Good morning. It's fantastic to be in Madrid and in Spain and to be in a theater. In fact, I was just thinking of the last time I was in a theater like this, this big, was growing up in India watching some Bollywood flick as a middle schooler and being completely taken aback by the size of the theater. Man, this is huge. So it's fantastic to be with all of you, especially at a time like this when there is so much talking about popular culture. Who would have thought that you pick up any paper, you pick up any news feed, and all you hear about is AI everywhere. And it's exciting. And so what I wanted to do today was to spend some time perhaps trying to give our point of view on what I think collectively this technology means to us and what we can do with it. To us, it always starts with our mission. Our mission as a company is about empowering people and organizations to achieve more. Right? That's where we start from. And whenever there's been a new technology, however powerful, we have always tried to focus on can that technology realize that mission, which is can we put it in the hands of people and in the institutions that they build, and can that organization, that person, really change the frontier of what they do. Right? In fact, we have this entire description of what a frontier firm is. Right? Because if you think about it, any technology is an input. Ultimately, what we try to do with it is change some output, and that's what the movement of the frontier is. And in practical terms for us, in the past we talked about digital transformation, and now we're thinking and talking about AI transformation. How can an organization use AI to change the customer experience, the employee experience, the internal operations, bend the curve of innovation? Right? Whatever goods or services you're producing, can you now do it with more throughput, with much better speed? That really is the bending of the frontier. Now, the interesting thing with AI is that you now have to perhaps take a step back and think a little bit on how to structure yourself for this new world. And I think it starts with a mindset shift because AI in some sense is a little bit of a horizontal general-purpose technology that cuts across roles, cuts across functions. So you want to think end-to-end workflows, end-to-end process, end-to-end task trajectories. Right? That mindset is slightly different because we've built up all of what we have done in our institution, in our organizations over the decades on a particular workflow. In fact, the last time you could say the workflow really changed, especially in the enterprise or in the work setting, was when the PCs became standard issue. Right? Sometimes I always describe this. Imagine in the 80s you were a company trying to do a forecast. How would you do it? Right? You would sort of have faxes going around and you would have inter-office memos and then ultimately there would be some kind of a paper forecast. And then suddenly email became standard issue. Excel was there and you would send an Excel spreadsheet and email and people would enter numbers and you would have a forecast. Right? They just changed the workflow and the work artifact all simultaneously. And that's what I mean by really rethinking how the workflow and the work artifact change and the mindset shift. So that means you need to be able to think about new skills that you need to do that, new tools to do that. Just like standard issue in the past was all these office tools. Right? I mean, one of the other things I think about is if somebody had come to us in the 80s and said, you know, three billion people are going to get up or four billion people are going to get up every morning and start typing, and we would have said, why? Like, why would we need so many typists? Except that's what we do and we describe it as knowledge work. Right? And that's what now the new skill set with the new tools is going to allow us to do. And one thing that I think we all are going to learn is how to measure the quality of the outputs we create. Right? So this eval is something that AI people talk about. Right? Whenever you're building a new model, what you're doing is hill climbing on an eval or an evaluation criteria. And all of us, so this is not just for labs or the hyperscalers or product makers, but it's for every one of us in some sense to have that high taste, high bar for what is the evaluation of what we are creating as an organization and to get clear about it and then use AI to transform that eval to a new frontier or hill climb. So that's a bit of how we think one has to think about AI transformation. And to that end, what we're doing is building a full stack. And I'm going to spend the rest of my time today just walking through this stack. But before I do that, I wanted to mention that perhaps it's important to think about two very important criteria when you think about AI. Since it's a learning system, of course, it means it's about intelligence and the intelligence that you as a company, you as a person, are going to build with AI. Right? So that's clearly going to be the case. But one of the things that you want to make sure is top of mind for all of us is that it's our intelligence, whether it's at an individual level or it's an organization. Right? This is not about celebrating some intelligence that's outside. It is about us being able to take the latest model but turning that into more tacit knowledge, more know-how inside the company. In fact, I'll talk about sovereignty at the very end. Of course, everybody's talking about sovereignty at the country level, but I would say one of the most important topics is perhaps sovereignty of a firm. Right? After all, what is a firm? A firm is about the tacit knowledge a company has thanks to the human capital that is there and even the digital systems. And so in the AI age, you want to think even more carefully that you are building that tacit knowledge and even embedding that tacit knowledge in perhaps a set of weights that you control, a set of agents or models that you control. Right? So it's not about celebrating anybody else's model. It is about your model that you have in your firm that makes you a unique participant in the economy. Right? That's at the end of the day the real currency for all of us. And that comes with this second consideration, that means trust. Trust everywhere. There's a technical aspect to it, right? For example, how do you make sure that there's real governance around your data? How do you make sure that you have security around anything that's happening, which agents are accessing? But the biggest trust is going to be a trust in ensuring that your enterprise value that you have built over the decades inside of your firm doesn't leak out to somebody else's model or somebody else's business. And that I think is going to be the two things that are going to become very, very important as you think through your AI transformation journey. So with that as a bit of a backdrop, I want to start with the first part of our sort of AI stack at the experience layer. In fact, if you look at the best way for us to introduce these powerful new AI tools is to put them where you already are working and then transform that work and that workflow. Right? So that's the approach we have taken with all of this Copilot layer. I describe Copilot as the UI for AI. It's about really taking the human and the human agency and the human ambition and really amplifying it by stitching in AI and then letting you then use AI to do things that you never imagined even that you could do before. Right? In fact, the way I think about sometimes is we've had these very powerful metaphors that have been there with computing. Right? Jobs had this of the bicycle for the mind, which I think was a very powerful one. Bill in the mid-90s came up with this metaphor where he talks about, he talked about information at your fingertips. That was another very sort of approachable way to think about computers, especially with the internet. And now I feel like we're at the beginning of a new era where the metaphor could be that we have infinite minds with all these agents that each of us can use to shape what we're doing, the artifacts we create, the work we do, and so on. And so that's what we are trying to get done, whether it's for information work, whether it's in coding, whether it's in security, whether it's even in science. And so that's sort of the Copilot. So if you take information work, what we've done is built this Copilot layer right into Outlook, into Teams, into Word, Excel, PowerPoint, and also built the Copilot app. Right? Effectively, think of it as a new module even, and this is a graph that's connected. It's stateful. So that means you can start one place and go to another place and that intelligence layer continues to compound, build, so that it helps you with what you're going to do next. Right? It doesn't stop in any one app. It's not captive. In fact, that's perhaps the biggest sort of misnomer. It's not about any one application. It is intelligence that is sort of ambient across all applications. Right? In fact, you can even think about, like one of the metaphors we're playing with is think of it as a composer that sits above all apps, right? And it's there everywhere. And that's really literally what at least Copilot is in its first version of it. Right? So you can imagine where the direction of travel there is. And these agentic workflows are already there. Right? We started with chat. Right? The first couple of years of Copilot was all about chat, chat, chat. But now increasingly, if I look at even my own daily usage, it's got many agents that I'm using all the time. And the most popular one that at least all of us, I think, have gotten used to in the last, what, 12 months or so, is Researcher. So that ambient video shows you, I go, you know, essentially assign a research task. In this case, I think it's some kind of a product strategy document I want the researcher to create. It goes off and, by the way, it's not just doing reasoning over all the data, but it's also using the computer. In fact, it has a computer use agent that it's using as a tool in order to complete the task I assigned it. Right? So that's kind of an example of Researcher. We built another agent called Analyst. Right? This is like having a data science analyst with you 24 by 7. In fact, this one I think is doing some kind of a campaign that I'm analyzing and I load up a bunch of spreadsheets and it goes and does all of the analysis that is needed and gives me insights like a data scientist would do. And the third one is the Excel agent. So we've not only built these agents in Copilot, but we have also built agent mode into Word, Excel, PowerPoint so that you can create these new artifacts. In fact, artifact creation has become just easier because you can single-shot it. But the more interesting thing is what you do with the artifact by using this agent mode and iterating on it, continuously refining it. Right? When I talked about that eval, right, you want, like a great DCF, you want a spreadsheet that does a fantastic cash flow or what have you, and you can get that out and then iterate on it, refine it, and that's sort of the new way to think about even the workflow because if you previously spent a lot of the time building the spreadsheet, you now spend a lot more time doing the analysis with the spreadsheet with agent mode. And so this is the transformation in information. And to show you some of this at work, I wanted to invite up on stage my colleague Anne. Anne, take it away.
Thank you, Satya. Hi, everyone. I'm so excited to show you how the entire Copilot stack is transforming the way people work. I start my day in Copilot, and the first thing I'm going to do is look at all of these models that I have to choose from. I'm going to pick GPT 5.2 into quick response because I've got a live demo and a lot to show you. Then I'll ask Copilot to prep me for my day. Copilot uses Work IQ to look at my meetings, emails, and chats. And it looks at its previous history of interaction with me to know what I prioritize the most and come out with a customized, tailored response. So, let's see what's going on today. First up, I have a time block for this demo here. So, I'm glad I'm in the right place with you all. And then if we keep going, we see at the top of my inbox, I have an ongoing customer conversation about a pending contract. And part of my job as an accounts receivable manager is to make sure our customers pay their bills on time. And to incentivize them to do so, I need to offer potentially a cash discount for early payments. And again, I have lots of options I can offer for such a cash discount, but I want to use Copilot to make sure that I'm following a policy-compliant approach. So, I'll ask Copilot, what are my options to offer this customer a discount? Now, this is where the power of Work IQ really shines. Copilot's looking at our policy documentation. It's looking at the context and history of that customer and this particular contract. And it's applying my role as an accounts receivable manager to drive what it is I'm specifically entitled to author. And here we go. We see how all of these things come through in the response. First up, I can see that we did indeed find that policy documentation. I can verify that the source is our finance SharePoint. So, we're accurately pulling those details. And then if I look through, Copilot's given me this great summary of all the different options. I can provide anything that requires additional approval beyond my manager level remit. And it ultimately summarizes we should start with our standard 2% cash discount for any payments made within 10 days of invoice. Now, that makes sense to me, but before I go off and add this to the contract, I need to assess the impact to our profitability. And what better place to do that than Excel? Here in Excel, I have a workbook that I've already built with agent mode that models the revenue on the contract as it currently is stated. And I want to add this nuance of how a discount impacts it. Right from here, I'll go to Copilot and turn on agent mode. So, I don't have to do all this work manually. I'll have the agent take care of it for me. I'll ask it to create a new sheet that models the impact of offering that 2/10 cash discount. And because the agent's grounded in the same work context I am, we'll give it access to that policy documentation file. And it can extrapolate additional details from that context. Then we'll kick it off. What the agent is going to do here is work directly inside my workbook just like I might or a coworker might. Unlike us, it goes quite a bit faster, but it still takes a little bit of time, which means now I have time I can use for other activities. All these insights that are helping drive business decision making, whether it's in Copilot, in Outlook or Excel, they come from our underlying financial data estate. So for our business insights to be valuable, our data needs to be accurate and up-to-date. Dynamics 365 is our platform for intelligent business applications, which enables us to do just that. Here in Dynamics, when I log in, I see I have a notification from the account reconciliation agent. That's a pre-built agent that comes out of the box in Dynamics, and it's letting me know there's a couple discrepancies in our books that need my attention. So, let's take a look. For each of our reconciliation exceptions, I get a natural language summary that immediately tells me exactly what's going on. In this case, a transaction was posted directly to our ledger, and I can see those posting details here, and it missed our subledger. And if you're not a finance person, don't worry. Just know this is not ideal. We need transactions in both places. And it's not just insight that the agent is giving me, but it's also recommending actions. It knows finance best practices. It knows our best practices. And in this case, it recommends we reverse the transaction so we can post it properly through the subledger. From there, all I have to do is confirm and commit the transaction. And I want to highlight what's happening here because it's so important. This fix is being logged directly in our financials. So our source of truth can remain just that. There we go. We got a confirmation the reversal was completed. And for any of my audit folks out there today, everything the agent does, everything I do gets logged in our audit trail. So we have that critical accuracy and accountability we need in finance processes. Okay, we took care of that discrepancy. Let's go back and check on our co-worker agent in Excel. And what we can see already is how we are on a brand new sheet that the agent created and it's making edits live right in front of us. We can even follow its thought process as it goes step by step in the pane over here. It looks like it's reassessing. And because we're doing a live demo and we've only got so much time left, I'm going to jump ahead to the same prompt that I ran earlier just to show you that output of what it looks like. So, we can see we have our discount situation broken out. It's pulled that detail of the 2% 10 days from the prompt and additional context from the document that I attached to it. It's looked at our monthly impact to revenue and ultimately we should have the chart I asked for as well over here on the side. What I want to stress here, it's not just putting static text in cells. These are dynamic formulas and visuals that work with me and the agent as we continue to work together and refine. And most importantly over here I have full visibility into all the data sources, calculations and assumptions the agent used to build out this context. So to recap, what did we just see? We saw how Copilot with Work IQ helps me work not just more efficiently but more effectively. We saw how agent mode in Excel is helping me very quickly strategize and make decisions that drive profitability of a contract. And we saw how agents in Dynamics 365 are helping us create a financial data estate that enables the whole business to make great decisions. And this is not just for finance personas. Everything I showed you today is 100% available. I hope you go home and try it out in Copilot, in Excel, and in Dynamics 365. Thanks. And back to you, Satya.
You know, I was, when I was seeing the demo, I was thinking, God, I started, you know, my career at Microsoft doing a lot of Excel demos. I had a lot more hair then and here I am, you know, in 2026 still looking at Excel demos. You know, one thing that hopefully you got out of that, what Anne showed was, you know, the design of AI systems, I think requires a particular thought that it doesn't add more cognitive load to you. Right? I mean, one of the things that could easily happen is because you have this powerful tool that can do things like create an unbelievable spreadsheet or go off and reason for hours and come back with some answer. And if all it does is cause you to then spend more time understanding what it did, then that's not great design. So one of the subtle things that we are trying to make sure happens is as we build out this Copilot layer, how it works with Work IQ and how you traverse your job and your task trajectory, the cognitive load has to come down. Even the aesthetic, like one of the things that Anne was showing in Excel in agent mode, right? Spreadsheets, the reason why spreadsheets have stood the test of time from whatever 40, I think we just celebrated the 40th year of Excel, is because it communicates a lot cognitively. Right? The fact that you have a tabular data form, you can sort of look at the formulas. In fact, I so remember the first time I sat next to somebody, you know, one of these Excel maestros was like sitting next to a piano player because the way they built the model, it made sense. And when you see the output, it made sense to you as a consumer of that model. And that needs to be true even when it comes to AI. Right? So it can't be a single shot at a model that nobody else can understand but it has the answer and then you're looking for hours as to how the heck did they find the answer. And so that's the type of stuff that matters in how AI gets designed into experiences. So it's great to see the diffusion of all of this technology right here in Spain. I had a chance to meet with many customers who are deploying, you know, Copilot. I had a chance to meet this one, you know, partner of ours, Ilunion, which is, you know, doing a fantastic job of not only using this technology but the social good. You know, how they employ people with disabilities in their workforce and empower them with technology is fantastic to see. They built an agent, in fact, using an agent builder inside Copilot for their law department to be able to reason over complex PDFs. In fact, that's kind of one of the most favorite features of Copilot is, man, PDFs are tough to parse and you can, you know, assign them to Copilot and it can come back and extract the right sort of reasoning on top of it and give you information. It's just great to see this kind of diffusion. So that was the first layer of the stack which is about building in AI experience into the flow of human activity and then really helping us raise the human ambition. The next piece of the stack is the agent platform. And when it comes to agent platform, the first place to start is the data layer, but more importantly not just raw data but uplifting the semantics of this data into what we describe as the IQ layer. Right? And Anne talked about this. Right? When she was using Copilot, she was essentially interfacing with this thing that we describe as Work IQ. And I would claim that one of the most important databases inside of a commercial entity is the information underneath their Microsoft 365 tenant. I mean, think about it. Right? Inside of Microsoft 365, you have all this very rich information, people, their relationships to other people, like your org chart, not just org chart in its formality but even who is working with whom on what projects. Right? I mean, think about how important that is for any company. You have all their calendars, you have all of their communications in Teams and Outlook, you have all their documents in OneDrive and SharePoint, you have projects and project schedules. So that is a very rich set of relationship and entities that now you can in fact use AI models to reason over. That's what Work IQ is. It's a stateful reasoning agent essentially on top of a very rich data model which captures all this tacit knowledge of an organization. The other place where we've done, in fact, you all have created a tremendous amount of semantic information is Power BI. Right? I mean, if you think about the number of dashboards over the years that have been created, someone took the pains to make sure that all that data was annotated with the right semantic annotation so that you could make sense of it. Right? This is some sales aggregate information, it is some forecast information, it's some supply chain information. Now all that can be brought together in Fabric IQ. And then Foundry IQ is another service we have built in to be able to in fact have an outer orchestrator of all these IQs. Essentially, you can have a plan that gets created that orchestrates Work IQ, Fabric IQ and any other document database or any other thing that you have. And now you can build essentially agents on top of all of this IQ layer. Right? So data is the best bootstrap, but more importantly not just raw data that you build a forward index on or you do a search on, but literally have a reasoning layer plus data available for any agentic system. So that's sort of the first layer of the agent platform. Then once you have this, the thing that you need is an app server. Right? I mean, with any generation of technology, you always built a middle-tier app server so that you could build applications on that technology. Right? It happened with the web. Right? We had web servers. We had it with .NET and Java. We had that with cloud. Right? We had cloud-native app servers. So this time around again for us it's Foundry where you have all the services to build agent and multi-agent systems starting with frameworks, with, you know, services for AI safety like guardrails, services for doing these evals, services to do fine-tuning, what have you. So these are a complete list of runtime services that you need in order to build agents and multi-agent systems. And on top of that, you have models. In fact, Foundry comes with 11,000 models. And you would say, 'Wow, why do I need 11,000 models?' In fact, it comes with all the popular ones. Right? You have all the GPT family, you have the Claude family, you have Grok, you pick your frontier model that you want to use. It's there. But it also has in fact all the models in all the domains that you want to be able to access and use as part of your applications. And in fact just to kind of give you an intuition for why the world needs more models, not just one model, we launched this thing called GigaPath I think a few months back. And it was done in cooperation with Providence and a couple of other partners of ours, University of Washington. And the idea was if you look at cancer treatment and immunotherapy, one of the challenges is you never know if a particular immunotherapy would work on a particular tumor. And so in order to figure that out, there's a test that one does. And that test costs a lot of money, takes a lot of time. And so the researchers put together essentially a spatial proteomics solution. So essentially using generative AI to simulate that test. Right? So instead of doing the test, what if we could do a simulation of that test and that way then you can reduce the cost of it all and make it available to every secondary hospital in every country so that they can now do that same test and make a difference in oncology. So to give you a flavor for this, let's roll the video.
Immunotherapy is really the most promising direction for taming cancer once and for all. The whole idea is using the immune system to fight cancer.
The big challenges we have in the field right now is to understand which patients will respond.
Spatial proteomics can simultaneously measure multiple proteins and create multiplex immunofluorescent images. It can tell you whether the patient might respond to immunotherapy or not. But unfortunately, to generate this kind of images takes days and costs thousands of dollars.
In collaboration with Providence and University of Washington, we developed this multimodal AI called GigaPath to generate spatial proteomics at scale. So we have trained the model using large-scale data analyzing millions of cells. The input image has limited data. We don't know where the protein is active. GigaPath can convert this image to a colorful multiplex immunofluorescent image. The color is the protein activation. They are the key factors driving diseases, especially in cancer. This can help us to predict patient drug response. We are now taking the first meaningful step to be able to generate this kind of data for everyone in the globe. The GigaPath model is now available to everyone through Microsoft Foundry. Our hope is to empower every secondary hospital in every tertiary city to be able to now conduct their own research.
We look forward to a future where we can actually bring this to the clinic, impact patients' lives, and bring the next generation of therapies to places where this would be otherwise impossible.
