Here we go. Really happy to have Gerrit on from Workday. We were joking earlier that we do it in German, but we won't.
That was German, yeah.
Yeah. The two Germans talking about the World Cup next year. Just, maybe introduce yourself, briefly. Like, you have an interesting background, as well. Like, and, like, introduce yourself and then talk about what excited you about Workday, joining Workday.
Yeah, 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 database business, BI business, planning business, HANA.
You know, 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 nine months ago, nine months in the making. And what excited me about Workday is that, you know, look, the bottom line is, you know, 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, you know, the phase of being focused on infra and model turns over to AI business applications. And I do think it's a time where, you know, 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 wanna be a part of that?
Yeah. No, exactly. Yeah. 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?
Yeah. Sounds good. You know, Raimo, I think that's one of the big things that, you know, I personally think people are confused about because we say, well, Workday is so penetrated, it's so present, and it's, you know, such, you know, such a narrow space in HR and Finance. But if you do just simple math, right, the TAM that you're operating in is roughly $200 billion.
Huh. Yeah.
You know, just do the, you know, you're a smart analyst, do the back of the envelope on, you know, 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 know, you can roughly segment it in three major ways. One is, you know, expanding the portfolio. One of the big initiatives that we are driving is going into frontline work.
It's roughly, you know, 3 billion workers 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. You know, we add new services for revenue management, for subscription services, a new expenses module.
You know, profitability and cost management. So we are really broadening the suite. That's category number one. And that's gonna allow us to just, you know, realize, you know, much more of the potential that we have right around our Core Suite. Secondly, it's all about markets and segments, you know. You know, Workday, as you all know, is a concentrated business when you think about it geography in Europe where you and I come from, right?
Yeah.
There is so much potential for us to grow. So we have a focus 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, you know, if you think about roughly the SaaS space, you know, of course there is the, the large enterprise space, there is the medium enterprise space, and there is the small enterprise space.
Workday is, you know, super successful in large enterprises. But then, you know, 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, you know, 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 to 10,000 employees than you are like a company which has 50 to 500 employees.
Yeah. Yeah.
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, you know, much more of the opportunities in medium enterprise space. So expansion of the suite. Geographic expansion and laser focus on mid-market are three immediate and very actionable growth factors for us.
Yeah. And then, if you look at the product, at the core product, is there anything on the technology platform that you think you wanna change? Or like, I mean, Workday started obviously later than a lot of the bigger ERP guys, like the Oracle, the SAP. Like, but if you look at the platform, is there anything you're kind of thinking?
Yeah. You know, look, you know, that's the reality is that, you know, if you write a line of code today, you know, it's legacy tomorrow. So when we say, you know, the technology stack, you know, to a large degree, this is, you know, actually the global scale of Workday, right? So when we think about, you know, Workday's ability as a cloud-only vendor, you say some of the competitors are newer. Actually, many of them are older, right?
No, I meant to say that. Yeah. Yeah. Yeah.
Like, you know, 50+ 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 dataset you have, how much do you leverage the process model that you have, how much are you able to contextualize AI, you know, in a certain process setting. And so there are so many things which are basically happening outside of how the core application stack is written itself. But also, you know, truth to be told, even before me joining, Workday already was on a major journey to renovate, you know, its core application stack.
Yeah.
With you know, 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 runtimes at Google.
Oh, wow. Okay.
So he's very proficient in that space. And, you know, we will still be always investing in our core and maturing it. But when you're to the core of your question, right, when we think about innovation at the current technology cycle, that question actually is, you know, not really the determining factor anymore.
Yeah. Okay. Makes sense. And then, I don't know how much Justin briefed you, but like on our side on the investor.
Very little.
Yeah. On the investor side, there's like this big debate going on about, oh, what's going to happen to this big system of records in this new AI world? You know, what's the future of SaaS? How do you, like you're more a Technologist, like how do you think about that debate? And what's the situation you see for Workday there as well?
Yeah. You know, specifically, I mean, we can talk in general and specifically. So I think, you know, what we have to be clear on when we say system of record, you know, 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 three key ways. The first one is AI is first and foremost gonna completely transform the user experience. So AI is gonna be the new UI. And I'm sure you have seen, we acquired a company called Sana.
Yeah.
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 backend processes. So when you think about, you know, about the 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, you know, 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. You know, right now we have roughly 10 organically, meaning non-acquired agents in the hands of our customers.
We have 5X of that already, you know, in the roadmap, you know, launching very quickly.
Yeah.
And if you combine that with the acquisitions that we have made selectively in the recruiting space, in the contract management space, you know, we are really having an incredible portfolio together already for the agentification of the core system. And the third pillar of our AI strategy is really centered around, you know, that idea of an open platform. You know, every large platform is an integration platform by definition. With Workday, you know, 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.
Yeah.
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, you know, through standard APIs, you know, called Workday Data Cloud. We have a new agent builder where people can build very quickly, in a non-technical way, agentic automation. We acquired Pipedream, which basically supports this with, you know, 3,000 connectors into the enterprise fabric, and that allows now the CIOs, if you think about the CHRO, the CFO, and the CIO as a third key persona.
Yeah. Yeah.
To innovate on top of the Workday platform. And we feel incredibly good about all three because ultimately it goes back to where we started at. 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.
Yeah.
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. We had Ali from Databricks yesterday.
You had them all. Okay.
Yeah. Yeah. We had them all. Yeah. Yeah. The what was interesting is like the importance of data came up again and again.
Yeah.
Especially, like, you know, you guys sit on a treasure trove of information that kind of keeps growing, and you said you wanted to be open as well.
Yeah.
Wanted to be open, but kind of maybe talk about that importance of data, what you do with data, you know, how you enable your customers to work better with the data as a kind of a way to.
Yeah, and I think it also builds back to your question around, you know, the risk of being disrupted now.
Exactly. Yeah.
From a small startup in that space, and I think, you know, it really comes from A. I think when we say data, I think that's incomplete. Now, if you are, you know, coming from a data company, it's what you have to say. It's everything is data.
Yeah.
You know, guess what? That was the story before AI as well, right?
Yeah.
So everything is data. In the AI space, you know, it's not just data, it's data, it's context, and it's process, right? It's always, you know, those three things coming together for AI Value Generation. When we think about, you know, what we are doing that sets us uniquely apart in that enterprise context is when we say, you know, 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 memory.
In the models itself. And the reason for that being is that one, it's highly churning data, right? Your transactions, if you will, you know, they're not just flowing directly into model training because guess what? Model training takes very long. It's very expensive.
Yeah.
Secondly, enterprise data, who belongs then, you know, who's the owner of the enterprise data? It's 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 its semantics, right? 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. You know, for AI, it's fairly easy to enter a space where you just draft off the public domain.
Coding is a terrific example. Coding's great because the models are incredibly proficient in code because they're trained on the public web.
Yeah.
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 that form, right?
Yeah.
Secondly, when you wanna run a process like a financial close or a supply onboarding, what data means in that moment and what data you need is very different, actually. I wanna also, you know, 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 number, one criteria which defines the acceptance of AI in enterprise.
Yeah.
That's accuracy. You know what?
What, what did you say?
Accuracy.
Accuracy. Yeah. Yeah. Yeah.
You know, again, right? You know, 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 non-issue. It's great, actually, right? Because you help with 80% or 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. You know, could you use that?
I call Sheldon. Sorry. No. Yeah.
Of course not, right? It's utterly unusable, right?
Yeah.
Or if you would do a payroll run and you would say, "Well, the payroll run is 80% correct." I'm not gonna tell you which employees I didn't pay correctly.
Yeah.
You have to figure that out, but it's 80% correct. It's clearly unviable, right? You know, it doesn't work that way, and if you now put all of those four things together, right, you actually do understand that as a startup without the 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's why, you know, you know, you know, that's why you actually see us innovating and, frankly, not much happening in the space around it. Most people who say AI for finance say better dashboards, you know, and this is not the AI transformation that I'm talking about.
Yeah. It's funny because like, in my first life, I was a PwC guy doing SAP systems.
And every time someone says like, "Oh, Vibe Coding can do this," I was like, "You haven't seen process." Yeah.
Exactly.
Yeah.
You haven't seen business processes.
Yeah. Yeah. Yeah. Good luck.
You gotta 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?
Yeah. You know, of course, you know, it's added value that we wanna capture. And we really wanna 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, you know, 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 is changed, you know, from.
Yeah.
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 assists in our platform.
Yeah.
And so a customer basically gets the opportunity to use AI credits, Flex Credits, as part of their Workday system. And they are basically the currency to pay for all of Workday's AI features. We have AI features that basically then consume, you know, 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, you know, in HR is employee service delivery. By the way, we are seeing fantastic results with that, right? You know, customers really reducing, you know, 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 with managing a benefit or, you know, another change, you know, in their work life, it basically then consumes these Flex Credits as monetized usage. And the way we have structured this 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, you know, 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.
Yeah. Okay. And then, how do you think about that equation then? Like, that comes up on our side a lot. It's like P times Q. So if I think about you guys historically, you priced on, you know, number of people on the platform.
AI might reduce that number, but then obviously you're adding a lot more value.
Yeah.
You talked about the Flex Credit there. How do you see about that dynamic? Because, you know, on our side, there's that concern. Well, it's less employees. Good luck to Workday, you know? But, like it does feel like actually with Flex Credit, agents, et cetera., you kind of have a very, very big offset there.
Yeah. 100%. You know, it's just basically a shift in, you know, where value accrues, right? In AI, the value doesn't accrue as much anymore at the user level. It accrues, you know, actually at the outcome level.
Yeah.
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.
Yeah. Yeah.
So we have both. We're gonna have, you know, we still have our seat-based, FSE-based model for the entire employee base because for many of our products, this is still where the value accrues and it doesn't go away. You know, from recruiting to learning, you know, there's still so much which the, where the value capture and the value accrual is happening on a per seat basis, the most natural way, how customers wanna 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 two. And, you know, we are already doing this.
Evisort, you know, the company that we have acquired for contract intelligence, you know, 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, it's the most natural way to say, "Okay, well, it's the number of cases that the system is handling," right? This is the most natural way for me how to size and describe value for it. So it's about balance.
Yeah.
Having both models because both make sense. And I think the reality is, right, I don't think in these doomsday predictions that it's gonna be one or the other, right? I think, you know, these are very reductionist views of the world. The reality is that usage-based pricing fast will continuously increase, you know, with the proliferation of AI. Seat-based model definitely is gonna change in certain domains.
But even if you're looking now 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 gonna be, you know, a tidal shift, you know, 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, you know, commercial model or one contract with our customers.
Yeah. Yeah. 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 know, you kind of walked, you know, part of that, you were part of like some of the acquisitions, but talk a little bit about like, you know, what you acquired, how that fits in, and what's the path forward for you?
Yeah. And I do, it's really for us about striking the ideal balance between organic and inorganic innovation, right? So, you know, both are things that you wanna maximize, you know, fully, right?
Yeah. Yeah.
As I said, right, you know, we have 10 organic agents, 50 on the roadmap. So there's a lot of stuff happening. And like I said, for payroll, for case management, for planning, an incredible number of customers who are getting tremendous value from. But we, of course, we also look in the market based on our rubric, right? Now, 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, you know, truly a leading AI experience that we see, you know, already. I'm using it every day, right?
I sleep with it. It's, it's cool stuff. Yeah. Yeah.
It's amazing.
Yeah.
And if you look at their engagement numbers, it's amazing how much engagement they drive. Now, connecting it to all of Workday, you know, truly puts us into a position where we have the leading AI experience on top of Workday. And secondly, puts 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, you know, underneath it.
Secondly, when we think about the best agents, you know, look, when we look at a complex Paradox, right, they have a fantastic recruiting AI system. It's a perfect complement to us. They are scheduling tens of millions of job interviews already, you know, annually. They are super significant.
Now, when we think about that capability and think about adding it to the recruiting AI that we have in our 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?
Yeah.
You know, why would we, right? It's right there. And it's doing exactly what we need it 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 pursue that. And on the platform side, you know, what we acquired was two very small assets, Flowise 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 platforms. Many of the workflows that a CIO drives today around HR and Finance are not, you know, yet things they could solve with Workday. For instance, when you hire someone, what do you wanna do next? You wanna send them a badge?
You wanna provision a notebook, get them hooked up on a mobile plan, send them the security training, typically what we would call employee onboarding and IT management. That is a process triggered out of Workday. Our customers are waiting for us to give them an AI agent builder so they can do the AI first and a connectivity suite so they can build that up across their entire enterprise landscape.
And the Flowise and Pipedream, they get exactly that. It's unified with our Workday Extend platform where we have already thousands of apps on it, incredible momentum. And I think this is just gonna unlock so many more scenarios for us on the AI platform consumption side. And we're gonna remain inquisitive, right? I'm gonna 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 gonna remain relevant for us.
Yeah, and then it was funny when you joined Workday. I kind of initially felt, I think he got the timing off.
Why? Why would you say that?
Because I remember the, like, you know, Workday spend, compared to the other SaaS guys, so much more money on research and development.
Yeah.
You know, like you're a very, very technology-driven, not like research, development-driven organization, like way more than the others. And then, like, then a little bit of the pressure came so that he has to come down. I was like, "Oh, like he comes and then his budget gets kind of reduced maybe, you know, I don't know." Like, how do you think about that dynamic about like?
I'm not as concerned as investors are about my well-being.
But how do you think about that dynamic about like, you know, 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?
Yeah. You know, I, this is not Workday specific for me.
No.
In all of the companies I've worked in, I always felt, you know, there's that idea that, you know, there is a trade-off between innovation and margin efficiency. And I think it all comes down to execution excellence. You know, 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 constrained in the ability to invest in innovation.
Yeah.
Or I felt, you know, I'm being tapped out on opportunities to free up more capital to redeploy towards innovation areas.
Yeah.
So I'm feeling very confident about that. It's funny. I think it was Toyota way back when who actually, you know, blasted that myth that you can't innovate and be margin efficient at the same point in time. I truly subscribe that you can. You can expect, you know, us continuing that way next year, right? We are aggressively expanding our investments into AI, into our core platform, as I said earlier, and our expansion. We're gonna also increase our margin, you know, as we guide it, you know, in the framework that we gave out by just realizing more of our operating leverage.
Yeah. Okay. Perfect. Hey, that's actually a very good closing statement, and you have two Germans on stage, so we need to finish on time.
I like it. Awesome.
Good. Thank you. Thanks for joining us.
Thank you for having me.
Thank you.
Thank you. Thank you. Thank you. A bless.