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

Okay. Welcome to our next session. Really happy to -- I need to get my questions all right. Here we go. Really happy to have Gerrit from Workday. We were joking earlier that we do it in German, but we won't.

Fellow German.

Yes. The 2 Germans talking about the World Cup next year. Just maybe introduce yourself briefly, like you have an interesting background as well, and like introduce yourself and then talk about what excited you about joining Workday.

Yes, happy to. So I'm Gerrit, President of Product and Technology at Workday, other than just being a fellow German. I worked 11 years at SAP, headed up SAP's data-based business, BI business, planning business, HANA, maybe some of you know that piece of technology. After that, worked at Google, here right around the corner. And then from Google joined Workday 9 months ago, 9 months in the making. And what excited me about Workday is that, look, the bottom line is I have seen enterprise application and how they are creating value, and I have seen how planet-scale AI systems are engineered. And for me, it's obvious what's infrastructure and what's value generation. And I think we're now at the point where the pace of being focused on infra model turns over to AI business applications. And I do think it's a time where Titans fall and Titans get created, and I think Workday is in an awesome position to be one of the new Titans. So who wouldn't want to be a part of that?

Yes. No, exactly. Yes. No, that sounds good. And the -- let's start with the core business. Like talk a little bit about the opportunities you're seeing there? And kind of what do you think in terms of innovation to kind of capture some of that opportunity?

Yes. Sounds good. Raimo, I think that's one of the big things that I personally think people are confused about because we say, "Well, Workday is so penetrated, so present and it's such a narrow space in HR and finance." But if you do just simple math, right, the TAM that we're operating in is roughly $200 billion, just do -- you're a smart analyst, do the back of the envelope on Workday's top line, right? We had 5%, 4% market penetration of that, right? So there is an incredible amount of space for us to grow, and we started to aggressively pursue that. And when you approach a market, you can roughly segment it in 3 major ways. One is expanding the portfolio. One of the big initiatives that we are driving is going into frontline work. So roughly $3 billion work on the planet are falling in the frontline work category. Workday has a very narrow offering in that space. Even Workday customers may use other solutions in that. So for us, very important that we capture that space around our customers and beyond that. We have other areas like in finance, where we are massively expanding our footprint. We add new services for revenue management, for subscription services, a new expenses module, profitability and cost management. So we are really broadening the suite. That's category number one. And that's going to allow us to just realize much more of the potential that we have right around our core suite. Secondly, it's all about markets and segments. Workday, as you all know, is a concentrated business when you think about it geographically. In Europe, where you and I come from, right, there is so much potential for us to grow. So we have a focused plan for Europe with country localization, with really bringing our offering fully market ready to the big economies in Europe. We just launched also new data centers in Europe with our EU Sovereign Cloud. We have new local partnerships with local implementation partners. And even beyond that, geographically speaking, we just launched India with a data center and a sales presence there. So in the second bucket, it's really being focused on tapping into more markets and specifically being super successful in Europe. And then thirdly, you can also think about the market segments, right? And if you think about roughly the SaaS space, of course, there is the large enterprise space, there is the medium enterprise space, and there is the small enterprise space. Workday is super successful in large enterprises. But then when you now look into the lower down segment, specifically in mid-market, we actually -- we have the same win rates in mid-market that we have in LE, roughly speaking. And that's because if you're a company which is 1,000 employees to 3,000 employees, you are more like a company, which has 5,000 to 10,000 employees than you are like a company with 50 to 500 employees. So Workday has an incredibly well-fitting product in that space. We just had to simplify our contracting, simplify our services model, simplify basically how we take this to market in a packaged way. We just launched it as Workday GO and now the new Workday accelerated package that will unlock for us much more of the opportunities in medium enterprise space. So expansion of the suite, geographic expansion and laser focus on mid-market are 3 immediate and very actionable growth vectors for us.

Yes. And then if you look at the product or the core products, is there anything on the technology platform that you think you want to change? Or like, I mean, Workday started obviously later than a lot of the bigger ups guys, they are more modern. But if you look at the platform, is there anything you're kind of thinking?

Yes. Look, that's -- the reality is that if you write a line of code today, it's legacy tomorrow. So when we say the technology stack, to a large degree, this is actually the global scale of Workday, right? So when we think about Workday's ability as a cloud-only vendor, you say some of the competitors are newer. Actually, many of them are older, right?

No, no. I meant to say that. Yes, yes.

I, actually, see all of them are like 50-plus years old. And actually, we are not -- I'm not worried as much about it. I'm a technologist. But when we think about technology going forward, we really have to think about it in terms of AI. And when you think about AI value generation, it's much lesser about how the application is written. It's much more about how do you leverage the data set that you have, how much do you leverage the process model that you have, how much are you able to contextualize AI in a certain process setting. And so there are many things which are basically happening outside of how the core application stack is written itself. But also truth to be told, even before me joining, Workday already was in a major journey to renovate it's core application stack with Kubernetes-based open standard programming languages, all augmented around the core. And one of the people I was fortunate enough to bring with me from Google, Gabe Monroy, he now leads our technology platform. He ran cloud run times at Google. So he's very proficient in that space. And we will still be always investing in our core and maturing it. But then to the core of your question, when we think about innovation at the current technology cycle, that question actually is not really the determinant factor anymore.

Yes. Okay. Makes sense. And then I don't know how much Justin briefed you, but like on our side...

Very little.

Yes, yes. On the investor side, there's like this big debate going on about what's going to happen to these big system of records in this new AI world? What's the future of SaaS? How do you -- like you're more a technologist, like how do you think about that debt debate? And what's the situation you see for Workday there as well?

Yes. Specifically -- I mean, we can talk in general and specifically. So I think what we have to be clear on when you say system of record, system of record is, of course, not an unattractive position, right? So the path is from system of record to system of action. That is the interesting piece, right? So how can I actually drive processes, value creation, user engagement with the data set that I have, right? So that's clearly the path. What we do at Workday, right? We structured our AI strategy in 3 key ways. The first one is AI is first and foremost going to completely transform the user experience. So AI is going to be the new UI. And I'm sure you have seen we acquired a company called Sana. So we are really quickly moving that space from a completely AI-driven user experience for Workday and beyond. The second piece of that is that a complete automation of all back-end processes. So when you think about HR and finance department of one almost, right, where you have complete AI-driven automation that is being surfaced through a conversational experience but drives massive efficiency gain and productivity gains at a platform layer. Workday is in an incredible position there because Workday already has 80 GA features out with customers, organically developed. We have roughly 1.2 billion AI actions year-to-date in the core platform already taking place. And we're using all of that to now build purpose-built AI agents to drive this automation. Right now, we have roughly 10 organically, meaning non-acquired agents in the hands of our customers. We have 5x of that already in the road map, launching very quickly. And if you combine that with the acquisitions that we have made selectively in the recruiting space, in the contract management space, we are really having an incredible portfolio together already for the identification of the core system. And the third pillar of our AI strategy is really centered around that idea of an open platform. Every large platform is an integration platform by definition. In Workday, we have hundreds of billions of API calls towards our platform today. So it's super relevant because it is such a key piece, people and money inside of an organizational context. So on the third pillar of our strategy, we are now unlocking our platform by making our data objects accessible through standard APIs called Workday Data Cloud. We have a new agent builder where people can build very quickly in a nontechnical way, agentic automation. We acquired Pipedream, which basically supports this with 3,000 connectors into the enterprise fabric. And that allows the CIOs if you think about the CHRO, the CFO, the CIO as the third key persona to innovate on top of the Workday platform. And we feel incredibly good about all 3 because ultimately, it goes back to where we started that the real differentiation in AI comes from data and process context. And because Workday being born in the cloud, we have one data model, we have one process model shared across all tenants, that's a huge advantage.

I wanted to stay on that data comment you made. We just had Salesforce on earlier, and we had Ali from Databricks yesterday.

You had them all. Okay.

Yes, we had them all. What was interesting is like the importance of data came up again and again. And especially like you guys sit on a treasure trove of information that kind of keeps growing. And you said you wanted to be open as well -- we wanted to be open, but kind of maybe talk about that importance of data, what you do with data? How you enable your customers to work better with the data as a kind of a way to...

Yes. I think it also builds back to your question around the risk of being disrupted from a small startup in that space. And I think it really comes from, a, I think when we say data, I think that's incomplete. Now if you are coming from a data company, it's what you have to say is everything is data. And guess what, that was the story before AI as well, right? So everything is data. So in the AI space, it's not just data, it's data, it's context and it's process, right? It's always those 3 things coming together for AI value generation. And when we think about what we are doing that sets us uniquely apart in an enterprise context is when we say the Workday data advantage, if you will, it really comes from, a, of course, having an incredibly broad and an incredibly deep data set of all functions in HR and finance, which gives us a lot of information that we can use to build and contextualize AI systems. But the second point is key, right, that much of what's happening in a business process is nothing that you can train a model on, right? It's not like the database is going away. The databases are just getting consumed in the models itself. And the reason for it being is that, one, it's highly churning data, right, the transactions, if you will. They are not just flowing directly into model training because guess what, model training takes very long. It's very expensive. Secondly, enterprise data, who belongs -- who is the owner of the enterprise? It is the customer, right? You cannot just like in the public domain, upstream it into a big model. It doesn't work that way. Third issue is it's highly proprietary in the semantics, but there is no common definition of what it means, right? It's a customer specific definition of what it means. And then the third element to that being is that now you need to understand how you take out of your data, the right patterns with the right process context at the right point in time. And this is why you need data, plus context, plus process. And that effectively creates an incredibly high barrier to entry for AI. For AI, it's fairly easy to enter a space where you just draft off the public domain. Coding is a terrific example. Coding is great because the models are incredibly proficient in codes because they are trained on a public web. If you go to HR and finance, none of this applies, right? Because they're not trained on the world's finance data. It doesn't exist in a form, right? And secondly, when you want to run a process like a financial close or supply onboarding, what data means in that moment and what data you need is very different, actually. And I want to also add one last point to that because I do think there's also one big misconception about data in the enterprise space. There is one #1 criteria which defines the acceptance of AI in enterprise and that's accuracy.

What did you say?

Accuracy.

Accuracy. Yes.

Again, right, we have to see where AI is successful and where it's not. AI is successful in coding because you generate code, the developer understands the code, sees it and corrects it. He understands the output modality and is proficient in it. So if it's 80% or 90%, correct, it's a nonissue. It's great actually, right? Because you have this 80%, 90%. Now you're an analyst. If I were to show you a dashboard, and I would tell you, it's 80% correct. I'm not telling you what's not correct, right? But it's 80% correct. Could you use that?

I'll call Sheldon. Sorry, no. Yes.

Of course, not. It's absolutely unusable, right? Or if you would run a payroll run. And you would say, "Well, the payroll run is 80% correct." I'm not going to tell you which employees I didn't pay correctly. You have to figure it out. But it's 80% correct. It's clearly unviable, right? It doesn't work that way. And if you now put all of those 4 things together, right, you actually do understand that as a start-up without a data, without the business process platform, without the business process context and without having the ability to guarantee the accuracy of that at 100%, you're practically not playing. And I think that that's why you actually see us innovating. And frankly, not much happening in the space around it. Most people who say, I for finance say better dashboards. And this is not the AI transformation that I'm talking about.

Yes, yes, yes. Yes, it's funny because like in my first life, I was a PwC guy doing SAP systems. Every time someone says like, "Oh, vibe coding can do this." I was like, "You haven't seen processes."

Exactly, [indiscernible] business process. You got to be 100% correct.

Moving on a little bit here, like if you do all this innovation in Workday, you're getting more AI, you get more agents. How do you think about monetization?

Yes, of course, it's added value that we want to capture. And we really want to strike the right balance between very easy to adopt, very transparent, very predictable to budget and of course, giving us a real opportunity to partake in the value creation for AI. And the reality is that when we think about this from a business model that seat-based models have limitations, obviously, right, because a part of the value promise of AI is that actually seat-based monetization has changed from a large spend on people to a software spend. So what we launched is a concept called Flex Credits. Flex Credits basically is our way for monetizing usage, AI assist in our platform. And so a customer basically gets the opportunity to use AI credits, Flex Credit as part of their Workday system. And they are basically the currency to pay for all of Workday's AI feature. We have AI features that basically then consume Flex Credits as customers are using the AI features and customers have the opportunity to buy more Flex Credits as they expand their usage. So very simply put, one of the most common cases in AI in HR is employee service delivery. By the way, they're seeing fantastic results with that, right? So customers really reducing their contact rates for their service centers through self-service AI for employee service delivery. And now when the agent is engaging with an employee, right, helping them managing a benefit or another change in their work life, it basically then consumes these Flex Credits as monetized usage. And the way we have structured is, again, right, making it super simple, super transparent, super easy to budget. And the key for us really is to make it universal across any AI. So what we want to enable our customers with is that once they are using Flex Credits, they can use all of our AI models, all of our AI agents without differentiation. It's simple that way. And secondly, on our side, right, it allows us to constantly introduce new AI features and new AI agents into that model that customers then can consume. So it's really striking a nice balance of customer value and value on our side.

Yes. Okay. And then the -- how do you think about that equation then like that comes up on our side, a lot is like P times Q. So if I think about you guys, historically, you priced on a number of people on the platform, AI might reduce that number. But then, obviously, you're adding a lot more value, and you talked about the Flex Credits there. How do you see about that dynamic? Because on our side, there's that concern, well, less employees, good luck to Workday. But like it does feel like actually with Flex Credit, agents, et cetera, you cannot have a very, very big offset there.

Yes, 100%. It's just basically a shift in where value accrues right? In AI, the value doesn't accrue as much anymore at the user level. It accrues actually at the outcome level that you're driving through AI. And obviously, you need to have a mechanism that allows you to capture the value where you're creating it. So we have both. We're going to have -- we still have our seat-based, FSE-based model for the entire employee base because for many of our products, this is still that the value accrues and it doesn't go away. From recruiting to learning, there's still so much which the -- the value capture and the value accrual is happening on a per seat basis, the most natural way how customers want to buy, how they perceive value. But when you think about AI, right, it's really about complementing that with an outcome-based model that basically monetizes usage of AI. And since we have both, right, we can strike a very nice balance across the 2. And we are already doing this. Evisort, the company that we have acquired for Contract Intelligence, it very naturally accrues value on a per-contract basis. So that's how it's being sold, right? And basically, what we do is now allowing our customers to use that on Flex Credits as a currency to pay for it. But from a user -- from a customer perspective, it's the most natural way, right? When we say, for instance, employee case management, right, the most natural way to say, okay, well, it's a number of cases that the system is handling or this is the most natural way for me how to size and describe value for it. So it's about balance having both models because both make sense. And I think the reality is, right, I don't think in these doomsday prediction that it's going to be one or the other, right? I think these are very reductionist views of the world. The reality is that usage-based pricing for us will continuously increase with the proliferation of AI. Seat-based model definitely is going to change in certain domain. But even if you're looking at our global market index in Workday, where we are tracking more people coming on the Workday platform or less, we are still seeing that growth at the same point in time, right? So the narrative is out there, and I think it will apply to segments for sure. But I don't think it's going to be a title shift, black and white picture of tomorrow. I think, actually, it makes us -- puts us in a strong position that we can serve both modality under one commercial model or one contract with our customers.

Yes, yes. Okay. Perfect. Makes sense. You mentioned a few acquisitions -- a few of the acquisitions that happened for you guys. Talk a little bit, like you kind of walked -- part of that -- you were part of like some of the acquisitions, but talk a little bit about like what you acquired, how that fits in? And what's the path forward for you?

Yes, it's really for us about striking the ideal balance between organic and inorganic innovation, right? So both are things that you want to maximize fully, right? So as I said, right, we have 10 organic agents, 50 on the road map. So there's a lot of stuff happening. And like I said, for payroll, for case management, for planning, an incredible amount of number who our customers are getting tremendous value from. But we, of course, we also look in the market based on our rubric. Our rubric is quite simple. First of all, does it help us to really make AI the new UI? Secondly, does it help us to drive agentic innovation to really automate big process change? Or thirdly, does it allow us to create an open ecosystem around the Workday platform? Now we acquired Sana because it is truly a leading AI experience that we see already. I'm using it every day, right?

I played with it. It's a cool stuff.

It's amazing. And if you look at their engagement numbers, it's amazing how much engagement they drive. Now connecting it to all of Workday, it truly puts us into a position where we have the leading AI experience on top of Workday. And secondly, put us in a position, right, where we have now a way how we can truly redefine the vast majority of someone's Workday by not only connecting Workday to it, but all of the other enterprise systems underneath it. Secondly, when you think about the best agents, look, when you look at a company like Paradox, right, they have a fantastic recruiting AI system. It's a perfect complement. They are scheduling tens of millions of job interviews already annually. They are super significant. Now when we think about that capability and think about adding it to the recruiting AI that we have now, recruiting platform, it's just such a strong synergy, right? It would be almost foolish for us to say, "Let's just build this ourselves," right? And why would we, right? It's right there. And it's doing exactly what we needed to do, and it's deeply integrated in the Workday system already. So it's like a match made in heaven. And of course, we pursued that. And on the platform side, what we acquired was 2 very small assets, Flowwise for agent building and Pipedream for connectivity. And here is the bottom line, right? As I said, right, we have hundreds of billions of API calls against our platform. Many of the workflows that CIO drives today around HR and finance are not yet things they could solve with Workday. For instance, when you hire someone, what do you want to do next? You want to send them a batch, you want to provision a notebook, get them hooked up on a mobile plan, send them to security training, typically what we would call employee onboarding and IT management. That is the process triggered out of Workday. Our customers are waiting for us to give them an AI agent builder, so we can do the AI first and a connectivity suite so they can build that up across the entire enterprise landscape. And the Flowise and Pipedream, they get exactly that. It's unified with our Workday Extend platform and we have already thousands of apps on it, incredible momentum. And I think this is just going to unlock so many more scenarios for us on the AI platform consumption side. And we're going to remain acquisitive, right? I'm going to push hard on organic innovation as well as if we see something that really makes a marked difference in AI as the new UI, agents for work or an open platform ecosystem, those are things that are going to remain relevant for us.

Yes. And then when you -- it was funny when you joined Workday, I kind of initially felt, I think he got the timing off because...

Why would you say that?

Because I remember like Workday spent compared to the other SaaS guys so much more money on research and development. Like you're very, very technology driven, like research, development-driven organization way more than the others. And then a little bit of the pressure came so that he has to come down. So I was like, "Oh, he comes and his budget gets kind of reduced," maybe, I don't know. How do you think about that dynamic about...

All investors as concerned as you are about my well being. I love it.

Yes. No, no. But how do you think about that dynamic about like having the pressure on -- or like having to deliver like margin expansion, while at the same time, there's so much that needs to be done on your side.

Yes. I -- this is not Workday specific for me. And all of the companies I've worked and I always felt there's that idea that there is a trade-off between innovation and margin efficiency. And I think it all comes down to execution excellence. The reality is we have lots of operating leverage across everything, our entire operating expenses. And you just saw this here, right? We pursued aggressive M&A. We delivered on our margin goals, and we invested into organic innovation. And I think the net of that is that it's the practice of being incredibly disciplined and executing very well on the top and the bottom line of your business because if you do, there is so much leverage that we have across the Workday business that at no point in time, I felt constraint in the ability to invest in innovation or I felt being tapped out on opportunities to free up more capital to redeploy towards innovation areas. So I'm feeling very confident about. And it is funny. I think it wasn't in Toyota, like way back then, we actually blasted that myth that you can't innovate and be margin efficient at the same point in time. I truly subscribe that you can. And you can expect us continuing that way next path, right? We are aggressively expanding our investments into AI, into our core platform, as I said earlier, in our expansion and we're going to also increase our margin as we guided in the framework that we gave out by just realizing more of our operating leverage.

Yes Okay. Perfect. That's actually a very good closing statement, and we have 2 Germans on stage, so we need to finish on time.

I like it. Awesome.

Great. Thank you. Thanks for joining.

Thank you for having me. Thank you.

Thank you.

My pleasure.