Global Keynote: The Beginning of Better | SAP Sapphire Orlando 2026

Since the dawn of time, history has been defined by a series of world-changing innovations. These groundbreaking moments were not always met with the appropriate levels of enthusiasm.

Well, that's the end of the raw food industry.

It's called a sail.

I don't know. We've always been more of a rowing-centric organization.

I call it inoculation.

Clever. But we have a lot invested in leeches right now.

Well, that's the end of Shakespeare.

That's the end of their careers. No doubt.

No more mainframes.

Well, that's the end of big data.

What is this called?

The internet.

I do not like it.

They're calling it software as a service. They think people are going to rent software.

SAP HANA. It's the end of legacy enterprise tech stacks with siloed ERP and analytics.

I don't even want to imagine a world without legacy enterprise tech stacks with siloed ERP and analytics.

So we're an autonomous enterprise now. You know what that means?

It's the end. Yeah.

The end of busy work, bottlenecks, and silos in the beginning of faster, simpler, better.

Welcome to the Autonomous Enterprise. It's not the end of anything. It's the beginning of better.

Please welcome to the stage, Christian Klein.

Of course, Christian. Please welcome to the stage, Philipp Herzig.

Good morning and thanks, Christian. We are so excited about Business AI Platform, a platform that is designed to close the adoption gap by delivering outcome, speed, enterprise readiness, and of course, all the context you need. It's the place where you build, contextualize, reason, and govern AI end to end. And let's have a deeper look into build. In fact, before we actually start building anything, it would be great to know what to build. What has the highest impact on the business? In the past, you had to do time-consuming research or do this thing we proposed called AI discovery workshops. Now, it is as simple as typing, 'show me my top three business challenges.' Now in this case, we are a pharma distributor. We move a lot of products around and need to align prices between manufacturers, hospitals, and purchasing organizations. Complex supply chains. Razor thin margins. Now, we have this new thing, the Process Consulting Agent from SAP Signavio that goes deep into the actual business processes of that pharma distributor to show us what has the highest impact. In this case, we have a huge margin leakage. This year alone, 24 million at risk. A great use case where AI can help. And the proposed solution, a Sales Pricing Validation Agent that helps to resolve those inaccuracies. Now, where do we build this agent? Of course in Joule Studio, it's the place where we extend, build, and integrate AI experiences like never before. Since its launch, customers and partners have built phenomenal things with Joule Studio already, like ABB, that now handles more than 15,000 RFQs automatically, every year, saving them millions.

These are just a few great outcomes, but we heard your feedback loud and clear. You all wanted more. And today, we're super excited to introduce Joule Studio 2.0 to build agents for business outcomes. Now, what is this new Joule Studio? Now first, it's intent-based, so it really understands your business challenge, and it's deeply contextualized in your business data and business process know-how. It, of course, comes with out-of-the-box connectivity to all your apps, and it guides you through full-fledged product requirements, documents, and technical specification. Also super important, it's eval-based so you can actually guide in a data-driven way your AI coding assistants. And it's SAP-managed and comes with all enterprise qualities baked in. In addition, Joule Studio is open by principle. You can choose from any third-party AI model. We have them all on the platform already. Of course, also SAP's own AI models, and you can build in the environment of your choice, no login. And of course your style. You can develop low-code, pro-code, everything in between. Now let's build something together with one of our amazing partners. And for this I'd like to invite KPMG on stage. Rob Fisher from KPMG, Global Vice President, right here. Rob?

Yeah. Well first it's great to be here Philipp. And that's right. We're living AI transformation ourselves, and we're learning from it every day. We're a GROW customer, and we know what it takes to modernize core systems on S/4 Public Cloud while still keeping the business moving. We've got a final rollout with about 270,000 users, and we moved early on AI. We deployed Joule for Consultants across 34 countries, and more than 3,000 of our consultants are using it already, and we're co-innovating with you. Our teams have built many agentic AI solutions, including 20 agents on the new Business AI Platform. And for one of the clients we're working with, we're targeting $120 million in reduced contract leakage. That is real, tangible value.

Absolutely. It's an amazing value. Hey, Rob, KPMG is a global leader in AI as well, and also, of course, very well recognized for its high-quality advice. What is the number one thing you're seeing here with customers?

Yeah, I think it's this clear shift, right, from pilots to integrated AI agents. Customers seeing AI not just as workforce efficiency and cost savings, but as core to competitiveness and a driver for unlocking more business value. And where we're really seeing the leaders really separate from the pack is in the execution and the organizational adaptability. You see leaders pairing the AI investment with the workforce transformation, and then embedding AI where the decisions and the accountability sit. And because most enterprises run on multiple platforms, that AI has to orchestrate across the ecosystem with trust built in from day one. And hey, that's where SAP comes in.

Rob, thank you so much. That was a great setting up the stage. And now we actually would like to implement the pharma distribution scenario on the margin leakage we just saw. And gladly, one of your colleagues, Chris, gladly accepted to do the demo. So we don't have to do the demo. You do the demo for us now. So that's amazing. Thanks for the partnership. And Chris, let's get up right on stage.

I'm excited to show some magic and really build something powerful for an organization. Here we can see Joule Studio where I can extend, build, and integrate agents. And what I really love about it, it's fully integrated with the SAP ecosystem, and I can build enterprise-grade agents without writing a single line of code. Joule has already taken the findings from the process consultant demo that we showed before, and I can use them to build the new Pricing Validation Agent. This is where we tell Joule what we need to achieve rather than how we actually do it. It's a game-changer for how we deliver value for our clients. And this is where we start to see Joule acting less like a tool and more like a colleague and coworker, where it checks and asks us critical, clarifying questions such as, what's the source of our pricing data, or what really drives success in this scenario? And as I'm going through these answers, one of the things that I think is extremely powerful is it's fully integrated with our agent landscape, where in this case, we can see that it's already identified two existing governed agents that we have deployed that already do part of the job. This is automatically included in our solution.

And from this generated intent, I get transparency into what is the problem statement, what is the business context, what are the relevant source systems and data domains that are included in this, as well as taking a look at what is the recommended solution for this agent. From all of this context, what I'm able to do is automatically generate the product requirements document that has historically taken us weeks or days to generate this type of blueprint that we now have at a click of a button and get in just minutes. What we're able to do within this product requirements document is verify that this is the intent, this is what we need to build this agent, as well as if we need to make any adjustments, such as adding a margin threshold of 1.5% to make sure it has that baseline. Now it's time for Joule Studio to start doing the building, and we can build the solution. And that's kind of really all of the heavy lifting that's done on my side. And we're able to go and generate, and I'm just going to move over here as we generate the actual solution. And as we're going through this and as we're building this, what we're able to do is jump to a completed agent already. And from this completed agent, what we're able to do is take a look at all of the unit testing scenarios that were identified and built, as well as take a look at the underlying solution with the integration with n8n, to see how each of these three individual agents are orchestrated together to solve this complex problem, and being able to see what that entire end-to-end workflow looks like, as well as we're able to dive deeper into the design and take a look at all of the configuration and capabilities that are built into this solution. Everything from what are the tools and skills to what are the individual evaluations that are needed for this solution. This is based on our data, our landscape, and really what is needed to check the success of this. And this type of solution is exactly what it takes to reduce pricing disputes and protect margin at scale. This is really how we turn expertise into agents that run the business from insight to impact to value. Back to you, Philipp.

Thank you so much, Chris. Fantastic demo. Thank you. And that's Joule Studio. That gets you to the outcome so much faster and far more accurately than ever before. We tested building this agent and many more on comparable platforms. And of course, what we cannot do is the code generation faster. In fact, because of all the context, it's a little bit slower. Joule Studio wins in every other dimension it has been designed for, bringing you to the outcome 10x faster, and it does it at a far higher accuracy because it has the understanding of the SAP domain, and it has the understanding of all the latest APIs you've got to use, and so much more. And finally, developers love it. We have given this experience to 1,000 developers who tested the alpha version of it, and the feedback speaks for itself. And that's the next generation of Joule Studio starting to roll out to first customers starting June.

Next in the platform, let's talk about contextualize and reason because, Christian mentioned it already, agents are only as powerful as the context they are operating on. But the problem still is, we know it, the data is fragmented, and it's trapped in proprietary formats. And of course, it's not semantically aligned. Now, Business AI Platform starts on a strong foundation, SAP Business Data Cloud, which acts as the business data fabric that gives you universal business context across your entire multi-cloud landscape, both for SAP and non-SAP data. Business Data Cloud is one of the fastest-growing products in SAP's history. Customers like Baker Hughes and Zalando are getting amazing business benefits from it already today. For example, by using any of the 300 data products that are coming out of the box managed by SAP so you don't have to do the heavy lifting. However, these data products are coming only from the SAP world. But of course, there's so much more data that needs to get integrated to empower our agents, like customers coming from Salesforce, employees coming from Workday, or any other application. This is where Reltio is coming in, allowing you to create the single golden record for all your master data end to end. So AI agents are able to reason over consistent master data. And to help you with your data modeling efforts, we are introducing Joule Agents into BDC as well. For example, our all-new Data Products Generation Agent takes the heavy lifting away and the same experience we saw in Joule Studio to entirely create new data products or derive data products in virtually no time.

BDC, of course, is open by principle. BDC Connect, introduced last year, allows you to integrate and share with a variety of data platforms, and today we are excited to add Amazon Athena to the mix as well. But we didn't stop there. We were so excited about our latest intent to acquire Dremio, a high-performance data and agentic lakehouse with cutting-edge data federation across any cloud and any traditional legacy database. Once this acquisition is closed, BDC will 100% support the open table format Apache Iceberg for plug-and-play interoperability with any data store, and combined with BDC, this will build the foundation to let your agents reason across data from any cloud, any environment, without data movement. Now with this, we have a solution to build up tables and data on the fly. And this gives us the next stage, which is predictions on the fly. Because the opportunity in Business AI was never just large language models. They're fantastic, but they're just one part of the equation. It is AI for decision intelligence, reasoning on structured business data. Predictions like demand forecasts in retail or predictive maintenance and asset management. To do such things, we had to rely so far on what we call narrow AI models, one for each task. Now, if you want to solve for 10 of such predictive tasks and say, for 10 company codes, you have to train easily 100 AI models. Months of work. And you need a lot of data scientists, which oftentimes you don't have. And it's a significant investment, hard to scale and adapt. So what we did is we basically put all of that into one giant model, which we introduced last year called RPT-1, which is the most performant tabular foundation model on SAP data. And today, we're excited to introduce the next model generation with RPT-1.5. This does not only allow for far better accuracy on tabular data, but also introduces an all-new chat API that allows you for on-the-fly what-if analysis and predictive simulation. And a big focus was on explainability. So you can always follow what the foundation model is doing based on the rows and the columns that led to the prediction. All of that without any AI model training because it just works on the fly. You can check it out today on rpt.cloud.sap.

But there's one more thing, because we are taking the next leap to define this category. We're excited to share our intent to acquire Prior Labs, a frontier AI lab for tabular foundation models, and bringing the latest TabPFN model family to life, actually today, on their platform. While RPT-1.5 is best in class for the SAP data, their TabPFN model family is best in class for non-SAP data unlocking industry-specific use cases, for example financial trading. And these are just a few highlights of what's new in contextualize and reason in Business AI Platform. Next up, govern. You know the thing, right? Agents are now everywhere. Some are great, some are not. And almost no one really has a consistent picture. No central governance and AI Agent Hub is the place that changes that. It's one command center to discover, manage, and govern SAP and non-SAP agents, MCP servers, and everything else in your landscape end to end. SAP AI Agent Hub is built on SAP LeanIX that's already used by more than 25% of the Fortune 500 companies. And today we're taking it to the next level. AI Agent Hub not only allows you now to discover all your agents in context, your landscape, or your business processes, but once you identified each of those SAP and non-SAP agents, you can actually control their risk, define architectural boundary conditions or compliance rules. And once you're done with that, you can mark an agent as verified and Business AI Platform and AI Agent Hub are making sure that only verified agents can operate in your landscape to ensure highest levels of control. Furthermore, once you then deploy these agents, of course you want to see what they are doing. We bring all the telemetry, all the observability into one place, and now you can navigate seamlessly into Cloud ALM from AI Agent Hub, so you can watch their health, their goal completion, or the tool calling correctness. In addition, you can then also from the AI Agent Hub go over into agent mining from Signavio so you can watch their behavior and find further optimization opportunities against your KPIs. And finally, we are also mapping all your agents into SAP SuccessFactors. So you can see the impact these agents actually have on your workforce end to end. The full governance lifecycle of AI Agent Hub will be available as generally available in Q3 this year, and is now part of SAP Business AI Platform out of the box at no extra charge. And this is the platform the Autonomous Enterprise is going to run on. With that, back to you, Christian.

We build Claude, the AI that enterprises trust most. At Anthropic, we build frontier AI systems, and I've learned that when the stakes are high, enterprises reach for the AI they trust. We've built the company around the conviction that as AI becomes more capable, enterprises need to understand how it makes decisions and to know it will operate within the boundaries they set. Earning that trust has costs. We've delayed launches over safety concerns and published research even when it's controversial. These are not easy decisions in the fastest-moving industry on earth, but we believe they're the right ones to make. Which brings us to SAP. The world's largest enterprises run on SAP, and that's exactly where trusted AI belongs. And that's why Claude models are available to SAP's customers on the SAP Business AI Platform. By combining Claude with SAP's depth and scale, we can help hundreds of thousands of organizations use AI throughout every part of their business. Inside Joule, Claude will power agents that take action across finance, HR, procurement, and the supply chain. This puts AI to work inside the systems you've already invested in and the processes your people already rely on. SAP is a great example of a company that is bringing AI into the enterprise in a way their customers can actually trust.

It is my first time. Indeed. It's impressive and intimidating.

Yeah, yeah. So let me give you a sense of that scale. We serve over 86 million U.S. customers and 7 million small businesses, and we process about 12 trillion in payments across 120 currencies every day, and technology is the backbone of all of that. It allows us. It's what allows us to serve our clients seamlessly and securely. But that scale brings complexity, which can slow you down and introduce risk. So we're very focused on moving fast without compromising safety. And that means investing in common tools and platforms that are easily deployed and available firm-wide. So in that context, SAP has been a critical partner for us. And one particularly notable recent milestone in that partnership has been our decision to invest in a major upgrade of our general ledger to SAP's latest version, in order to bring our entire financial ecosystem into one unified, intelligent platform.

Yeah, I agree. I have to say at JPMorganChase, we like our unicorns with only two ears. Please. Thank you very much. It's very important. But no, AI is the most significant trend in technology in a generation and frankly, in society at large in a generation. And as a result, everyone, I think all of us, are experiencing pressure to use it everywhere. But in a finance organization, the risk of that is that you sprinkle AI on broken processes, and you miss the opportunity to retire technical debt and modernize the entire ecosystem. So from our perspective, AI is only as good as the data and processes underneath it. It can't reach its full potential in a fragmented legacy environment. So we're very focused on end-to-end process re-engineering. And through RISE with SAP, we're embedding intelligence directly into the workflows. We're already starting to integrate agents to make the day-to-day management of the ledger more efficient. One example is an upstream agentic solution that proactively catches issues with systemic feeds, so we can correct them before anything posts to the ledger. And over time, it learns patterns so we can fix root causes permanently. Importantly, the agents that we've built are not inventing their own business rules. Those rules, rather, come directly from SAP's embedded control framework, and every AI-driven intervention is logged and fully traceable, giving us the transparency and audit trail that we and our various stakeholders expect.

Yeah. Now the partnership between JPMorgan Payments and SAP combines enterprise software and AI expertise with an extensive set of integrated payment solutions at real scale. What this means in practice is that clients can seamlessly access JPMorgan Payment solutions directly within their SAP system everything from trade finance and real-time reporting to global payment rails without leaving the workflows they already operate in. We're continuing to develop new solutions together that enhance digital experiences and help our clients grow. And this partnership is only getting deeper. We're now beginning to work with SAP team on exploring agentic capabilities to create a much more automated and intelligent treasury environment for our joint customers.

Sure, Christian, I can do that. I'm going to use three words: scale, speed, and trust.

Thank you very much. Thanks a lot.

It's great to be here at Sapphire. What SAP and NVIDIA are building together is one of the most important platforms in enterprise AI. NVIDIA's supply chain is incredibly complex. Millions of parts, hundreds of partners and factories, all connected through SAP. But what's changing is not just how enterprise systems are managed, it's how work actually gets done. We're moving from hand-coded software to AI that can understand, reason, and act. AI no longer simply answers questions. It works for you. And enterprise systems are where work happens. Finance, supply chains, procurement, and every workflow in between. SAP is the foundation of enterprise. And now they're building the agents that sit on top of it, trained on proprietary data with the skills to act. Soon, every company will have a workforce of agents. These specialized agents will not replace enterprise software. They will make enterprise software more powerful than ever. And with NVIDIA, agents can be built, evaluated, deployed, and governed. We created OpenShell so agents can act inside a company safely and with clear boundaries. And this work is happening as an open-source project. So everything we learn goes back to the community. SAP is embedding OpenShell across its entire platform to secure enterprise AI at scale. Agents can take action, but only what they're allowed to do. They can access data, but only what they're permitted to see. They can execute workflows, but every action is traceable. This is how we make AI safe and useful. It understands the business. It follows the rules. It uses the tools, and it moves fast. Thank you, Christian, Philipp, and the SAP team. The Autonomous Enterprise is no longer a vision. It starts now.

Please welcome to the stage, Muhammad Alam.

A big welcome to Sapphire. And thank you for being here. So we didn't just build a new Business AI Platform. We built it. And then we used it natively to really completely change the portfolio of products we have into a truly Autonomous Suite. Underpinning the Autonomous Suite are out-of-the-box agents. Hundreds of agents cutting across all core business processes. These agents come together into what we call assistants or Joule Assistants. We've mapped these assistants to roles across the core processes of an organization, because we know that the first step in realizing value from AI is to empower your people to do more, do it better, or do things that just weren't possible to be done before. These assistants can be human-triggered by humans. They could be system-triggered with humans in the loop, or ultimately set up to run autonomously, collaborating across multiple assistants once you have built the right trust and confidence in the results of their execution. They're also designed with outcomes as a core objective. Each assistant has a defined set of KPIs that you can expect it to deliver. The outcomes are then tracked in the SAP AI Agent Hub that Philipp showed to ensure full transparency and accountability to you and your business, because ultimately what you want is positive business impact, rather than just a count of agents deployed as the measure of success for your business.

We also know that no two businesses are alike, so not every agent is going to work completely out of the box for you. And for this reason, we've made extensibility a core design principle. You can extend any of these agents by adding tools, workflow steps, and even code through the same simple experience that you saw in Joule Studio earlier. This allows you also to connect them to your non-SAP application, because we know you're going to have to do that. You can, of course, also add completely new custom agents to any of these assistants to complete the autonomous execution for your enterprise. But we didn't just stop there. We know that the traceability and auditability of the agents is critical for you to build your confidence and leverage them at scale. And that is why we're proud to announce that we follow a SOX audit-compatible, ISO-certified development process for building these agents. All our agents are built on this certified compliance framework, developed in coordination with our own auditing firm, as well as an external audit partner to ensure audit readiness at the agent level, particularly for the audit-relevant agents. Multiple assistants then come together to form an autonomous domain. In Autonomous Finance, for instance, we have assistants for AP, for AR, for treasury, for controlling, and so on. An assistant for every role, each with its number of agents and with its uniquely defined outcomes. We have built these autonomous domains again, not just for finance, but across the entirety of your core business processes. So let's go through them. In Autonomous Spend, we have assistants from supplier management to sourcing, contracting, and buying. For Autonomous Supply Chain, we have assistants spanning the entire make-to-deliver processes. For Autonomous HCM, we have assistants like recruiting, payroll, and career and development that cut through the entire HCM set of processes. And finally, for Autonomous CX, we have assistants covering sales, service, commerce, and marketing processes. All of these domains then come together to help you become an Autonomous Enterprise, allowing you now as an organization to do things better, more efficiently, and unlock value that you just weren't able to unlock before. Across all of these domains, we're proud to be launching 224 agents and 51 assistants, a number that will only grow monthly from here on out.

So we built all of these assistants, and we made extensibility a first-class principle. But we realized that there was still something missing, something big. Your company context, the organizational memory of your company that sits in the minds of people that have executed those processes for years and years. This knowledge, often referred to as decision traces, often sits as unstructured data encapsulated in process models, policy documentation, even chats and long emails, making it effectively invisible to any agent on any agentic platform. So we built something to address this tribal knowledge gap. Something we call the Company Memory. Company Memory is a knowledge management layer, you can call it a context graph, for your company specifically. It's built on the same foundation as SAP Signavio. So it's already natively aware of your process models and your process insights. You can additionally feed it your policies and procedures, document relevant content from Teams and Slack channels, and even long email approval chains or exception chains, from which it then extracts what we call process atoms, which are small structured units of business process knowledge. Each one can guide an agent on what to look for, what to do, and more importantly, what not to do. When an agent makes a call, it also maintains traces for full transparency and control. When it runs into an exception, it adds to the Company Memory on how it dealt with that exception. You, of course, have the power to directly update a rule, review a rule, a process atom manually, and once done, all agents adapt to it instantly. This is what allows us to bring the best of the public LLMs, SAP's unique context layer, and your own company context together to really deliver the results that you can rely on.

Now I know what some of you may be thinking. You love the assistants and the agents, and you love the Company Memory story, and you can't wait to deploy in your landscape, but you still might have a long way to go before you complete your modernization journey to our latest cloud applications. However, your business, of course, expects you to deliver value today. Well, we've thought of that too, and we're really excited to announce that we are enabling a significant percentage of these Joule Assistants and Agents to work in hybrid landscapes with the ability to connect to your S/4 on-premises and ECC landscapes. This will be available for our customers that have started their modernization journey on RISE, of course. We've done this so you can start generating value from AI today on the SAP Business AI Platform while you're modernizing your estate. Finally, as you would expect, we're also proud to reiterate our commitment to openness and enabling A2A across all of these agents and assistants, because we know that some of you may have, unfortunately, selected other agentic platforms for your organization. So instead of having to rebuild all of these core business process agents from the ground up, you can use A2A to connect these with your platform of choice to get the benefit of the outcome-driven, out-of-the-box, certified Joule Agents and Assistants. Okay, so we've talked enough about these assistants. Now let's take a look at one of our more popular finance assistants in action. Please join me to welcome Sophia Levins onto the stage. Sophia?

Thank you Muhammad. It's incredible to be here with all of you. You've heard about the Autonomous Enterprise. And what I get to do is show you how it feels to work in this new way. Today, we're looking at the Financial Closing Assistant. And in an era where companies can run supply chains in near-real time and answer customer questions in seconds, the close can still take a week, sometimes longer. Not because the work is hard, but because the work is fragmented. All right, let's dive in here. What we're going to start with is looking at last quarter and how that went. What you'll see is this company has 12 legal entities, four ERP systems, six different currencies, hundreds of different reconciliations that depend on a human in one country waiting for a human in another. The close is not slow because financial teams are slow. It's slow because the work is person to person. What I'm going to show you is a new model, not automation. Automation runs scripts. What you'll see here is that Financial Closing Assistant, hard at work, coordinating across all the different agents. It's also brought to my attention the 18 things that need a bit of approval. We're going to start here with the Accounting Accruals Agent that has four proposals for me to review. You can see these have the details we need and a confidence level that's been set by my company. I can see this professional services accrual is just below the confidence I set. I approve, and the system remembers. Now, over in intercompany reconciliation, which is typically the hardest part of close, you can see most of these are completed and working on it. You also see we have some variances, each with the root cause already done. You can see timing, cut off, and the occasional genuine dispute. One click matches the records on both sides in both currencies. What used to take weeks of emailing back and forth now happens in a single click. A pattern was recognized because the system has that company memory. It learns how your controllers think. Every approval, every override, every comment becomes part of how the agents behave next period. The system doesn't close the books. It learns the company that's closing them. Let's look back to what changed. So I can easily go back to Q1 and ask the system to compare with what we just saw in Q2. And what we're going to see here is that in Q2 close, it was faster, with less variances, with less items for the human to deal with. Each close will teach the next one. The system gets sharper, the exceptions get rarer, the close stops being something you survive and becomes something that can run itself. Okay, Muhammad, back over to you.

Thank you. So as exciting and energizing as the topic of financial close is, it's a bit of a joke. I'm sure Dominik here, our CFO, is thinking it's actually a true statement. We have over 12 customers that are already on their way in deploying the close that you just saw on our new Business AI Platform in their environments, including Dominik as well. For SAP, for many of these assistants, not just this one, we've been working with early design customers not just to build, but also to deploy and validate the results. So let's look through some of those stories. In finance domain, for instance, Bayer is leveraging our Cash Collections Assistant to handle routine work autonomously while engaging humans only when needed, resulting in, of course, faster execution, smarter outreach, and more importantly, accelerated cash flow. In the spend domain, Novartis leverages agents to autonomously execute high-volume sourcing that has resulted in faster cycles, stronger compliance, and outcomes at scale for sourcing events. On the supply chain side, Kaeser Compressors utilizing our Product Design Assistant to help define product requirements, create and update product design, and hand it over to the execution engine. On the HCM side, Ericsson is leveraging Intelligent Goal Recommendations to implement aligned, consistent, and measurable goals across their 85,000 employees, so each one of them can have personalized recommendations on their own journeys. With the Merchandising Assistant in CX, Petco turns fragmented and inconsistent content checks into a guided workflow that scores content readiness, optimizes the product data, and surfaces assortment gaps for AI-based discoverability across all their channels. We're proud to be working with these organizations and many others to unlock value through SAP Business AI.

Now, there's one thing that we've shown you all throughout the keynote, but we haven't yet explicitly talked about, and it's actually been present and working hard to even introduce all the speakers. And that's Joule itself. With Joule, we are fundamentally reimagining how users will interact with SAP applications in the future. It's probably no surprise to anyone here that as SAP, we're really very famous for our amazing user experiences in our core ERP applications. Though I think infamous might be the right word, I think our CEO actually was brave enough to show even some of our great screens from 10 years ago, I think. So we thought, since we're so famous, why don't we just completely change the paradigm of user interaction? Some call it zero UI these days, though the word headless also seems to be in vogue. We like to call it an app-less experience and a no-apps experience. App-less, meaning an experience layer that allows you to interact with your underlying applications without having to be in them. And no apps, meaning you don't even need now an app for it. The capability will generate itself on the fly for your users to use either one time or repeated use without you having to go build them. All of this and more is what is available in spaces in Joule. Spaces is also where our agents interact very immersively with the user, what you saw just now in the Financial Close Assistant with their human counterparts. Let's roll the video to take a deeper look into what spaces are.

Spaces in Joule Work have been designed to help you get your job done. These spaces are unique, context-driven, built for the task at hand. They're outcome-driven, where humans and autonomous agents work together in harmony. The right information and actions are generated for you, providing structured insights and full control. Work directly with your content. Confirm decisions. Complete workflows, all without switching between applications. They are dynamically generated, transactional, and analytical content that bring powerful business insights directly into your workflows. You always stay in control, and Joule Work comes with a full dark mode because great software adapts to the person, not just the task. This is how modern work will get done.

Hopefully. That's exciting. Joule spaces, what you just saw, along with a completely reimagined Joule conversations experience and a brand-new Joule Studio 2.0 that Philipp showed you, is now part of what we call Joule Work. With Joule Work, we're excited to announce that we're taking a massive step forward in supercharging the capabilities of Joule as we know it today. Internally, we even refer to this as the V10 version, though our marketing folks didn't like us branding it like that externally, so we kept it simple, Joule. Today, the Joule you know is RAG-based, limited in context, governance, action, space, and scalability. With Joule Work, we're bringing a cloud-based agentic harness to Joule along with computer and file access, better support for open standards such as MCP and A2A, and access to a more complete knowledge base. And of course, amazing visualizations on the fly. In the world of SaaS, we believe not all applications and application providers are created equal. At SAP, hopefully you see that we're busy rewriting the apps and the platform and the experiences playbook in a way that will unlock value for you that has never been possible before. Through the power of our Business AI Platform and our Autonomous Suite, we have agents that drive outcomes, assistants that empower every role, autonomous domains that transform entire functions, and a Joule experience that reshapes employee engagement. But we're still not done yet. While the autonomous domains cut across all core processes, we know every industry has its own unique value chain. How we help you unlock that potential, unlock the potential of that value chain is up next with Industry AI and Sebastian Steinhaeuser. But before we do that, let's hear from some more of our customers who are deep into their AI journey with SAP. A big thank you for listening and thank you for your trust.

AI is an important element for us. It's about identifying those use cases that really provide the most value.

SAP Business AI has been designed into the core functions of DataXstream's OMS+ Platform. It's the Swiss Army knife of it. This transformation not only saves thousands of work hours annually, but also optimizes costs.

SAP's CX AI toolkit, it works and it helps our reps focus on our customers and what suits them best.

The Invoice Assistant with S/4HANA is cutting the average invoice inquiry from 30 minutes to just two minutes. Our accounts payable backlog is down by 25%.

Every large enterprise is somewhere on a transformation journey. The companies that get this right won't be the ones with the most AI. They'll be the ones with AI that understands how their businesses operate like SAP does.

Please welcome to the stage, Sebastian Steinhaeuser.

Woo. Hasn't this been great so far? Now, as Muhammad said, there's at least one more thing to complete the Autonomous Suite. Let's start with a quick raise of hands. Who of you is using SAP 100% in standard? Raise your hand. Trick question. So now raise your hand if you are using SAP with industry-specific solutions and extensions. Yes, I thought so. Industry solutions have always been the superpower of SAP, and this year, we bring that power back with full force. While some are just now discovering concepts like forward-deployed engineering, we have always known since our founding days, real enterprise transformation requires deep industry understanding. And we understand how your assets are maintained, how your manufacturing processes run, and how your brands win on the shelf. And we do so across 26 industries like no other software or AI company on this planet. Hey, Joule. Tell me more about the industry-specific capabilities SAP provides.

Hey, Sebastian, here's an overview of SAP capabilities across more than 25 industries.

So now we are taking this deep domain expertise and turn it into autonomous solutions. This is what Industry AI is all about. Industry AI is how we bring that industry-specific process logic, the data context, and the regulatory rules into unique AI-powered solutions tailored to your core business. And we do this together with all of you on the Business AI Platform, delivered end to end by SAP as an outcome instead of SKU patchwork. We are starting with a select group of customers given the high demand, and we are delivering autonomous industry solutions starting with seven, from Autonomous Adaptive Production to Autonomous Project Delivery. Close to your core operations and tightly integrated with your core execution systems, where each one caters to multiple industries, delivering value for customers like CHS, Roche, Deloitte, Vestas, and so many more. Already now, the initial feedback has been phenomenal. Let's dive deeper into one of these industry domains. Let's take Autonomous Regulated Manufacturing. What you see here is a typical pharma process, from planning to production to batch release. Every batch, every change, every material must be traced, documented, and audit-ready. Already today, the life science industry. Where's the life science industry? Anyone? Ah. Already today, the life science industry bets on SAP to manage this highly complex and regulated process. Now with Industry AI, we take it to a whole new level and deliver end-to-end, multi-agent, audit-ready processes that detect disruption early, keep operations within regulatory limits, and simulate compliant recovery options in real time, ultimately enabling a fully validated, market-specific batch release as a business outcome. A great example here is Takeda. They see outcomes such as an up to 25% reduction of revenue loss due to stock-outs, and an up to 5% reduction in safety stock levels. Let's take another example. Revenue growth management. CONA, the IT group of the largest North American Coca-Cola bottlers, manages a very complex direct-to-store delivery process. In today's volatile environment, exceptions during delivery settlement can happen very, very frequently. The starting point was a fragmented and manual exception-handling process. Now with Industry AI, they can auto-resolve standard exceptions across all routes and escalate non-standard cases with clear root causes and recommended actions. Or let's take a final example. Let's take Autonomous Unified Commerce. Here, we are working, for example, with Salling Group, Denmark's largest retailer. Introducing new products across categories, suppliers, and channels is complex. Before, this was a manual and fragmented process. Now Industry AI helps to orchestrate the entire flow. Products are automatically classified, promotion decisions are AI-driven, and supply chain actions are triggered autonomously. But now let's hear from one of our customers directly about their Industry AI transformation journey. H&M is one of the world's largest fashion brands, with over 4,100 stores across 81 markets, and I am super excited that they are joining us today to talk about how business AI looks like when it's truly embedded in a company at global scale. I'm thrilled to be joined here today by H&M Group's Chief Digital and Information Officer, Ellen Svanström.

Thank you Sebastian. It is so good to be here, and I'm so happy to be talking about our digital and AI journey, because we are on a journey to strengthen our position as a fashion brand, and our customers are at the heart of every decision that we make. To succeed, we've been focusing on building a strong digital foundation so that tech at H&M becomes a true growth enabler.

Well, Ellen, before we go deeper into that, can you tell us a bit more how your journey has looked like so far, what your transformation journey is at H&M?

Yes. Love to. We're focused on leveraging the latest technologies to enhance both precision in demand and supply, gaining real-time access to data across our entire supply chain and financial processes. To unlock the full potential of new technologies like AI, the right digital foundation is needed to ensure that we can standardize and harmonize key business processes, and SAP sits at the core of our processes enabled by RISE, Business Data Cloud, Commerce Cloud, and SuccessFactors.

Awesome. So with such a robust foundation in place, now, what's next? What changes for H&M, and where are you today on your journey?

Well, on top of this foundation, we are layering in AI, using it to improve our operations, reimagine our stores and supply chain, and empower our store colleagues with more precise decision-making. To make this real, we introduced a new innovation framework designed to help us test, explore, and scale new ideas faster. Through this way of working, we shaped a clear vision for a more data-driven supply chain that gives us real-time insights into our store operation, which is actually what you're seeing here on the screen. And we brought SAP with us on that journey. We feel the collaboration between H&M and SAP is truly unique and it lets us test, explore, and scale digital and AI capabilities.

Of course, I'm glad to hear that. So can you give maybe one specific example of what we are doing together?

Yes. Love to. One example is our Store Intelligence app. It is a comprehensive solution for tracking store performance in real time and all the way from the sales floor to the market level. It allows us to understand and steer our business based on live feedback. And together with SAP, we developed the Store Intelligence Agent that prepares store readiness and suggests next best action for our store managers. This is what you're seeing again on the screen. And this isn't just a single agent. This is a system of agents working end to end across our value chain. So when the store receives a recommendation, it is based on what's happening real time across different business processes. For example, a week before a major stadium show here in Florida, a new trend starts to gain momentum. The Store Intelligence app scans market signals, local demand, and based on this, it recommends which key item to feature, in what quantities, and where to place them for maximum impact. The result will be a store that feels fresh, intentional, and perfectly aligned to what the customers want.

So this is really an awesome example of business AI in action at scale. What else are you looking at right now?

Well, another example is a customer-facing solution that we're exploring. This is featured in the in-store mode in the H&M app, where customers get a personalized and locally relevant store experience. Together with SAP, we are developing an AI-powered Store Concierge Agent. This goes beyond optimizing the store, and it connects the entire customer journey across digital and physical channels. The store concierge brings the online and in-store experience together in one continuous, personalized experience with outfit recommendations, real-time availability, and contextual advice. It's about making every interaction relevant, helpful, human, powered by AI in the background. Well, in fact, Sebastian, if you had this morning asked the agent what to wear for Sapphire, it would actually have had an answer for you.

Well, take a wild guess. I might have had that problem just this morning. Big keynote, big stage. And I wasn't really sure what to wear.

Well, then, let's see what the store concierge would have suggested for you. So here, if I wrote Sebastian is giving the Sapphire keynote in Orlando together with H&M. What should he wear? Then, see, now the agent will have an answer for you.

See, they know Sebastian very well. So. And the good thing is that there's also a shop-the-look option now. So, Ellen, look at my outfit. The store concierge, I think, did an awesome job, right? Advising me what to wear. Actually, my daughter said that it was perfect. You look like you always look.

Yeah, I'm very happy with what we put here together, and I'm overall super excited about the journey we are on together.

Me too. I think we're off to a really good start. Thank you Ellen.

Thank you Sebastian.

And with that, back to Christian.

SAP is becoming a Business AI company. That's 100% accurate, unlike your unicorn drawing.

Today we showed how to turn the promise of Business AI into reality. SAP Business AI Platform provides the data, process, and governance AI needs to deliver accurate and secure outcomes at scale. We introduced the Autonomous Suite where applications reason, decide, and act for you. And we showed how we manage change management with RISE. Together, with customers and partners, we showed how SAP is helping companies realize the vision of the Autonomous Enterprise.