First of all, congratulations.
Thank you.
That's where it's at the moment. It's really exciting times if you think about the technology world. One of the things that was really interesting when I was here at Insider a couple of weeks ago was this notion of the frontier transformation. Can you talk a little bit? You know, what does it mean to you? What's the message here?
Yeah. So we're really working with customers to have a 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 you know 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 you know tech to business you know misalignment with business goals. Others are tied back to sort of the disorganization of data in the enterprise and like lacking a real data estate or foundation to build quality.
Yes.
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. You know, what are you doing to enrich your employee experience and tie that back to your own KPIs? How are you 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.
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. 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, s o 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 or contrast that to, like, you know, previous, you know, tech transformation where you'd like to move to the cloud, we have the internet, etc.? 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? You know, how do I throw tech at a 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, you know, the impact on white-collar work and how's it gonna 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 in 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, you know, 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, advancements 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 in 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.
That's actually a good segue into my first question for Craig. Like, our industry, like, financial services, you know, 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, you know, I think we'd all agree the opportunity is quite, quite significant, and virtually everything about AI is changing at a pace. When I sort of reflect on, you know, some of the key trends and probably reflecting on the more recent influence of sort of generative AI, you know, sort of five 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's starting to change roles and workflows in a really fundamental way.
You know, 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, you know, in a position to create personalized development content. You know, 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, you know, across operations, across technology, across risk management. I think the second thing that you see happening, Raimo, is this shift from, you know, 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, you know, each agent in the organization, be it human or AI, understanding their permissions, 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 gonna 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, you know, data fuels AI, but too often that data is, you know, 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 lifecycle, and while you don't have the lineage and controls in your data that 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. You know, I think we all sort of understand that to use AI at scale, yes, I need, you know, access to powerful models across multiple clouds. Yes, I need increased compute density. Yes, I need, you know, much greater power capacity. But I think the really important shift is in resilience.
That as AI, in particular, agents and autonomous agents become more integral to critical workflows in organizations like financial institutions. The expectations on resilience will actually rise quite sharply. And I think that's gonna have a profound impact across the entire stack, from networks to multi-cloud infrastructure to data platforms to APIs. And I think you're gonna see much greater focus come onto this question of resilient AI provisioning within organizations. And then finally, I think, you know, the AI is emerging as much as a threat as it is an opportunity. You know, you think about those traditional cyber exploits, you know, identifying vulnerabilities, polling of systems, creating malware. All of that can be executed now at a scale.
Yeah.
And pace that just wasn't attainable before. It's probably the new exploits that are emerging or the new opportunities for exploits that are emerging that are most concerning. You know, think of, you know, a mass disinformation campaign executed across multiple social platforms, a vast target audience using deepfakes executed almost instantly. You know, 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.
Yeah. And then Judson, Craig mentioned a couple of interesting points. Therefore, as a really core kind of point here as well, how do you think about it at Microsoft in itself? Like, you know, yesterday I was talking with Gina from ServiceNow and asking her about she's eating her own dog food.
Yeah.
She calls it champagne, actually. Like, how, what's the, you know, how are you going about it? And how do you think about that data aspect as well?
Yeah, 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 two 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 'cause 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 a thousand 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 hoovering 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. It knows your 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 service. 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 sip data through a thousand 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 wanna run on.
And then, of course, the final tier, the Foundry layer where AI applications are aptly actually built. We have unlocked all of the knowledge bases that sit into agent-to-agent communication, all of the Azure Search foundations, all 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, you know, 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. So it's been a big investment for us, and I think a pretty strategic advantage for customers that rely on and trust Microsoft.
Yeah, perfect. And then, Craig, if you think about it on the Barclays side, like, there's so many potential things, you know, that you, that AI could touch. Like, how do you think about our journey? Like, how do you prioritize about that?
Yeah. So look, I think we were quite early into generative AI, you know, largely off the back of, I think, the investments we'd made across infrastructure, data, security, those foundations, you know, and if I sort of wind forward to where we are today, it's emerging out of what you might wanna think of as the creative chaos phase.
Yeah.
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 three areas that I think are priorities. The first, and, you know, Judson touched on this is process transformation. And, you know, I tend to think of, you know, generative AI in particular as almost the third generation of automation. You know, the first rule-based, the second RPA-based, now language-based. And it's when you, 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, you know, real value there. And I think this is, you know, 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. To colleague enablement, just getting the technology into the hands of colleagues, allowing them to innovate, simple augmentation, you know, more complex autonomous workflows. For me, you know, the challenge there isn't a technical challenge. I'm not even sure it's a skills challenge. Yes, skills are important. It's absolutely a mindset challenge. It's about how you actually inspire people to sort of embrace the art of the possible, you know, set aside traditional ways of working, invest the time and the energy in discovering what you can do with this technology. And I would say, you know, 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, software development. You know, 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 cross-functional teams of agents and engineers having enormous potential. You know, 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 two years ago. Now, I would say this, Raimo. If you were to 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. You know, we're still learning. The technology's evolving really quickly.
So what's key is, yeah, be curious, be adaptable, keep really focused on customer value. It is the only way I think you can approach this.
Judson, from your customer conversations, like, I'm sure you're getting that as well. Like, how do I think about the return?
Yep.
like, you know, of all these kind of adopting this innovation, et c. Like, what's the conversation like behind?
So that was the second big part of our work at Ignite. I mentioned intelligence and trust on the trust side. You know, trust means a lot of things. We want.
Yeah.
Of course, there's a security element to trust, which I'll come to, but there's just even the, you know, 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 frontiers. Is that something that we can trust? You know, can we trust that the ROI at the end is actually going to be something that, you know, is makes the juice worth the 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. We turned on the product, 'cause 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 organizations 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.
Yeah.
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 a 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 the ROI on the overall process." So you can actually zero in on that, go in and understand what the model is under, 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 gonna take these agents, you know.
Yeah.
To production in the wild.
Yeah.
So, you know, 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 and say, "Well, here's what an AI claim costs to process per claim." And if you're gonna do, you know, 100 million claims in a year, great. This is the bill. Here's the ROI. You actually see these things upfront.
Yeah.
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 gonna be a huge lift for the acceleration of, of real AI adoption. Because if you can build with intelligence.
Yeah.
And then trust the process and understand the ROI before you invest. We think it's a pretty big unlock.
Yeah, and talk about trust. Like, Craig, you know, when we made the decision to roll out Copilot.
Yep.
To me, that was probably the single most kind of project I've seen in Barclays since I've been here.
Yep.
And that's kind of many years.
Aided by my friends to the left here.
Yeah, yeah. Can you talk to that a little bit? Like, you know, 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 outcome so far?
Sure. So look, I think when we started with Copilot, we started, as you do, with often, often with new technologies, which is sort of 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. 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, you know, the same enthusiasm, the same curiosity, the same desire to discover the potential and capability of this technology on a much larger scale? So we leaned into that really hard. You know, we ran three 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, prompter farms, hundreds of demos, and fostered quite a sort of a vibrant community of interest, you know, across firstly Teams and then Viva Engage.
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? You know, according to Viva Insights, we've just crossed, you know, a million hours of productivity, through that process. But actually the, you know, the other measure, I think, is just the demand that continues to exist. And that demand has meant that, you know, the enablement that was to run to the middle of next year will now be concluded at the end of this year. You know, where to next?
Really, it is about continuing to inspire, continuing to educate, hold up the role models, putting new capability, you know, Copilot agents into the hands of those early adopters and innovators and, you know, seeing what they can do with autonomous workflows in support of, you know, themselves and their teams.
Judson, is it? Are we like a typical customer here? And then, the other thing, it does feel like the Copilot initially was seen as like a, okay, I ask a question and, and do something more. But now, like, it feels much, much broader. And I, I'm only now realizing, okay, this is.
Yep.
Actually the gateway to everything.
No, like, we've had a very strong relationship for a long time, and you know, 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, you know, helping us even taste-make the product itself, so Barclays on that side of things is, you know, 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, you know, comparison of the Copilot journey itself, if 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.
Yeah. And then, last question for me, and then I have to let you go. Time flies, flew by here kind of quickly. If you look at 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, and it's more like, you know, we saw Copilot, but like, you know, 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, you know, 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's some predictions on the ROI and the outcomes. I think that's a huge confidence booster for business in the adoption cycles. I do think you'll see a lot more definitive cases of real growth, and abundance versus simply efficiency and productivity.
Yeah. Craig, for you, same question?
Yeah, so look, I think it's redefining what digital transformation means for organizations, you know, like ours and increasing its urgency. You know, 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, you know, 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.
You know, Judson called out the one that I think's absolutely right, which is this sort of frontier transformation. It's, hey, look, pick three or four key journeys, three or four end-to-end processes, go deep. Not experimentation, real, real transformation. And in that, solve not just with AI, but the integrated capability of digital data and AI, because the three in combination are far greater in capability. Then I think this issue of just preparing the organization for adoption. You know, the technology's ready. The question is, is the organization ready?
Yeah.
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, you know, I think they're critical.
Yeah, but it sounds like an exciting journey, and I'm.
Indeed.
I'm glad I'm on as well. Thank you. Thanks for joining us.
Thank you.