Okay, good afternoon and good morning, Fred Boulan from Bank of America's software team. We are delighted to be hosting Muhammad Alam, Head of Product Engineering and Member of the Executive Board at SAP. From a format perspective, we'll go through some, some questions together, before opening up for Q&A. For Q&A, you need to email me your questions, so usual email, and I will go through them. So before that, I will go through a quick Safe Harbor statement. During this fireside chat, SAP will make forward-looking statements, which are predictions, projections, and other statements about future events. These statements are based on current expectations, forecasts, and assumptions that are subject to risks and uncertainties that could cause actual results and outcomes to materially differ. Additional information regarding these kinds of risks and uncertainties may be found in SAP's filings with the SEC, including but not limited to the risk factor section of SAP's 2024 annual report on Form 20-F. Great. Welcome, Muhammad. Thanks a lot for taking the time to be with us today. It's been precisely a year since our conversation in the same format in our virtual tech trip. I guess perception on software has gone full circle since then. Very good timing to catch up. Maybe first of all, if you could start with a quick intro on your role at SAP, your background, and what's your vision for the SAP product suite? Yeah, no, sounds good. Thanks, Fred. Thanks for having me here, Fred. I'm excited to be back again, so again, my name is Muhammad Alam. I am the Executive Board Member for Product and Engineering at SAP, and I'm responsible for effectively all our products, our engineering, our design, our product strategy from applications and our platform product as well. You know, in terms of our vision for the product, product suite, the applications that we have at SAP, it's relatively simple. You know, we believe, and we've got a very simple point of view that the application data and AI flywheel is what generates the most value for customers: a seamless application data and AI layer applications where users work on a daily basis, which create the data, data that's harmonized already across the end-to-end business processes, which allows AI to reason over it, to be able to come up with the right recommendations, and those recommendations then get embedded back into the applications that users use on a daily basis. And this flywheel creates the most value. If you try to do this application data AI flywheel in a disparate way, that means from an application perspective, you have to take the data somewhere else. You have to then harmonize the data. You have to make sense of the data. Once you've done that, you have to then sort of apply a disparate AI layer on top of it, think about all the security, the privacy, and the permissions that you have to apply to it, again, in a bespoke manner. And then once AI generates the recommendations, you have to plumb that back into, again, a disconnected application layer. So the more this application data and AI layer comes together seamlessly, it creates more value for customers, not just in a vertical manner, but also in a horizontal manner, meaning that if you have an app data AI flywheel from one vendor on procurement, let's say, and a different vendor on HCM, and a different vendor on finance, that still isn't optimizing for the global optimum, for the global maximum, right? Because you still have three different flywheels there. Where it creates the most value is if data across your business processes— finance, spend, supply chain, HCM, customer experience— comes together, pre-harmonized across, which is what we provide with AI that can reason across the whole, not just in silos. It gets the biggest outcome and the most optimal outcome, and that, from an SAP perspective, is what we're focused on in unlocking for our customer to make sure the application layer is a, is one of the most comprehensive and complete in the areas that we play in. We're making sure that in each of the areas, we're best in class, and for each of these areas, they're integrated out of the box seamlessly for our customers, which has massive TCO benefits, but then the data layer gets harmonized. Then, of course, the AI then reasons across the whole, providing a global maximum as opposed to a bunch of local optimums, if you will, that otherwise then customers have to manually plumb together, to stitch the story, which, while it can happen, it will still be, from a value perspective, far less than what it would be if it just comes seamlessly across those three layers. That, in core, is our application strategy and our product strategy. Each one of those three layers, we want to make sure, even though we are very comprehensive, because I said we have finance, supply chain, spend, HCM, CX, and then we have the data layer and the AI layer, that we know customers don't only live in just an SAP-only world. So at the application layer, we have our business technology platform that allows you to both integrate with non-SAP applications as well as extend and build your own custom applications and ISV solutions. On the data layer, we've got partnerships with Databricks and Snowflake and Business Data Cloud that allows you to bring in or zero-copy share out to other data assets in your organizations, again, harmonized with the data products and the data model for some of your most mission-critical applications, such as finance and supply chain. And the AI layer with the Joule and our agentic capabilities, you obviously cannot just reason over SAP applications and build agents, but you can connect them with non-SAP applications and build agents that work across non-SAP applications too. So we want to make sure we obviously are best in class in the SAP landscape that we play in, but not just that, but that we can expand into and provide the holistic needs for our customers, so hopefully that makes sense. Great. So maybe, I mean, you've covered a lot of things, maybe at a high level, before we go into some of those items in a bit more detail, but how do you think AI will change or transform the way your customers, the kind of product that they want, looking at it on the multi-year view? Yeah, I mean, I think it's, I mean, this is sort of the biggest question out there, right, as to how, what, in everybody's non-existent crystal balls, what would the world look like, from a customer lens perspective? And we actually outlined a very, again, a very simple point of view that resonates and is informed by a lot of customer conversations that they're, you know, the way we think about this is there's sort of at least three large steps you can argue today, what we want to do and enable our customers, because largely, if you think about customer organizations, it's humans, it's people that are running those organizations, and there's defined role constructs, and functional responsibilities that people are working on. So we want to make sure first and foremost, we're making those roles, those people more efficient, more productive, and smarter with our AI. So we've outlined this concept of AI assistance in Joule that brings together N number of agents or AI capabilities, but let's say for your accounts receivable agent, or your controlling associate, or your customer service agent, or your planning associate in supply chain. We want to make sure that there's an AI assistant available for them that makes them, again, super productive, far more efficient, and they can understand and reason over things that they wouldn't have been able to do, as humans, because the data that now everybody has access to is massive. So AI assistance is first and foremost. Let's make the people a lot smarter, faster, and efficient. As then these people develop more trust in the AI, and the AI becomes more complete in terms of the functional things it can go do for that role, we step into the world of autonomous execution, right? So as the role, as the person becomes more confident, they can say, "Hey, this set of customer service requests that are coming in, I'm actually okay with the AI agent taking them, reasoning over them, finding out what the right response is, coming up with the response, sending the response to the customer, waiting for feedback, and closing out the case, because now my trust level is so high that I can let that process run effectively in a touchless manner on an autonomous execution." So we believe these assistants, AI assistants over time will lead to autonomous execution in different functions of the business. We're seeing proof and results of that already. Customer service is a great example. We think about that on the financial close side. We look at some of the stuff on the supply chain side where you can take a demand signal, understand what the impact on the supply is going to be, and come up with the right request for how do you need to make sure that you can meet the change in the demand, if you will. That's the second part. So make humans and the people better, then you get to autonomous execution. And the third part is deep research. Like, you know, today there's so much data. You need to be able to reason over that data, uncover things that would take humans either a very long time or be very hard for them to be able to go do, to come up with the right strategies and the right recommendations to either increase top line, reduce costs, or be more resilient, so this combination of AI assistant, autonomous execution, and deep research is what I think organizations, over time are going to continue to rely more and more because it makes their function more efficient. It allows them to do more through autonomous execution and uncover the deep 10X, 100X opportunities through deep research, if you will. Okay, excellent. The big debate out there has been around agentic AI and a lot of concerns on how, if you push that logic, that can replace scale fast. You've been advocating the kind of opposite, but, you know, it would be great to have your kind of your vision in terms of how agentic AI will potentially impact SaaS and more specifically SAP. Yeah, I mean, I think, again, this is one of those, those bigger debates, if you will, that are happening through the course of the year as well. And our, our point of view, again, is very simple and informed by customers. That says, listen, I think business applications will be reimagined with AI and experiences. There's no doubt about that. Now, the question is, what would that reimagined set of business applications look like? And we believe, that there's at least five patterns, and we'll see more patterns evolve in terms of how the applications would change. And most of these patterns that you will see, there isn't where, there isn't a place where the application itself goes away, because where does the execution happen? Where does the compliance and the regulation and all of that stuff happen, right? But these patterns range from you have an application today. This is pattern number one, where now AI is deeply embedded already. So anything that you do is enhanced and enriched by AI to make you more efficient. You know, a simple example of this would be take your SuccessFactors, your HCM application. Now, as you need to do performance feedback or job description, AI can help you do that far more effectively, if you will. The second pattern is agents. Agents will automatically be part of these applications that allows you to do certain functions and make those roles that we just talked about far more efficient. So on the finance side, for instance, the application's still there, but there are agents that can help you do accruals management in a much more continuous manner and effective manner than what humans would be able to do. Agents that can do dispute management when an inquiry or a dispute comes in for your open AR or AP and come up with the right recommendations, so the application gets enhanced by agents that are just available. Now, the third case is autonomous execution. As we discussed, let's say customer service, for an example, the application's still there because you need to sort of work through, have the knowledge base, have the cases come in, and all the warranties and things like that, but an agent can look at the case and be able to go resolve it all the way to the end in an autonomous fashion. Now, a certain percentage of cases, a human might still need to touch because they're more complicated and things need to happen. The fourth pattern, it becomes a little more interesting, which is what we call the app-less experiences, right? Where the application is still there, but does a user need to touch the application? We believe that in certain classes of application, that will probably go away. So let's take expense management and Concur, for example. Do I really need to ever go into a Concur expense application to complete it? Probably not, because an agent can see your receipts coming in from your credit card provider, understand the policies, reason over it, sort of prepare the expense report because it knows when you traveled out, when you traveled back, and all the things that happened in the middle. It may ask you a question proactively through Teams or something that says, "Hey, this dinner you took, did everybody attend? That was in your calendar, so I can complete the expense report." And you say yes, and it asks, "Should I go submit it?" Like, like a human assistant would. And then the agent can go submit it. If there's more complicated, sure, you can get into the application and do it. We do believe there's going to be some app-less experiences like that. But again, that doesn't mean the application doesn't exist, because there's tremendous amounts of regulatory compliance, per diem requirements that are different by region that need to exist as well. That reasoning engine still needs to exist. The agent just creates a far more better experience for you as a user. And then you maybe get to the fifth pattern, which, you know, we're calling the no-apps experience, where you can either effectively [vibe-code] or generate an app, or get the information you need in a conversational experience without needing an app in the middle. We do believe this pattern we already see as well. But this pattern, we think, and we're seeing largely applies to where people were already building low-code, no-code applications. So it'll impact that area first or custom applications. So if you're going to go build a custom application, do you need to go build it, or can you generate it through vibe coding, use it, discard it, and then keep going as well? So we'll see all patterns, and we're seeing all of these patterns in our applications as well, and we're building that too. So in most of these, as you can see, the application still exists, if you will. Now, we do have a point of view. Like if you go back to the product strategy part, right, Fred, we said, listen, these app data AI flywheels can't exist in silos in a single landscape, because that effectively means somebody needs to then still bring them together. So what we do believe how the SaaS landscape will change is we will see that the best- of- breed players, as they're called today, struggle to find relevance in the future. Because if you're just a procurement application SaaS provider, and certainly you can put some AI on top of procurement, but you're leaving a pretty long last mile for the customer to take the data still somewhere else out there, match that up and harmonize it with finance and SCM and others, apply AI with its own custom security model that cuts through your application, because a user never only lives in procurement, right? They do multiple things, and where a customer can get a broader end-to-end business process suite, which has, again, best in class capability with data already harmonized, it would just make sense for a customer to go do, so we do believe there is going to be a disruption in SaaS, but it's going to lead more to first the standalone single lane players, as I call them. They will struggle first to find relevance because the TCO for the customer to now take them on, integrate them, pull the data out, apply AI, make the AI consumable is just not going to be worth it from that perspective when the capability differentiation isn't going to be there as well. Now, the third dimension for this is, you know, you can argue from a business model perspective, users and user-based licensing. And if you think about us from an SAP perspective, you know, less than half of our cloud revenue is actually user-based. Most of it, or a larger majority of it, is more outcome-based. And what we are going to continue to see the shift as we move from AI assistance to autonomous execution to more value-driven stuff, that the shift to outcome-based is going to be more and more. Outcome-based could be, you know, you know, it could be a bunch of business document metrics, or it could actually be value generated in terms of value realized in terms of efficiencies or savings as well. Those are the things that we're also continuously evaluating. From a journey perspective, just specifically on the business model with the role of AI and agents, we do believe it's going to shift more over time to outcome-based. We're already less than half from a user-based perspective. We don't think users are going away because they, you know, a lot of organizations have a lot of work, a lot of work to do. It might still be the same users, but more outcome. It will shift more to outcome-based. Okay, I mean, that's a good, very good question. We get a lot around the business model. I mean, you're touching it. I'm just going to just maybe go a bit deeper on that. So if you think about traditional SAP users, I mean, some of them are still seat-based, especially in the legacy on-prem world. But if you think about, I mean, when you have this conversation with clients on that kind of change in business model, is this something they're open to? Do you think there's an outcome where it's actually a net positive from a discussion perspective? And the concern out there is that you will need, you know, if your products are so much better and more efficient, you will need less users of SAP, right? So we'd be keen to understand how you see the equation playing out. Yeah, I mean, I think, listen, I think there's no organization that have come to me that says, "Hey, as long as the value is there, I'm okay with the value or an outcome-based measure or metric." I think the premise is can you create the value? And we believe we're in a very strategic spot with having, you know, core finance systems that we run, core supply chain, core spend, and others that end- to- end we can create the value. And as you talk about the value, then sort of an outcome-based business model resonates from our perspective generally well with customers. It doesn't necessarily matter if it's more users or less users as far as long as the outcomes are there. So, you know, I think in general the conversation that needs to be had is what's hype versus what's value? Can you realize the value from it or not? And as long as the value can be realized, I think there's good precedence that customers are willing to pay a percentage of that from a price and a cost perspective to whomever sort of generating that value. What we see today broadly in the market is, again, the talk of the AI hype and the bubble that's there and what it's going to lead to and the billions of agents. Like none of that is a reality for most customers, if not generally, you know, a large number of customers. I'd probably stop short of saying all customers. Now, it's when you talk about the hype without realized value, then it becomes a tougher conversation. But as long as there's confidence that you can create the value, as we just discussed in the three questions, we haven't seen pushback on it. Yeah, great. Let me talk about the product, and the kind of business AI vision that you've been, you've been kind of laying out. Can you update us on where you are on the roadmap in terms of agents? I mean, you know, off-the-shelf agents you've been, you've been pushing. Any metrics around traction and adoption of the business AI proposition? Yeah, I mean, I think I'll share, I think some of the metrics that we've shared, publicly here as well, and then just some more broader statements. I think today we have over 34,000 cloud customers that use SAP Business AI. We have shipped over 400, what we call premium or embedded, AI use cases. We have about 40 Joule agents that we've shipped and over 2,100 Joule skills, to our customers. Now, you know, I'll sort of put this a bit in context, and then we've also outlined now these role-based agents for your accounts receivable, your accounts payable, controlling, and things like that as well. I think, you know, I'll say this here openly, I guess, to this group, is 40 agents might seem, when folks are talking about hundreds of agents or thousands of agents, 40, like is that super impressive or not? But this is where I think going back to the hype versus reality, the noise that's out there, I mean, 600 agents are great, but the question is who's really using those 600 agents? We're taking a lot more customer-driven, customer-first pragmatic approach to say, "Hey, the agents really need to create value that is landing and we're working with customers to deliver," as opposed to let's just call anything everything an agent, agentify things and say we've got 1,000 agents that as a customer you struggle with to say, well, how does it relate to my accounts receivable department or my accounts payable department, if you will? That's why I think the feedback we get is, listen, I think you're probably one of the more pragmatic ones to say 40 agents, but these 40 are the ones that are hardcore agents that deliver the value that we're working with customers to deliver on top of the embedded cases that I don't know if you want to just go rebrand them as agents, but those obviously also provide acceleration and value from an efficiency perspective to our customers. Those are the 400 embedded use cases, and so forth as well. We also now, at our SAP Tech Ed event, talked about our new Rapid One model that allows you to do some predictive functions and capabilities on tabular data, if you will, in a, I'll call it a generative AI manner, if you will, which historically would have required a lot of data scientists and machine learning folks, if you will, in an organization, so we're bringing the best of the generative, the text-based to now the tabular data with the Rapid One SAP Foundation model that we talked about that reasons over numbers to again create more value and adoption from an AI value and an adoption perspective from our customers too. Then the Agent Builder, which allows customers to build their own agents outside of the ones we're shipping, is also something we're working with many customers on now, and I believe it goes GA here at the end of the month. Excellent. A question we get a lot on GenAI is, what's your approach around monetizing this? You know, maybe you can split it depending on the type of patterns you kind of laid out earlier, but, you know, it would be great to understand what is your commercial approach, the different models you've been testing. I mean, I know there's been a lot of discussion in the industry about how we get customers to reward us for the value we deliver, so keen to understand the approach you've been taking, and anything you can share in terms of, kind of what you've seen from a specifically from a customer standpoint, in terms of monetization. Yeah, I think there's four or five, and we can go through them. I think there's again super simple to understand. I think the first one is there's a set of AI capabilities that we believe are just now part of the core application because it realizes this vision of application data and AI seamlessly bring together into a singular experience. Those are just part of the application that if you have one of our applications, you get those AI capabilities and you can get value from it. So that's part of our core applications. The second set is, and this is where we did a shift, that we announced at Sapphire that's resonating well with customers: we said, hey, there's a set of agents and AI capability that, based on the workload, if it's finance, spend, supply chain, HCM, and others, that you get in a per user per month basis where you can use as much of it as you want. And we're going to continue to add more AI capabilities and agents to our finance per user per month AI package, if you will, and you can use that across. So that's simple. That gives predictability to customers to say, hey, that if you're using our spend applications or finance and others, that the innovation that we have shipped and the innovation that we'll deliver is going to be part of this. Majority of our out-of-the-box agents are part of this as well. So from a customer perspective, it gives them predictability. It gives them ability to see plus. It allows us to continue to drive innovation without having to, you know, charge for every single innovation for every customer or a customer to worry about, well, this new feature is going to cost me more or charge me, you know, it could be like a runaway usage. Now, the third part is there are certain cases that are more consumptive in nature. So for that, we have AI units. It's more of a consumptive, monetization package that says, "Hey, the more AI extraction you do from documents, the number of documents that obviously is more of a consumptive one in nature." And you can obviously go forecast that out based on, the documents that you want to. go. So we've got an AI unit-based model as well for the consumptive type scenarios that don't lend themselves well for any of the first two models. And then we have custom AI that you can go build as we talked about with Agent Builder and Business Technology Platform. And of course, it's custom and it follows a consumptive AI model from that perspective. And then, you know, those are the models that we have that seem to be resonating well with our customers, both from a simplicity perspective and then creating value. Great. So now maybe moving on to the competitive position. So you've kind of explained why you're saying the kind of suite approach will differentiate SAP. We'd be keen to hear, you know, how SAP is differentiating around AI integration. In particular, I would be keen to hear your thoughts around your direct competitor on the ERP side, with OCI. I mean, do you think this is emerging as a new type of competition from their standpoint, or do you think it's just a different debate, and the approach you have in terms of application layer will enable you to remain and to keep that differentiation? I mean, I think specifically as it relates to Oracle, I mean, I think our point of view is a lot of where Oracle's focus and growth is coming from is from the infrastructure side, and the partnerships that they're sort of driving there. While where our core focus continues to be on the end-to-end sort of cloud applications, end-to-end business processes and the industry depth and this app data AI layer that we create. Now they have that as well. But for us, this is our core focus. We're not getting distracted by now building data centers or in the infrastructure. We have that in strategic areas, but we also have what we believe is also an advantage for us through our business technology platform that we can partner with hyperscalers and are able to deploy our stack and our app data AI value layer in any hyperscaler in the regions that are in the customer's, wherever the customer's choices, if you will. And that gives us both a level of flexibility and an ability to then really focus on creating value at the top of the stack as opposed to going to the infra layer of the stack, from that perspective. So that continues to be our focus. And we believe, at the application layer, it's a different debate, as you said, I think. And in the infrastructure one, our point of view is clear. Now, what I would say, I think from a competitive differentiation perspective, I think you alluded to what we discussed already that, you know, if you think about, you know, we'll see a level of, I'll call it commoditization or simplification in the application layer. We believe, as our customers are telling us, it's going to be towards more of the suite players as long as they can deliver the best in class capability integrated out of the box, because the value on the AI then gets realized more easily. So the best of breed players will struggle, is what we're seeing and what we think will happen from a competitive perspective. Our value proposition, our differentiation there is very clear. Like if you want to build a supply chain plan, of course you need financial data. If you need spend, you need to be able to look at it holistically. Now, when you look at against suite players, I think the things that also differentiate us from that perspective is, you know, our business transformation suite, right? So if you look at Signavio that we have that looks at the process of our customers, and what you can generate both from a process mining and now an agent mining perspective that we've talked about, as agents and innovation that we're landing, it gives customers this frame that thinks about, because customers think about their businesses from an either industry lens or a process lens. Signavio gives us the significant advantage that on top of the application, we've got the world's leading process mining and process modeling solution that with the right agentic experience and the right agent logs can give you better insights as to which agents are working well, which ones are not. That gives us again an area of differentiation. If you think about with our digital adoption platform with WalkMe that sits across again, not just the SAP applications, but your entire landscape. And when you look at some of the agentic AI that's out there and what you can do with Joule action bar, it gives a level of differentiation from an AI perspective that certainly has the deepest integration with SAP applications, but through WalkMe, it's any application and can transcend all of that, if you will. We believe that's again a very different, sustainably differentiating area for us because WalkMe is also, as hopefully most of you know, one of the leaders in the digital adoption platform. You think about LeanIX, which already has a view of your enterprise architecture and your landscape being the AI agent hub alongside understanding your data. Like it gives us again a significant advantage to say, "Hey, not just agents with SAP, but we have a full view of our entire enterprise landscape and architecture and where we're leveraging agents and how." Now, like all of these things alongside, you look at Business Data Cloud and our partnerships with Databricks and Snowflake, that gives us sort of the leading data platform and data warehousing capabilities with our data and what you can do. I think creates a level of differentiation that we believe adds up to, you know, a very differentiated value for customers. Okay, great. I've got a few more questions, but I also have a number of questions from the audience. So I'll take those later. No worry, I have them. Maybe moving on to the cloud migration, if you think about your current SAP ERP customer base, can you give us an update on where they are on that journey to the cloud and what's the strategy to accelerate that migration? Yeah, I mean, I think, listen, I'll, I'll share some of the numbers that I believe Christian and Dominic have already shared in the past that from a, our, our peak support revenue was $ 11.9 billion in 2022. We still have, roughly over $10 billion, $ 10 billion- $ 11 billion in 2025. So we still have a massive runway left for ERP cloud conversion with revenue uplift sort of coming in the years. We see the base to migration of the cloud continue to increase. So the growth in going into our RISE and GROW offerings continues to increase. We already see about a third of our ERP customers have initiated ERP cloud journey. And then from there, obviously there's a significant upsell cross-sell that happens too. So, you know, that's sort of where we stand from, you know, maybe call it from a numbers perspective, but then we're also investing significantly as we just talked about. You look at our business transformation suite, how do we help customers get to the end state in a predictable manner. We're also investing heavily in AI-driven migration tooling like Joule for consultants, Joule for developers, as well as migration tooling that helps make this cost and effort for our customers to get to the cloud landscape in a much more simple and predictable manner. Not just obviously the tooling, the transformation suite, and AI, we're also investing heavily from an SAP perspective with architects, enterprise architects that help our customer through that journey with the right decisions on the enterprise architecture side, on the data architecture side, and on the business process side as well. We've got a heavy focus in supporting our customers through this accelerating migration journey that we see of our installed base. Great, and maybe touching on Business Data Cloud, I think that was launched for general availability in April. You've been talking about a very strong traction and pipeline on that product. So it'd be great to have an update around the adoption, the ambition for the product and maybe help us understand the economics for SAP with Databricks. I mean, at some level, I mean, I think, listen, I might get into trouble with this, but I wish, I wish I could share some actual numbers here, but I'll sort of leave this to our investor relations and Dominic and Christian to share, at the right point. But we do see a very significant uptake of Business Data Cloud, both the pipeline as well as the customers we have. And not just that, the usage of the customers we already have in the nine months is all very encouraging for us as one of our fastest growing products in the recent past, if you will. I think one obviously data point that you can look at, from as a validation of the success and product market fit it's finding with our customers is the partnerships that we just continue to announce. And the ones we've announced, I think are in numbers, you know, far less. We've announced quite a few, far less than the numbers that we're already working with other partners to go announce because the partners are also lining up to say, "Hey, listen, we really want to be a part of this." So obviously Databricks was our leading partner and we've got a special relationship with them as part of the Business Data Cloud. But then we also announced our partnership with Google BigQuery, as well as Microsoft Fabric from a zero copy share perspective. And then a deeper partnership with Snowflake as well, which has not just the zero copy share, but also the ability to use Snowflake as an extension solution on SAP as well, because we got that feedback from a lot of our customers. And then we're working, as I said, on many more partnerships too on the data side, not just data platform providers, but enrichment providers and, and providers that have data access. Like we announced the Adobe partnership that brings sort of the, the marketing and the customer experience data that we have with the financial and supply chain data that we have. So that continues to also go really well in terms of the economics. I mean, I think the way our customers see it and the way we've sort of positioned the economics of Business Data Cloud is, it's in my mind very simple. And I look at this both as a TCO reduction and a TCO shift from services to SaaS. And I'll, I'll explain what that means. And that's why it's resonating so well. You know, from a TCO shift perspective, it's not that the customers aren't spending significant amounts of money in taking data out of SAP applications, either through tooling that they've bought or SIs that they've engaged. And they engage significant either internal headcount or SI headcount to harmonize that data then across SAP and non-SAP applications. And not just that, that's a, you can argue, a one-time effort, but it's not because you have to then ongoing continually maintain that data asset in this disparate manner. So the value proposition for Business Data Cloud is to say, "Listen, all of this stuff that you bought bespoke, disparate tooling had a lot of humans and SIs harmonizing the data, managing the governance of it is now available as a SaaS service." So the data products from SAP applications are now available, managed, governed with semantical richness that you otherwise just wouldn't have been able to go do anyway in the previous way. And the quality and the harmonization of it across it, like all of that is now as a service. So on one hand, it's a shift from what you were doing, call it in a services or a tools-based heavy manner, to a SaaS experience that now comes with SAP, if you will. And if you look at it from a TCO perspective; A, the value is far higher. And I would argue in most cases, the TCO, ultimate TCO from a customer spend perspective is actually lower for them to be able to get a product that's higher value, if you will. So that's the economics that's driving it. It's a shift of, call it services to SaaS that sort of increases the [TAM], if you will, of where we go out. And in a way where it also expands that to say now with our partnerships with Databricks and Snowflake, and we can also have a larger data purview with zero-copy share for non-SAP data or non-SAP data in Business Data Cloud as well as some customers are doing because they'd rather have a single data platform to really then drive the AI layer on top of it. So it also obviously feeds into the multiplier effect of AI because data is the fuel for AI, if you will. So hopefully, that provides some context. Okay. That's very clear. Maybe one question around the upsell journey. So I think one interesting data point from the Sapphire was that you've pretty substantially upgraded the upsell expectation you have when customers switch to the cloud. And I think there was a the big delta is product innovation, which is really AI. But you'll you still have that kind of more traditional modules like BTP. So it would be great to understand like a you know concrete case of where you see those upsell as customers move to S4 in the cloud, you know, what's going on in terms of upsell journey, most popular modules that are being consumed, and how you see that you know those cohorts developing once they've moved to the cloud. Yep. I mean, I think it's, again, this story is very simple as well. You know, cloud ERP is sort of the leading anchor leads to an upsell from, you know, let's go back to our app data AI. It leads to business technology platform as that natural extension platform to make sure that you can meet the unique needs of the customer. So BTP continues to be a big one, both from a SAP build perspective in Integration Suite. And then certainly Business Data Cloud, right? Because as you move into the new environment, you need a way to be able to sort of have the data in a managed governed manner. So you can apply AI and then AI on top of it becomes, again, a not just a natural upsell, but we're seeing that now as the bigger pull as well, increasingly as the reason to go to the cloud as well, so you can unlock more value too. So those three continue to be the very logical ones, if you will, as an upsell. But then certainly from a cloud perspective, we also see significant expansion into this end-to-end business process context as well. So we have our supply chain applications. We have Ariba on the procurement side where we announced the next generation of Ariba built natively on BTP, seamlessly integrated to our cloud ERP. That's getting very positive feedback. We've got SuccessFactors. And we're seeing great growth, great momentum on our customer experience applications as well. So that continues to be, again, a cross-sell capability there from that perspective. And then certainly as part of the journey, we talked about our business transformation management suite as well, which is, hey, you, as you go through the journey, you know, LeanIX creates significant value for you and understanding your landscape. Signavio gives you that process map of your organization and WalkMe gives you that adoption platform. So this whole story sort of comes together in a nice way from that, upsell and a cross-sell standpoint. And in each one of them, as we talked about again at the, at the very beginning, if you step back and think about it, right, in each one of these, if you go back to that simple frame of app, data and AI, right, because apps is where you have our cloud ERP, all the adjacent line of business applications. We can also put our BTM suite there as well, LeanIX, Signavio on the data side. We've got Business Data Cloud with SAC, with what we're doing with Databricks and others and AI. You have a significant opportunity in what I would call workloads that we may historically have not played in as much. So on the application side with Build and BTP, you can build non-SAP applications. That's obvious on the data side with our partnerships. We're seeing non-SAP data is one that is coming together in certain customer workloads to say, "Hey, we need a data platform that's cohesive on the AI side." You can use AI now to stitch together not just AI on SAP applications, but AI across your broader function. Because generally the core of an organization is what SAP powers, right? It's your finance or supply chain or spend, but there's a lot of adjacent applications. So we're seeing AI being sort of this umbrella thing that pulls even more usage, and capability through Agent Builder and others and non-SAP workloads too. Great. Q uestion. I got a couple of questions from investors. I'm just going to go through them. Firstly, in terms of M&A, any capabilities that, you know, might be missing in the overall value chain, and in particular, I mean, there's a bunch of questions around the merits of bringing some partners in-house, and so, you know, maybe you can help us understand to what degree, for instance, integrating SolEx makes sense from a data standpoint, data understanding, data monetization. Okay. So I think I'll answer it a couple of different ways, right? I think the first part of the question would be, I think we're always looking at strategic M&A targets, if you will. We just recently completed SmartRecruiters. So in HCM portfolio, we felt, hey, this is an area we needed to bring in a best of breed player that our customers were, are asking for more innovation. And that has landed really well. And we're seeing some significant positive momentum there. Similar to that, we continue to evaluate across all of our domain areas, if you will, on where do we need to go show that up. We have also done, you know, some interesting partnerships as you think about in the customer experience space. We launched a loyalty management solution, but that is partnered with a provider that we've OEMed with significant expertise and SAP integration and data capabilities on top of it. That's resonating very well. It brings us the maturity of a product with the customer base that completes our story on the commerce side, and that's again resonating really well for us. You know, we were looking at M&A not only just across sort of the application side. We're obviously looking at it again, just continue to use the frame of app data and AI. If there's things on the data side that we need to go do, besides the partnerships that we're doing, obviously for us continuing to bring in the world's leading data engineering platform with Databricks as an OEM seamlessly into BDC is resonating very well with our customers as we see. We also saw that in this space, there were a lot of customers that had already bet on Snowflake as a platform. So they wanted that option as well. They get the full value prop of BDC, and this is where our openness is what drove us to say, listen, we want to bring this because it's a customer choice already, Snowflake into a fold as well. And we did that as a SolEx because obviously then if customers have that option, they can go with Snowflake as well. And then Databricks continues to be deeply embedded part of Business Data Cloud, and then the zero-copy share solutions as well. In AI, we've announced significant partnerships as well. We announced one at TechEd with n8n that comes together and brings their agent building capabilities with us too. So I think, I guess that's how I would answer it. If there's any specific aspects, I'm happy to go deeper into it. We also did in the procurement space, if you notice, you know, we've made a decision early on to say, hey, contract management as a space has got a lot of sort of best of breed domain level innovation that's happening. And this is where we partner deeply with Icertis. And we've now launched a SolEx solution with Icertis. And not just that, with our next-gen Ariba application, we're actually providing a seamless singular experience on top of Icertis in our procurement platform that from a customer perspective, they wouldn't be able to differentiate whether they're using Ariba or Icertis, but they get the value and the power of two organizations innovating aggressively in each of the domains as well. So we'll continue to look at how do we meet the customer needs and the gaps and the white spaces that we have and where there's other opportunity to go to. Very quick follow-up. When you have a SolEx agreement, do you actually have access to data or it stays at your partner? If you both had an offering. It depends on the nature of the SolEx, if you will, and how the integration with the applications have set up. I guess that would be the short answer. There's no single way we do it, if you will. It depends on the nature of the integration that we've built with the provider. Okay. A few more questions from investors. I'm going to go through. I know we have taken almost an hour already, but question around how SAP is using AI internally. I mean, I guess it's a big topic from you know Sebastian and he's part of the operation, but keen to hear from your standpoint on the engineering product side, et cetera. Is there further you can do in terms of OpEx and cost management, after the kind of big efficiency ramp-up we've seen the last few years? I mean, looking forward, in your organization, it looks like the velocity of innovation on AI is accelerating. To what degree you can leverage that internally? Massively. I mean, I think we've already realized significant benefits, as you can see in the ratios that we have out there, and we continue to expect the ratios to generally continue to trend in the direction that they have been in the recent past, if you will, but alongside that, I personally know, and we're working towards that, that there's a massive uplift in throughput that we can still continue to derive with all the AI toolings out there. We are looking at every, effectively, you can say at some level it's a little bit of a hyperbole because you can't really do every single one, but every single sort of new tech that's out there that affects software development, not just in development, but product management, design, UA, QA, and how can we bring that in and scale that to our 35, 000-40,000 colleagues in product and engineering to increase the throughput, if you will. Alongside that, we also continue to look where do we optimize, where do we focus the right skill sets on. I expect our throughput from an innovation perspective to continue to skyrocket, if you will. At the same time, our ratios continue in the trajectory that we've sort of articulated with you all. Cool. Next question around your mid-market offering GROW. It's been also kind of a bit of a new growth area for SAP, not really the kind of core of the proposition historically. Can you discuss a little bit where you are in terms of the product, the availability, the go-to-market on that, the momentum you're seeing in GROW? We're seeing a really tremendous momentum in our, you know, I'll call it our S/4HANA public cloud solution, our SaaS solution, if you will, that has now finance, a supply chain package, supply chain premium in finance base. The uptake that we're seeing is, it's, again, one of our faster growing areas, if not the fastest. I don't know the actual numbers in my head to be able to lay that claim here definitively. But what we do see, of course, is a significant uptake in new from it, which is what the design principle was in the GROW motion in the mid-market space. But we're also seeing a very real interest from what I'll call the upper mid-market or lower enterprise that says, "Hey, we really want the value proposition of a true SaaS cloud ERP, and we're willing to go through the business process change and re-engineering to be able to benefit from having a SaaS cloud ERP application. So we're actually, from a product perspective, continuing to add a lot more capability. But then at the same time, from a go-to-market perspective, we're also spending a lot of energy and investments to continue to, if you will, fuel the machine, and meet the demand that we see out there alongside significant partner motions and ISV motions that sort of complement too. This is an area, of course, we're also seeing a significant amount of AI adoption too, because the app-data AI comes seamlessly together. Think about the agents or the AI experiences in the core ERP applications that are just there natively for you enabled in the next update without you having to do anything. We're seeing some very significant usage there. Excellent. Well, I think we're up on time. Look, I think you've given us a very a bit assessment of your, I guess, perspective around cloud. In, well, the whole AI side in general, BDC, GROW. So lots of areas of growth for you. So exciting space, a lot of partnerships. I mean, you mentioned Microsoft Fabric, et cetera. I mean, there's a lot of innovation on the BDC. So look, I mean, thanks a lot for giving us some of your time, Muhammad. It was a pleasure to have you on our field trip. And, yeah, I mean, talk to you soon. And for our audience, you know, we stay tuned. We have, we start again in five minutes. Thank you very much. Thank you, Fred. Thank you, everyone.