Microsoft Corporation (MSFT) Earnings Call Transcript & Summary

Good morning.

Good morning, everyone.

Good morning for day 2. Actually, I'll leave out the day with like 2 congratulations. Judson and Craig, both of you have expanded roles in the organization. So first of all, congratulations.

Thank you.

Thank you.

The Judson, so at the moment, it's really exciting times if you think about the technology world. Can you -- one of the things that was really interesting when I was here at Incyte a couple of weeks ago was this notion of the frontier transformation. Can you talk a little bit -- what does it mean to you? What's the message here?

Yes. So we're really working with customers to have sort of an evolution in the wave of AI transformation because if you look at the first couple of years of progress, largely focused on efficiency and productivity and largely tech-driven. And if you look at the corpus of AI projects out there in the market, I think Craig would agree with this, you see that there is an extraordinarily high failure rate of AI projects, north of 80%, depending on the research that you study. And if you pick apart the reasons why, some of it is classic tech to business, misalignment with business goals. Others are tied back to sort of the disorganization of data in the enterprise and lacking a real data estate or foundation to build quality and capabilities. And then if you poke further, it's down into not having the right kind of AI development tools that allow researchers to become productive in an environment. And so if we look at by contrast, the places where we have seen strong success across our customer base, across multiple industries and around the world, it's largely tied to this notion of business-led transformation. What are you doing to enrich your employee experience and tie that back to your own KPIs? How are you're looking at it in the frame of customer engagement, driving top line revenue, not just bottom line savings? How are you reshaping business processes, not just throwing tech at existing ones, but actually stopping and saying, how do I create an AI-first business process that's grounded in human ambition and empowered by assistance in an agent ecosystem? And then four, how are you putting AI to work for innovation? If you have this success framework where you're tying it back to business-led transformation and then applying the technology portfolio to it, the success rate goes up materially. So we're coining this notion of frontier transformation as being a business-led AI evolution that really allows for a fundamental reinvention of the business empowered by AI rather than the other way around. So it may sound like a subtlety, but there is a massive difference in how we're tracking progress with customers on these business-led transformations. And so much of what we've announced in terms of product portfolio is meant to help and stimulate that kind of growth.

Is that -- and how do you compare and contrast that to like previous tech transformation, we had like the move to the cloud. We had the Internet, et cetera, like this feels slightly different.

I mean I think it's fair to say almost every wave of technology has been largely grounded in efficiency, right? How do I throw tech out of process, make it faster, drive down cost? And AI certainly has the potential to do that, right? I mean much of what you read about is the impact on white-collar work and how is it going to change? But if you pull the thread on it, the bottom line is AI can do a lot more for humanity and needs to do a lot more for humanity than simply drive efficiencies. And so if you look at AI and drug discovery, for example, we are shaving an order of magnitude off the cycles that are required to get new drugs to market and new drugs to market safely without having to risk human trials, only to have them fail in the last mile. And there's objective evidence of making real progress there. The same can be said with material science, advanced -- it's in quantum and even in our own Quantum labs at Microsoft, I think, is a big reason why we're ahead in getting quantum chipsets to market is because of the use of AI and material science. And so if you pivot away from, gosh, how can I just save money and eliminate jobs with AI versus like, hey, actually, how can I get more done? How can I unlock creativity and innovation with AI? It's a remarkable change in terms of the business outcomes that you can drive.

And then that's actually a good segue into my first question for Craig, like our industry, like financial services, we -- sorry, if I say that, like historically, you're not like first adopters, but like I see a big movement around AI. How do you feel about AI for us?

The trends. So look, I think we'd all agree the opportunity is quite significant. And virtually everything about AI is changing at pace. When I sort of reflect on some of the key trends and probably reflecting on the more recent influence of sort of generative AI, sort of 5 things sort of come to mind. Firstly, I think you're starting to see the shift from experimentation into operationalization, slower than, in fact, what we might have anticipated a little bit to Judson's point. But AI is starting to change roles and workflows in a really fundamental way. Take your customer service agent as an example, who now has real-time expert support, guiding them through a call, summarizing the results of that call, assessing their development needs throughout that call and then in a position to create personalized development content, that's not augmentation. It's a shift in the way work is happening and it's a shift in how capability is being developed. And you're starting to see that pattern across operations, across technology, across trust risk management. I think the second thing that you see happening, Raimo, is this shift from AI use in back office to the point of customer interface from human in the loop to sort of more autonomous workflows. And the significance of that shift is it elevates the importance of governance. And by that, I don't mean just guardrails. I mean understanding the role of each agent in the organization, be it human or AI, understanding their commissions, understanding the decisions they make, where you need to have oversight. And I think the organizations that get that right will establish autonomy with accountability. And within financial services, that's going to be key. I think the third thing that I see, and Judson touched on this, is whilst we often talk about AI, the real battleground is, in fact, data. Yes, of course, data fuels AI. But too often that data is not accessible, not of the right quality, not structured in the way to support real-time decisions. And I think most organizations are discovering the same fundamental truth. And that is you cannot scale AI while your data is trapped in silos, while you can't link activity across the full customer life cycle and while you don't have the lineage and controls in your data to have trust in that data. So while AI often gets the headlines, actually, it's in data that I think real advantage will be created. Then I think you're seeing infrastructure being recreated. I think we all will sort of understand that to use AI at scale, yes, I need access to powerful models across multiple clouds. Yes, I need increased compute density. Yes, I need much greater power capacity. But I think the really important shift is in resilience that is AI, in particular, agents and autonomous agents become more integral to critical workflows in organizations like financial institutions, the expectations on resilience will actually raise quite sharply. And I think that's going to have a profound impact across the entire stack from networks to multi-cloud infrastructure to data platforms to APIs. And I think you're going to see much greater focus come on to this question of resilient AI provisioning within organizations. And then finally, I think AI is emerging as much as a threat as it is an opportunity. You think about those traditional cyber exploits, identifying vulnerabilities, polling of systems, creating malware, all of that can be executed now at a scale and pace that just wasn't attainable before. But it's probably the new exploits that are emerging or the new opportunities for exploits that are emerging that are most concerning. Think of a mass disinformation campaign executed across multiple social platforms, a vast target audience using deep fakes executed almost instantly. That's a very different type of threat that we now need to begin to mitigate and arguably probably one of the more important of those sort of -- those trends.

Yes. And then Judson, Craig mentioned a couple of interesting points, data as a really core kind of point here as well. How do you think about it at Microsoft in itself? Like yesterday, I was talking with Gina from ServiceNow and asking her about is she eating her own dog food. She's had champagne actually. like what's the -- how are you going about it? And how do you think about that data aspect as well?

Yes. So the data piece of it is super critical for us. I mean we just had our Ignite conference a couple of weeks ago and had over 70 product announcements, but really in sort of 2 major areas, one around intelligence and the other on trust, both sort of really grounded in data. And what we did on the intelligence front was a pretty massive lift to help our customers get their arms around their data in a much more usable way. Let me explain this in a little bit of detail here just so you all can understand because there's a fundamental difference on asking AI engineers, even the best and brightest of AI developers to go after data sources using connectors and APIs versus using a semantic layer through what we call MCP servers, allowing agents to speak to agents. If you take a model provider's approach towards trying to synthesize a business process, a model provider will take data through 1,000 small straws, hoover it up and try to inference over that data, and we use inference as if it's a super cool smart technical word, it's fancy guessing. So you're hovering up tons of data through lots of small straws and guessing on an outcome. What we've done by contrast is to serve up these intelligent layers. The first one being what we call Work IQ. Work IQ is basically the brain inside of Microsoft 365 Copilot. It knows how you work, with whom you work, the content over which you collaborate, and it knows it precisely, not guessing. And those are actual workflows. When you delegate an important task, it knows to whom you delegate it and has the history of how you've done so for years. We've served up Work IQ now, not just as the brain inside of Microsoft 365 Copilot, but basically as an Azure meter. So you can build agents on top of this intelligence layer that provide a higher degree of accuracy, speed and trust. You can reason over confidential and encrypted documents, if that's what your rights and privileges entitle you to do. And then it's a material leap forward versus what a model provider can do or any other company that is trying to somehow sit data through 1,000 straws or frankly, an enterprise customer trying to have their AI engineers do that themselves. It's super hard. We've done this at every layer of the stack. We've also done it on Fabric. We released Fabric IQ. Fabric is our data services product that allows you to reason over multiple different data sources. So BigQuery running on Google Cloud platform or Amazon S3 data stores and of course, all of the Azure data services and even data services running in your environment. With Fabric IQ, what we've done is we've taken the semantic layer inside of Power BI. So the sort of the one binding link across all of those data services across multiple clouds is the fact that most of them use Power BI to understand the semantic layers within their business. We've also served that up now as a singular API, so you can reason over data far more accurately in the way in which your business understands it through Fabric IQ regardless of what cloud you want to run on. And then, of course, then the final tier, the foundry layer where AI applications are actually built. We have unlocked all of the knowledge bases that sit into agent-to-agent communication, all of the Azure search foundational elements into Foundry IQ. So you can develop agents far more effectively and efficiently than ever before with this intelligent layer. And everything I've just described to you is model diverse. So a new model comes out tomorrow, great, snap it in. We support over 11,000 models. It's open and heterogeneous at every layer of the stack. And so we think -- back to this like how do we have to drive frontier transformation versus throwing tech at existing business processes and hoping things get better. You have to serve up this layer of intelligence to allow business to get more work done. And so it's been a big investment for us, and I think a pretty strategic advantage for customers that rely on and trust Microsoft.

Yes. Perfect. And then, Craig, like if you think about it on the Barclays side, like there's so many potential things that you -- that AI could touch. Like how do you think about our journey? Like what's the -- how do you prioritize about that?

Yes. So look, I think we were quite early into generative AI, largely off the back of, I think, the investments we've made across infrastructure, data security, those foundations. And if I sort of wind forward to where we are today, it's emerging out of what you might want to think of as the creative chaos phase, lots of experimentation, rapid learning, uneven value. But I think through that, a level of insight around where the opportunities are and how we need to go about executing against those opportunities, sort of 3 areas that I think are priorities. The first, and Judson touched on, this is process transformation. And I tend to think of generative AI, in particular, as almost the third generation of automation, the first rule-based, the second, third base, now language-based. And it's when you combine the potential of AI, digital and data that I think you create an enormous opportunity to rethink, redesign process to unlock true -- real value there. And I think this is Judson's point around frontier transformation. It's about working front to back and left to right across the organization, and that's exactly what we're doing. Two, colleague enablement, just getting the technology into the hands of colleagues, allowing them to innovate, simple augmentation, more complex autonomous workflows. For me, the challenge there isn't a technical challenge. I'm not even sure it's a skills challenge. Yes, skills is important. It's absolutely a mindset challenge. It's about how you actually inspire people to sort of embrace the art of the possible, set aside traditional ways of working, invest the time and energy in discovering what you can do with this technology. And I would say that's a war not yet won, but certainly one that we're really focused on. And then finally, tech modernization. And that goes beyond just software development. It's about how we're using AI to shift from legacy architectures, legacy technologies, legacy practices to more contemporary ones. It's an area where we see sort of crews, so cross-functional teams of agents and engineers having enormous potential. It's allowing us to refactor legacy technology in a fraction of the time that we previously did. So the opportunity to accelerate tech modernization far greater than what we might have seen even 2 years ago. Now I would say this, Raimo, if you'll ask me this question again in 12 or 24 months' time, I may very well have a different answer, right? And I think it's the nature of this technology. We're still learning. The technology is evolving really quickly. So what's key is, yes, be curious, be adaptable, keep really focused on customer value is the only way I think you can approach this.

And Judson, from your customer conversations, like I'm sure you're getting that as well, like how do I think about the return like -- of all these kind of adopting this innovation, et cetera? Like what's the conversation like?

So that was the second big part of our work at Ignite. I mentioned intelligence and trust. On the trust side -- trust means a lot of things, of course, there's a security element to trust, which I'll come to. But there's just good old-fashioned trust in the business and trust in the partnership is this journey that we're about to embark upon of embracing AI to reinvent business processes and become frontier is that something that we can trust. Can we trust that the ROI at the end is actually going to be something that makes the juice worth to squeeze, right? So we announced a lot of new capability around this observability aspect that Craig mentioned, we announced a new product called Agent 365. What Agent 365 allows you to do is basically visualize all of your AI artifacts across the entire enterprise, whether they're built on the Microsoft platform or any other third-party platform. And allows you to register these agents, provide identity and access privileges to them. But far more importantly, it actually allows you to visualize how they come together in workflows and how they come together with human interaction across those workflows. So we turned on the product because part of the new role is I've inherited IT at Microsoft. We turned on Agent 365 before we launched and announced the product. And we have 138,000 agents being used by 88,000 employees on a weekly basis, which I would offer you all up to turn on Agent 365, it's free in your environment because I would be willing to bet you have more AI happening inside your organization than you know about. But the beauty of this is you can instantly understand how these agents come together in a process, in a workflow and then also the usage intensity so that you can then go back and optimize. And say within a supply chain flow, for example, you can say, well, we have an out-of-stock agent and an inventory agent and availability to promise agent, wow, it looks like the availability to promise agent is getting hit by a lot of people. The usage intensity is really high, and it's costing us a lot of money and actually impacting ROI on the overall process. So you can actually zero in on that, go in and understand what the model is underneath that agent, fine-tune the model, optimize the process and streamline it. And you can actually do this as a part of the dev process even before you decide to say, I'm going to take these agents -- production in the wild. So we could meet with a large insurance company who wants to reinvent claims processing with AI. And before they have to commit to some massive body of work, we can actually run water through, here's what an AI claim cost to process per claim. And if you're going to do 100 million claims in a year, great. This is the bill. Here's the ROI. You actually see these things upfront, manage the security aspects of the same. It all binds to the same identity platform that you use for your employees and your contingent staff, you can register your agents that way. And you can use all of our data classification tools to make sure that the data over which those agents are reasoning is what you, in fact want. And so this idea of providing observability at every layer of the stack with transparency around the ROI for AI is also something we think is going to be a huge lift for the acceleration of real AI adoption because if you can build with intelligence and then trust the process and understand the ROI before you invest, we think it's a pretty big unlock.

Yes. And talk about trust, like Craig, when we made the decision to roll out Copilot, to me, that was probably the single most kind of project I've seen in Barclays since I've been here, and that's kind of many years...

Aided by my friends on the left here.

Yes, yes. Can you a little bit -- can you talk to that like it seems like there was an urgency that I haven't seen before. What was the thinking here? And what are you seeing in terms of outcomes so far?

Sure. So look, I think when we started with Copilot, we started, as you do with often with new technologies, which is sort of a small pilot to understand impact. I think what was really surprising, Raimo, was the groundswell of interest in being involved more so than I think any other technology that I've had the opportunity to bring into an organization. What we learned from those early pilots is that when we put the technology in the hands of colleagues, they did innovate. They did find new ways of working for themselves and their teams. And that gave us confidence to scale and make the commitment to enabling 100,000 colleagues across the organization. Now with that commitment then came also a challenge, which is how do you create across the broader organization what we actually saw in those early adopters and innovators? How do you inspire the same enthusiasm, the same curiosity, the same desire to discover the potential and capability of this technology on a much, much larger scale? So we lent into that really hard. We ran 3 global hackathons, 6,000 colleagues involved in those, another 8,000 that we didn't have capacity for. And I say that just as a measure of the scale of interest across the organization. That wasn't just tech, that was organization-wide. We ran escape rooms, prompt the fonds, hundreds of demos and fostered quite a sort of a vibrant community of interest across, firstly, teams and then Viva Engage. So a lot of energy in creating sort of not just skill and understanding, but also the mindset shift and holding up role models within the organization. Where are we today according to Viva Insights, we've just crossed 1 million hours of productivity through that process. But actually, the other measure, I think, is just the demand that continues to exist. And that demand has meant that the enablement that was to run to the middle of next year will now be concluded at the end of this year. Where to next? Look, it is about continuing to inspire, continuing to educate, hold up the role models, putting new capability, Copilot agents into the hands of those early adopters and innovators and seeing what they can do with autonomous workflows in support of themselves and their teams.

Judson, is it -- are we like a typical customer there? And then the other thing I had to -- that one is like it does feel like the Copilot initially was seen as like, okay, I ask a question and do something more, but it -- now like it feels much, much broader. And I'm only now realizing, okay, this is actually the gateway to everything.

No. Look, we've had a very strong relationship for a long time. And Craig and I have done a lot of work together across our teams. The partnership has been strong because of the feedback loop between the organizations. I think Craig has been very strong at helping us even taste make the product itself. So Barclays on that side of things is a strong early adopter. I think the other thing just to even kind of come back to the Frontier concept, it's no accident that Craig is now in the role that he's in, right? Because you move from just leading technology to leading the business and applying AI for the outcomes is frankly where we're all headed. So if you take that back to the comparison of the Copilot journey itself, you were to make an abstraction, Copilot is for AI like the iPhone is for personal computing or like Windows was for the PC. It's a platform. It's designed to drive a lot of great personal productivity, and it is achieving that. It's the fastest-growing product we've ever launched with the highest utilization. Agents are basically like the apps on your iPhone. They provide that accelerant into the process that you're trying to achieve, the ambition you're trying to unlock, the creativity or the innovation that you're trying to go and pursue. And so this next step that Craig talks about connecting agents back into the flow of how humans get things done, how they innovate. That is where we are right now, sort of the apex of where we are with the bulk of our customers. We're super excited about the adoption of Copilot. We think we're just getting started in terms of the real AI unlock because if you couple human ambition with Copilot and an agent ecosystem, that's really the formula for really driving true frontier transformation across every facet of the business across all industries.

Yes. And then last question for me, and then I have to let you go. Time fly -- flew by here quickly. if you look out the AI potential that you see out there, how do you think about kind of adoption curves, but also kind of the next steps that are coming out of that? And it's more like we saw a Copilot, but like what's the stuff that we are not thinking about at the moment?

I think it actually comes down to this reinvention of the business that has to be pursued, right? And so there is so much we can achieve with the technology that's less of the question and more of the applied use of the same. I'm very excited about where we're headed with these Agentic business processes with empowering our customers with this intelligence layer that allows people to build those more effectively, more efficiently and with greater confidence. And then at the same time, the counterbalance on observability so that there are some predictions on the ROI and the outcomes, I think that's a huge confidence booster for business and the adoption cycles. And I do think you'll see a lot more definitive cases of real growth and abundance versus simply efficiency and productivity.

Yes. Craig, for you, same question?

Yes. So look, I think it's redefining what digital transformation means for organizations like ours and increasing its urgency. It is a uniquely powerful capability that changes everything. It changes how we think about designing personalized intelligent digital experiences. It changes how we think about creating autonomous workflows. It changes how we think about modernizing our technology. And I think it's both distinctive but also new. So there's a sort of a window of opportunity at the moment to differentiate. And I don't think anyone can afford to be left behind, which is why you see the greater urgency. But I think it requires some really important shifts. Judson called out the one that I think is absolutely right, which is this sort of frontier transformation. It's, hey, look, pick 3 or 4 key journeys, 3 or 4 end-to-end processes, go deep, not experimentation, real transformation. And in that, solve not just with AI, but the integrated capability of digital data and AI because the 3 in combination are far greater in capability. Then I think this issue of just preparing the organization for adoption. The technology is ready. The question is, is the organization ready? And I think that's a different question. It comes down to how you get the technology in the hands of people, how you create the right climate, the right mindset, how you start to educate leaders to manage teams that are now hybrid teams of agents and individuals. And then this sort of question of just getting the foundations right, like -- getting infrastructure right, getting data right, getting security right, none of that stuff is glamorous. But ultimately, it will set apart those that actually pilot AI and those that really scale the capability, and I think they're critical.

Yes. But it sounds like an exciting journey. And I'm glad I'm on as well. Thank you.

Thanks.

Thank you.