SAP Financial Analyst Conference - May 13, 2026

Yeah. Hello everyone. Welcome to all of you here joining us on site in sunny, sunny Orlando. And of course also welcome to all of you joining us virtually. What a difference 12 months can make. 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 and now we are talking about the future of the software industry in the age of AI.

And after yesterday's keynote, I would like to share a little bit more about what is actually the why to win for SAP in AI. Why will SAP be a winner? And then second, of course, what does it take to become a winner in AI? And to start, maybe a quick look back into our cloud transformation because, 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.

Now first, when you remember, we acquired a bunch of cloud companies starting 2012 and then when we took over, we said hey, we want to harmonize our portfolio. And harmonization means we want to actually build a best-of-suite, we want to harmonize data, we want to harmonize processes. And I will come later to that why this is so important that customers and our stack are not sitting on a bunch of data silos and not on a bunch of broken business processes, but that we are now really building AI on a harmonized foundation.

And second, I mean, what was equally big I would say is actually the transformation we did on the go-to-market side. I mean, in 2019 there were not many people in SAP who know how to, you know, manage subscription consumption, not how to, you know, actually drive adoption of consumption-related business models. There was a lot of change happening inside SAP to not only equip our sales force for that but then of course also to change the whole operating model.

And then third, no transformation actually just, 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. And that needs to happen at fast speed, at fast pace. And we also changed our culture so that we are really also having, you know, our employees understanding that this change is actually needed and that there is a clear plan in place which we all have to execute now.

And then you of course see it in the numbers. I mean, Q1 earnings in my eyes was really great. I mean, we are outperforming all of our competitors. And of course even in the cloud world, you know, the ERP, the SaaS apps, they will not go away. I mean, there's a lot of potential still to grow our business in the cloud but of course now much more with the value equation, you know, happening on the agentic AI layer. So we transformed SAP once and yes, I can tell you, we do it a second time.

Now 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. Philip and I showed yesterday in the keynote, you know, what does it really mean? What kind of value will the autonomous enterprise deliver to our customers? Today we want to share a bit more insights, you know, what is really differentiating SAP from the rest and also then of course, you know, how are we going to make it happen, how do we develop it.

And 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 will also need some transformation on the go-to-market side. Thomas will share more details about that but obviously, you know, there was a certain reason why we also did certain reorganizations, 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.

And 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 and did we have enough people to know how to actually operate a cloud at scale? Did we actually have 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 and the same needs to happen now all over again.

And then of course with Sebastian on the operations 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 SAP deliver AI at scale, at speed, and then of course also how can we also deliver the efficiencies? Yeah, you're going 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. But 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.

Now coming to our why to win. We won ServiceNow, we did win a few best-of-breed applications in the past because some of our acquisitions made the mistake to not choose SAP. Don't ask me why but, you know, every time when we then migrated away from these best-of-breed or now running 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 a million of correlations between logistics and finance and there are another two 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. And now think about it, at that point it was about harmonization, now the agents need a graph where they can correlate this data. So it's actually 7.5 million multiplied by a factor which is not imaginable where we now need to make sure that we are also bringing this semantically rich data context sitting in our ERP into the new platform I will talk about in a second.

And then obviously we are running thousands of business processes in these companies and, 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 agentic AI use cases all day long but if they don't understand your process logic and you cannot also flexibly extend that because every customer has a different way of doing sourcing, has a slightly different way of running a payroll, obviously it's not going to work. But all of that sits in the brain of a company and this brain is actually the ERP.

So, I'm very confident that, 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 Purposity, we didn't start with the business side. We started with the platform. Why? Talking about lessons learned. We know that Joule is not perfect. And when you are talking to some of our partners and customers, you probably 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 handholding? And I can tell you by the way, great examples, we can deliver world-class AI, but also I have to admit there's probably a lot of handholding behind to let the agents connect to the right data fields and making sure they understand the process context.

And then Muhammad, Philip and I were sitting together and said hey, this can't be because, you know, our agent builder was on BTP, the data layer was on BDC, the process context sits in Signavio and in ERP and in some other places. So 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. And I hope you have seen with the new better version of the platform and in one month this platform is GA. Yeah, we are going to deliver now an agent builder where you can bring your own tools and on the building side you can use Anthropic, you can use OpenAI. Next week we talk about Mistral because of sovereignty in Europe. The differentiation on coding an agent is zero. That's why you don't have to come to us.

But then when you start developing it in Joule Studio 2.0, you're going to see that aha, I'm developing now a pricing agent. I need access to the material data in S/4. My pricing data sits either, you know, in Salesforce or in SAP. I come in a second to non-SAP data. Ah 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 and then finally, you know, I come to the governance part in a second.

So the community will see in a month from now together with our own developers already, you know, building agents with that. Now they're going to see aha, now I have the context, now I have access to the brain of every company. But here we don't stop. I know there are certain concerns around BDC, what data do we share, what data we don't share. We actually share raw data, we do zero-copy share, you know, so that people don't need to move the data around. That's good for TCO. That's good for cyber reasons. So we do that.

But then actually what happens on BDC and only on BDC, we join data. We are not a data product alone, it's raw data. But then when you join data, when you build a semantical module, suddenly you can say, aha, my pricing agent actually needs data from SAP and maybe from Salesforce or maybe from another CPQ solution out there in the market. And then you can do these data joins. And the way how Muhammad and Philip now engineer that is you can actually say okay, I join data, customer data between SAP and Salesforce and now I link it into the knowledge graph 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, etc., etc. We build this really, really rich semantical data layer between SAP and non-SAP data.

You have seen our acquisition of WalkMe so that we also then can take care about the master data quality. Again, because agents need high quality data, they should not compensate for a broken data model. And then last but not least, of course, with Databricks, we have a lakehouse to really also develop own agentic AI scenarios with our own lakehouse built on Apache Iceberg. So that is actually the context layer and that is the heart. And then we are also now launching SAP domain modules. So we are training these 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, etc., etc.

Then last but not least comes the governance layer and that is actually really everything what I showed yesterday which sits under the iceberg. You can build, you know, of course a lot of fancy agents with LLMs. They will miss the context but especially you're going to deal with a ton of complexity in the governance part. I actually had in the commerce area a customer of ours telling me yesterday, Christian, we built over 50 agents and now I have a problem. These agents actually don't understand really the pricing logic. They actually share pricing data with consumers they should never ever share. They are for sure also not actually adhering to certain regulations with our data privacy standards in Korea or actually with the data privacy standards in Germany. I mean, this is completely out of control. I can double my IT organization to really make sure that the governance layer is actually working.

I said yeah, but hey, SAP is investing over 600 million each and every year for the localization of our software for the certification. I mean, here is the logic. It sits in our software. So that's why we said hey, when we are talking about the business AI platform, here's the build layer, here's the context layer and here is the governance layer. Very, very important.

And then, you know, other CEOs are debating oh who has now the orchestration layer and who owns the agent layer. I actually, we give this away for free. Why? Because we want to monetize, you know, the success, the value of our agents. We want to have the best HR agent. We want to have the best source-to-pay assistant. We want to have the best record-to-reporter assistant. We want to have the best planning assistant. But the orchestration, if customers want to really also plug in third-party agents, of course, this is for free.

And I know there was some talk about this API policy. The only thing what we actually manage now with the API policy so that we manage actually the access to our APIs, to our MCP servers so that we really can guarantee, you know, high governance standards and second obviously that we also can still guarantee with millions of API calls coming in that we can manage the system performance. But everything what you're going to build on the SAP business AI platform, you know, you can use any model you want, no differentiation. But then comes the context and then comes the governance and that will be world-class delivered by SAP and some of that is actually already there.

Now the autonomous suite, when we are now having the platform, I mean yesterday we presented okay, what is actually then our vision of running how an autonomous enterprise will run. So first you're going to 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 Joule 1.5, you saw Joule 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.

And then next to role centering, obviously it's very important that it's outcome-based. So I just had a US public sector customer four rooms away. Actually they asked me a question. I now build a custom agent and okay, the agent understands, you listen of course but actually, you know, now to change something in the system of record and to write back into the system of record it's not possible. And actually that is not good because I want to 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. So we are going not only from listening, understanding, we are going all the way down to execution and there's always a human in the loop but the customer can decide okay how far do I want to go with regard to the level of autonomy I want to give these agents.

Very important, audit-ready. 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. No, we are going even one step further. We actually telling you when you run your financial close with our assistant, hey this is actually SOC certified, this has all the certifications you were used of running your financial close with S/4HANA, with our software.

Extensible by design. When I talk about learning of Joule, my god, maybe we should have thought about that before. But 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, simpler. But we have different policies and sometimes, you know, we have new ideas about new policies, new pricing policies, etc. So extensibility is super important. There will be not this one standard, you know, agent for pricing, not this one standard agent for sourcing and customers will also have different policies so that's why extensibility is key.

But what is also very key and I hope you saw that yesterday with Joule Studio 2.0, oh, 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 the new platform to a customer right now and they are building actually a personalized AI agent for commerce and actually we said hey okay, you actually have this one more step you want to have this agent to do, you go in and say okay, as a business user you can say when you have the authorization right, you can just say okay, with Joule here is one more step, the 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-premise 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. So the speed, the agility and the extensibility is very, very important. And think about that now you could build all of this with Anthropic. Let's assume that your business is changing so fast every day. Not only the tech world is changing. I mean, how do you want to actually make sure that these agents are always extensible, everything when everything is custom? Have fun with your IT organization to make sure they understand all the business model changes, all the business process changes the business wants tomorrow and not in three months.

So industry AI. Yeah. You know, some customers told me Christian yesterday in the keynote, so happy that we heard this term industry again from SAP. I can tell you that when we sometimes say SAP is so sticky because we are the brain of every company. But 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.

And especially in these domains here, what Sebastian and the team picked. I mean, look at that. When you do asset management, when you do commodity management, yesterday we showed unified commerce, there is no scenario, not one single one who doesn't need a tight integration into the ERP of SAP. Not one single one. And in many, many cases again you need to even have a write-back because otherwise forget about autonomous and forget about running autonomous asset management when you cannot maintain or change certain assets or trigger actions in the SAP system to actually, you know, trigger a predictive maintenance or get one worker from place A to place B in order to fix the asset. How is this possible? You can always say it's autonomous but what you are missing is the last mile with the deep integration into the ERP and that's what we are going to deliver.

And then yes, Palantir has the copyright for the FTE but, you know, Hassner actually told me just four weeks ago 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 Kona about last mile delivery, talk to the truck driver, talk to the people in the warehouse to understand not about how to incrementally apply AI on top of our transactional application, no, just to completely reinvent this process because now truck optimization happens with AI agents without anyone, you know, sitting in front of Tableau or PowerBI and trying to somehow manage it on top of a transactional system. No, that is now done via autonomous industry AI use cases.

We're pulling now together our IBUs, our industry experts with our industry developers. You're going to see in that space that we will also build here a huge ontology layer for each industry. We are now started with, you saw H&M yesterday on commerce, on retail, oil and gas, everything around related manufacturing because this is high value. This is, you know, these are complex scenarios but they are high, high value for all of our customers. And actually by the end of the year we want to actually scale this business to over 200 projects, more to come.

I can tell you after I told everyone yesterday call Sebastian if you want to do something, his inbox is exploding and the pipeline looks actually anyway really, really good because again what is the why to win? The why to win is it is so natural that you need to not only collaborate with the brain, you need to also write it back and the agents need to understand the full logic of our suite.

Now when we are now building the autonomous enterprise, when the platform gives everyone a great reason why to build agents with SAP, why to win autonomous with SAP, now one lesson I learned during my whole career is technology means nothing if you don't bring it to adoption and every 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.

And that's why we said hey, we really want to also deliver at Sapphire a new Rise with SAP offering. And 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 but, 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 actually start to show value and we can't have our customers waiting for three years until they finish the modernization of the system to actually use AI.

And to underpin that we said hey, we are not only saying this, you know, in some nice words at Sapphire, no, we contractually commit to that and we come on site and 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. And now of course I heard, oh Christian, you changed your strategy because now you're also allowing it for on-premise systems. No, no, no. It's also not a defensive move.

What we are now doing is after a ton of feedback. I mean, think about that. There are customers here who are actually shifting now and harmonizing over 100 ERP systems over the next years. I come to the acceleration for that. But now of course these customers are saying Christian, my CEO, my CFO is telling me hey, I need to deliver AI tomorrow. I can't wait here until I have finished all the modernization. And even for some of the on-premise systems, I mean, they are mission-critical systems in the company. I see great value AI use cases. If I'm not doing it with SAP, I do it with someone else custom but I would love to do it with SAP.

And that's why we built this on-premise connector to really now also then connect, you know, our assistants and our agents to these on-premise systems. But 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 because needless to say, yeah, when I talk about extensibility, if you have a heavily customized landscape obviously there is more handholding, more extensibility required than when we are talking about the clean core, a very, you know, clean system with regard to a lot of standardized business processes and a standardized data model underneath.

Then ERP migration acceleration. I can still remember when Alex Karp called me and said, Christian, I would love to switch to SAP, but oh my god, my CIO tells me this takes that long. That needs to be done faster. I said, Alex, we're on it. We have Joule for consultants, 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.

And when I'm now talking to Accenture, to Deloitte, to PwC, everyone, everyone is investing into that. Somehow we are disrupting someone's business model here but, you know, that is overdue. And so that's why we are now also today in Thomas' keynote, we launched our AI-led ERP migration platform where SAP on its own is shipping newer systems for data migration, for business reconfiguration and we are very confident by the end of the year when we are testing this now with our customers that we can actually cut the time and the effort and the expenses by sometimes up to 50% when we are deploying all of these assistants now available for the ERP migration.

And then we are pulling in Tricentis for test automation, AI-based, we're pulling in Palantir, we're pulling in Accenture and there are many, many more now to join so that journey will be accelerated without any doubt. And then the customers have both, they have actually a very harmonized data model, 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. And 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.

Now talking about the go-to-market model, Thomas will deep dive on that. So I want to keep that short. Look, at the end of the day, what we are going to do is you can show a lot to customers on PowerPoint. What is more convincing is that 4,000 of our pre-sales experts can already write code prototypes. That is what we need to show in industry. 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.

And here I mentioned already before, we didn't want to add new roles to our go-to-market model, we just simplify that. But then with the consultants we have, the data consultants, the LOB consultants, the industry consultants. I mean, they can 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 reskilling, but we have it. And when I see the amount of consultants who can today code already with our new platform with 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.

I come in a second to the people section and of course that we need to use our own agent toolchain is of course a mandatory task to all of our people in the post-sales area. And then yes, when you actually then see when they have adopted the suite, when they have adopted, you know, BDC and now with the data catalog and you can then build these data joins and you can add it into the knowledge graph. We of course also see that the AI consumption is already going up because now finally we see okay, as in ERP, a best-of-suite, best-of-suite will win, not best-of-breed. And then 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, top experts. But 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 going to see productivity gains. But again, I also want to highlight, you know, to get the best ontologist, the best data scientists, to get the best full-stack developers. I mean, these are all the roles, data AI architects. These are all the roles where we also then hire from the top, top universities in the world and bring those in.

But even more important is of course the reskilling and here you see, I mean, you know, we call it mandatory, I actually call it, you know, it's actually an offer to our employees to have a bright career, you know, within SAP to enjoy all of these learnings. And the managers are always in the lead. It's not about, you know, that we have oh here's it and here's your AI tool, no, we really want to have across SAP the managers and some front-runners on AI showing about what is possible with AI in the one or the other job. And we are doing this across SAP obviously with more intensity in engineering and as I mentioned, you know, the write 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. And of course we see a huge potential to make this even a bigger number than two billion. And again, don't only take this as, you know, efficiency. I mean, we will take some of that money of course also to reinvest but again, it's not about the quantity of people in development. It's about having the best people in certain areas.

The market, I mean, believe it or not, I'm 100% confident that our SaaS, PaaS business will not go away. AI agents don't work without a brain. The brain is SAP and 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. But this is why I actually see a unique chance now with the suite. We always said the suite will win. I guess in the age of AI there's one more reason to believe that.

And then of course when we are growing our market share and there's no doubt that we are going to grow our market share even further then of course we rather see this 5 trillion market here as an opportunity because if our platform now delivers what we here presented and it will, and when we then deliver this high-value agentic AI use cases for the different domains plus industry AI, there's no doubt that we can accelerate now also our growth towards 2030.

Here one last point on the commercial modules. I said this at earnings so, you know, over two-thirds of our cloud revenue is already today seat-independent, meaning it's non-user-based. So we have all kinds of value metrics so no one needs to be scared that oh, efficiencies, users not anymore needed and suddenly the revenue goes down. No, no, no. We price a lot of our apps already today value-based. And then of course you see now with AI, with the platform, with our migration tools, with the autonomous domains that of course, you know, we see that at least we're going to see a 30% consumption-related revenue share in our cloud revenue in 2030. Again, there's some time to go, but we see that we are on the right path. We have the right strategy and the execution is there.

Now look, that's how I see our growth journey. There was first an enable AI, maybe we can also say 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 Joule agents and then 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 systems 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 TechEd and there is more to come. So when the platform now starts to scale and the autonomous suite will become bigger and more mature and with higher value, I mean that will of course help the adoption of AI massively. And then last but not least, I mean we also then have our migration tools and we definitely want to leverage them and we will monetize them because again this is a big, big market, 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-led migration tool.

And then of course for me it's actually now very important to build the community on the platform, creating the belief in the ecosystem with the new platform being in better in one month's productivity. And actually we built this, and then we build the graphs for the different industries. 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. Signavio, 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. And that's actually what I believe will happen within a year from now. Yeah. And I guess this year was a very, very important Sapphire for us to show that we are now really accelerating both on product, go-to-market, people, operations. And when this all comes together, there's 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, but thing number one is I want to at least share with you that as we think about our product strategy, what were the shifts that we saw both in the tech and in the market that guided the strategy that you saw yesterday and what Christian just walked you through, and how internally are we really mobilizing that strategy to build the products that we believe our customers need. So that's one, the shifts that we see, and hopefully in an effort to see if those are the shifts that you see in the industry as well. The second is the product strategy. A lot you saw yesterday. Christian covered a lot in detail as well. But related to the product strategy, what I also want to attempt to do is share a little bit more of the conviction that we have as to why this particular strategy is both differentiated and unique than the strategy that you're hearing from some of our other tech peers as well. Because at the category level, things are probably starting to sound very similar, right? Everybody has a set of agents. Everybody has an agentic platform. They think that it's the world's best. And people want to control the orchestration layer as well as the engagement layer as well. But I want to sort of build a little bit more conviction in you, at least share the conviction we have as to why what we're doing and what Christian just walked you through is different than the rest of the tech space.

So let's start with the shift. You know, the basic shift that we see as 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. And that has been the SaaS applications now for the recent past if you will. But what's happening now is AI instead of being in the SaaS applications, you can put on top of the SaaS application. So there's a new layer on top and that's what everybody's trying to vie for. So what that means though is two things if you think about 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. But 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 going to be disrupted that will go away that can probably entirely be subsumed and consumed by the agentic layer. But as you think about finance, as you think about GL, as you think about supply chain, as you think about core employee management, that layer isn't going away because it has a level of 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. And that hence comes the new layer on top of it. But what that means is the more this new layer takes foothold in our customers' landscape, for us two things would happen. One, if we can provide this new layer of compelling agentic experiences for the customers to consume out of the box as much as possible with the right extensibility, we will get a share of the value that we're creating for our customers. Hence the autonomous suite. Two, even if the customers then say choose another agentic platform to build that agentic layer on top, for the class of business process domains that we live in, the platform underneath will still continue to grow as well the more you interact with it. Hence our openness that from an 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 going to win because the SaaS layer that we are underneath that needs to exist for it to consume. So there's going to be a level of growth that we're going to see in both, if you will.

But this to me as you think about the shift in the tech landscape, right, in the stack that hasn't happened in multiple decades, 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 because it sits underneath that highest layer up top and they're going to be a pull through on that but of course we want to 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 are products in Ariba, right? There's sourcing, there's contracting, there's buying, and there's invoicing and any features we ship sort of stayed within the boundary lines of whatever the scope that we addressed in those applications that could be S/4, SuccessFactors, and anything. The mindset shift now is this agentic layer at the customer level that they're going to go build is going to be one agentic layer for a source to pay if they're using us for Ariba and they've got two other tools completing that process. We by definition whatever agentic layer we provide has to factor in the additional tools that they have because a customer will only have one layer. If we only provide agents that work on our app, they're going to have to find another layer on top to rule it all that can connect to those other applications because the customer is not going to stitch together that new layer up top by multiple agentic platforms. So that's why as you now think about the core properties of our agentic platform, the new business AI platform, extensibility and our partnerships with n8n is so core because there's hundreds of connectors that n8n brings that's embedded in our business AI platform. The extensibility that says hey because we generally are the core of that process, right? 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. So 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 will extend with n8n or the vibe coding experience that you see with business AI platform and Vercel and others to expand. So 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. But in the SAP customer base that are running these processes, chances are that if you get 60, 80, up to 90% of the process that you run on the core application with that new agentic layer out of the box with our autonomous suite, you'll go cover the rest of it with us as well. And as you do that, the stickiness of let me go do a few more things would come into play as well. That's the app boundary versus landscape boundary. That's so core to the product strategy that you saw.

The third one, and we've been, that's been received very positively, is listen, historically we've said you won't get the value that you're going to get from our innovation is when you get to our latest products but that's shifting now, right? Because this layer up top is loosely coupled with the platform layer. So we have to shift our product strategy as well to say listen we're going to build this agentic layer, the autonomous suite, that is loosely decoupled with the application 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. And hence it allows us to now make the commitment to the market that says hey as long as you're in the modernization journey which is valuable in and of itself because there's components that are going out of support you need the compliance, the regulations and things like that that the modern solutions have. We will as SAP allow you to take that agent and connect to your ECC and S/4 environments. 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. And 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 want to make the cost of getting to the modern solutions much faster as well. That's what Christian talked about with our modernization assistant as well. But 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. So as you think about SAP now in 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. And not just that, it has also those additional tools and maybe non-SAP applications that sit adjacent to us because we have the right being one of the largest tech partners for most customers, right? It's either us or a hyperscaler. Those are the top 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? And that's what Christian walked you through. The layers on the right unfortunately don't map the layers on the left, but if you would allow me to, the 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. Now that's the agentic layer, right? Agents can run headless, but agents need a place for the user to interact with them when there's an exception and hence comes Joule. So those are the three core things. Now 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 going to 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's saying, 'Oh, I've got the finance agents that can really run close for you,' but they're not doing supply chain, right? And they're not doing HCM. And a customer doesn't want 22 different agents running in them, right? 22 different platforms, agents on different platforms because that proliferation, the security nightmare is going to be pretty massive. So, we're one of the only ones again in our 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 and not just that with industry AI we're going to go to your core processes last mile distribution, distributed energy, unified commerce, adaptive manufacturing and we're going to go build those as well to complete your story on it. So that's what these three layers are. But I'm not going to spend because I think Christian did an amazing job makes me proud sitting there on what these platforms are and our differentiation. But what I do want to spend a few minutes in each one of those is talking about what are our differentiators and uniquenesses, right?

And starting with the autonomous suite, right? The one in the middle. We're of course building 200 agents that we talked about. It's going to be more than double by the end of the year. There's a set of characteristics that these agents have that are unique. One, because we run the underlying application we can be far more committed in terms of the outcomes we think that these agents can generate and not just in aspiration but in reality because we know what that data model is we can track those outcomes and we can prove that in the AI agent hub that that's there because that's the biggest issue that some of the customers have. Second, they are extensible and that needs to be there like I said because it needs to be that landscape view. Third, the auditability part of it and the traceability is unique in what we can offer because we have one of the world's leading process mining platform in Signavio and we're using the same tech to have agent mining logs for customers to see how these processes work and second from an 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. And 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 going to get into it right the ability to take an idea on what agent you need to go build and take it to GA at an enterprise level is what this platform enables us to be able to do by the end of Q2 in under 10 days and we're going to get to under five days and 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 and what you're going to find is timelines that's from 3 to 6 months or even further because there's a lot of complexity at an enterprise level. You have to account for the under the waterline iceberg that Christian talked about that we have platformized as a platform in our agent governance and business AI platform. So this ability to innovate at a pace that's going to be very hard for anybody else to be able to go do is what's the differentiator for our platform.

Moving on, we've talked about this. I'm going to skip this. Is the fact that we've really expanded the TAM without risking our modernization story to say hey the AI consumption can now expand massively to the customers' ECC state and the S/4 on-prem state alongside the cloud state is a big, big plus for us with not just hey you can go custom connect to ECC but there is a connector that we've built that is part of our agent gateway that truly understands the ECC data model and the patterns because we're of course the writers and the publishers of that software as well. Moving on to the platform layer. Again, I'm not going to spend too much time here but I want to sort of give you at the highest level the strategy here, right? We're partnering with things that we believe are going to be commoditized in the AI tech landscape too. Meaning the public LLMs we believe is a commodity. Of course, that's where a lot of the early money and revenue is going. But the race between an Anthropic, 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? So, we want to make sure that we bring the best of that to our agentic platform. Second, the design experience of the agent is also going to be a commodity. Like, do we want to go build a design experience that's better than any? Of course, we want to, but is that sort of really what our secret sauce is? No, it's not. So we want to go partner with an n8n, a Vercel to say hey if customers are choosing an experience to go build something we just want to make that part of the business AI platform in a way that we can add another n8n 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 want to spend 30 seconds that I don't have on this slide still which is listen the public LLM part is what people can generally go build an agent on and they're getting smarter every day, right? The Opus 4.6 and the new models are going to continue to get very smart. But the things that we do that again as you sit in another tech conference that you should think about like does this really exist when they say they're going to be the best agentic platform is a knowledge graph of our canonical model, right? That's already over 7 million fields and the relationships that we need to go maintain because that's very hard to go do. The process graph because we cover end-to-end processes, 120 top level but thousands below as to where things fit. We also have domain models over two 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 in SuccessFactors ours is always going to come out to be closer to the pin on the first iteration than just using the public model. I mean that's just common sense but we'll prove that with eval as well. And the fourth one is predictive models. This is where we shine because we don't just have the shape of the data as our IP which is the knowledge graph. We have the data itself with tens of thousands of customers in aggregated, anonymized manner that we're training this model on to say hey we can give you predictions on the fly and really make machine learning science a thing that could be disrupted next with a model business right these are the tabular models so we've done that for SAP data which is Rapid one but then we said it's not going to be enough just for SAP data because customers always live in a world where they have a lot of non-SAP data and hence Prior Labs because we want to bring the best of both to be able to bring into our context these 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 because it's not an or it's an and. And then finally that's the SAP context it's the customer context that we already know that we can embed in the platform which is every customer has extensions so the knowledge graph is not just what we canonically ship but the thousands of customizations and extensions 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. And finally, the agentic runtime which has the governance built in which has the sovereignty built in because it's natively on BTP. It can run anywhere you want. Like you add all those three things and it gives our agentic platform a differentiation that's going to be very hard for a hyperscaler to go do because that's a bit hard from a sovereignty perspective. They don't have access to the shape of the data or the data at the scale or the predictive models and so forth. But anyhow, this is what leads to you can see the eval 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. And you can see we beat each one of them every time and that's only going to get better. Now the public models are going to get better but of course the context that we have and the company context that we build every time a customer runs on it is going to get even better and better. So both will continue to show some difference and movement if you will.

Now finally I'll close with because we've talked about the data we've got the Relio and the Dremo. Again on the Relio part you should think of that we have MDG but we knew because we have to go from app boundary to landscape boundary that we have to provide a solution for non-SAP master data management hence Relio 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 Dremo on top of BDC to be able to go to so you can see that story is now building up from a clear product strategy perspective. And finally I'll just stop here governance we've talked actually the reason why because you'll 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 so LeanIX is one of the leading enterprise architecture platforms which already understands your landscape beyond SAP as well as the best place to understand where all your agents are and that's why we already have 55,000 agents registered on them and they're not all SAP agents. Most of them actually are not SAP agents but LeanIX already knows them because they are the enterprise architecture solution in our customers' landscape. Signavio already does mining. So adding agent mining was just in the world's best mining platform. Another capability to add. Cloud ALM was already a leading platform for observability and we continue to add to it. So our right to win in agent governance and then finally sort of tackling it all the way to the end to SuccessFactors to say listen the total workforce discussion for our customers going to be pretty important one as it is for us as Sebastian's going to talk about that your total workforce is not humans or it's not FTEs and contingent but FTEs, contingents, and agents and how we've brought that together with SuccessFactors is pretty unique. But finally I'll stop at this and then hand over to the next presenter here is the Joule, the engagement layer and what's our right to be in this. Now, there's 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. Now, there's some good successes as well, but we know as Christian talked about earlier that we had a lot of learnings in Joule. So if you look at what Joule 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 because 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. But this is a massive uplift. So you will hear feedback if you call a customer to say, 'Hey, what do you think about Joule?' And I think some of it is actually fair. There's obviously success stories like Ericsson out there too. But this is what we're completely reimagining. And then finally, you know, we announced something called Joule Work. This is that engagement layer, right? And the reason why we believe this is something we again have a right to have a significant market share in 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 and ECC. So for us to provide an interaction layer that understands those applications to say hey now we're actually solving our evils of the past because we have a lot of bad rep on user experience as well. You can argue in our earlier core ERP apps is something that we believe that will resonate and is resonating with a lot of customers as well.

Now I'll stop with this and hand over to I think Thomas 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 Figma. It's not vaporware, right? We have a lot of early customers that through FDE as we've been working with. The logos that you see here, the examples that you saw all day yesterday and what you're going to see today are people, customers that we've been working with through this FTE program that we've had now for about a year to be able to go work with them. They have the experience on the new platform. The others, and there's a lot of them, are still working on our previous platform because we're now just publicly announcing our new stuff and mid-June is when we're going to scale out the roll out of it to any customer that wants it as part of our early adopter here. So 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 one that was just announced at Sapphire or is it the old one? And you need to be able to discriminate in your assessment to say, hey, what strategy are you outlining here? And as Christian pointed out, I think the industry AI part, I didn't touch on it, Christian did, but it's a big, big focus for us. And this is where we're setting up an organization that have now thousands of FTEs from SAP with already deep understanding of industry and our products that are going to engage with customers at scale, majority of them in engagements to activate these domains and this industry AI as well. So now is when we're ramping up, hey, we've got the platform that we feel is now in some cases better than best-in-class because it's using the best-in-class and adding our context, that slide if you remember on top of it. And we want to go unlock value if you will. So hopefully this makes sense. Now I'll hand it over to Thomas. Thank you.

So hello and also welcome from my side and Dominic 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. So let's give it a try. No, but I'm really looking forward to talk with you about how we evolve our go-to-market model in the company and in order to do so I think it's always good to basically remind ourselves in which world and environment our customers are living because that's basically the expectations we want to not only meet but actually exceed. And when you look at the world I think we all see the volatility, we all see uncertainties, we see that the world is changing faster than ever before, geopolitical fragmentation, economic uncertainties, supply chain disruptions and this is just compounding and for sure this is a pressure on all of our customers. In parallel we see this little disruption called AI but we also talked today a lot about that so on the one hand side there's cost pressure on the other hand side there's how to get value of AI and that's coming together so the customer expectations are raising up and for sure they want to have faster outcomes because the pressure is high, the world is changing more quickly but also here it's clear that resiliency is becoming more important for them as well. Resiliency is one of the highest value drivers they see and then for sure a huge pressure in the market about return on AI because everybody is piloting and pinging some AI capabilities but also if you see the various research in the enterprise context for sure it's clear that it's a little more complex and that's what we discussed the last two days here at Sapphire how we as a company want to help our customers to overcome that but 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.

So for sure we focus on adoption services. So our services transformation really driving to get into an AI engine, a deployment engine for our customers. How we evolve our business model with consumption-based, outcome-based pricings in that sense. Also thinking about the consumption and value realization which we see with all of the agents, all of the AI capabilities what we drive and also our responsibility to help our customer in doing so. And that's why we basically also elevated our services and support portfolio to the next level and included the AI capabilities and deployments as part of our success plans with our customers. That's also 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. And with that said also we simplify the entire customer engagement throughout the customer value journey. And this is for sure something where the customer experience is improving dramatically. Gives us also the opportunity to inject the relevant resources in the mix. And I will come to that as well and Christian alluded 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're at the customer because that's what you do. But we should also ground ourselves where we are. I mean, we talk about 440,000 customers. If we zoom in now to the global Fortune 500, 91% of them are SAP customers and 68% of them are already using SAP Business AI. That's the scale what we have and scale for us is a critical component which we want to enable with our workforce.

And if you think about this customer value group which we brought together touching the entire customer lifecycle as I mentioned the seamless journey is something what we want to enable but also think differently how we work so also embracing AI and Sebastian will talk about how we internally embrace AI to serve our customers for sure we change the entire motion about consumption models and that's also by the way part of the bonus plans of all of the people in this organization so harmonized KPI which is fully consumed ACV on our customer side also leveraging the deeper expertise and especially with AI it's clear that we need to think about how can we embrace AI in a complex enterprise landscape and that means we use our architects to make that happen so for the deployment of AI we leverage our colleagues as part of the success plans to really activate all of these cases for our customers and that's I think a critical aspect what we want to see and then we talk 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 grow 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 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 and I also want to touch on that one a little bit in a second. Now if you think about purely the cloud growth from 2020 to 2025 and think about the new customers, the new logos which we acquired, they lead up to close to three billion in cloud revenue which we see. But you also clearly see already here the indication how much cross and upsell is happening there. So our new cohorts which are coming since 2020 are significantly accelerating with cross and upsell the revenue as well. What we also see because our mid-market engine is really now at steroids where we also use the indirect channel partner territories and here you see some of the numbers I mean 1,000 partner territories, more than 3,000 sales partners which we have and they also when you see the cloud revenue growth comparison of the indirect channel and the direct channel are really at speed and that's what we see on aggregate which means our Grow with SAP offering for small and medium-sized enterprises, for startups, for scale-ups is extremely important for us and also here I mean if you think about companies like a Snowflake, Databricks, I mean for sure they want to grow infinitely and which is the single only one ERP system on this planet which you cannot outgrow, which scales infinitely, it's SAP. And 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 systems, you just can grow.

But then you even come to customers like Nvidia which shows how amazingly these systems can scale. When Jensen said that he wants to do one trillion in revenue next year, this is one ERP system. This is just unlimited scale and we saw the other customers on stage today which clearly shows that this is a big advantage. So basically already you see the contribution of 20% of our cloud revenue growth in the last five years coming from new customers and we see with market 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. But 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. But also here you see if you think about the numbers again, how much cross and upsell we do in our entire installed base. So it's a nice mix of new customers, about customers migrating to the cloud but even more aggressively the cross and upsell what we see across our installed base which I believe is a huge opportunity and you see some of the facts here on the right side as well. 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 solutions in place. So you 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 Maya's point the more you aggregate in this agentic AI world bring together the more value it adds. So 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 and for sure the business transformation management components are already a vehicle because for sure to support the transformation to and accelerate the migrations. They need these tools like Signavio, LeanIX which we directly also package together for our customers. But also in a RISE migration what is important for you to know it's not just that we do 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. So we see thousands of ERP systems which we migrate into the cloud which means you see the ramp and the journey over time which means that the revenue is increasing over time even further. In the meantime to give you some context we operate more than 190,000 system IDs for RISE with SAP customers at scale. So it's the largest operating scale in the market and growing. And here for sure on top we use already the Business Technology Platform for all the 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. And that led also to the establishment of the new success plans which we have in place and here we've embedded the AI activations. We've embedded that we help our customers building up AI COEs. So it's all

Natural part and also we evolved our engagement model with Rise with SAP. With our enterprise architects, we actively help our customers to get to that point. And for sure, with our agent-led migration and tool chain, we not only support the migration 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 to 50%, that also means that we can also shift this capacity in order to support our go-to-market capabilities, exactly as Christian mentioned, from the 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. And that with the scale of the growth we have with new logos is super critical for us. For the remaining tickets which actually get opened, already 20% get actually fully solved by agentic AI.

And actually, customer effort score, so the customer satisfaction score for our customers, is even better as if it would be a human. So we see even a further customer experience improvement with AI. So AI is not only for the productivity but also for the customer experience in itself.

Now, we talked about these migrations and I think I don't want to go into details in light of the time, but think about this market of 100 billion plus for SAP migrations. And for sure, with our agent 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 innovation, which is a good thing.

And another good thing is there was a lot of worry in the market about, oh, what is happening with the maintenance end? Yes, ECC maintenance end is 2027, the extended maintenance is 2030. Now with AI, we can help our customers to accelerate these transformations into the new world. And that's also a benefit for our customers which we leverage here big time.

Another growth lever is sovereign cloud. And here, just to remind ourselves, we talk about 60,000 critical infrastructure customers. 23 NATO armies are running fully on SAP. Just to give you some context where we are also in the defense industry. You've seen customers like Lockheed Martin on stage. RTX, deals across the world are betting on SAP. Nine out of 10 defense manufacturers here in the US rely fully, 100% on SAP. That's where we are.

And because of our architecture, because how we engineered the sovereignity, we can serve those markets. And I think this is a huge opportunity what we have. What is important is that if you think about sovereignity, what does it really mean? What's the definition of sovereignity? And what we offer which is unique to us is on the one hand side a data, so an operational sovereignity, a legal sovereignity, especially if you think about also Europe and Germany and the likes, and also technical sovereignity which is absolutely essential.

We all deliver that in various forms of shapes. In the US we have NS2. In Germany we have a vehicle called DOS Cloud. But also we have capabilities like sovereign cloud on-site options where even for really critical customers in highly defense activities, bring our sovereign cloud onsite on the customer's place with the physical security of this defense organization in itself.

And this flexibility what we have is based on our platform which is on the one 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 test 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 need.

And that's differentiating us in the sovereign cloud. And that's for sure a huge growth driver which we see especially based on the geopolitical evolution what we see in this world.

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. And here it's actually my pleasure to welcome on stage the CIO of Ericsson, Marlon Pson.

Good to have you here with us. And perhaps we start with, for the audience, a little bit to set us up with kind of what triggered and started the Ericsson transformation and the challenges you were addressing on the outside with us.

Yeah. No, and absolutely. And now think about where you are today because you were 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.

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.

No, absolutely. So we talk about more than 80,000 users who use that actually on a daily basis and that's real today. And that's what you heard, that appreciation how people will start working differently, that's exactly what we want to do. And that's exactly where with our customers we bring that not only to our portfolio but as Marlon and Christian shared really also with these new capabilities now on steroids, which is certainly amazing.

Now, if you think about a little bit your 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. So where do you see us and our joint journey also for the next couple of quarters to come?

Well heard, you heard that 100. But also I think it's a good example because you mentioned it. I mean, Rise with SAP, BDC, the flywheel with AI and how that's coming together in real life on a customer. And I think that is for me the perfect example how to drive that change. And just perhaps as a last question because I truly believe in the world we are living is also a little bit about 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. So give us some context about also from a leadership perspective what you see in the age of AI.

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

So, 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 Ericsson is a perfect example. More than 80,000 people experience SAP's business AI every single day. 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. So I'm super excited about what's to come. And with that, thank you so much for your attention. I want to hand it over to Gina. Thank you.

Thank you, Gina. By the way, I love what Gina showed, not just because it's super important, but also because all of that is powered by our autonomous HCM solutions. And that's just one domain. But now, look, my job as a COO typically is to keep us on time and in budget. I have some good news to share on the latter and I will make up some minutes on the former. So hello everyone, great to be back.

Now my real job is actually to turn SAP into an autonomous enterprise and that means both in terms of making our vision real for SAP and at SAP. So both in terms of what we commercialize for our customers as well as how we run SAP itself. So let's first dive into the commercial side. You already heard from Christian about the gradual mix shift, incremental consumption growth on top of a very resilient subscription model. Let's click a bit deeper into these different models starting with the subscription side.

Now here we see significant resilience already today where you can see the majority of our subscription base already, so 55% is non-seat-based. That's even excluding the consumption share. And there are some great examples here like BRIM, billing and revenue innovation management measured on revenue spend. By the way, a great reminder that we are the company behind rolling the software industry's commercial infrastructure. So 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 SuccessFactors. Now you might say oh total employees that's a disruption risk. I would say no. Think of, I mean Starbucks presented down there with hundreds of thousands of employees at the front line. So even there I would say this is super resilient. And then on the cloud ERP side, by the way, we have a battle-proven model that has been driving automation for decades and decades and decades where the dominant share is also coming from non-seat-based components already.

So let's move on to the consumption side. Christian and Thomas already talked about accelerating consumption growth. You will see that both on the autonomous layer that Muhammad described, so through consumption of Joule 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. And 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 and think about the trillions in services we address with that. Second example is industry AI. Here's where we deliver autonomous outcomes. Think of an autonomous batch release for a pharma company we talked about yesterday or an autonomous maintenance ticket closed for an oil and gas company.

So 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. So that's it on the commercial model. Now let's turn into how we actually turn SAP itself into an autonomous enterprise.

And let me start with a very clear statement here. You heard us talk about investments, but our commitment remains absolutely clear. We are continuing to drive increased operating leverage with expenses growing 80 to 90% as a percentage of revenue growth with continued improvements across all key KPIs, decoupling the expense growth from the revenue growth and that despite significant AI investment.

Now how are we going to do this? First of basically by demonstrating that our AI is how we scale SAP profitably by actually also simplifying SAP. Take for example the operations function, since I took over we drove for more than 30% productivity in that function but really then by turning SAP into an autonomous enterprise itself. My team is fully focused on making this vision real, transforming our internal processes and driving AI adoption.

Acting as our own customer 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-digit amount of value realized in budgets today, and we are committed to deliver over two billion in productivity by 2028. And that's a value that has already measurably increased since we first talked about it. I think it was in Q4 2025 based on basically what we showed you yesterday, the significant jump we've made in the AI we are shipping to our customers as well. And that means delivering productivity gains in every single function north of, and sometimes significantly north of, 20%.

Now let's take a closer look at some of these domains, starting with 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, Dominic, from planning to risk and compliance to invoicing and reporting, our AI assistants and agents are already taking over strictly governed process execution. And the operational impact we do already see is material. We see 45% productivity gain in contract management, over 40% time reduction from offer to collected cash, and not to mention there the significant jump in intelligence we can arm our people with when planning by making every finance professional a Business Data Cloud user.

Let's look into 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. So 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, and Muhammad talked about it. In a nutshell, what we see is a compression from starting to build an agent to going from months 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, sorry, AI Hub on top of BTP using our Joule SDK for pro-code development. And then, of course, our ABAP developers have Joule for developers as well. Then 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. So 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.

And then on the governance side, that's basically typically in the development process the hardest work, and Muhammad already talked about it. It's basically taken care of in the majority for agent development for our teams based on the AI agent hub, which takes over the majority of lifecycle tasks so you don't have to build it. So the intent is clear. It's basically not to reduce our R&D ambitions or R&D investments, but to unlock more innovation for every euro we invest into the R&D function.

Now Gina talked about the workforce side of the house. We are already operating here a highly, highly efficient shared service center 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. So around 15 hours saved for simple onboarding per year, 25% productivity gains for performance preparation tasks, 80,000 hours saved in early rollout across as many, many operational HR workflows already within SAP.

And with that, we are really turning Gina's function into a truly experience-led, high-productivity HR function. A 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. And proactively builds succession plans. And the impact is tangible. We already saved more than 45,000 hours per year across SAP, and that's in the initial rollout phase. So we don't even yet see the full productivity impact. That results not only in productivity but in a much more personalized employee experience that gives additional productivity gains and enables us to do better talent planning.

Now, last but not least, that's my favorite area of productivity and AI: autonomous customer experience. So our focus here is really on optimizing the end-to-end customer journey from demand creation to adoption and expansion. The journey 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 a half hours per day roughly with AI guidance on implementations and consulting activities. Account executives gain more than 20% productivity in lead qualification. Ultimately, really what we do is we free up time of our customer-facing teams to do what they should do: spending time with the customer.

By the way, it's also a great example here how this AI layer and the underlying architecture layer fit together because we are continuously also modernizing our internal stack here. We just went live with 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. And basically what you see here is how we built 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. And what we achieved with that, basically, of the two million cases that we don't deflect, we get more than 10 million cases a year. And you can imagine that stack was already highly, highly optimized. By now, 100% of these remaining two million tickets get a very strong recommendation from Joule already for our technicians, and actually 20% of these hardest-to-solve tickets are now solved fully autonomously. And that's early rollout, so you can imagine the productivity gains as well as, and Thomas mentioned that, the improvements actually we are seeing even in customer experience scores on support at the same time.

So one thing to close off is also very clear for us: you cannot have autonomy without governance. That's why we have established a strengthened and holistic AI lifecycle 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. And that comes with rigorous value management. So I can commit more than two billion to Dominic 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. And these productivity gains with that are not just claimed but realized, measured, and continuously improved across hundreds of use cases. So I hope you take away from this short presentation: we are firmly on the journey of running SAP itself as an autonomous enterprise. The very same vision we provide to our customers is what we are driving internally at SAP as well. Thank you. And with that, over to Dominic.

So, maybe I start. Thanks, Adam. So first of all, I mean, I showed some of the cases we have. We have over 300 cases. We see significant productivity, and that productivity of north of two billion we committed to you and is committed to our budget. And we see that actually across every function. I mean, we see a significant, the north of 20% productivity lift we expect from go-to-market to corporate functions to development. And then if you compare, well, usual productivity tools, then the Cloud Code of this world, and what we see actually, it's a good healthy mix. Take development, I think all of our people have Cloud Code. I know all of our people have Cloud Code available through BTP, by the way, which takes care of a lot of the governance, IP concerns, and so on that we have, so that's a very good model. But the real productivity, 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 knowledge graph available, the agent lifecycle management. Actually, 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. But with what we are shipping with Joule Studio 2.0 and the SDK that goes with it, take care of a lot of that for our internal development teams. And with that, what we've seen is actually a significant uplift on top of frontier lab AI model usage. So we measure 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 a significantly north of 30% acceleration in terms of development velocity that we are seeing. But then I always like to say the amount of unwritten software in the world is infinite. So I think this unlocks great opportunities for us to provide more solutions like in the industry AI space. And then we have a full roadmap. Every function of SAP is co-innovating with Muhammad's team, Dominic'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.

So we, I mean, we looked at that. I cannot disclose the numbers, but we are a RISE customer. We of course use our full suite. And if you go across AI and BDC, it would be a measurable, measurable uplift we would see on top of that if we were a paying customer. Luckily, I don't have to pay usually for our own software, but yes, absolutely. And I think that's also one of the sessions where, 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. So we would pay a significant uplift on our Joule and AI assistants already today.

I mean, absolutely. What we see, what I showed in the slide, first and foremost we see with the Business AI platform for sure the consumption-based scale in that sense, so the cross and upsell within, and that's something if you think about the more data which is processed, the more agents which are running, this is a compounding effect. And as you see the cross and upsell potential in this Business AI platform capabilities, this is for sure a huge opportunity for us, for our customers. So basically 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 transformation, which is also...

in a consumption-based model as well, which for sure will continue, and that gives us, quite frankly, a lot of confidence that this is exactly in that direction. And we see, I mean, also talking here on the floor, I mean, there's huge excitement about the potential by bringing all of these things together in this flywheel what we described earlier.

Yeah, and 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 OCI, 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. And so 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.

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 '26, '27. Then we had three impacts. We had the trade disputes, then we had the Iran situation, and we also had this shift of saying we're going to in '26 reduce the hours we bill in services and use more hours to adopt. So that teaches you that you have to be always very careful to say that this is what it is. The very strong point we can make, and it's trivial, is that we can't see a scenario where this company would shrink in real terms. But to the contrary, it's more a question about how do we modulate the growth rates we are currently seeing to the upside? How much to the upside? Are there some risks of microeconomy? Is there some headwind indeed from some SaaS apocalypse stories? So that's the modulation, but in general, we feel that the underlying growth trajectory, the puts and takes are very favorable in total for SAP. So 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 this 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. And we learned about the context, we learned about the governance. I mean, you do your channel checks, we of course got a lot of, you know, feedback. And I guess when I, but now when I look at the feedback what we are getting on the better version which now will become Joule on the platform, this time I can say we are coming out of this learning curve. And also with RISE, I mean, you know, when we launched this offering, there was so much learning, yeah, which we then said, oh yeah, Signavio and then LeanIX, and we need to govern and support the customers even more with the architects. And the same is true here, but I see also now a phase coming where we say, wow, now there is this, you know, point where we see, oh, now we can start scaling, yeah, because now we see the accuracy, now I see in the better testings, oh, the accuracy, the compliance, the output is getting better. We measure that. And so that's why I have the confidence to say we are over the learning phase and now we in the scaling phase. And then to Thomas' point, what is now very important that also our ecosystem is embracing the platform because similar to the ERP world, now we need partners, ecosystem, customers building on the platform, building the extensions, building new industry AI use cases. And when that is happening, then you really then go into the accelerate phase what we also outlined on the slide. Yeah. But of course, you know, Dominic is right. You never know what happens next in the world. But just looking at our AI journey, we are definitely now entering the next phase.