressure is high and the world is changing more quickly. 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. For sure, a huge pressure in the market about return on AI, because everybody's piloting and POC-ing some AI capabilities. also, if you see the various research in the enterprise context, for sure it's clear that it's a little bit more context, complex. That's what we discussed the last two days here at Sapphire, how we as a company want to help our customers to overcome that. It also means for us that 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. For sure, we focus on adoption services. 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 pricings 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. 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 a reason why we brought together all the respective customer functions from pre-sales, sales, post-sales, service and support, and cloud operations to really have a delivery engine for our customers to make that happen. With that also, we simplify the entire customer engagement throughout the customer value journey. 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, and Christian also already hinted to it, how we leverage our technical skills on the services side, also in pre-sales 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 important rule. 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. Scale for us is a critical component which we for sure want to enable with our workforce. If you think about this customer value group which we brought together, touching the entire customer life cycle, as I mentioned, a seamless journey is something what we want to enable, but also think differently how we work. Also embracing AI, and Sebastian will talk about how we internally embrace AI to serve our customers. For sure, we changed the entire motion about consumption-led models. That's also, by the way, part of the bonus plans of all of the people in this organization. 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. That means we use our architects to make that happen. 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. That's, I think, a critical aspect what we want to see. We talked for sure about also how we leverage AI itself to proactively guide our customers, to proactively deflect tickets in support and the likes to really put that on steroids. Now, if you think about the growth lever, I think there's a common misunderstanding and belief 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, for sure, 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 innovations, what we see with data and AI, but also the huge opportunity we have in sovereign cloud. 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, they lead up to close to EUR 3 billion in cloud revenue, which we see. You also clearly see already here the indication how much cross and upsell is happening there. 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-led territories, and here you see some of the numbers. I mean, 1,000 partner-led territories, more than 3,000 sales partners which we have, and they also, when you see the cloud revenue growth, comparison of the indirect channel and direct channel, are really at speed. That's what we see on aggregate, which means our GROW with SAP offering for small and medium-sized enterprises, for startups, for scale-ups, is extremely important for us. Also here, I mean, if you think about companies like Snowflake, Databricks, I mean, for sure, they want to grow infinitively. Which is the single only one ERP system on this planet which you cannot outgrow, which scales infinite? It's SAP. Once they sign up with GROW with 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 want, you just can grow. 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 one ERP system. This is just unlimited scale. We saw the other customers on stage today, which clearly shows that this is a big advantage. Basically already you see the contribution of 20% of our cloud revenue growth in the last five years coming from new customers. 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 for 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 this 2x multiplier. Actually with RISE with SAP, we, in the moment of the landing of the offering, already do up and cross sells in that motion. That leads up to the 3x potential what we see. Also here you see, if you think about the numbers, again, how much cross and upsell we do in our entire installed base. 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. You see some of the facts here on the right side as well. If our customers use our Cloud ERP and our BDC as a data platform, then you see that nine out of these 10 customers have more than four cloud solution in place. We clearly see that RISE, but also BDC, are starting this flywheel from AI data and applications, and with that also the cross-sell across the portfolio because, to Muhammad's point, the more you aggregate in this agentic AI world bring together, the more value it adds. One plus one does not equal two, but actually three 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 a RISE with SAP conversation. For sure, the business transformation management components are already a vehicle because, for sure, to support a transformation, and accelerate the migrations, they need these tools like Signavio, LeanIX, which we directly also package together for our customers. Also in a RISE migration, what is important for you to know, it's not just that we do one deal 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 five or six from a system landscape perspective. 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 IDs for RISE with SAP customer at scale. It's the largest operating scale in the market and growing. Here for sure on top, we use already the Business Technology Platform for all the custom extension, for the custom build, 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 two years ago to really shift away from a traditional services business, but really going to an AI adoption engine to basically really focus on adoption and consumption for our customers. That led also to the establishment of the new success plans which we have in place. Here we've embedded the AI activations. We've embedded it. We help our customers building up AI COEs, so it's all natural part. also, we evolved our engagement model that with RISE with SAP, with our enterprise architects, we actively help our customers to get to that point. For sure, with our agent-led migration and tool chain, we not only support the migration and 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 efforts for 35%-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 pre-sales 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 deflected by self-service, by AI capabilities, that the tickets are even not getting created. That it 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. Actually, customer effort score, so the customer satisfaction score for our customers, is even better as if it would be human. We see even a further customer experience improvement with AI. 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 100 billion+ for SAP migrations. For sure, with our agent-led 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 one 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 innovations with that, which is a good thing. Another good thing is there was a lot of worry in the market about, oh, what is happening with the maintenance end? Yes, ECC maintenance end is 2027, and 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 sovereign cloud. 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, Diehl, Thales, across the world are betting on SAP. Nine out of 10 defense manufacturers here in the U.S. rely fully 100% on SAP. That's where we are. Because of our architecture, because how we engineer the sovereignty, we can serve those markets, and I think this is a huge opportunity what we have. What is important is that if we think about sovereignty, what does it really mean? What's the definition of sovereignty? What we offer, which is unique to us, is on the one hand side, a data sovereignty, an operational sovereignty, a legal sovereignty, especially if you think about also Europe and Germany and the likes, and also a technical sovereignty, which is absolutely essential. We already deliver that in various forms of shapes. In the U.S., we have NS2. In Germany, we have a vehicle called Delos Cloud. Also, we have capabilities like sovereign cloud on-site options, where even for really critical customers in highly defense activities, bring our sovereign cloud on-site on the customer's place with the physical security of this defense organization in itself. 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 STACKIT or T-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 a Cohere, Mistral, OpenAI in this platform to be able to not only deliver our entire cloud portfolio, but also the agentic AI world which our customers needs. 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. In 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 firsthand about how they work with SAP, what they do with us in the cloud, with AI specifically. Here, it's actually my pleasure to welcome on stage the CIO of Ericsson, Malin Persson. Thank you. It's 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 you were addressing on the outset with us. 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. It's quite cumbersome. There's very few customers, and they are struggling with their margins. In order for us to be relevant, we needed to transform. 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- Yeah accommodate, right? It also drives complexity internally. The way we were operating was really hampering us from both safeguarding bottom lines, but of course also be relevant towards our customer base. Yeah. No, and absolutely. Now think about where you are today because we 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 had together? No, great. As we started out, of course, this was more about the clean core and what can be utilized on that to actually be a catalyst for necessary simplification and transformation. As we have progressed, of course, it's evident, you know, the trajectory of the development of AI is just massive. Of course, when we're talking about Gen-A, GenAI and agentic, there's a lot of things that needs to be done, and I think that we have truly showcased how we are partnering up on that. I think the communication you did on the BDC last year- that was a missing piece of the puzzle. Yeah. 'Cause that was really the foundational element that, you know, kind of allowed us to fix the fragmentation that we had. When we're looking into where are we 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. 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 for me. It saves huge amount of times. 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 employee 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 employee experience and how the people in the daily work fundamentally start changing to work based on AI? Of course, it's starting to change. I think we are beyond that phase when it's more exploration. All our employees actually have access through SuccessFactors now the thing, which of course is simplifying data. Personal productivity is obvious, right? Right. That's already there. Then we have the agent build of different sorts, which is getting much more in the hands of the democratization of quite a few users. No, no, absolutely. We talk about more than 80,000 users. Yeah who use that actually on a daily basis. That's real today. That's what you heard, appreciation, how people will start working differently. That's exactly what we want to do. That's exactly where, with our customers, we bring that not only to our portfolio, but as Malin and Christian shared, really also with these new capabilities now on steroids, which is certainly amazing. If you think about a little bit your journey, 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. Where do you see us and our joint journey also for the next couple of quarters to come? I think, to be honest with you, we actually changed our data strategy. That was a necessary thing when we're looking into agent-to-agent flows, right? When you communicated BDC last year, we actually flipped things around. We are depending heavily on your trajectory and roadmaps within the full domain of the autonomous enterprise, right? We are moving quickly. We have already quite a few things in live running first quarter, the ambition levels for 2026 is that we're going to have more than 100 in live running. Well, you heard that, 100. 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. 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 transformations now embracing AI in the enterprise and some customers for sure struggle a little, and you fundamentally changed this upside down in Ericsson to really be AI first and do all of that. 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. When you're talking about AI first, that can easily be something that is just a buzzword, and then you're facing massive resistance in entire organization. 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. 'Cause if you're just saying that it's for productivity to reduce workforce, of course you're going to get that resistance. I think that leadership in this day and age, 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 story on stage here with us. Thanks so much. Thank you so much. I think what you have seen is here, AI is there, AI is here now, driving value for our customers already now, and 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. 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 with the right organization in place. I'm super excited about what's come. 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. Actually, you have heard Christian presenting the strategy. He was setting the strategy for the autonomous enterprise, and my job is now to focus on what we are caring about next. 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. 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. autonomous enterprise means actually that we have now agents in place that do the work proactively end to end. you have humans in the loop that is making the decisions and also the judgment, because it's important that agents are operating in boundaries with clear accountabilities and also clear governance around that. this is actually why value is created only when technology operating model, but also the workforce capabilities move in lockstep. 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 top, 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. I'm 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. To achieve that, we have now built the transformation backbone, how we call it, with four integrated pillars, and this is all supported with our skill-led foundation. Because with that, we can drive actually scalable and repeatable execution, but we can also expand execution efficiency, which is also extremely important. Let me quickly walk you through the four pillars. The first pillar is very much about enablement and adoption. Extremely important one because we are investing heavily in our employees through Let's Work the AI Way. 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 it, have to experiment it, have to use it in order to get adjusted and learn about that. This is of course also reinforced and with targeted up 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. It's all about structure and process redesign because organizational development is so important though when we are moving towards an autonomous enterprise. As humans and also agents work together hand in hand, we have to redesign the co-roles because now you have an overlap of tasks. Agents will take over tasks, 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 split. We also have to change organizational run models and also processes. 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, that 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 tasks and accountabilities, together between split between agents and also humans, and that's why we also have to make, deliver, buy, borrow, build, and also automate decisions. Then we have to say, "Okay, where do we invest now also in strategic AI skills?" SWP 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. Those are the questions we are also answering with the execution backbone. This translates of course into the right decision to say, "How do we do also talent shifts?" Yeah, 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 targeted up and reskilling for our 110,000 employees that we are securing also critical AI talent and critical AI skills. This goes hand in hand. It's all three that are important. Because skills are changing fast, we are always talking about a so-called skills flux. Because the duration or the shelf life of skills is only six to 18 months, we always need to have a system in place that gives us a current view of our capabilities. We're building now, a 700 skill taxonomy. This skill taxonomy we have translated now into updated job and skill profiles, extremely important, and we will also have that up and running in our SuccessFactors growth portfolio by mid of the year because we have to measure proficiency, and we also have to see, okay, what is our skill inventory? You need the transparency, otherwise we are unable to drive the workforce transformation. This data is also informing us again, okay, where do we have to hire and where do we have to also improve our learning portfolio? Bottom line is we are rewiring the organization to enable AI, but to also in building also the execution engine that makes AI outcomes also repeatable. Extremely important. Now let's go and deep dive a little bit also into the pillars. The first one is, as I said, AI transformation rarely fails on technology. It normally fails on the lack of adoption. That's why we also drive enablement and adoption through our unified approach, how we call Let's Work the AI Way campaign. 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 cannot stay actually with only localized or very fragmented usage of AI. We have to make sure that we scale across the organization. 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 at SAP to use AI, right? To make sure that we are freeing up time also of our employees for higher value tasks. 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. It's important to keep the culture strong and to always make sure that we are putting it into new context for our employees. We are also doing that to apply performance management. In 2025, 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. One of the flagship formats 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, 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. From there we say, now we are scaling actually also the enablement with three levers. The first lever is actually that we say is skill-led learning. Skill-led learning is, I said it before, that we have now AI-based and role-based skills and learning journeys, we're investing heavily also in trainings. We have 1 example I would like to share. This is our AI developer program with two learning tracks. The first one is Joule for Developers. It's for developers who are then building extensions and integrations with Joule Studio. The second track is actually intelligent agents. This is for developers who are building autonomous agents or multi-agent systems. 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 projects. We also have launched just in January this year, our so-called Learning Navigator. This is a single point of entry for our AI trainings. We have meanwhile more than 49 learning journeys for the selected profiles, we have more than 500 skills who are directly linked to trainings. We keep that also current actually with quarterly learning priorities. We also set out a target last year to say 15% of the working time should be also invested for learning. 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. We will also make that adoption sustainable even further also after Sapphire, because we are putting our two hours of protected learning time per week for our employees. Because this is the feedback we're receiving also in the employee surveys to say, "Give us more time to learn." This is what we are 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. We are running code camps across SAP. Code camps are hands-on build sprints where cross-functional teams are working actually on real business AI problems for our customers. 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, 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. Those are tools like Joule Work or also Claude Code. The third lever is how we scale is actually through our multipliers. This is also when it comes to change management, when it also comes to large scale adoption, it's a peer-to-peer influence. We have meanwhile an AI ambassador network with more than 9,000 ambassadors. We have, since this year, beginning of this year, increased that network by 60% alone. This is also how we try to remove the adoption bottleneck. Let's come to the second pillar. This is something where we really have to look how can we rewire SAP. We are experimenting, and I also say that very clearly, that is something where we have pilots, and I will also speak about it in a second. It's important that we have a very clear North Star of how does an agentic company looks like, and how can we also design an operating model that is actually integrating human and humans and agents into operating model. It's very clear that humans have to stay in the top, at the top, and always have to stay in control. I think this is extremely clear. When you look at the current models we are having, we have verticals, we have org boxes, and now we have to shift into layers because you also saw before on the slides, how it works. 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? Then we also have the second layer, the second layer is how we call it the orchestration layer. This is where Joule sits, and this is where then Joule is orchestrating the agents. Then you have the execution layer underneath. The questions we are asking at the moment is, okay, how do we bring these three layers in sync? What do we have to change, especially on the strategic decision layer, because this is where our team sits. Then we also have to ensure how do we integrate actually agents together with our teams. The separation is of course deliberate because humans have to decide actually what and why we are doing things. Joule orchestrates then the how, and then the agents actually execute at scale. 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. This is where we can also see where execution discipline is also most visible to our customers. We have introduced last year the so-called AI native harmonized product operating model, in short, HPOM, which brings product engineering and UX under one shared accountability. The point is here that we have fewer handoffs, that we have faster decisions, and also faster time to market. That was the foundation to say, okay, how can we now evolve the system end to end? How can we now integrate all the questions I have asked before? How do roles 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? We have tightly connected everything in order to start shifting also the operating model in P&E. First of all, of course, we had to look at the roles and responsibilities. We have a deep redesign of the most critical engineering roles as engineering and technology roles with very explicit AI responsibilities and oversight. Also here we are already considering actually the impact of the tool usage, but also the integration of agents. When you look at skills, we have now infused more than 30 AI skills into our skill taxonomy, because at the skill taxonomy, what I mentioned before, the 700, is not static. This is changing all the time. We are adding every quarter, we are adding skills, we are removing skills again because of the relevance. Just recently we added 30 new AI skills also into the taxonomy. Those are skills like context engineering, rapid field prototyping, or also AI assisted development. Everything is directly embedded into the role architecture. Here to date, when you look at learning, in the P&O, 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. We also have tested more than 73 tools that informs also the SAP-wide adoption. In June, we will also start rolling out the code camps in Muhammad's organization in more than 26 locations in order to make sure that we bring new teams together and that we have hands-on learning again. We also shifted the hiring. I will also say a few words about that in a second. We hire for AI critical skills through very targeted and very active, proactive sourcing in AI hubs around the world. We are hiring in Munich, Berlin, Singapore, Palo Alto, but also in Bangalore. Then change management also here, we have used existing formats already because it's important that the context is relevant for the employees and if we are not putting artificial change formats on top. We have used existing formats in order to drive the change. Actually P&E is for us, the foundation. It's a blueprint for us. We are now seeing, how is this evolving, also with the North Star I have explained before in mind. 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. Let's look next also, how does it look now for the overall workforce investment and mix. 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. Our internal agentic AI roadmap 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 it impact also the role compositions? Which task is automated? Which tasks are stay, and how do we then have to redesign the roles. First of all, I also want to share, okay, where are we at now leaning in? Because we have to make strategic investments in certain profiles. Just as an example, we're investing here very clearly in machine learning engineering, we're investing in data science, enterprise architects, but also business AI and data platform consultants. You also have roles that needs to be reshaped very clearly. We have one example, because as 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 redesigning the role, but that we also have the right learnings and the right trainings in place for our employees. Roles such as quality management and user assistance developers, supporting roles and shared service roles, those are roles that needs to be reshaped when you also have the vision and the new North Star in mind. We also try to make that, of course, actionable. How do we make that actionable? We have defined now, on the one hand side, core AI skills that go across all roles, context engineering, AI-assisted prototyping, AI trust and verification. You can see that on the right side of the slide. We also have come up, as I said, with the AI skills that are now embedded in the role architecture of selected profiles. Here you see machine learning engineer. The AI skills we have embedded are, for example, agentic engineering, semantic retrieval, or evaluation and benchmarking engineering. 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 at the market? This is actually how we try, how we create structural operating leverage. We are reallocating capability to where AI can scale execution. Now let me also share how are we now investing also in these profiles and how do we also approach the hiring? We understand, of course, that AI execution requires also different talent in very specific roles, and this is just an example from the roles I showed before on the previous slide. 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, senior expert talent. I have spoken about up and reskilling, but I would love to speak about now how hiring has changed using the four profiles as an example. First, what's very different in our hiring approach, and you saw that already on Christian's slide. Because we say we have a three-to-one ratio that means that we are instead of hiring three developers or standard developers with standard skills, we are now investing also in top caliber AI talent. Because on the one hand side, the productivity impact is meaningful, and especially in development, where we see roughly 30% uplift already. That doesn't mean that the cost base is going down, because we have to invest in this scarce talent, and this scarce talent is quite in high demand at 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 comp package at the moment in order to be competitive out there. 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. We are also scaling senior expert hiring where it most directly drives build velocity, but also customer value realization. You can see it also in the numbers already how we are shifting and how we are changing our hiring. You can see the numbers that our AI relevant senior hires have nearly tripled from 2023 to 2026. Since 2025, we have made already 6,000+ AI relevant hires in engineering and technology alone. In data science and also machine learning engineering, we have seen a 200% increase in hiring just about the last six months. We are also bringing in exceptional key AI executives from the market because the caliber of talent is necessary to accelerate also our AI ambitions. what is also important is that we are also building our own internal pipeline, because even though we have more, 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, instilled also as a curricula, as a strong pillar. 80% of vocational trainings, are already in AI relevant degree programs, especially for machine learning, engineering or even data engineering. what is also maintaining strong that we are also this you saw in Christian's slides, that we are also collaborating with top tier universities, like Stanford, TUM, or UC Irvine. 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. With that, I would love to hand over to Sebastian. Thank you very much. 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 one domain. 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 latter, and I will make up some minutes on the former. Hello everyone, great to be back. No, my real job is actually to turn SAP into an autonomous enterprise, and that means both in terms of making our vision real for SAP and at SAP. Both in terms of what we commercialize for our customers, as well as how we run SAP itself. 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, where you can see the majority of our subscription base already, so 55%, is non-seat based. That's even excluding the consumption share. There are some great examples here, like, BRIM, Billing and Revenue Innovation Management, measured on revenue spent. By the way, a great reminder that we are the company bankrolling the software's industry's commercial infrastructure. If anyone can be flexible for commercial metrics, that's definitely us. There are many more examples here. One I like to call out is SuccessF actors. 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 frontline. Even there, I would say this is super resilient. 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, where the dominant share is also coming from non-seat based components already. 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 described, so through consumption of tool assistants and agents, as well as on the business AI platform layer, where we already see significant growth in things like AI and BDC. 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, in our ecosystem investment, in simplified commercial models, and so many, many more levers to ensure we see this accelerating consumption growth, and we're 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. To give two 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 their migration cost. 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 a autonomous batch release for a pharma company we talked about yesterday, or an autonomous maintenance ticket closed for an oil and gas company. In a nutshell, here we will be monetizing on a value basis, really the outcomes and savings that we prove to deliver to our customers. That's it on the commercial model. Now, let's turn into how we actually turn SAP itself into an autonomous enterprise. 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 despite significant AI investment. Now, how are we going to do this? 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. Really 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 zero. 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 EUR 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 the at 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 Asam. From planning, to risk and compliance, to invoicing and reporting, our AI assistants and agents are already taking over strictly governed process execution. The operational impact we do already see is material. We see 45% productivity gain in contract management, over 40% time reduction in, from offer to collected cash. 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 one example, the financial planning assistant, which supports budgeting and forecasting, detects margin compression risks early, identifies actions to protect profitability or increase top line. Collaborating AI agents 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. Here, of course, we are deploying AI tooling, Joule agents, and our own business AI platform, as well as best-in-class third-party tools. Muhammad talked about it. In a nutshell, what we see is a compression from starting to build an agent to GA-ing 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 builder for low-code and pro-code development. Every developer at SAP has access, for example, to cloud code via our own agent hub on top of AI hub on top of BTP using our Joule SDK for pro-code development. Of course, our ABAP developers have Joule for Developers as well. We contextualize these agents with easy access to Business Data Cloud, more than 300 data products available to all of our developers and our Knowledge Graph. It's a super fast process, and honestly pretty foolproof, 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. On the governance side, that's basically, typically in the development process, the hardest work. 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. 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 efficient shared service center setup. By the way, same true for finance and many other areas. Now, in autonomous HCM, the objective is really to elevate the employee experience as well while taking away burdening administrative tasks. Our AI assistants support the full employee life cycle, and the impact is adding up quickly. Around 15 hours saved for simple onboarding per year, 25% productivity gains for performance preparation tasks, 80,000 hours saved in early rollout across many, many operational HR workflows already within SAP. With that, we are really turning Gina's function into a truly experience-led high productivity HR function. Good example here is our career and talent development assistant. By the way, all of that is either live or in rollout within SAP. It supports creation of development plans. It automates talent discovery. Gina talked about how critical that is for us, as it is critical for many others. Proactively builds succession plans. The impact is tangible. We already saved more than 45,000 hours per year across SAP, and that's in the initial rollout phase. We don't even yet see the full productivity impact. That resides not only in productivity, but in the 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. Our focus here is really on optimizing the end-to-end customer journey from demand creation to adoption and expansion, the journey Thomas talked about. Joule becomes really our single engagement layer already for our sales teams. Customer-facing colleagues save more than 15% for their preparation and coordination of customer meetings. Our consultants save one and half hours per day, roughly with AI guidance on implementations and in 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 with the customer. By the way, it's also a great example here of 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 the new version of CPQ, our own product for quoting, which actually led to an 80% reduction in quoting time, in time to produce a quote. With then now AI agents coming on top to further reduce the time spent on quoting, which is a tedious activity, as you can imagine, especially for large and complex customers. Take one example, Thomas already touched on it, something I'm very proud of, which my team in SAP's IT built on top of SAP AI platform, with really the golden path we provide for pro-code based development, through the Joule SDK in this case. Basically what you see here is how we build a fully autonomous software support multi-agent system. It has, I think, 26 agents, 60 knowledge sources, all of that connected through Business Data Cloud, all of that built on BTP. It has human-in-the-loop capabilities. What we achieved with that, basically, of the 2 million cases that we don't deflect, we get more than 10 million cases a year. You can imagine that stack was already highly optimized. By now 100% of these remaining two million tickets get a very strong recommendation from Joule already for our technicians. 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 scores on support at the same time. One thing to close off is also very clear for us, you cannot have autonomy without governance. That's why we have established a stringent 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 of three months. That comes with rigorous value management, so I can commit more than EUR 2 billion to Dominik in productivity, but also with rigorous change management in partnership with Gina on how we roll out these capabilities using all of our own tools to do this. These productivity gains with that are not just claimed, but realized, measured, and continuously improved across hundreds of use cases. 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. With that, over to Dominik. Also warm welcome from my side. It, it's great to have so many of you here. It has been a pretty wild ride over last year. Let me just kick it off now with some numbers, and then I go to a more conceptual part about the autonomous enterprise for finance, my board area. Last but not least, and I wanna also go back to the numbers part. The first point I wanna make is, while there has been a rollercoaster on some metrics, like our share price, there has been actually 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 US dollar exchange rate. There's 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% one year ago. That's interesting because when I then quizzed the investor relations department, what is actually our weighted average cost of capital because I want to back solve what's the growth assumption for SAP, I hear it's between 7%-11%, and the median and the mean is pretty exactly 9%. If I deduct that 7% free cash flow yield from the 9% weighted average cost of capital, and I go back to business school, corporate finance 101, I see the implied perpetuity growth rate is 2% nominal, and that's a negative absolute growth rate in a perpetuity. Basically, we get a very clear message from the market, "You're going to die. You're going to be kind of disappearing in the world economy." world economy is growing at 2.5% real, you're real zero or shrinking. now what I wanna do is share with you why I'm not convinced about this hypothesis from my point of view. I wanna sneak first in the shoes of somebody who has been a CFO role for a large group for now in my sixteenth year, and not only at SAP, but also before, and share with you how we view the world and what the AI component can do to us. First of all, I think we need to conceptualize two hemispheres in what is a finance department, and I wanna characterize one as deterministic. 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 high degree of repeatability, that when there is a certain input, exactly the same output will come. 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. In the executive board in Germany, all of us sign with our blood that these documents are correct. In U.S., the CEO and the CFO are signing that. There is just no tolerance for risk in these processes. This is really what is the absolute key priority of any finance department. I always say very kind of jokingly, "Staying out of jail." We wanna just make sure that the assurance levels are really met. By the way, sometimes we have some errors. These errors tend to be always related to the more probabilistic world I come to later, namely humans doing stuff. I can tell you when these 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. I think it's not only the audit committees that should care about it, but investors should care, too, because after WorldCom and Enron, I think, it showed that these internal controls have a certain meaning. Now comes the fun part for CFOs, 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, tacit knowledge. You require subtle reasoning. Inputs are unstructured. What do I mean by unstructured inputs? Think about us quoting to a customer. When we do that, then suddenly exceptions are popping up. 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 Klein has promised to some customer at some point in time, or even a board member who's not even there anymore, or this is the pricing we had whispered into the ears five years ago to somebody in the supply chain, or some professional that was in another company leaving to another company and now that kind of knowledge about pricing has migrated to another company. 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 at 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. By the way, this is a compliance risk. It's extremely difficult to put controls around it, proper authorizations. Yeah, 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 guardrails. 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 absolute paramount. We have to combine that kind of neural part of the world, which the more algorithmic, deterministic, symbolic part of the world. 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. 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 a deterministic world. 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. Let me share some deep convictions we have on that front. The AI models are not substitute for traditional deterministic systems, and I give you three reasons for that. First of all, I mentioned all the shortcomings of probabilistic and what trouble they kind of induce. 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 quasi zero. In contrast to a kind of prompting process which is requiring 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 can be dolled 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. It's also that this kind of preexisting knowledge, what is embarked in our systems, is not publicly available. Yes, of course, you can run pre-training 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. That's again, a reason why the deterministic world will not disappear, and we will continue to use the software. Next point is the quality of the agents really depends on the quality of the underlying systems. 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. 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. It's all about having the quality of the data, context-rich, having a golden record, and Reltio, 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's 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 where tokens are crunched today, these are often one-shot type of transactions. It's a customer calling a call center and saying, "What do I need?" It's over. If you make a mistake here, you've spoiled that one transaction. In these processes we run in finance, we have often multi-step transactions. As many things from a quote all the way to the cash-in, all the way to the financial disclosure, where when you only make one mistake in one step of this huge chain, it's over. You have the wrong number in the disclosure. What that all means is that the probability of failure in one single step is compounding through the process. It's like in automotive manufacturing, when you have one part that is wrong, the whole car is broken. That's very different from this one-shot 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 is very different in the type of world, in this kind of transactional, long chain world that we are exposed to in finance here. Third one is on the probabilistic side. Yes, it is absolutely super useful to use these large language models. There are good ones out there we can use. We also have now this RPT-1 model on large tabular data, which is again proprietary SAP data in SAP systems, and now we extend that with third-party data, by virtue of the acquisition we have recently done. I do not need to go into details. I think Muhammad has really explained that extremely well. Last but not least, and maybe most importantly, I think it is a huge mistake to think that vibe coding is automatically the same thing as creating enterprise-grade ERP system engineering. We benefit a lot, as has been shown before. We gain huge productivity with that. The new long pole in the tent is really to making it enterprise-grade, to put what I call the trust wrap around it, to satisfy all the compliance requirements, to govern it properly. It's almost like an insurance policy for the customer. 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 wanna put everything they can under that SAP trust wrapper to say, "This is the thing. This is my insurance policy that 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. Also, it's flawed 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. The higher share of the total cost of ownership of the software is actually in that maintenance cycle. Here I summarize some topics where you can see how we think about it in finance. I don't wanna 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, and I also wanted to be modern and use an AI agent to coin something, is that kind of symbiosis, that neural symbolic system of action to become an autonomous enterprise. 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, one year added. The CAGR on cloud revenues over the timeframe has been 23%. 25% if you focus on what is really the center of gravity of our activity, which is the fast-growing SaaS PaaS layer. You know that infrastructure as a service is phasing out. The only specialty, so to speak, we are going to push is the infrastructure for sovereign applications. 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 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 durations, it's also driven by steeper ramps. I think you heard us talk a lot about that in the last quarterly calls. We think that actually the numbers that we can produce are giving a lot of substance to the growth story. The TCB growth in, inflection, so to speak, is good for a 2 percentage points acceleration and helping us, so to speak, to offset that kind of expanding base we have to compare ourselves against. 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 EUR 1 billion over the year 2025. You see that we continue to believe that over the time frame through 2030, we should see that maintenance base be cut in half. 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, 2027, and then the extended maintenance, 2030. There's one thing I want to highlight on top, which is that actually the ratio of conversion today is about two-thirds coming from ECC and one-third coming from S4. We are not only converting legacy directly to RISE, but also the S4 is going more and more to RISE. When customers start the journey, they are not taking the detour through S4 anymore. They're really going straight to the RISE offering on S4 in most cases. Why? Because of what had been depicted before. The processes themselves are massively re-engineered with AI. If you do the blueprinting of an S4 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 are not 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 that canyon in two steps. 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 S4. Margin-wise, I think the most important message here is that this 80%-90% operating leverage, as we call it, I see, i.e. the total expense growth versus the revenue growth, is still intact. Sebastian mentioned the more than 2 billion AI efficiencies will embark over the next years through 2028. 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 the gross margin is more or less stable, yeah. If you say that's the operating leverage will not come much from the gross margin but comes more from operating expenses, that's a fair assumption to take. 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 the last three tuck-ins, they were kind of depending on which one you look at, more or less early stage. We are talking about transactions that are dilutive, have a J-curve, but this will not bring us to decommit from these numbers, but we create the efficiencies to fund that aggressive acceleration of our roadmap on cloud. Last but not least, cash conversion, always dear and dear 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've 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. 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 accrue in the P&L for stock-based compensation, cash out versus P&L, and this gets you to these numbers. This is also the reason why we think actually, we are going to grind up further on the famous rule of forty target that we have set a while ago. Capital allocation, no change on the policy. 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, opportunity here? Time is really of the essence. 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 the Strait of Hormuz 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 a quarter of that already, and now the remain to do, which is about three quarters of the EUR 10 billion, will become quite linear. You know, we don't speculate on the share price here. We just do that in a very disciplined, rigorous fashion over the next one and a half years. 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've been always pushing back on giving long-term guidance on growth. Some people said, "Dominik Asam, 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 have 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, that this is a sustainable enterprise. 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. 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. Good afternoon. 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 back to the stage the SAP executive board. Great. Welcome back, everyone. I hope you had a good, quick lunch. Still have. 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 one second. We're now gonna open it up to questions from you in the audience. If you would like to ask a question, please raise your hand, and we have mic runners here to bring you the mic. Let's get started with Adam, please. Hey, it's Adam Wood from Morgan Stanley. Thank you very much for the presentations. Maybe if I could dig in a little bit around how SAP is using its own technology, 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? 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 on it? It feels like you're being judged a little bit differently in terms of purchasing decisions than maybe some of the other players. Sorry in the market. Got carried away in a conversation. Yeah. Well Muhammad is from the off. Yeah. There we go. Maybe I start things, Adam. 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. We see that actually across every function, I mean, we see a significant north of 20% productivity lift we expect from go-to-market to corporate functions to development. And then if you compare usual productivity tools, the Claude Code of this world and what we see, actually, it's a good, healthy mix tech development. I think all of our people have Claude Code. I know all of our people have Claude 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. What we found is actually when our developers worked on enterprise-grade code, this is now vibe coding, that actually the productivity gain was still there, but the real uplift came when we now infused things like the domain models Muhammad talked about, made our KG available, the agent lifecycle management. In many cases, the 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. With what we are shipping in the, with Joule Studio 2.0 and the SDK that goes with it, we take care of a lot of that for our internal development teams. With that, what we've seen is actually a significant uplift on top of Frontier lab AI model usage. We based and measured productivity, and we see right now, I think a good measure, I can say, on GitHub pull requests, I'm not saying that's the natural yardstick. We see significantly north of 30% acceleration in terms of development velocity that we are seeing. Then I always like to say the amount of unwritten software in the world is infinite. I think this unlocks great opportunities for us to provide more solutions like in the industry AI space. We have a full roadmap, so every function of SAP is co-innovating with Muhammad's team, Dominik's team, Gina's team, to make sure the Joule assistants 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 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, forgiving ability to mark up code that you got 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. 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 discuss here. 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. We're going for that more ambitious goal, and that's why it takes a little bit. You made the point, though, if you were paying for your own software that your spend with SAP would be accelerating- Yeah, so. in a way you wouldn't. Sure, sure. I mean, we looked at that. I cannot disclose the numbers, but we are a RISE customer. We, of course, use our full suite. If you go across AI and BDC, it would be a 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. Yes, absolutely. I think that's also one of the sessions where, like, 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. We would pay a significant uplift on our Joule and AI assistance already today. Okay, let's go to Toby, please. Who's next? Hi, it's Toby from JP Morgan. 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. Could you perhaps talk through specifically, you know, what gives you the confidence sort of in that consumption ramp and, you know, any more detail you can share around the big kind of product components that would drive that sort of ramp in consumption revenue? Thank you. Yeah. I first a small comment on the financial part. The key message we wanna 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 you 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 this 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. If you have that leverage with the customer because they wanna 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. 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, you wanna, or Christian? I mean, absolutely what we 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. That's something if you think about the more data which is processed, the more agents which are running, this is compounding effect. 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. Basically, that's the majority of the consumption-based revenue, for sure. On top of that, we also talk about some of the large scale RISE transformations, which is also in a consumption-based model as well, which for sure will continue to continue. That gives us, quite frankly, a lot of confidence that this is exactly in that direction. 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. Yeah. Maybe one last word, what was actually for me a little bit of a reminder about our cloud transformation five 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 I 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 OpenAI, with Anthropic, 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." Let's not forget that. Also to Adam's question that, you know, there's always, you know, what we are selling is the end-to-end. We sell the LLM plus, you know, the governance and the context part. Okay. All right. Let's go to Jackson. Thanks. Jackson Ader at KeyBanc Capital Markets. Thanks, guys. The one that I had was if I do some envelope math on the cloud starting point for revenue in 2020, you know, 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, 7.6. If we think I'm not asking to, like, you know, I'm not gonna, 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, but, like, should AI be an accelerant? Should we expect a shorter, like a compressed time to doubling that cloud revenue because, you know, these AI products in a consumption model? Just curious how we're thinking about, like, this new base in the next five years? Thanks. Please. 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 2026, 2027, then we had three impacts. We had t he trade disputes, then we had the Iran situation, and we also had this shift of saying we are going to, in 2026, reduce the hours we bill in services and use more hours to adopt. 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. 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 macroeconomy, is there some headwind indeed from some SaaS apocalypse stories? That's the modulation. In general, we feel that the underlying growth trajectory is. The puts and takes are very favorable in total for SAP. That's why we said it's more an opportunity than a threat from our perspective with all the reasons we tried to give in the session. Maybe, you know, 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." We learned about the context, we learned about the governance. We, I mean, you do your channel checks. We of course got a lot of, you know, feedback. I guess now when I look at the feedback, what we are getting on the beta version, which now will become GA on the platform, this time I can say we are coming out of this learning curve. also with Wise, I mean, you know, when we launched this offering, there was so much learning you know, which we then said, "Oh, yeah, Signavio and then LeanIX, and we need to govern and support the customers even more with the architects." The same is true here, but I see also now a phase coming where we say, "Wow, now there is this, you know, point where we see, oh, now we can start scaling." Yeah? Because now we see the accuracy. Now I see in the beta testings, oh, the accuracy, the compliance, the output is getting better. We measure that. That's why I have the confidence to say we are over the learning phase, and now we are in the scaling phase. 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 extensions, building new industry AI use cases. When that is happening, then you really then go into the accelerate phase, what we also outlined on the slide, yeah? Of course, you know, Dominik is right. You never know what happens next in the world. Just looking at our AI journey, we are definitely now entering the next phase. Okay. Let's do Charlie, and then we can do Ben afterwards. Yeah, Muhammed, I liked 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? 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? 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, you know, 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? Yeah, 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 up top, if you will. I do believe, I think the thesis around our growth, you know, going back to a couple questions as well, you know, if there's a belief in the thesis that, hey, the token consumption in the world is gonna grow and is gonna grow exponentially in some ways, and that's sort of what we've been seeing, then you can sort of separate that growth into two buckets. Certainly, there's gonna be one on the consumer side, and then there's one gonna be on the enterprise and the commercial side, right? On the commercial side, there's gonna be some maybe on the SMB side, and the other's gonna 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 experience is creating value for those enterprises, there's gonna be a few things, right? We think uniquely to whatever's the best LLM out there on top of what we can add creates the most value. 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, 'cause the ability to shift from one LLM to the next LLM while the stuff on top continues to run is very simple. It's gonna get even simpler. The science around what LLM should you use for what workload is also gonna get more sophisticated 'cause you don't have to use the most expensive one for the simplest tasks, right? 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 the. That's where we shine, right? to me, I feel like we've got the unique value proposition to be the layer up top to benefit from the thesis of token consumption explosion by providing the value to the customer, and that stands. Now, let's say that doesn't happen for a percentage of our customers, 'cause 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 plan 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. there's gonna 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. We will obviously monetize that as well. Not as much as the scenario A, but we're gonna be in that cycle one way or the other, if you will. 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, that's the customer context, the company memory, but the ability to switch out that is super simple. anyhow, it's a bit longer and complicated answer, but 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's announced, two things Christian announced yesterday, if you remember, is not just this 100 million dollar for our partners to rewrite and re-platform 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 runtime, right? Runtime happens later. That's where N8N also makes a lot of money. We're saying to our customers that, "Listen, we're gonna 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 gonna give you runtime free as well." 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 wanna be that agentic layer for you." Hopefully that makes sense. Dominik and Christian may want to add something. Perhaps 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. Also we are more prescriptive how they need to work in order to reach the quality levels, but also the acceleration in all of what we want to achieve. I'll give you an example. I mean, all of our RISE with SAP validated program partners commit on the methodology and the tool chain. They all use Signavio, they all use LeanIX, and Joule for consultant and Joule for developer, which is super critical to reduce the cost for our customers. 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 likes, they talk about thousands of users for Joule for consultant and Joule for developers, which they now include into the project work, and we will for sure see more. We also have to a point on incentives for this world, also have customer incentives, which we also associate with some of the RISE with SAP transformation, maybe for short on purpose, wanna also invest into the AI adoption in as we discuss. 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 Joule capabilities, whether he puts something in between or not. I mean, that is really triggering a lot of value creation, for us. 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 wanna hear that word from an antitrust point of view anyhow. 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. Last point I wanna 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 Joule, honestly, at some point in time? If you are a kind of higher level executive in a media company, maybe you have different needs, and then you kind of make that Joule capability connected with Copilot a kind of invisible slave below, so to speak. As long as we get the license for that customer too, commercially, it also gives us a lot of value. Maybe just on the internal side, because the question, I mean, I would say actually that engagement layers is ours to win and not to lose. What I've seen with the initial employees that have access to the new Joule experience, it's exploding. I think there's this misconception I hope you took away. I mean, lots of our application base is already, in a way, lights out, and I couldn't care less if it's a user or an agent that's triggering an invoice, that's triggering a purchasing order. Our commercial model is immune to that, and I'm seeing that within SAP too. 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. 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 month. Great. Ben, do you wanna go next? Thank you. Hi, it's Ben Castillo from BNP Paribas. Thanks very much for your time today. 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, you know, extremely focused on delivering that at least EUR 2 billion of AI efficiency. When we're looking at that gross investment wallet over the next three or four years, mid-single digit billion EUR, 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. I guess my question is, you know, that 80%-90% feels quite conservative. Why could that not be maybe slightly more aggressive and maybe be below 80%? The follow-up question might be, well, if it is in that range, you know, 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 what, you know, why it's needed to be that high? Thank you. Maybe, maybe I start with some ideas on that. It's true, it's a big amount of money and a lot of firepower. We don't wanna 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. I did mention that behind some tag-ins we did recently was also very much the idea of time to market. 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. 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. We've done that in the past. I mean, we didn't even think about BDC three 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, they 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 and leveraging AI internally. 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. Maybe to add, first of all, you will see Dominik and me being relentless in ensuring these productivity gains are net 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. 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. Yesterday I saw the head of our finance shared service, 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? Yeah, thanks. 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. There is this kind of small set that are very advanced in their AI journey. Some of them have even been a bit vocal, kind of, you know, how this has enabled them to cut back maybe even on 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? Just a quick question for Gina. You talked about the evolution, you know, kind of the skills mix, investing versus reshaping. Maybe can you give us a bit of a timeline for that and also what you think that will do to the overall headcount of the company? Thank you. Yeah. Let me start, and I give, you know, my pitch I just have 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're running round about 40 ERP system, even four or five non-SAP acquisitions. Now they ask me that question, said, "Oh, Christian, you said in the keynote, you know, we can use AI also for S/4 on-prem or even ECC. What should we now do? should we just, you know, build now the agentic AI layer, or should we start modernizing?" 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 hand-holding, yeah, to, you know, 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 agentic AI layer. We can do that. Muhammad built a connector, so we can do that. 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 down. I guess, you know, this is not. Also for many customers, it's not really an either/or, it's really about, you know, both. 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 is a lot of pressure on them as well, similar to Dominik and Sebastian here, to deliver these efficiencies, yeah, what we are all shooting for. 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 this benefit with this business context, business data, business processes, and governance. This governance aspect should not be underestimated how much simplification it brings, because again, in the end of the day, systems need to be auditable, audit does need to testify, otherwise people go to jail. That is certainly an aspect where nobody is joking around. I give you perhaps one very concrete customer in that sense. Always good to talk about concrete customers. Bayer in Germany, 1 of very advanced technology-wise customer which we have. Basically we, with the new platform, went to Bayer and said, "Look, let's go in an FDA approach." Now basically actually this weekend, which is coming, they go live with 8 agents from SAP, seeing the new power, and they basically leverage the platform. They also fully embrace Joule 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, two months ago with all the adjustments and evolutions, because what Mumu had said, the innovation speed is unparalleled actually. It's unbelievable how quickly we add more content, more context into that. Now Bayer is rolling out Joule for Consultant for all SAP professionals in Bayer. Actually even more so, they now ask all the system implementers to do the same because they expect the productivity gains from Joule 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. 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 total different world. I'm absolutely convinced, quite frankly, also after that SAP Sapphire, this will be a big boost in AI adoption, by the way, around the world. 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, you know, taking it from the perspective of they've bet on an agentic AI platform. They don't want to go change that because they've already deployed a bunch of agents on top of it. You know, there's a few customers in that category, right? Either they've done that or they've built their own because they believe nobody, or they can't use anybody, anything that's off the shelf, if you will. I think that I haven't run into a customer that has done that but still doesn't see value from the autonomous suite. 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?" 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 layer." The A2A allows us to have an and story as opposed to always an or story if you already bet on it. Now as you do the and, this is where the beauty of, and the context comes in that now as I continue to add SAP agents, I can build on this platform but still have orchestration on whatever I built internally. This and to me is the answer to those class of customers, if you will. It's not an or discussion, or are we going to try to go compete and say, "No, get rid of that completely now we're in the game and you should entirely use us. You asked on the timeline. I think it's difficult to put a timeline out because this is an ongoing effort. 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?" This is the first labor. As I also said, 85% at the moment in the most important profiles, in order to build up the AI skills is that we are saying we do up and reskilling. 15% for the roles we are getting from the outside market. 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? What do you automate? This is a constant cycle. I think we cannot say what is the timeline. We have to make sure that we have the right skills on board in order to deliver to our customers and to our products. We have an ongoing effort in order to do that. From a head count point of view, it's very much that we're keeping that flourish. Okay, let's go to Mo and then Michael. Gina, Lena. Yeah. Yeah. It's Mohammed Moawalla from Goldman Sachs. Thank you for the presentations. There's obviously been various discussions in the marketplace around the momentum, around the migration or the transformation cycle. In your opinion, to what extent is, you know, the sort of perhaps the lack of a roadmap 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. Now post the announcement of the roadmap, stuff around Joule 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. 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? What's been the feedback also on the kind of the roadmap from customers here? I can get started, please, team, share your feedback. I mean, especially after that week, I mean, you know, of course, we have customers who didn't yet move with us to the RISE journey. This was less of a, "Okay, we don't see the value." I mean, many customers, you know, 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 one or two years." These customers said, "SAP, we are going to make the move, please give us the time, give us a year until we have figured out how we wanna run this company." 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, you know, 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. 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 one on while we are together modernizing your landscape." All two 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. You know, oftentimes it's also a customer has already started a journey. Then there is still, you know, started with 10 ERPs, but they have 50 out. Give these customers time. You are touching the most mission-critical system in the company. That's why you see also in our total cloud backlog always this ramp, because exactly that. Customers wanna time that also in the right way. I think that's an important perspective. If you look about the support revenue base, already a portion of the support revenue base is already a customer who signed up to RISE. Based on, as I mentioned earlier, customers with 350 productive ERP systems need one or two years to move all of them to RISE. For sure you see the shift in the support revenue. That certainly is something. We've clearly seen acceleration of, and expectation on acceleration based on the migration, the agent-led migration which we've put out. I think that's certainly something what is helping our customers also in light now what we discussed for the maintenance end which we see with ECC. We are quite positive there. It'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?" 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 vibe code 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 standard to 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 per mil of revenue type of tickets. Why would a CFO say, "I take that risk, bet my job, to do all that myself"? It might not even be cheaper because they will also crunch a lot of tokens. They will need to maintain that, and they can get all that hassle that, and sometimes it is 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? I mean, I meet many customers on a peer-to-peer basis. Most, actually, almost all customers I meet, they know that plastering AI on top of something that's broken three levels down on the only thing it gets you to is with light speed into an RPA 2.0 disaster, and the only thing that will go through the roof is token consumption. Actually, I don't meet customers, rarely, 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 empathize with that with for running our own IT operations. I don't sense there's hesitancy always stay on-premise. It's timing. Then, of course, they love everything we can do to reduce the cost and time to get there. Let's go to Michael, please. Thank you, Alexandra. Michael Briest to UBS. 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? 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 we have with AI go out of the box. 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 we go to market. We're very prescriptive on that one. Basically, all the net new names and logos you will see going on will be by definition GROW with SAP customers. For sure we leverage significantly here on the one hand side of scale with our indirect channel, what we have in the partner ecosystem for the mid-market. Also, we leveraged opportunity with private equity companies, with all the portfolio companies, where 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 a modern, cloud ERP with AI embedded. With that, again, they can grow infinitively. For me, this is absolutely clear that this is the predominant, net new name, engine for the company. We talked last year about apartment-driven territories. We see phenomenal growth in that area. Yes as well. The way I would react to your question on rule of forty is the following. We have a deep conviction that no matter whether it's nominal, 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. There will be a trend of grinding up the rule of forty performance. Now, how fast that really goes depends on so many factors. Again, I would kind of be cautious on that. It's of course a journey that we wanna consistently pursue. We had some bumps in the road now with the topics I mentioned before. Honestly, with whatever might happen Strait of Hormuz and some commodities running out of steam and being not available, and there might be some kind of meltdown scenarios I can't talk about. That's why I would also refrain from giving a specific timeline 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 wanna be hung up with them. Okay, let's do one final question, and let's go to Fred. Hi there. Frederic Boulan, Bank of America. Thanks for taking the question. If I can stay on the AI topic, can you discuss your AI ambition? I think in the past, you talked about that billion plus opportunity. Can you be more specific about your ambitions, timeline, et cetera? I think last year you had this kind of 5x multiplier with product innovation. Keen to hear your thoughts on that. Secondly, coming back on the AI commercial model and economics, there's a lot of announcements around AI capabilities for free, pushing FDEs. Can you discuss the economics of building versus running agents? What kind of margins can you make on that revenue stream? Thank you. I mean, look, let me start and please chime in team. I mean, first, what can we monetize with AI? I hope this really came across today. First, we have, of course, the platform. 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, you know, is of course all going predominantly to SIs. If you add this all up together, it's a sizable potential. Think about that. When now Thomas comes with his AI architects to the customer and says, "Okay, Joule, here's the platform. Now we can extend it." There is a root cause. The master data quality is not so good. Okay. In the platform, let's pull in Reltio. You know, 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. 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. Now that the platform is coming to life, you know, that is, of course, for us, giving us an enormous scale. When you take those weak components together, given where the solutions are, where the assistants are, I can now say with way higher confidence than two 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. You know, 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 invest needs to be amortized. It's a little bit hard to speculate on where exactly might the cost of token go. Can you extrapolate from the fast decline in the past? Is this sustainable? so forth. 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 one and Aleph Alpha, we don't do pre-training, so we do inference, so it's 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. RPT one and Aleph Alpha on this tabular model, it's 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. 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. Recall we said we don't want to focus on expanding margin on cloud in percentage terms, but what we wanna really achieve is the incremental euros of gross margin from the cloud that we wanna maximize. Great. 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 at our Q2 earnings in July. 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.