Workday, Inc. (WDAY) Earnings Call Transcript & Summary

Please welcome Investor Relations Vice President, Justin Furby.

Thank you. Thank you. I love that video. In fact, I was actually thinking we could just end it there and go right to Q&A if you guys are okay with that? We've got a few [indiscernible] out of that. Welcome to Rising. Welcome to Analyst Day. I'm really thrilled to have you guys make the trip out. We've got a great group here in Las Vegas. A great group on joining us on the webcast as well. So appreciate all of you. I know there's a lot going on in the market in this week as well. So I really appreciate you making the trip out. There's a lot of energy in this room. We're going to bring it up though as we go through the course of the day. We're going to show a lot of product. We've come a long way just in the last year. So excited to show you and if you took in the innovation keynote this morning. You saw some of that. We'll show you a little bit more for you all and give you a look into what we've been working on over the last -- really, last year, but in the last 6 months, in particular, a lot of acceleration around innovation. This is what we're going to expect today. It's going to be about 2 hours or so. We're going to go across most of the -- much of the leadership team and talk a lot about innovation and then how we're actually delivering it to customers and what that adoption cycle looks like. And I think as we go through the course of the day, and we'll close that with Rob will talk about -- and this is a question we get a lot, what does that journey look like in practical terms when a customer goes down that path with us. So this is my obligatory safe harbor statement. During today's presentation, we will make certain forward-looking statements. Actual results will -- actual results could differ materially. Please see our latest Form 10-Q filed with the SEC for a discussion of key risk factors. With that out of the way, it is my pleasure to invite on stage our Co-Founder, CEO and Chair, Aneel Bhusri.

Right here. I was just watching to see how you were doing. So good afternoon. Thanks for being here. It's great to see so many familiar faces. I hope you all saw the keynote today in the Q&A, you can ask any questions about that, that you'd like. What I'm especially excited to hear about, you're going to hear from all the product and technology leaders that are setting the pace for the new set of products. Zane's got a lot to cover as well, talk about the market opportunity, the strategy we're executing against. And hopefully, what you see is that there's a big bench of talent at Workday that's driving the company forward. And in many cases, new talent because it's a new era, it's a new time and we've got largely a pretty fresh team driving us into the future. I talked about the refounding moment in the keynote. How many people were at the key to everybody? Some people weren't. Okay? What were you doing? If it wasn't for AI, would I have come back to Workday, I don't actually know the answer to that question. I get asked that a lot. But once AI showed up, I felt like I needed to be back, and I had many discussions with my Co-Founder, Dave about the future and what makes a great technology company. What that's a technology company pass the test of time as a company that people admire that does good work for their customers. And it's winning across multiple technology generations. So we were a clear leader in cloud. I was part of what happened client server. But [ PeopleSoft ] didn't make it to Cloud. Workday made it to Cloud and at Workday needs to lead in this next generation of AI, and that's a large part of why I came back. I also came back, which is about driving innovation, but I also came back to make sure we're refocused on our customers, refocus on our employees. And so I'm 9 months in, so far so good. This is not just another product cycle. This is something like none of us have seen. Everybody wants to have all these hyperboles around what AI means, but I've been around long enough to know. I've never seen a technology like this before. Simply put is trying to replace the human brain. No technology in the past has done that. It's all been around increasing levels of automation. And so, do I know where this technology is going to go? No, but I do want to have a say in where Workday heads with this technology as it continues to evolve on almost a daily basis. So just to remind you, I'm not going to go into detail. You've been covering Workday for a long time. We've been at AI and machine learning since 2013. One of the reasons is in the founding principles of Workday's, we thought data would be really valuable at some point down the road. We weren't sure how it was before machine learning existing. But as soon as machine learning showed up in that '13 time period, I was actually on the Board of [ Cloudera ], one of the pioneers of [indiscernible]. And I saw what was the start of something really powerful and magical. We just kept investing along that trajectory. And today, those bets along the way you paid off, we're still down to one data model. One data model means we can aggregate all the data across all of our customers and train the models in ways that nobody can touch. Not ADP not SAP, not Oracle. They have their customers on multiple systems. And on the HR side, no one has the scale of employees that we have at 80 million to begin with. One security model. You don't have to adjust security going from version to version, which means when you're using AI, it only has to traverse that one security model for whatever applications using one version of the system. Every customer on the same version. So when you come out with a new agent, you don't have to try to customize it between 6 different versions of legacy systems. One agent works on all of our customer systems. And so that's where we are and lots of transactions, 20 years of trust, which is not something to underestimate, especially with so many new companies emerging. They don't have the data, they don't have the trust, they don't have the ability to actually drive the models the way we do. To get that trust with customers, it took a long time, but the way we translate it is into this universal master service agreement that lets us user data in an aggregate way anonymous way, but that lets us train the models in ways that no one else can do. The other part of what we're doing is just understanding that AI is just very different. It's not about determine -- it's not about an end-to-end business process, it's about probabilities and reasoning. And I said this in the keynote this morning, you can't get a payroll 90% right. You can't get a close 90% right. You have to be 100% right, 100% of the time. Reason can add an element that helps you move a process from step to step but it can't replace that process. But when we can take what we know with our platform and tie it to AI, AI work starts getting done on behalf of people and business and it can rely on those guardrails. So that really is our differentiation. There are a lot of spaces where AI can just run the muck. Maybe it's in the creative space where you're just creating new content, you're creating new images, but not in the HR and finance world. These spaces are highly regulated. They have permissions. In some places in the U.K., you track religion in the U.S. it's illegal to track religion. You can't have agents that are just running across your data without any insight into the rules and regulations that run their business. And when you get to the financial side, same thing. So those same security permissions, workflows and biz rules, these are what customers rely on. And so that's what makes for lawful use of AI. And that lawful versus law list distinction is really important to us for those that were you in the audience. You saw the ad that we did with patent manning on the [indiscernible] versus [ Lawlis ]. In the spaces of HR and finance and ERP, that really, really matters. And so there was a talk about SaaS apocalypse, which I never believed in, but now I think it's becoming clearer in our space that companies like ours, AI is an opportunity to leverage those business rules, leverage those guardrails and build AI on top of it, which for better or worse means that SAP and Oracle will probably find 2. They have deeply embedded business processes and functions. We'll see if they can build AI on top of it, but no one's going to rip out SAP manufacturing that I've ever talked to. I mean no one's ripped it out for 30 years. Why would they do it now? I talked about the distinction about the deterministic rails. That payroll example is one I always come back to. What we need to do as a company and part of our refounding is this differentiation is take these deterministic rails. Every business process has a start and an end. Some business processes have 20 steps along the way. A lot of that work between those steps are very mundane. They could be automated. There's a human involved. Well, AI can actually move the process from step to step. And in some cases, just redefine that business process. And that's where Workday comes in to take the strength of those deterministic applications and now come up with the agents that can move work across these different business processes. The last part I talked about is operating with a start-up mindset. And coming back, you heard this in my keynote if you were there. I didn't really have a have a guidepost of how do you do this when you've been gone for 3 years, and new technology emerges and you come back. So I watch a lot of Steve Jobs videos. And I've said this many times as much as he was a great product leader, he was an equally great management, thinker and leader. And so his thing when he came back to Apple was to think about Apple as a start-up again, and that's what we've done. We've got the scale. We've got the security. We've got the platform of 11,500 customers, but we need to innovate like a startup. And so we've organized that way and how we're running the company and how are we operating differently. We're doubling down on what matters most, concentrating on AI on our platforms. Our core apps are still important. But I'll just say, talking to customers that 100th feature in HR or finance is not as important to them as the new capabilities in AI. Having said that, we still have a lot of our development team still working on those features because we do have a backlog of things we need to work through for our customers. But it also means doing fewer things. When I came back, there were 50 agents, now they're 20, 1Kinrossin production, 3 more to come down the road, focus on ones that really matter. That still sounds like a large number, but it's not when you look at the scale and breadth of our platform. And moving away from niche products, focusing on the ones that really matter. We are a leader in HR. We're a leader in finance. Let's work on agents that really leverage those capabilities and really help our customers at scale. And we are moving much faster. Hopefully, you saw that today. A lot of that work got done in the last 9 months. I'm honestly blown away. I'm so proud of Gerrit and Joel and [ Yasmin ] and Max and Gabe and let what they've done in the last 9 months I've never seen before, even in the early days of Workday we didn't innovate that quickly. Part of it is just the AI technology allows you to do that. Part of it is the mindset of the team. We are working at speeds I've never seen before, and it's starting to pay dividends. And you'll see that maybe not this year or I don't know when you'll see it, but it's going to start seeping into our business as well because these agents are truly very transformational for our customers, and they will generate a lot of revenues for us. A couple of examples. Workday Decision Intelligence built out organically concept to GA in 4 months. Singapore payroll using AI localized at just 8 weeks, we would thought that would have been -- before that, we thought it was a year project and now it was done in 8 weeks. 10x faster than anything we've done before. So that speed really does matter. And I guess the other part, which I've mentioned a few times is we're making the ownership much clearer. When you hear from our product team, [ Jasmin ] knowns Data Cloud, [ Ellett ] owns Agent factory. [ Joel ] is our Chief AI Officer, Gabe owns platform, [ Maxon ] applications. When I came back, it was very diffuse AI was owned by all the application teams. APIs were owned by all the application teams. And frankly, we're moving too slowly. When something is being done by someone in this 10% of their time, it doesn't get done. When you're up against a start-up they're devoting 100% of the time of activity. We had to do the same thing. And so putting leaders closer to the work, close to the customers, making them accountable. That's what we've set out to do. Let me quickly talk about the 5 ways to monetize Workday. I talked about the different opportunities. So I'm not going to spend a lot of time with this if you were in the keynote, you saw the 5 use cases. So let's just turn it into monetization Agents. Obviously, we're going to sell Agents on a Flex Credit basis. APIs, if you don't want to use our agents on the front end or you want to use something like cowork, cowork by the way, [indiscernible] integrates Sana. It doesn't even integrate directly to our base level APIs that integrates through Sana. Workday Data Cloud is a way you want to do deep analytics off to the side and marry with a whole bunch of other data sets. We've got the Workday Data Cloud, Workday Extend AI, if you want to build your own custom applications, again, Flex Credit based. And then, of course, we still have our core applications that we monetize. So 5 different ways. It really does cover every use case. We're largely neutral on which ones you choose. Do we make more revenue on agents? Yes, but APIs are ones where we don't really have to have a sales and marketing costs, so that can be equally powerful. Workday Data Cloud, brand-new opportunity that we really didn't have in our business plan, same with Extend AI. So this model of monetization is still new to Workday, but it's coming together in a really nice way, and customers are embracing it. We still have work to do on the Flex Credit front to make sure that they understand how that works. In a few minutes, you're going to see them all in action. And with agent harness, which we announced this morning, we get you that same foundation, trusted foundation to every agent. Gabe will share that in more detail. On APIs, direct way to access and monetize agents. Customers won't always start inside Workday. They happen in another third-party application. That's okay. These APIs are purposely built purpose-built for AI. I talked about the Data Cloud. Extend AI, that used to be Extend Pro, as you know from our earnings reports. It's been one of the fastest-growing revenue streams we've had, and we're seeing customers build agents in a very short amount of time. They're incredibly powerful. And we're not going to build all the agents that run at Workday. We don't need to, but Extend AI lets us participate in the building of those new agents. I do want to remind people that we're not stopping investing in the core applications. We are continuing to invest in those core applications. So you'll still see us adding new capabilities. There's new areas like financial consolidations that we probably need to do a better job of. So there are still business process applications. And any time we build a new business process application, creates data that feeds the model. So we're not done with that area. But we have such good products in that area. Customers are more focused on the features around AI. So there you have it, 5 ways to monetize Workday. Each one matters in its own. But together, they reinforce a very open platform. Take what we built with Workday and use AI to make it better. With that, I'd like Zane to join me on stage, and we'll get into all the financial questions you want to get into. So thank you.

All right. Good afternoon, everybody. It's good to see so many familiar faces out there. So Aneel, as CFO, I feel like I'm obligated to tell all of you as much as I know you like bigger numbers, bigger slides. This backdrop was repurposed from a meeting we had yesterday. So we're thrilled that you're here. This isn't all for you. This was actually intended initially for our exec symposium, and this room was 2 or 3x the size it is now, and we literally had hundreds and hundreds of CXOs listening to our story and talking about AI and looking to us to understand where we are with AI. So it was an exciting time. It's an exciting time to be here at Rising 17,000 attendees in person 14,000 virtually, I believe. So just a great time to be here.

It's fun. It's fun to be back. It's just an exciting time to be back building product. It's feel like a kid in a candy store right now.

Right, right. And for those of you who missed , I know a number of you got in this morning, obviously, you'll be able to have a chance to see the keynote, but we've got all the talent here to give you a private showing as well. So with that, Aneel, you've been talking about market size and market growth. And for those of you who were here last year, you saw that we had a slide up look similar to this, except instead of $276 billion of TAM it showed $188 billion. So we're very proud of the growth here that you can see both HCM and [ FinsTAM ] has grown nicely, both through innovation as well as just the market growth. So that's approximately $176 billion as we calculated today. And the area we're most excited about is the $100 billion opportunity for us under AI and platform. And for those of you who were here earlier, you saw the power of Sana, you saw the power of Workday Cloud and our Agent ready tools that are really driving that $100 billion of opportunity, which is more than 2x what it was just last year. And what this does include are some of the interesting things that we're now getting into notably ITSM and of course, a big announcement for those of you who missed it earlier today with C1. So I thought this is maybe a good opportunity, Aneel, to talk about the C1 opportunity, how you've been thinking about identity.

Yes, I'd first start by saying. I've been back for 9 months, I've talked to a number of customers. When you look at that AI and platform circle, they are looking to us first. I'm not one to exaggerate. I think most of you have known me for a long time. Our window is not forever, but we have a window 12 to 18 months where if we deliver our agents, we're going to be the default choice for our customers. They trust us. We already have their data. No start-up has the data we have access to, they believe in the guardrails we have, and they want us to help them make their businesses better. So we have this window where I have yet to meet a customer who doesn't say, we're looking to you for the agents, please deliver. So I just think that, that $100 billion opportunity is ours for the taking. I'll just say like Data Cloud, I didn't really have that forecast into our business. I think Data Cloud could be a huge new revenue accelerator. So getting to Identity, I've been talking about identity for a long time. I was one of the founding investors in Okta I love that space. And I think there's a nuance that's lost on the market. HR systems are the source of Identity. We have massive share in the HR space. Not saying I would do this, but if we turned off access to Okta to our HR system, they'd be in a lot of trouble, which just tells you we're at the front end, we're upstream of all the identity systems. It's a very fragmented industry. So HR is the source of identity. Now we're providing all the governance around it for not just humans but for agents. We can work with other IDPs whether it's Microsoft or Okta, but we now have that agentic layer that manages human and agent governance around all the things that are going to happen in the world of AI. It's incredibly powerful. It's going to take a while for the market to realize. It's going to take us a little bit of time to ramp up the sales but I can tell you, it's going to be very disruptive to the identity space, and we're now in the security space in a place that we have a right to win. So I'm very bullish, and you guys have all seen all the reports about Agentic Identity and Security, where we're now front and center in the middle of all of it. And [ C1 ] is a very special company. So you'll learn more about that over time.

That's right. That's great. And as we align with the growth and opportunity for AI, you can see the improvements that we've seen in this category on a year-over-year basis. We mentioned in our second quarter earnings call that we're approaching $600 million in annual recurring revenue with our AI agents and our AI portfolio, which is up significantly over 3x what we shared with you just this time last year. So while it's still early days, really tremendous growth in this area. And it's not just the breadth of agents. And again, those of you who saw the keynote this morning, you can see we have a number of very exciting opportunities and agents that we're building, but it's also the depth. And if you look at areas like Sana, Extend AI, [ Hard Score ] and Paradox, they're all growing nicely, 3 of them are over $100 million, and Sana is approaching that number very quickly. So we're very excited by that opportunity and obviously the growth in our agent capabilities. As of the second quarter, we had 300 customers that had signed up for Flex Credits but as Gerrit will talk about, we have over 5,500 that are using a number of our organic agents already. So it's really getting that flywheel started. Rob is also going to talk about the opportunity on a customer basis and how you see that flywheel build up with an average customer. So we're excited by that opportunity in aggregate. Now when we break that down and we look at our customer base, we've been focused on the dynamic with the customer base. And obviously, this, as you all know, represents 60% of our trailing 12-month subscription revenue. So we see this as a tremendous opportunity to leverage AI and if you look at that opportunity just over the last year, it was the second quarter where we saw AI become just over 50% of that mix between core and AI. So 50% in the second quarter, we expect to end the year with more AI going into the customer base than just the basic core. And then as we look to next year, we estimate roughly 70% of that selling motion will be AI-driven and that revenue will be AI-driven. So it's tremendous growth. And Aneel, maybe just touch on some of the customers that you're talking to this week and how they're leaning in on the AI story.

Well, so just last week, I talked to [indiscernible] at Accenture, they're rolling out the self-service agent to 400,000 employees, right? And that application is based on Flex Credits. You can just think about that Self-Service agent. Joel talks about as employee concierge. It's probably 30% of what it's going to be in a year, but it's already an incredible application. And I think it's the first killer application and I suspect many of our customers are going to deploy a BMO is the other one, and they're well along the path and very happy. The customers are also very excited about the developer agent they want to build their own agents. So are the service partners, by the way. I wouldn't ignore them in terms of talking to the service partners, the ability to build custom agents that they can resell to a whole bunch of their customers are really powerful. Then we have all the functional agents. But I mean, across the board, a huge amount of interest in what every conversation we have with the customer start around AI and our agents.

Right. And it's really that flywheel effect. If you look over the trailing 12 months, we had just under 900 customers that purchased at least one agent. And the uplift we got from that subset of customers was roughly 12%. And you're going to hear from Rob where the idea is to get those agents out with those customers, and you'll see how that can ramp significantly higher than the 12%. So it's still early days. We're starting to see appreciable improvements and increases. And we're excited about what's still to come with the existing customer base, which highlights the other part of the opportunity, 40% of our opportunity is still around net new and how growing both internationally and how we're leaning in, in particular around the medium enterprise space. Rob is going to talk a lot more about our initiatives there, and you've heard of Workday go and really how we think about not only selling AI into those opportunities but leverage you talked about around the world as far as looking at Singapore payroll and all of the initiatives we have to localize our product and accelerate that road map, but there's also the time to completion, the time to execute and the time to get those products out there. So it's an exciting time with medium enterprise, we've got a lot of initiatives, and we do believe that that's a key part of our growth with net new opportunities going forward. So again, a nice mix there.

Yes. And in the medium enterprise, a lot of the international market is medium enterprise. I happened to be in Europe 2 weeks ago, met with both large- and medium-sized companies. I was kind of surprised how far their thinking is on AI. I thought maybe they're going to be a little slower. They were -- Europe, in general, was slower on cloud. They're not slower on AI. They're embracing AI in the same way, and I think it's really exciting.

Yes, and a great opportunity for us. We've actually, as you know, seen good success on our international markets over the last number of quarters. So we expect that to continue. And then the focus ourselves, Aneel talked about a startup mentality and culture. And when we think about investing in growth earlier this year, we talked about really understanding what that opportunity set is. And today is going to be more about the product set. So we're eager to hand over to Gerrit and his team to talk about those products and get you excited about those products. But it's everything from rethinking how we deploy and how we engineer those products to the go-to-market side, Rob is going to talk about how we use AI and leveraging AI in our go-to-market capabilities, but also thinking about the whole adoption, whether it's forward deployed engineers. And how we lean in with our customers. It's really around how they adopt. And that's what they want to know and want to learn from us.

So when I think about what we're doing in AI, we have to be customer 0. We have to be the best user of our technology, and we have IT, what we call a BT at Workday, a leader Ronnie, who is totally embracing that. And so when I look going forward, there are a couple of things that come to mind. I don't personally think about running the business for margin. I think about running it for growth. But if we run the business the right way, the margin should and will get better over time for multiple reasons. We get back to startup mode, we have to reduce layers. We just did a reduction as painful, but 2.5% of the company, and I promise you it wasn't for cost cutting. It was purely the fact that in some places in the R&D organization, there were 10 layers between me and someone actually building code or writing code. That's not how you move quickly. They were -- those individuals were complaining about how long it took for them to get anything approved. So we slashed a lot of those layers to get back to that start-up mindset. So not about cost but about speed, about ownership about moving quickly. If we are a good user of our tools and other tools that are AI based, actually, we're going to get benefit for margin. And so we're going to set pretty aggressive margin targets to drive us to use these AI tools. And I mean, we have to eat our own dog food or we shouldn't be selling these products to customers. And so far, the early results are very positive. But we're looking at the products across all of our software providers and how we can use them to use AI to drive margin. They may not be in terms of more reductions in force, maybe there are. But I think it's -- I think there's also the opportunity to just not hire in areas they were hiring in the past and just leverage AI instead.

And we've been doing that through the course of the year with headcount, Aneel mentioned earlier in the year, our intention was to keep headcount relatively flat. Again, we want to be very thoughtful in where we invest and how we invest as well as a company-wide thorough review on how we think about people, process and systems all working together to just get better at what we do. So a lot of opportunity there. And then our last slide before we hand it over is, of course, around the framework. Now none of this, we talked about it in the last earnings call, so it may be a little anti-climatic for some of you in the room. But we're obviously holding to our guide this year. We mentioned on the call that we expect to have 11% in subscription revenue growth into FY '28. We still believe there's some upside there. I mean, Aneel, if you want to touch on some of what you're seeing through the course of the week. But I think we've mentioned that a lot of this is about adoption and how customers are embracing the technology, and that's where we see with the proliferation of agents that we're building that there is some potential for upside into FY '28 and beyond.

I'm hugely bullish and optimistic on what we're doing on the Agentic front. Just before this meeting, I met with 4 different customers, and what they're frankly worried about is the token costs tied to those agents which tells me they want to use the agents, and we have to get smart in how we work with them to make sure that the cost to run away from them. But that's a good problem to have because I think we can -- as a better problem to have than they don't want to use our agents. They want our agents. And in some cases, they badly want our agents. So I'm very optimistic on that. It's hard to -- it's hard to figure out what that looks like in a year? I know it's going to be great. Where along the curve does it happen? I don't know. But then I look at the new areas around identity. I look at the new areas around ITSM, and those are all to me, upsides in the model. But AI, I think we're in the process of catching a tiger by the tail hard to figure out when these agents, we've got 17 agents who are all young agents. When will they actually take off. But when they take off, they're going to be huge.

Great. As we talked about through this conversation, and we mentioned already, we've obviously focused on non-GAAP operating margin and expect that to be greater than 33%. But as important to us is SBC as a percent of revenue. You continue to see the decline. We expect next year to be roughly 15% but it is something that we're focused on and expect that number to decline even beyond FY '28. And so we'll start talking more about non-GAAP operating margin, including SBC, and you can see nice growth here and expecting over 17% next year with that important metric. We know it's one that you all follow closely as well. And with that, we're also focused on how we deploy and how we think about capital allocation. It was just this time last year when we announced a $5 billion buyback, I was thrilled to see that happen sooner than expected. Market conditions obviously helped in that regard. But we also have $4 billion authorized for an open-ended buyback as well. So excited about how we're balancing that. And then the last line item here, I know it's very important to all of you, and that's free cash flow per share. We are focused on that as well, expecting $13 this year and $15 into FY '28.

Yes. I guess I'd say just on the Agentic front, the Agents, the APIs, Workday Data Cloud, this new consumption model is a beautiful model. it's a flywheel. Once it gets going, it really gets going. And it's new to us. It's not new to us as executives or leaders. It's new to Workday, and it's new to you and how you look at Workday but it's a far better model than a people-based subscription-based model, which will continue for a long time. But I'm super excited. I see the bills we have to pay to Google Cloud and AWS and Azure. And I think why I'd like to be in that business. And now we have an opportunity to be in that business. And we have to do it in the right way for our customers. But once you start using these agents, I've done it myself with the Sana Self-Service Agent. You get kind of hooked on it. And it just keeps -- you got to stop yourself because I realized I was probably burning through a lot of tokens. But I do think it's something that people are going to underestimate how powerful this is going to be for Workday. I'm confident that. I'm not one to get way ahead of myself, but I see it, I see it. When exactly it happens. I can't give you a date. I think it will happen over time and then it'll just explode as customers get real value out of these agents, and that's the key. They need to get real value out of these agents.

Great. So with that, the most important time here is with Gerrit and his team because we want to have this be more you seeing the products in use. We would like to invite Gerrit up. And then Aneel and the team and I will be here later on for Q&A. So with that, let me invite Gerrit.

Thank you [indiscernible]. Hello. Good to see everyone. Did any of you catch the innovation keynote earlier to this today, some not in [indiscernible] hey, Mark, I know I saw you. Anyone else go to , okay? I'm going to do just a very quick recap of some of that right for the some who may have missed that. But we're going to keep it brief because I know I stand between you and some pretty awesome product demos. So I want to have it going on a double for all of you. So yes, it's a pretty incredible technology, right? And we all can agree to that quite easily. An interesting thing is though that on the enterprise side, except for very specialized products like coding, the break really hasn't happened yet, right? It's really -- it really is being applied to the old ways of working. It's just making everything a little better. And if you look at total company output, total productivity, the impact has not been as large as of today. And this is actually where we think there is a massive opportunity for Workday if you understand, hey, what drives it? And why is that really, right? What is preventing that from happening at scale? And understanding that when you actually change your perspective and look at it from a Workday lens, there is actually a lot of growth that Workday can drive from AI. And [indiscernible] 2 categories. What does every single company right now go through. No matter the industry, no matter country, right? More or less, but in broad strokes. Everyone is working through the AI transformation. And what are they all trying to figure out, how do they need to reconfigure their organizations to be AI led? How do the jobs change, their profiles change, what skills do they have, what skills do they need, what system that sits in the middle of all of that, Workday on the HCM side, right? There is a very interesting narrative. And I think some of you are actually guilty of pushing that and that's that interesting thought that AI is just a squeeze, just an efficiency gain, right, do more with less. That's all AI can do, right? You automate, right? You're automating your automating your automated, right? But it is actually a quite limiting perspective. And I -- well, I would say it's a real misunderstanding, right, because the whole promise of AI is that actually you can drive, create productivity, address new market opportunities, saw a few customers better. It's a story of growth. That is what every company is trying to figure out. Not every company is trying to be only more efficient. Everyone wants to grow, right? And that's the big promise of AI. The opportunity that we have now at Workday is that on the one side, we are building that system of work intelligence that allows you to completely transform your workforce. Again, which every company has to do. Everyone tries to figure out what do I need to hire, what skills do I need to build? Where do [indiscernible] talent the best, right? The age of over-hiring is over, right? Companies don't hire anymore to grow. That was the old recipe. The new recipe is that you actually grow the company and then you may hire, right? Maybe? And then secondly, what is actually preventing all of those organizations from adopting AI at scale. No one has a recipe for how to actually do that. And it starts with very foundational primitives like security and identity and permissioning and guard rails. I'm sure you all read the headlines with the hugging phase incident right of OpenAI model breaking out and hacking website, right? And that sounds really concerning enterprise context because there are so many things that can go wrong when you say enterprise, from the obvious things like paying people to the real out things compliance when closing the books, right? This is what makes it hard. But what if you could solve the problem, right? What if you could have a platform which lets companies deploy AI at scale in a safe, reliable and in a lawful way. 2 massive opportunities for Workday. Just very quickly on the first one, right, because there is, again, also this idea that it's only compression and compression and compression. And yes, if you get very specific in graduate hiring in computer science, yes, there is a compression. But if you look in virtually any other function, the picture changes dramatically. Hospitality, health care, companies are looking for people to grow. If you look in functions like finance, what you all know, right, it's a very efficient function, but many CPAs actually are close to retirement. So it's a huge talent shortage on that side of the world. And now we're looking at the new roles that AI is creating forward deployed engineer. Every company right now in the tax base is trying to build a forward deployed engineer function. It has grown more than 7x over the last year in relevance. The job was practically relevant before. AI is in every job title right now and all the job postings. And most interestingly, outside of tech, marketing, sales, customer service, it is really changing the job profile. And the second thing which allows companies to do is actually changing how they define, how they work, what their workflows are you saw today and you're going to see later Work Intelligence, which is really a strategic tool kit for companies to drive the transformation of work for themselves. I just want to quickly recap on the progress that Aneel spoke about, right, because it's easy to forget how much we have accomplished and how transformative that change has been. Last year, we said we're going to completely reinvent the user experience of Workday. Today at Rising Sana is the new Workday home experience. You saw it earlier in the quick screen video, a complete reimagination of how work gets done. I give you one example because it shows so nicely what it means when you free people from the work about work stuff and give them time to focus on what's important. One example that you all may like, I like it, that most people like us taking time off PTO. Sana cuts the time down by more than 80%. If you look at our customers, that is equal to 1.6 million working hours safety every year. That's just one tiny example. Now just think about if you redeploy that time to something which is productive versus managerial, it shows you how big that leverage function is. It is insane. We talked about the open platform strategy and Aneel spoke about Data Cloud and what it can drive. We have seen such incredible momentum with our AI developer tool chain. We have trillions tries froze of data and work the data cloud already growing 75% year-on-year. And all of this is driven by customer demand, who wants to use that data for their own agentive transformation for their own [ UI ] use cases, use it as context, bring it together with auto data for analysis, all possible. And based on that idea of giving enterprises strong guardrails we have had our open platform for AI and early adopter for 4 months. More than 6,000 applications develop, multiple hundreds of agents built with it. And it really stems from that need that yes, I want to have a frontier model, of course, why wouldn't I? But I want to give it a safe context and guard railing system so I can run it reliably and lawfully. And last but not least, last year ago, we were just getting started with Workday organic agents. We had a couple, but I think it's fair to say not many of them have been in production yet. Today, we are at approximately 6,000 customers who are using Workday Organic UI in production. We just achieved a milestone of 1 trillion users for Sana Self-Service Agent. That just shows you that incredible potential that we have and really reinventing the work across finance and HR. So bringing it back to the convictions that we have, right, how we think our portfolio strategy, why C1 makes sense. You may wonder. And what we're going to show you next. The key stepping stone to overcome is that AI needs to be trusted in an enterprise context. That is foundational. And you would be surprised how hard that problem is to solve because guess what, current guard-rating mechanisms, current way of thinking security, all of that is conceived from a human right, from a human continuity of from something like a service account. No one has really thought about, right, what it means in the age of AI because an AI model can now act on my behalf and is doing something very different if it's acting on your behalf and doing something else, right? And yet, I need to understand the security model per request at the right time to order to enforce it correctly. And it's very important when we say that AI must be trusted that we mean with that at all the AI an enterprise context needs to fulfill that guarantee of being reliable auditable and secure. The second thing which I have just said, and that's driving so much of our momentum this moment of redesigning work, really reconfiguring every enterprise in virtually all aspects team, talent, organization process and giving them a system where you actually move from this age of copilots, that is really the limiting factor of standalone AI, right? It's that smart site panel sitting on the side, having no understanding of how to actually change the process itself because you just prompt it in a traditional way of doing work. And like I said, there are some domains like coding, but this is just incredibly AI friendly. But in 99.99%, that ain't the case. Companies were fundamentally different. And that redesign moment allows us to introduce these autopilots. And they are incredible game changers because you all think economically, right? I would say, roughly speaking, in economic terms, AI assistant equals to software spend. Autopilot equals to labor spend, right? Because it's actually doing something independently behaving like someone performing that function. And last but not least, this idea of making AI a growth driver through your talent by actually putting the org and the workforce transformation in the center of that conversation. And like I said, every meeting I had, be it with customers, prospects, partners, even before I joined Workday and worked for a very different company before him, every company was trying to map out the AI transformation. And every of these companies were starting with how do I actually reconfigure my organization, my talent around it. That puts Workday in the center of all of that. So work intelligence, that analysis platform for recomposing work on a continuous basis, that's the brilliance of work intelligence and its counterpart decision intelligence that the enterprise is not sequential anymore, right? Something that maybe you have all like annual plan, annual budgeting, zero-based budget and all of that stuff going to a continuous fluid model that you constantly redeploy and reassess in your talent mix, that's Work Intelligence. I spoke about the autopilots and recruiting is one. Sana [indiscernible] is another. Sana [indiscernible] is yet another one. We are bringing all of them to market because this is where we are seeing the biggest unlock and a trusted agent platform underpinning them all, it's the blood form that we are building on. And that's why we're as confident in giving the same platform to our customers. And again, I want to address it because you may have that false on prediction that should I use a frontier model or the trusted agent platform. This is not how it works. It's just like saying, should I choose a CPU or a cloud hyperscaler, right? You have to think of it as a value chain and a stack, right? We are using all of these models, and we are providing all of these models, including [ CHEP ], from type safe, recent innovation on the block, if you will. And we are exposing them and giving them everything which they don't have, like the trusted context, enterprise guardrails, workflow awareness, domain knowledge and HR and finance. And we're bringing this, which you saw an innovation keynote into all canvas, it's very important to us that Workday meets you where you are. This is why we are introducing candidate search and application in ChatGPT. We announced [indiscernible] partnership with Anthropic for Sana and Cloud. That's how it all comes together in the AI ecosystem around us. I want to say just 2 quick things about C1 because it is not just about acquiring a company. For us, this has been, like Aneel said, an incredibly strategic move for 2 reasons. One is identity is fundamentally broken, comically broken, I dare say. What is the source of all identity in any enterprise? Who knows, who is getting hired who is moving teams and who is leaving the company. Any guesses? You're not a shy [indiscernible]. Are you usually having the sharp questions and the sharp minds. So the HCM system, of course, entity vendors are basically trying to organize themselves around it but it has big limits. One big limit is there is a huge delay from hiring someone to provision access or from some leading and deep provisioning access, big question in the compliance space. There is even a bigger one when you think about AI. Who knows what the person actually got hired for? What's their job? What are they working on? Who are their teammates? If you know all of that, what can you actually automatically infer an access rights and how intelligently can you automate them in a way no one else can. If you're a software developer, what you should have access to based on the true work graph of the environment, is a huge potential. And then secondly, the reason why C1 is so specialist because it thought about it not from the old ways of the human identity, but the task-based identity, permissioning work that's happening right now at a task basis. This is what makes it unique. And this is what makes it so incredibly valuable for AI because it solves that biggest problem, how do I do contextual missioning for AI request on a real-time basis. And again, something for the quantitative side, the estimation is that only, I think, in 2 or 3 years, agents probably will outnumber human 50 to 100 to 1. That is a real growth opportunity because guess what? The physical world is limited. There are only so many workers in the world. And there is a limit to the growth what it ever can be, but a digital universe, it's unlimited. Again, when you think about your economic model, this is why this is so interesting because you can have indefinite economic growth because you are not resource constrained in the way you were in the higher to grow economy and C1 basically gives us the platform to be the security enforcement and identity layer across all of it. It also completes our growth model of work. In the world of AI, there are just limited ways of creating real value. One is you can provide the very foundational base model, you can conceive it as evolution, that's model training. That's the best metaphor I have. It's like evolution. It creates something with a base intelligence. And then everything material building on top of it and around it is contextualizing it through tools that you give them or you can call them harnesses, right? Things you give it to act. Data and context, you give it to allow it what's relevant, what needs to happen, what's the goal and how to do it and embedded in the real-time flow of work. And now with C1 in identity, right, we truly have a gross finance, HR and IT. We have an incredible TAM opportunity. And Zane and Aneel spoke about it. So I'm just going to skip ahead and tell you, I'm as the product person, I have it easy, right? I'm saying by the numbers. I'm not a product visionary person, and it allows me to set it actually, if our TAM opportunity is much, much bigger, much bigger than this. And here is why. We have the opportunity to go into new areas that no company, not even Workday could actually enter the way we can now. Just think about employee service, all types of employee service delivery. [indiscernible] that was a question of expensive software and shared service centers. Sana Concierge and our Self-Service AI already reflects more than 25% of HR cases today. and gives back our customers so 20% gain in productivity. This is just the very beginning. With C1, you saw how we extend this into all of their [indiscernible]. We're going to do this on all of the finance scenarios as well. This is going to be a hyper converged AI service center that you're going to provide. Secondly, we are going to really bridge the gap from work about work that get people actually frustrated and also gets in the way from doing value-adding work in their companies through auto pilots to a place where actually people can work all of their time and what it takes to drive the business forward versus alignment, coordination, management alignment, reviews, et cetera, et cetera. It's a massive simplification of work. And if you heard yesterday, [indiscernible] had basically right now, companies are regaining control of what they were actually meant to do because all of that mundane work is being automated away. Talent acquisition is the best example where you can actually see how that transforms in a complete agentified way. And last but least, finance. Finance is an annoyingly efficient function, right? So when you think about total spend, right, of a company, finance is tiny. But this is not the opportunity in finances. How much money do companies lose through compliance spend T&E is the biggest manageable expense line that exists huge problem. How much money is lost on incorrect payrolls? You would be surprised. So the economic potential for us in finance is that it's not about making finance less. But actually having all of those financial controls being applied continuously and in real time, and actually having that perfectly executing company at any second, not just at period close. So I know I said a lot and I'm going to give it now over to the demo team, which is going to show you examples of all of that. But you've got to remember when you think about Workday, Workday wins in all ways. Whether you're accessing our platform for age and ready tools and Cloud or Copilot. It's our AI APIs on our Flex Credit consumption models. If you're building agents and you want to run them on safe enterprise guard rails and you deploy them into Workday, you are sitting in our run time and again, right, Workday directly is able to partake in the value creation. And last but not least, if you're going for the full, and there I say the best experience for HR, finance and IT, you're using Sana, you saw how transformative it is. And all of those 3 things come together in one agentic workplace, again, right, that they be partaken. So this is really the key, right? No matter what path a customer chooses and maybe I'm telling you a little secret here, there won't be a single clear-cut customer who's saying, I'm only doing 1 or 2 or 3. Customers are doing all free simultaneously for various different use cases. And the greatness of our model is that we are part of all of them. So I said I keep it short. I hope I could share with you the most relevant pieces of Workday's strategy. And now I would like to hand it over to Joel and Charlotte, who are going to show this to you, [indiscernible]. Joel and Charlotte, over to you.

Thanks, Gerrit. It's fantastic to be here with you all. I joined Workday through Sana. I now serve as the Chief AI Officer. And [indiscernible] was really quite simple. The company that owns the workflows is going to be dominating the world of AI. And since we brought the sort of start-up spirit and frontier of AI in Sana, with the enterprise readiness of Workday and now rolling it out to thousands of customers around the world. Together, we're moving from AI that is simply responding to questions, copilots, if you like, to agents that can safely do real work out to pilots. I'll show you what that means for some of our customers and why I think this creates a significantly larger market for Workday as well. Workday's opportunity, of course, starts at the core, but it doesn't end there. AI is helping us innovate faster across financials, payroll, recruiting, and planning. And I think the larger opportunity is really transforming the work that is around all of these systems. And that's really where we're focusing a greater share of our R&D. Most of the enterprise AI systems today here sort of co-pilots to sit on the side panel, answering some questions, maybe supporting you with individual tasks. We call that single player AI. Our agents can coordinate and perform entire workflows. They anticipate what needs attention, the advice on the best path forward and they can act within company policy and controls, can actually get work done. So this expands our TAM truly beyond the software budgets into the labor and services spend attached to that very workflow. And I think this slide does a good job at showing why this opportunity is so large. Across each one of these categories, we already serve thousands of customers, more than 80 million users under contract. So we aren't entering these workflows from the outside. We already have the customer relationship. We already have all of the operating context and every repeated workflow is an opportunity for our agents to do more work. And that creates measurable value for our customers, but it also gives Workday multiple paths to expand within our installed base. So I think this is really where Workday has a real advantage. We already understand these workflows. We all have the context across people, money and processes to get work done here and we operate the systems where all of the action needs to happen. And this really enables us to have agents that do real work. They don't answer simple questions they actually execute the entire workflows. Customers get capacity to move faster and Workday grows as more and more and more of these agents run through our platform. Let's just take HR and IT service delivery as an example. It's high-volume work that causes HR, IT and multiple systems, but our customers are spending tens of millions of dollars on outsourcing firms on. Today, this market still starts with the ticket. So in May, we introduced the Self-Service Agent as well as Sana for ITSM. And today, we're bringing all of those capabilities together around our new offering, Sana Concierge. This is a new way to deliver employee service across HR and IT. And I think it's really delightful to use. Employees can get help with onboarding, live, payroll, access, IT support all of that in a single place. As an employee, I just type what I want and Sana takes care of the rest. So instead of these employee systems, creating tickets, sending that to a person, Concierge is built to complete that request itself. It's the patterns that we've seen applied to customer service around the world, but now working for you internally. It understands the employee. It understands the company's policies takes actions across Workday, but also all of the connected systems around it. And when judgment is needed, it prepares a case for a specialist. So with the history, supporting context and the next steps already serve their a specialist can approve it. And this is very, very difficult to replicate without work this context and processes. Customers get fewer tickets faster resolution and lower support costs. And this is a radical change. We already heard 25% increase in resolution rates across Self-Service Agents. We're only getting started there and tens of millions of dollars folks are spending on outing spend here, and we think we can make a significant cut too. But also employees become productive sooner. I'll show you shortly what that looks like but for Workday, this also creates a much broader sort of premium offering for our customer base. It also creates recurring usage across the employee life cycle. Actually just saw a twit before I came up here and with someone about our new user experience and said, "Oh, damn, will start loving to use Workday now." And I think it's a pretty good proxy. People love coming back to our product experiences now. We're bringing a consumer-grade DNA to our product experiences and customers can't stop to use them. So we have a single employee that now can instantly onboard to the company. We have HR and IT service delivery that can run on out the pilot. It's a really incredible experience, and I actually want to show you like what it all looks like. So let's start by an employee getting onboarded here. So historically, this would have been an employee jumping across dozens of systems, creating a bunch of tickets, escalating that to humans, a human resolving taken a lot of back and forth. Here, you can see the onboarding experience with Sana, it's all set up from day 1. Sana, it looks at all of your systems, what you will be working on and so on and sets up your plan. So here, for example, you can see the Sana and learn experience has created all of the courses and the training sessions, already scheduled them for you and what you need to get done. Sana has already gone ahead and scheduled the knittings with all of the folks that you need to meet your first week, your manager, your direct reports and some colleagues that you will be collaborating with. And also, through Sana for ITSM, as you can see here in Concierge, it's ultimately all of the setup of all of the tools that you will need access to. So on day 1, this is all set up for me and creates a very delightful onboarding experience. But I can also just go straight to the Concierge experience. And this is literally a concierge. I can type in whatever I want access to. So for example, if I need access to Cloud, Sana will route that, look at the approvals required for this, whether it's safe to provide any access. And it looks like this is all good. So I can just hit submit and Sana will give me access to this system instantly. So that would have historically taken a lot of back and forth, created tickets being escalated to a human was now run by out the autopilots. And that's what we in by autopilot, autopilots can actually get the work done for you. But we've also been really pushing on the user experience. So what we've been working on here together with the team at [indiscernible] is dynamic UI. This is instant UI. This is UI that's literally generated in hundreds of milliseconds. And Workday is the first company on the planet to bring this to the market. So here, it means I can write something like what's my first paycheck, Sana will reveal that for me instantly. I can type a query like who is Victor Miller. And it's so fast and it feels so obvious that you barely notice when it's being generated. Here, I type something like whose Victor Miller and it pulls all of the details for me instantly but it can also create these richer components like a calendar view. So I jump in here, and I want to schedule a meeting with Victor, it sends that out. And for frequent workday actions, such as filing a PTO or a similar task. Sana generates the calendar schedule pre-populates all of these different fields. So it all looks good, and then I can send out that request. So as you can imagine, a lot of companies are building conversational interfaces to solve this task. For very few tasks, that's actually the most efficient UI, UI augmented AI is really powerful, and we're really excited about the product experience that we've created here. So since joining, Sana is entirely reimagine the user experience. We brought a lot of new agents to market, and I couldn't be more excited about this progress. But to share a little bit more about what we've been working on, I want to hand it over to Charlotte. I'll see you soon.

Thanks, Joel. That was what our new experience looks like for our employees. But what we need to also focus on is how we're looking at new decisions about the workforce itself. Right now, I'm going to play the role of a CHRO, and I want to review our hiring plan for the next quarter. So I simply ask Decision Intelligence. Once I do a quick refresh. Sorry, everybody, live demos. So want to review my hiring plan for the next quarter? So I'll ask Decision Intelligence to help me understand what's happening. The current plan includes 50 new hires. But I need to know if that's what's right for the business, do we still need those 50 new hires? I can ask Decision Intelligence to understand based on the work we know about the team. Now it uses our work intelligence capabilities, understanding the tasks and the skills that we have within our workforce and identifies the work that can either be automated or augmented by AI. From here, it's going to find out any duplicated work. Underused capacity, Skills Cloud will show the capabilities of the skills that we already have and with external connectors, we can bring in third-party benchmarks as another source of context. The answer is we don't need 50. We actually need 42. That will reduce hiring costs without relying on a simple headcount cut. So I ask Decision Intelligence to update the plan. But before acting, I want to understand the broader impact. Decision Intelligence actually remains that we even fill 9 of these roles internally. It searched my entire global workforce for suitable employees. The employees that have the skills we need, the experiences and have even indicated those career interests. Now this reduces the need for external hiring. It creates meaningful growth opportunities for employees and my plan now actually reflects the skills, capacity and the actual work. This is far more precise than a traditional headcount plan. But every workforce decision has a financial impact. So as a CHRO, I decide to share this plan with my partner over in the CFO world. We'll now move into the [ SG&A ] experience once this is shut out. So I share this with my financial adviser, [indiscernible], this is my CFO, and I'm going to be ready to review this. However, from a CFO perspective, this is the decision intelligence is bringing the same data. There is no copy and no sync between HR. HR does believe that we could fill these roles internally. But from a financial perspective, I want to actually understand the impact that this will bring. So from there, I noticed that we see that we have faster internal hiring recommended. And this could mean faster revenue growth. So I ask that same Decision Intelligence about if we fill these roles internally, what is our revenue potential. And it's going to model that outcome i.e., stock rates, ramp dates and finance is now using a model that is no longer just cost data. This information includes people, skills. It's got the ramp time to the different roles. And it's even bringing in pipeline data and demand data from Salesforce. Decision Intelligence analyzes all of those inputs. It shows that we can actually support our higher revenue target and this target is based on roles that we can actually fell. It isn't just based on hoping that we hire faster. Now I want to look at the information behind this. HCM provides the skills and the interest but I can see what the handoff start to happen. I see [ Peakon ] is providing the sentiment insights and confidential comments, allowing me to better understand my employees. Financials has brought in the actual cost Salesforce provided that pipeline and the demand data. And normally, this type of amalgamation would take weeks in several spreadsheets. Now I have one with the sources actively identified and clearly traced. I now have a plan that by business can execute. Decision Intelligence doesn't write back anything on its own. It waits for me to review and approve the changes. Once confirmed in the working plan, the approval train will always be visible. All of Workday security and approvals remain intact. So what did we just do? We analyze the data, we made a decision and we recorded it. That is the value of owning the full decision loop. But as AI companies as AI helps company move faster, financial controls must keep up and traditional controls often test a sample after the fact. And by then, the damage may already be done by an issue that was caused. Financial test suite continuously test transactions as they happen. Here, I see it's examining the work completed by people and agents. And in this example, it reviewed 90,000 transactions but it's identified a small number of risks that do require investigation. Let's start with [ PercutaPay ]. Here, I see various tests covering several payment risks. These tests include duplicate invoices and cutoff issues. We can also now evaluate actions taken by the agents themselves. In this case, this test reviewed more than 400 actions by an AP agent. However, these same controls applied to people now applied to agents. But I want to really investigate this payment change to bank account details after they were changed. And this is where I noticed that there were suppliers, some suppliers that change the bank details shortly before payment. The agent identify the risk, place the payments on hold, and now I can either [indiscernible] the hold, cancel the payment or investigate further. To do this, I can see the transaction lineage. This shows who approved and who initiated each step. From there, I can quickly see what changed, and I can also confirm whether it was within our policies. But based on this, I can decide I might want some more coverage for these high-value financial transactions. So I can go to my test library, where I'm able to see tests that come from both Workday and our third party partners like KPMG. KPMG here is providing a 3-way match exception detection, allowing me to do that as an AP cutoff. However, based on what I just saw, I really want to focus on building out a test for segregation of duties. And now we see a test is provided by Workday that will check whether or not one person can change, think details and pay. Definitely something we shouldn't be allowing. That is a combination of admissions that introduces significant risk. So I ask the financial test week to build out this and configure this new test. Before publishing, I'll be able to review the methodology that was used in order to build out this test. And I can even see a preview of the people and transactions that were flagged as a result of this test. Now I noticed that there are some responses, but the agent also anticipates and advises some changes we can make to this financial test that will improve the outcome. So I accept the recommendation and rerun it and see the new results. Once I'm satisfied with this test, I publish it available to the financial test suite. And already within this, we see the results and it immediately begins running across all of our transactions. This makes continuous compliance possible before an external audit. Now let's look at a very different process, talent acquisition. This makes Workday a major gateway between employers and talent. However, as recruiters, it still requires significant administrative effort. This includes screening, sourcing, scheduling and all of the related follow-ups. The talent acquisition agent changes that. So now recruiters can focus on the people and the business of judgment. Here in our talent acquisition agent, we see a new role that was created that didn't previously exist, such as our AI safety engineer, normally, this information would wait until I logged in. Like most recruiters, I am managing a lot of different things. The talent acquisition agent doesn't wait for me. It continuously runs and scans every open requisition. Now I see that it's flagged a potential hiring risk. The AI safety engineered doesn't have enough candidates in the pipeline to reach a potential target higher date. The agent diagnose the problem before I even look at the requisition. And it's found out some previous applicants and members of our talent communities that I can reach out to. These could be silver medalist or the second place run up in a previous requisition or we can even utilize external partners such as find them and hire [ EZ ] as a sourcing opportunity. So now the agent has drafted a potential reach out to 15 candidates. At this point, I can review and use my judgment to approve it. This is hours of sourcing saved and now becomes more focused on a review. So after I approved that outreach, I see that applications have started to arrive, very exciting. It helps me identify the strongest candidates faster. I'll be able to see and understand why a candidate is a great fit. This is bringing together our higher scope paradox and recruiting capabilities all in a unified experience. But importantly, I also see the gaps and why they might need more attention. However, I think this candidate looks pretty good. And so the recruiter is still going to be responsible for the decision. I move this individual to into you, but the agent has made this decision easier and better involved and now the agent starts the interview process. It checks availability from the candidate and the hiring team. It sends the invitations and handles any of the rescheduling. That removes a large amount of administrative work. So during the interview, it means we can focus on the most important thing, the human connection that still matters the most. So we start the interview. And when it's time for the interview, the agent is there. Doing the interview is the actual fun part. Writing the feedback that may be not as exciting. The agent steps in with prepopulated responses based on transcripts and notes, I'm not starting from scratch. This gives recruiters and hiring teams, the time they need back in their day, so they can focus on their teams and the interviews and removing the administrative burden. This interview then becomes another signal about the candidate. So from here, I'm able to see and understand more about the agent signals and what they're using for candidate rediscovery. It might decide to rediscover this candidate for another role in the future should we not move forward with them. The organization also means in control of all of this. In the control plane, this defines what the agent can and cannot do. It also defines what must wait for a person. This means the agent moves quickly, but within the boundaries that are set. Now we transformed the recruiter experience. But we've also made applying easier for the candidates themselves. People are now discovering jobs in entirely new ways. The application experience should meet them where they are. Today, I'm going to use ChatGPT to look for a new role. Little awkward with my entire leadership team currently watching me to apply for a new role, but we'll do it anyway. The candidate profile now gives me one identity across Workday hosted applications. I no longer need to create another account for every single employer. I verify my identity through a quick text exchange. Sana then [indiscernible] role, based on my skills and who I am as a person and the experience that I might want to consider. From there, I can see how well my skills fit these particular roles, but also where it may have gaps. At this point, I decide whether or not I want to apply. I answer any of the in-line question is and the application experiences right in the flow of that work. And then from there, I'm able to directly submit my application. There is no longer a long form of fields and candidates and passwords to remember. This ultimately can help strengthen candidate pipelines by reducing candidate drop-offs. So you see in a lot, but I would like to pass it over to Joel to close out a little section.

Thanks, Charlotte. Wasn't that really good? I mean you just saw how we're completely transforming the work across all of these workday processes. So of course, as we do this, we drive tremendous efficiencies for our customers. But I also think there are many illustrations of how this is opening up entirely new sources of TAM for Workday. In recruiting, for example, as you just saw, we are completely changing the economics of recruiting. Today, we have over 400 million applications on the Workday platform just last year. And this creates an opportunity for both Workday and our customers, where our customers can continue owning the relationships and all of the hiring decisions but the agent takes on more and more and more of this work. And the more work that our agents take on, the more our opportunity grows. It's no longer based on the number of users on the platform. This opens up a labor pool of more than $100 billion just on the recruiting space alone. So these examples represent a much broader road map for us. Our agents can anticipate, advice and act across many workflows and over time, they take on more and more and more of this work. Of course, it starts with these individual tasks, but then it expands into the continuous coordination across complete workflows. And where we're focusing Workday is where we have that differentiated context, where work happened repeatedly at scale and where that customer outcome is measurable. Workday against new products and more platform consumption as a function of this, but we also get deeper engagement inside of our core applications. So today, we showed you some of these examples of where Workday can transform workflows directly I'm a very simple man. And my thesis here really is we own the workflows. We build agents that can deliver on these workflows, and we expanded the TAM to take on labor as well. It's an incredibly exciting time to be building AI at Workday. But our customers also need an open platform that preserves this governance. So I want to hand over to my good friend and our CTO, Gabe, to show you what this looks like across the full ecosystem. Over to you, Gabe.

Hey, everyone. It's good to be with you today. And I'm excited to dive into some of the coolest tech that is, I mean, really transformative. I mean, it's such a special time to be in the industry right now. And Gerrit had a point that I want to pick up, which is enterprise AI only works if it's lawful, not lawless. And that means running inside of enterprise rails versus around them. Now what happens when something like that could go wrong, when you take a sneak peek at some of the creative. [Presentation]

Funny, right? But AI that's almost right is wrong. It's true, right? You can't get payroll right 99% of the times, it's got to be right 100% of the time. And this is true in a lot of different scenarios in the space of HR and finance. And that's why Workday's role in this transformation is so critical. It's also why Workday is evolving from a system of record that you log in to at your desktop towards an open platform that you can build on and that you can easily extend. We call it Workday's Trusted Agent platform. And it's differentiated on 3 core points that drive our strategy and drive our platform business. The first is that we're open by design. This is a key to our monetization engine. I think Aneel and Gerrit both mentioned this. and we'll talk more about it. The second is it's backed by builders. It's important because everyone today is a builder. Natural language has democratized the art of engineering these systems, right? So we want to create that product like ecosystem and flywheel. And the third, and this is really critical, and this is something only Workday can do is we are lawful by default, and this is our unmatched competitive moat, and we're going to cover each of these. So let's start with open by design. And if you remember nothing else today, I think this is the one to remember. And the exact path Gerrit mentioned, this is the 1 that lets customers connect their own agents straight into Workday. Now again, Gerrit talked about this, but it's really important. A year ago, we stood up at Rising, and we promise an open platform. Today, our trusted agent platform makes that real. What does that mean headless access to HR and finance data. Every capability required to do that is now live in GA. And the cool thing to me is we're customer 0 for this stuff. So if you take a look at the demos that were in the innovation keynote, all the stuff that Joel just shared, all of that is powered by all this headless technology, right? We're building on top of the same stuff. But it's also a major monetization shift for us. This is where we can create a brand-new product-led flywheel that's going to drive platform consumption and really extend the growth of the business. And I mean, look, we don't have a lot of clarity on what this looks like yet because it's still early days. But we know there's tons of demand out there for folks building, whether it's personal agents or different ways of driving the headless business. It's really a big demand. And so to kind of walk you through how this works and some of the challenges, frankly, that we see with this, we're going to walk you through a company that we set up here, [ Acme Industries ]. And I want to be very clear, this is modeled after the very real Fortune 50 customers that I see on this headless journey. Now meet Maya, she's an Engineering Director for HR IT at [ Acme Industries ]. She's you're going to do a very common task, right, building an internal mobility agent, very common thing that we see from our customers. So you could do it the way that we see a lot of customers do it. Own stack headless. You need a few things, way to pull her open job recs, a way to move people between different roles. And all of this critically, and this is the part of what you guys pay attention to is it's got to be scoped to what Maya can see and what Maya can do. There's a couple of ways to go about this. And before I show you the right way, I actually think it's more instructive to show you the wrong way first because this is where I think the market has it wrong. I know certainly some of the customers have it wrong on this. And it's wrong in the sense that people think it's safe to build agents on custom MCP engineered solutions and or data that's kind of extracted out of Workday and put into some external data lake. And I want to show you guys, this is not safe. This is actually causing a lot of problems. It is lawless, as we say. So let's show you head list the lawless way. So I'll move over to the demo here. And what we're going to start with is here, we're obviously in cloud. So we're going to think through this internal [indiscernible] agent. But first, I want to show you, this is the pattern. So Acme Workday MCP. This is not Workday. This is built by Acme. By that, this is a true story, okay? I have lots of customers who are going about it this way. Think of it as like they're creatively taking a way around our guardrails and our path here. Well, a lot of them don't mean to do that, but they are doing it. If you take a look at what's inside of the MCP server, we got some read only tools, we've got some right tools, should be enough to execute what we want to do. So let's just try out some of the just required to build this agent. So I'll paste in, share me my open job recs, or sorry, show me the job recs. Now the word my is doing some work here, right? My open job recs refers to Maya, okay? Now you're going to see we get 30 open job recs back, okay? So that's first part. So it looks like we got a tool came back. Second part is let's move KC on to my team as a senior engineer. These are the building blocks of internal mobility. So this works, we can probably build a whole agent that does this. And you'll see that we're submitting some stuff, and it looks like it's done. Now here's the problem. This was supposed to go through approval chains and workflows and business processes that we've set up that customers have set up inside of the tenant. None of that actually happened in this case. So this is a big, big issue for that, okay? Now let me show you another problem here. This is kind of the other pattern that we see with our customers, which is I want to take the data inside of Workday. I want to extract it as like a snapshot, put it into a different system like Snowflake, which is what we see here. So you can see we've got this Workday snapshot. We have all these tables. We've got all these different table here, all the different data inside of Workday. So it's quite helpful to have this data in a lake. And you can see here as our job requisition feel. So we should be able to ask similar questions. Let's try it again. Show me my open job recs. And we're getting 30 back. And what I'm noticing here is this is actually account executive roles. This is the sales team. These are not the right roles for this agent. This is actually a completely permission agent because remember, Maya is in the engineering department. Why is Maya [indiscernible] roles from sales, marketing, HR, this is actually not what we want. So just sort of a crazy example here, but we'll go paste something else in, what's the CTO salary just as an example. I mean, this definitely not come back. We take a look and here it is. It's coming back. And I'm sharing this because this is very real, right? When you extract the data out of Workday put it into a data lake. All of the rules come off the data. And so these kind of things are not only plausible. This is, in fact, how this looks. Building agents against us is very risky. So pulling an agent at the lake. For a lot of customers I talked to, it feels like the obvious next step for their data strategy. But what it's really doing is it's quietly turning a governance system like Workday into an ungoverned one. Now I do want to be clear, data lakes are awesome. They're actually great for lots of things. They're great for joining your other enterprise data with Workday data. They're great for reports and analytics at scale, RAS reports, things like that. But hopefully, you saw in that example that you can export the data from Workday. You can't export the rules govern the data. Maybe you could try and rebuild them, but that really doesn't work long term as a strategy. And until today, we haven't really offered our customers a great answer to this dilemma. But with that, let's see headless now the lawful. All right. So we'll move back -- and this time, we're going to start on the data side, this is in tableau, and we're going to take a look at this connection. You could see there's a client ID in secret. I know you guys probably don't quite follow exactly what this is. This is Maya, logging in as Maya, okay? And getting low level access to Workday data through our live data query product. Now we're going to ask that same question, paste it in here, show me my open job rec. And what you're going to see is we're executing that query. Now we got a different response. We've got 16 open job recs. And I can tell by looking at these that these are actually scoped to engineering. So these are the right roles. So I said before, you need to see what Maya needs to be able to do what Maya can see. So this is what Maya can see. Now the other cool thing and this is something I hear so much from customers is look, Workday has this unbelievable object graph that has been the secret to our succes for so many years, it powers all of the 20 years of HR and finance success that we've had at Workday. But it's also been tough for our customers to get raw access to this. So what you're seeing here is a SQL query that select statement there. And this is something our customers have been begging us for Developers get raw open access to live Workday data without making a copy that leaves the rules behind. All right. So let's move on to the MCP front here again. And you can see this time this is the official Workday MCP server, what we call our agent ready tools, now generally available. We'll go ahead and connect this up. And the first thing you notice is we got to log in, okay, because this is going to be done as Maya. So we're going to sign in as Maya, and this is now connected, then we'll go ahead and start some of these props again, show me my open job rec. Again, the identity piece is so critical to this to get right. And you can see that we're getting 16 open job recs, these are all mapped to the engineering role. And again, if we go ahead and move Casey, we're going to see a better outcome in this case which is a not only are we going to find hopefully right, Casey, which we did who's a software engineering data platform. But what we also did is we started an approval process and workflow versus just executing the approval right away, which is what often happens in some of these other failure mode cases. Now the other thing I just want to point out because this is the really exciting piece is check out how many tools we have. Look at this list of tools about 400 tools. We have read tools, right tools, I'm not going to read through all of these, but this is the surface of Workday. By the way, we're constantly expanding this list. Customers ask me like, how many tools, like when do you get to done? Honestly, I don't know where ever going to get it done. There's always going to be new combination tools we're going to build for customers. So incredibly exciting potential here through the headless enablement at Workday. And why is that will head list used to force a choice between speed and control. And now today, that choice is gone. Now lots of great capabilities, but let's talk for a minute about what headless enables, what it enables for our customers. If you think about Workday, in some ways, Workday represents your company's institutional memory. I like to think of people and money data as like the base of enterprise needs, right? Everything in the enterprise from a data perspective, it all derives from your people and your money and all of that context, you'd be locked away. Starting today, any agent that customers build anywhere can get to use that context and use it safely as defined by our guardrails. Now what can this unlock, lots of things. Just a couple of examples that come to mind. Field operations, imagine an agent that is able to assemble a field crew, the perfect crew, right, the right experience, the right -- based on performance history, kind of pull that all together, do that without any data leakage, really exciting. Maybe on the R&D side, even closer to my world, the agent could, let's say, model the financial cost of a team's big new bet, right? We're going to go build something crazy and do all of that right inside [ Jira ] where the engineering team works or maybe retail operations where the agent can match staffing needs directly to point-of-sale traffic, balancing real-time availability with exact cost center performance. This context changes everything for our customers. When they have access to this headless [indiscernible], who knows what they're going to build, who now much usage and demand this is going to drive. We honestly just don't know because this is so early. But my bet is, given how foundational authoritative people and money datasets workflows the demand for this is going to be incredibly high, and we're already seeing signals of that now. Now to bring this all together on open by design, Workday's now have us, agent ready tools, GA tons of momentum on this. The demand is just incredible for this, as I said, 500 tools at 4-ish, 500-ish. We're shipping about 130 of these a month, by the way. So the momentum is really accelerating in terms of our ability to churn these out cover more surface area of the product. And by the way, I do want to mention, I think this was hopefully noticed in some of the news, but we have been partnering closely with Anthropic and some of the other AI labs on this MCP surface. They are our customers. And so there's not only like the fact that we're shipping things, but that they're getting used by the likes of Anthropic is, I think, an important proof point. Now Workday Data Cloud. Also, GA, live data [indiscernible], that capability I showed you before, SQL access to the Workday object graph, this amazing piece of technology that is propelled Workday for so many years now GA, right, live direct SQL access to Workday data with the rules built in. And the feedback from EA customers is, look, they want options. As Gerrit kind of laid out those 3 [indiscernible], they want to be able to choose the right path based on the right use case, right? Some paths are going to be headless, some paths are going to be subagents, some paths are going to go through the Sana workspace and some of the unbelievable end-to-end product that you saw in Joel's demo,right? And like we win when our customers win, and that can happen on any path. Now shipping tools is only part of the job. The other part is making these tools effortless for folks to use because today, everyone is a developer, every one is a builder. And I'd like to reflect on the AI companies that I see, right? If you go to any of these big AI companies, when you -- you get to sign up for the products, you get to use the product. You don't have to talk to a salesperson first. And I think Workday has really kind of thrived in that prior model, but that's not the future. The future is log in, sign up, get an account. And that when we do that right, it fuels a product-led flywheel that will drive pipeline on the sales-led side. There is the 2 key updates that I want to share on that front. Starting today, you can get a free account on developer.workday.com, and you can also get instant access to a developer tenant. And that allows you to basically probe on the MCP surface. Fun little fact, we did this [indiscernible] keynote today where we shared this news biggest applause line of the day was actually this, right? Because folks who try to get access to Workday the tenants know it's been pretty frictionful right? Trying to get a tenant. It's also been expensive frankly, for Workday to provide these, right? Because these systems are architected for pretty large scale. And so we had to actually build and engineer a new kind of lightweight small-size developer tenant to create this product led flyway. But the net result is you can now go to a developer.work.com, sign up, get the tenant, build your headless app, promoted into the marketplace and probably do this with whatever tools you want to use, right? So the cursor cloud code, VS Code whatever. We also have our developer agent, which is our prescriptive way of building agents. They give it as like the guided path for building agents the way that we think they should be done. That product has been a rapid success. Since June, 125 customers have built 500 agents and 6,000 forms-based apps with developer agent. In fact, I have one customer who built 25 agents over a weekend, this is a media customer, totally insane velocity that we're seeing. Now what does this mean for financial levers. First, on the ecosystem front, we have tripled our developer community in 2 years. A lot of momentum here but we are only [indiscernible] to begin. We believe that with this product-led motion, we're going to be able to 5x our developer ecosystem from where we are today. The second is marketplace expansion. There is still a huge need for content on the marketplace that is areas of the product that Workday kind of first-party road map is not going to cover. We think there's a great opportunity for developers, whether they're affiliated with a service integrator or a major partner of ours or maybe unaffiliated to build apps to monetize those apps and to part in our ecosystem. And thirdly, there's a need for a global talent pool. And we hear this a lot. Workday is obviously a global company, but we need help. Our customers need help implementing Workday, deploying custom AI, headless integrations, building advanced workflows. And we need to make sure that there's a base of talent who understands Workday worldwide and these solutions that we're describing a real that global talent community. So product-led flywheel, developers, marketplace adoption ultimately long-term pipeline. The final pillar and the crux of that commercially, you folks just saw, and this is really the secret sauce. I mean, this is the moat right here is lawful and the ability to map to our guardrails. Anybody can wrap in LLM, I think that's pretty clear at this point. Nobody else has 20 years of HR and finance logic that is driving core business workflows for companies at the scale and import as Workday. It's just a fact. Agents with a capital L sorry, our lawful with the capital L when the reasoning and the execution run inside of the platform that governs it. In other words, the reasoning has to sit next to the system of record. And I'm excited to share the work to agent harness, which is what does this is now GA, the trusted run time for enterprise AI. So walk into some of the details on this. Now I want to be clear, this is unlike MCP, some of what you saw before. This is about keeping the execution and reasoning on Workday, sort of the token path, what we sometimes refer to as the better path. This underpins every agent in Workday, by the way. So every demo that you've seen, all of it runs on top of this agent harness. What does it have? Memory, guardrails sovereign routing, sovereign governments, I talked to a lot, they need to make sure that the AI is all within the country boundaries. Full visibility into reasoning and context, all of that in one place. And what we're now doing is we're actually opening up this harness like from a tech perspective and letting folks build directly against it, right, making sure that these layers are all available directly to the customers who want it. Now one point I want to make on context just to try and pull out the differentiation here. Imagine that you're trying to find a successor at a company of about 40,000 people, okay? 40,000 folks in the organization. You need to churn through, I don't know, 10 years of people and performance history, that's going to be about a gigabyte of data. sensitive data by the way. And if you're using Cloud or Gemini outside of Workday, you're going to have to painstakingly extract a gigabyte of context across to some other harness tool call by tool call, pulling a gigabyte of data, data that probably shouldn't be leaving Workday, by the way, and send it over the public Internet, by the way, right? With Workday agent harness you don't need to do that. The context is ambient. Nothing moves because the system of record is right next to where the reasoning is happening. This is engineering physics, and this is part of Workday's structural moat. Now why is lawful by default giving us a moat at some other pieces here. LLMs lack business logic and permissioning. Nobody else can match executing inside of Workday. We also have platform stickiness, right? This capability, these capabilities agent harness, for example, in [indiscernible] our core HR and finance workloads while driving high trust AI. And it also solves the #1 CIO we hear about, which is risk mitigation and this idea and this concept of lawfulness, I think -- or sorry, a lot of listeners. I think when folks start to see that they really perk up because intuitively, they understand how much risk there is here. So a few takeaways for you, open by design, new platform consumption revenue backed by builders, this hybrid sales-led and product-led motion. Ultimately, our ambition here is to scale that ecosystem I referenced by and fuel pipeline in the process and lawful by default. No one can match our HR and finance core that really drives a lot of the differentiation from the agents that run on this platform. Now we're taking governance even further I think you've all seen the C1 acquisition. Fun fact about this, we actually surveyed our customers about this. And Gerrit, you have a small-size survey, but 70% of them told us that they want HCM and Identity Governance combined into one system. I'll never forget, I was on the phone with a mining company and they told me -- the CIO told me it was, "Why are you making me buy a [indiscernible] system for this? Like this should be one system." And I took that as many months ago, and I was wow, this is -- there's something really here because identity is so central, as you can probably see to sort of this lawful lawless piece. It's all about identity at the end of the day. And Workday, as Aneel has said, is the upstream provider. It is the source of truth from which identity derives. And so we're doing that today for humans, but as we look forward to AI, when we look at the growth of nonhuman identities, they're going from about 1 to 50 to like 1 to 70-ish. In other words, 70 nonhuman identities for every single human identity. That's the projection over the years to come. This acquisition puts us in a really incredible spot to start to capture value around how can govern that in conjunction with the rest of the org chart and the rest of the business processes that we provide on the HCM side and do it all with one solution. So super excited. Together, we're going to become this identity governance and security enforcement layer for not just people but also for agents and do that across the enterprise. So for customers, what this concretely means is no more accounts with keys to the entire enterprise universe, right? Permissions get rightsized to exactly what's needed, exactly when it's needed. One platform, one set of rails. That's the platform opportunity. And I think with that, we're going to hand it over to Rob. All right. Thanks, everyone.

Hi, everybody. Good afternoon. Great to be here. I am so excited to be able to take what you've just seen to market. And you can see from the keynote today, customers are really, really excited. So I'll walk you through our go-to-market motion, how it's evolving and share a closer look at our customer journey as they adopt our AI solutions. So let's just jump right in. As Zane and Gerrit, our total addressable market is substantial, spanning both new customer acquisition and expansion of our existing customer base. And on the ground, I can tell you, almost all of my conversations, our focus on AI APIs, data cloud and how this connects with finance and HR. To capitalize on opportunity we're seeing, we're sharpening our go-to-market focus and investing across the following key growth areas: customer base, AE, COE, medium enterprise and global growth. Customer base expansion remains our single largest and fastest route to revenue. As Zane shared earlier, we are on track for AI to [ repin ] over 50% and of our incremental customer base ARR for this year. We're committing substantial go-to-market resources to capitalize on the deep trust platform stickiness we've built with existing customers. And this investment is broad-based within our customer-based team, but it is strategically weighted towards larger customers. This is where we see the highest expansion potential and the most immediate predictable pathway to cross-selling and growth, particularly as AI adoption accelerates. We're not just adding sellers though. We're also adding specialized COEs to incubate, scale and mature our growth products. Our current areas of focus are on Sana if you didn't get it, talent, platform and spend across both organic bills and acquisitions. The COE is partnered with account executives and work across the complete go-to-market motion, selling life cycle, driving real execution, deal execution, strategy, enablement, evangelism, demand generation, marketing and customer insights. Our 4 deployed enterprise architects work closely with our customer pilots during the sales cycle to activate the agents and accelerate enterprise AI adoption. Partners are key. We're empowering the GSIs such as PwC, Accenture, Deloitte, KPMG, to build differentiated IP and AI offerings that reinforce Workday's core specific to industry, personas and geography. Over the past year, we have sharpened our focus on the medium enterprise segment First, we focused our EMI upmarket land team on the category of 500 to 3,500 employee segment. This is our sweet spot. And we've been more intentional about this. As a result, we've seen improvement in win rates and an increase in net new deal sizes nearly 20% year-over-year. We also simplified the packaging and implementation model and expanded Workday go into more than 2 dozen markets. We're seeing good momentum as we continue our rollout and expansion, including strong win rates and faster deal cycles. In Q2, Workday Go volume increased more than 5x over Q1. And within the ME customer base team, we have increased our capacity and aligned our teams with the COE sellers that I mentioned earlier. This is driving nice uptake of AI solutions within our ME segment. In fact, our customer expansion rates in ME are very similar to what we're seeing in [indiscernible]. Switching gears to global growth. We're seeing strong performance out of certain markets like the U.K., Canada, Japan and parts of Northern Europe and ASEAN. To sustain this global momentum, we're taking the following actions. We opportunistically adding capacity in high-performing territories. We're opening up new markets targeted at international markets, where we are scaling our value-added reseller program. We launched our VAR program in Feb of 2026, onboarding 20 unique partners now reselling across 14 countries. Workday's AI is grounded in deep enterprise context, speeds up the customer's part the value by combining business logic, unified data and strict governance. We are seeing strong uptake of AI across several areas. In total, 25% of our core customer base has adopted one or more SKUs, representing $600 million of ARR, and that's up just over 10% of our customers a year ago. We also see more repeat buys. This is a very strong signal of the value that our customers are achieving. And as Zane mentioned, the monetization we are seeing is driving a healthy balance, as you can see from this slide. The growing base of our customers build a flywheel of value statements that we can leverage. In addition to building commercial momentum, we are really pleased with the adoption we're seeing across our newer initiatives. Over 50% of customers have signed our AI terms of service, and we're beginning to use -- and they are beginning to use their entitlements, and we see a clear path to 75% over the next few quarters. We have more than 125 customers and partners in our EA program for MCP, which is now in GA, we've already signed our first wave of customers on the Workday Data Cloud since our launch a couple of weeks ago with a rapidly building pipeline. We've also signed 18 of the most significant brands in the world in our Sana Enterprise [indiscernible] program. These are companies that are deeply co-innovating with us over the next year and have agreed to service the references. We forgo 1 year of licensing, but we are thrilled with the partnership here and believe it will lead to much larger long-term opportunities. While much of the $600 million of AI today is sold via traditional subscription license, a growing mix is coming from Flex Credits, a usage-based pricing model we announced last year at Rising. These are typically 1- to 3-year commitments from customers that are tied to measurable outcomes, not token consumption. For example, when we sell Flex Credits tied towards a recruiting use case, it's based on the number of applicants. We are very early in the Flex Credit model but momentum is building rapidly. We have nearly 300 customers that have moved on to Flex Credits, including nearly 200 customers we closed in Q2 alone. There are a couple of things that's happening in the background that should drive an accelerated uptake of Flex Credits as we move forward. One, our first-party agents continue to mature and we are accelerating the amount of innovation we are bringing to market. You could see it year-to-date. Because of the Flex Credit model, which is fungible across our identic portfolio, the more solutions we bring to market the more measurable value our customers will get and the more appealing the Flex Credit model becomes. Two, we are opening up our MCP and Data Cloud programs into GA right now, which should also lead to increased momentum as customers leverage the power of the platform. So let's take a look at 2 very distinct customers. I think the one might be the same as Gabe mentioned, putting Workday AI to work to reinvent how their businesses how operate and scale. Our first story is about a health care company with nearly 70,000 employees. They turn to Workday to optimize their data in a runway for AI, enabling them to maximize resources and focus their dollars where it matters most, the patient. In FY '25, they adopted Hyatt score, which enabled them to develop the AI governance strategy in line with Workday's responsible AI model. And that foundation paid the way in FY '27 to add Flex Credits to get them capacity to roll out solutions, including Self-Service Agent. Ultimately, AI adoption across this journey has driven a 50% uplift in ARR. Our second story is a large manufacturer /mining company in Asia Pacific with over 60,000 employees. Initially, they came to Workday to solve challenges with manual recruiting tasks, long hiring cycles, excessive administration workloads for their managers. What started as a single HR platform deal has grown into a strategic partnership, a cycle of investing, measuring the result and then expanding. In FY '26, they adopt a Hyatt score to reduce the burden of manual task on talent admins. 9 months later, they closed a deal for Paradox to layer conversational AI and candidate nurture on top of it. And today, we are seeing continued investment outside of talent with [indiscernible], Extend AI and Sana. The payoff, they've automated scheduling improve candidate communication, reduce hiring time, cutting the time of managers spending on recruitment in half, and their AI journey has driven a 40% uplift in ARR. This one I'm really excited about. In the last few minutes, I want to talk and quickly touch on the opportunity that I and I know the teams are very excited about. The CIO is an emerging distinct buying center alongside HR and finance. This dynamic is one of the many reasons I'm excited about today's announcement. AI is pulling them into the Workday conversation in ways we haven't seen before. And this represents a huge opportunity for Workday. To capture it we'll be focusing on the CIO persona with our innovation offerings like Workday Data Cloud, Extend and the AI agent use cases. Since we announced Workday Data Cloud last year, we've moved from concept to GA in less than 12 months. We see tremendous interest with more than 1,000 enterprise customers of ours that are leveraging Snowflake and Databricks for data warehousing, and have already closed 20 deals with rapidly growing pipelines. [indiscernible] on this momentum, we're also expanding our partnership with Google Cloud and AWS to deepen integrations and broaden our reach across cloud ecosystems. The deal economics are already proving out. Platform adoption is driving meaningful ACV expansion opportunities for Workday. We are seeing growing interest in Extend AI to build both traditional and identic applications on Workday. And there are 2 different types of opportunities here. Convert the more than 1,000 customers from traditional Extend towards Extend AI. This drives a roughly 20% increase on average to what they were paying with the traditional extend license. Two, drive first-time platform customers, and this is the biggest driver of growth with Extend AI. It's also worth noting that for customers to leverage our agents ready tools at MCP server, they need to be licensed on Extend AI as the first step, and this is helping drive momentum. We are just getting started here. Nearly every customer I meet with is excited about leveraging our platform and MCP for certain use cases. Our MCP program is in GA, and I believe this creates a tailwind moving customers on to Extend AI and Flex Credits. And Sana Enterprise is a central AI orchestration engine across the enterprise. And you all heard exciting announcements this morning about C1.ai, which opens up a much broader opportunity with the CIO around Identity Governance, a top priority for many customers we believe only accelerates with the proliferation of AI. It's an incredible opportunity for us to leverage our trusted position within the enterprise and C1's innovative technology to drive adoption. So to bring it all together, the runway ahead of us is vast. We're excited about it. Our teams are excited about it. You can feel the customer excitement. We're excited about how this go-to-market motion will evolve over this year, and we're confident that this will position us to drive rapid customer AI adoption and capitalize on a TAM that's growing both the land and expand opportunities. So thank you for your time today. And with that, I want to invite back the leadership team, and we're happy to take questions. Thank you.

We are going to go around the room. I know there's a lot of people, and we're not going to get to everybody, so I apologize in advance, but we're going to -- why don't we go [indiscernible] on your side, we'll start with Alex. If you could just say your name and then make sure they can see you.

Alex? The button turns it on. Broken tech.

Thank you guys for hosting. Alex Zukin here with Wolfe Research. Really inspiring presentation kind of at all levels, the tech the interface, the modules, the TAM expansion opportunities. I wanted -- Aneel, maybe to ask you, it feels like all of -- there's kind of a big debate about headless becoming the new kind of interface layer for interfacing with enterprise workflows as much as applications or software agents. What is -- what do you anticipate those economics to look like? Some of your other kind of compatriots are leaning heavily in kind of, I think, as you guys are on the head list strategy. And consequently, they think they're able to drive significantly higher unit economics from that strategy. And then there are some others that are leaning hard in the other direction. So how do you expect this balance of trade to kind of result when you're interfacing with customers? And then maybe, Zane, for you, I know we're not doing midterm or long-term targets here. But maybe just the conviction, given the amount of innovation you've demonstrated in maybe the sustainability of double-digit growth as an aggregate kind of concept?

Sure. So I guess at the highest level, I don't think anybody really knows how it's going to play out between headless and your own front end. Now I think a lot of our competitors and peers don't have anything like Sana. So they default to headless because they don't have their own promp-based AI-driven user experience the way we do. And I'd say the other piece is we are an HR system. So we do naturally touch every employee in the company, not just a group of employees, maybe in sales or marketing or services, SHR, you touch every employee in the company. So I do think we have a different strategy. Now if people want to do headless, they work our APIs. And my expectation is we'll be margin neutral. The beauty of APIs requires a lot less sales cost I'd always like people to use our user experience. But if they don't, our guard wheels that are exposed to our APIs are super valuable. That's the real value of the underlying system of business process maturity model, org chart. So I kind of think we win either way. But I think so far, what we're seeing is customers really like our strategy where we have the user experience that's best-in-class. And that's only because of Sana.

And just to add today, we didn't announce APIs in Claude. We announced Sana in Cloud, right? So this is an [ AI-to-ei ] ntegration, which means the reasoning intelligence happens on the Sana side, right? So I think the interface is a bit of a blade and relevance. The real question is, there is the reason there's a reasoning to happen.

And I'd just add, getting beyond FY '28. We intentionally wanted to highlight for all of you the incredible work that this team is doing on these agents and the receptivity and adoption that we're seeing from the customer base. As Aneel put it best earlier today, our intention is to focus on growth and to drive growth, and we're excited by this consumption model and it there, but our intention is to lean in and continue to grow the business. So that's the intent. As Aneel said, we'll see how things play out. There's a lot of interest and a lot of adoption to come, but we feel like we've got a tremendous portfolio to drive that.

This is John DiFucci from Guggenheim. Gerrit, you said that other than coding, the AI breakthrough hasn't really happened or not yet anyway. And it took a long time for the large enterprise to really adopt Internet-based applications. And Aneel and Dave saw that coming and got into it before it happened and actually, was ready for it when it came. I guess, how do you think this market evolves for the use of AI by businesses and how is Workday positioned differently, and this is really, I think, for Gerrit and Aneel differently for this major technology paradigm shift than the last one?

Do you want to start? Should I start?

You're smarter, you start. If you haven't figure out he is smarter.

This is smart way of handling that question actually. Yes, the reason is that for the transformation to really happen, companies need 2 things, right? One is they need to actually understand how to transform their existing work, right? The success in AI is not in applying it to the work you already do. And the reason why we see so much success in coding is we code to a large degree is language. It's a very fiber problem. There is high proficiency in the models per se. There's not a lot of domain specificity to it. There is so much code in a public web that you can train models on. And it's mostly driven from a very simple business process, right, your right code and you submit it and you should review it maybe in between and then you deploy to production. So it's a very simple workflow. Now if you take any other enterprise workflow, right, which brings together multiple functions. For instance, finance and HR, you have hundreds of steps, thousands of variations, different people interacting with each other. And one key part, which is why we announced broke Intelligence today's actually getting to comprehension and to understand and understanding about how does work actually mapped to the language of AI models, which is tasks models fundamentally don't think in jobs and job profiles, that's like amorphous icing that space. They have task proficiency. They can do one thing really valid, maybe another thing. But I think in the middle, they can't do renewal, right? So you have to extract all of the things they can do from the things they can't do and maybe also the things they shouldn't do. And then you have to project this on a new workflow configuration inside of [indiscernible]. That is the fundamental step for a scale adoption and that's what makes Workday so strategic, right? Because fundamentally, you talk about an HR people and organization transformation first and foremost, right? Usually, a saying keep saying, technology comes last, right? First, you actually have to change the business and the process. So that's why work Intelligence is such a big stepping stone. And then secondly, and this is why C1 for us is such a big part of our strategy is that even if you have an understanding of where you apply AI to, now the question becomes, how do you do this in a secure, reliable and lawful way? And the problem is that none of the recipes that you have in classical European. Let's just say how we thought about ERP, enterprise resource managing, right, the builds of material and the supply chain and blah, blah, blah, right? Everything that was built for that doesn't work for AI. The unit of work is different. The cost model is different. You need to have no real-time economics based on real-time spend against tokens because it becomes such a big part of the operating expenses. And you need to think about security, right? How do you do this and stay compliant. How do you do this and stay safe. So you have to fundamentally rethink what the platform and enterprise who runs on, which used to be our [indiscernible] actually does. And this is what agent system of record, C1, right? This is why we are bringing all of these assets together because we think with that work transformation, also the business applications are going to reconfigure themselves around agentic identity, a genetic work and agent to human collaboration.

Yes. I guess the only thing I'd add to that is, just look where enterprise enterprises have adopted AI and specific to our world, kind of on the edges so far, recruiting, not super high risk, and it's a pretty contained environment. Same thing with what we're doing with contract intelligence. This next wave of agents actually go deep into the bowels of an organization, and it requires real transformation. The best place to start is at the top, which is why Sana and Self-Service Agent or the Concierge product is so important. But then you start drilling into middle. The financial audit agent, which is my favorite agent because it's going to automate the financial audit process, will be really powerful, but that will require a lot of workflow changes.