Workday CEO Carl Eschenbach: Building the System of Record for the AI Era

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Carl Eschenbach: As soon as you go in and you talk to the enterprise—a CEO, CFO, CIO, whatever it may be—it immediately goes to an ROI conversation. Like, “This is going to save me a boatload of money.” And in some cases it is. That being said, if you only focus on the benefit of the ROI, what happens is immediately, if you’re an employee, you start to think, “Man, my job’s being replaced.” We need to flip that narrative as well. We need to start to think about how do we use this technology to drive ROI, to get dollars to reinvest in the business to drive growth. We need to talk about AI from a growth value proposition, not just an ROI value proposition. And that’s how you get the employees and agents and AI to peacefully coexist in the enterprise and really drive growth for companies.

Pat Grady: Greetings. Today we’re joined by current Workday CEO, former Sequoia partner, and perhaps the most beloved executive in technology, Carl Eschenbach. Workday, as you may know, is a system of record for people and the numbers produced by those people. As we move into a world where people are complemented by AI agents, Workday is on a path to become the system of record, not just for people, but also agents—and of course, all the work done and numbers produced by those people and those agents. Carl talks about Workday’s three-pronged approach to monetize AI through seat-based pricing, role-based agents, and consumption-based API access. He also talks about how to implement AI and how to transform a company while preserving your culture and values. We hope you enjoy.

Carl, welcome to the show.

Carl Eschenbach: Thank you for having me. It’s great to be here with both of you. I couldn’t think of two better podcasters to be with: Sonya and Pat. Come on!

Pat Grady: We couldn’t think of a better venture capitalist who is moonlighting currently as a CEO to be doing this show with.

Carl Eschenbach: Okay. Moonlighting. I like that. Actually, I am moonlighting because I have responsibilities here with you and my other job, right? Some days I wonder which I’m doing more. No, I didn’t say that. I’m doing the other one more, if I’m honest. But I appreciate still being part of the partnership. It’s an incredible opportunity to still engage with what I think is the most iconic venture capital firm of all time, Sequoia. And just to be around it in some capacity, and see people like you and all my partners is truly a blessing for me to still be involved. So thank you.

Pat Grady: Thank you. We really appreciate that. We’re going to start with a question on Workday.

Carl Eschenbach: Okay.

AI and the system of record for people

Pat Grady: So you guys are the ultimate system of record for people. In a world where AI is replacing people, what happens to the system of record for people?

Carl Eschenbach: Interesting. So if I may, we are the system of record for the two most critical assets of any company: their people and their money.

Pat Grady: Okay.

Carl Eschenbach: Because we have the financials platform.

Pat Grady: Good distinction.

Carl Eschenbach: And interestingly enough, they’re one and the same. And we get asked—and I get asked all the time, “Your system of record in the enterprise. AI is coming. It’s a transformative technology. It’s going to disrupt everything, especially in the enterprise, and companies like you might be in trouble.” And I would say with a bias towards being biased, I think we’re in a very unique position. And let me explain why. Today, 20 years old as Workday, we’ve been managing and supporting our client’s most critical assets as I said, Pat, their people and their money.

Pat Grady: Yeah.

Carl Eschenbach: And we now have a database, a highly-curated database of more than 70 million users that is processing 30 percent of the job recs in the United States last year.

Pat Grady: Hmm.

Sonya Huang: Wow!

Carl Eschenbach: And when you think about AI, at the end of the day it’s all about data. I think you guys would agree with that. Now it’s not just about data, it’s the context of the data.

Pat Grady: Mm-hmm.

Carl Eschenbach: And when you have that much data, and you have the context of it and you’re in the actual workflow, the business process workflow, you can then use agents to drive actual results that people see value in doing, in using. This is where we start to talk about things like role-based agents and the impact it can have on the enterprise.

The other thing I’d say when you have 11,000 customers, you’ve been around 20 years, you’ve been that core system of record, it led to us becoming the system of truth. People trust us. And anytime you go through one of these major tectonic shifts from one technology to the next generation of technology, I believe, especially in the enterprise, the enterprise customers look to us and say, “How are you going to help us with that transition, just like 20 years ago you helped us with the transition from on premises to the cloud for HR and finance?”

Pat Grady: Yep.

Carl Eschenbach: So the combination of the data, the context of the data, we’re in the workflow, the trust we have. And I think at the end of the day, as we talk about agents—and I’m sure we’ll talk about it here—I think domain-specific agents with highly-curated data are much more beneficial in the enterprise than just general purpose LLM models that are out there.

Pat Grady: Agreed.

Carl Eschenbach: That’s why we think we’re uniquely positioned. I get asked all the time are the incumbents in trouble because of the startups? And quite frankly it’s not an ‘or,’ it’s an ‘and.’ There’s going to be success across the board. You guys have a bunch of great companies that we work with that actually bring more value to the system of records. So it’s both. I think there’s opportunity for everyone, Pat.

How accessible is your data?

Pat Grady: And your point on trust is a good one. And that’s definitely been a recurring theme amongst the application-layer AI companies that seems to be working. Let me ask you a question on product, though. So let’s say that I am an AI developer of some sort, or more practically, Sonya is an AI developer of some sort, and she shows up at Workday because she heard that there’s this treasure trove of information that you’ve amassed over the last 20 years. All of these records and all the context around the records. How much of that is actually available for you to go build cool AI stuff with, and how much of that, due to permissions or information architecture or 20-year-old company stuff, how much of that is not available? Or said differently, in actually building with AI, all the data that you’ve amassed is obviously an advantage. How accessible is it, and what roadblocks are there to making the most of all that data when you try to turn it into product?

Carl Eschenbach: Yeah, it’s a great question. If you look at the amount of companies and technologies that access the Workday system of record, it’s a lot. For example, identity companies. For example, active directory. When you onboard a new employee, guess where they all come and do that? On top of the Workday platform. Today, they do it through an open set of APIs, general purpose APIs that we put out there for people to develop on and connect with us.

Going forward, as we start to think about this world of AI and the potential of all of these agents entering the world in the enterprise, someone has to think about how you onboard them in a secure, responsible, ethical, compliant way, and make sure their identity and access controls and all the data they get access to has governance around it. Today, that’s the risk. That is the risk in the enterprise. When we talk to CIOs, the first thing they say to us, “We love agents, we love this technology, we love what potentially could happen with agents, but we are concerned. We’re seeing agent sprawl across the enterprise.” My analogy, Pat, is remember when we always used to talk about shadow IT creeping up?

Pat Grady: Yeah.

Carl Eschenbach: And how do you get it back because of the security and compliance controls that you want in a corporate headquarters or a corporate function? That’s what we’re seeing. Now what we’re seeing is people want to bring agents into the enterprise. They want access to our records, they want to onboard them just like we would a human worker, they want to do it with a digital worker. And what we’re building is something called an AI gateway where you’re not going to be able to do that to a generic set of APIs that we have today. You’re going to have to come through this agent, if you will, gateway to onboard into the enterprise through us.

Pat Grady: Yeah, say more about this. So people are using Workday—or will be using Workday—to onboard AI agents analogously to how they might onboard human employees.

Carl Eschenbach: Bingo. I’m going to hire you as my next sales rep, Pat. So what we announced—that was a good setup and you didn’t even know it. About a month and a half ago, we announced something called an “agent system of record.” And think of it as the unification of a system of record between human workers and digital workers. Just like human workers get onboarded in the enterprise today, they come through Workday, they get onboarded, they get assigned to whatever organization they’re part of, they get their benefits. They get tracked, they get monitored, and they get looked at for performance reasons. And then you do benchmarking of all your human employees to drive workforce management and capacity planning.

The world of AI comes about. Agents come into the enterprise. Everyone says they’re going to come into the enterprise. Okay, how do they get into the enterprise? Who onboards them? What organization are they part of? What access rights do they have? Who controls them? Who measures them? No one today. We think in the future, and as we talk to our customers with this new agent system of record, that unification of human and digital workers on a common platform is the path forward. And then once that’s in place, you can start to do workforce management, workforce planning around all of your workforce because it’s no longer a workforce of human, it’s a workforce of human and digital workers. And that’s what we’ve developed, and we’re in the process of bringing to market.

Sonya Huang: That’s so fascinating. So it’s a system of records for your people and your money, but your people includes your human people and now your agents as well.

Carl Eschenbach: Exactly. And there’s a lot of fear out there, Sonya, that these agents, as they come into the workforce are going to completely displace all the human workers, right? And we’re going to—you know, I hear some of my peers go out there and talk about we’re going to see a 20, 30, 40 percent reduction in the human worker going forward by all of the agents in this whole AI revolution. I fundamentally don’t believe that to be the case.

Pat Grady: Yeah. It’s interesting. I remember 15 years ago hearing Aneel articulate the vision for Workday. And part of it was the reason people and financials go together. And the analogy to the last generation before that, which is the ERP systems of yesteryear, SAP and such. Physical manufacturing was the economy, and so the same system that took stock of all of your machines was the system that was your financial backbone. And with the original Workday, we’re moving into a knowledge-based economy. And so the same system that takes stock of your people is the system that provides your financial backbone. And now as we move into an AI-driven economy, the same system that takes stock of your agents is the system that is also your financial backbone. And because it’s fluid between the people and the agents, it makes sense that it would all live in one place.

Carl Eschenbach: And if you think about the Workday platform, everyone thinks about us having a HR platform and a finance platform, but if you look at the architecture, they’re one and the same. Exactly as you just described. Now layer on top this new workforce called digital workforce on top of it. That benefit you’ve got for the last 20 years continues, but it brings these agents into the enterprise in a much secure way.

How do you take agents to market?

Pat Grady: Yes. Yes. And then practically speaking, how do you take this to market? How do you roll this out? Do you need some sort of a standard unit of pricing and packaging that an AI agent builder can adhere to? You know, a person is a person, and granted there are lots of different types of people, but everybody’s got a date of birth, you know, everybody’s got a title. What are the common characteristics of AI agents that will allow them to sort of map into this framework that you guys are now providing?

Carl Eschenbach: And how do you price them as well?

Pat Grady: How do you price them?

Carl Eschenbach: Also, how do you go to market, right? I remember being at Sequoia—I’m going to back up and that’ll answer your question. Do you remember when this whole—actually, I was talking to Roelof about this last week. Everything was going to go PLG.

Pat Grady: Yeah.

Carl Eschenbach: Everything’s going product-led growth. You don’t need sales reps, you don’t need 37-year sales veterans like myself anymore. You don’t need the enterprise, you don’t need the top down. I’ll tell you, the times are coming back. With AI, it’s a top down sale in most cases. It’s not product-led growth up through the enterprise. You’re selling to the heads of departments or heads of functions or a CIO.

Pat Grady: Yeah.

Carl Eschenbach: So I think the distribution is coming back to a much more in-human sales motion. We’re definitely seeing that.

Pat Grady: We’re absolutely seeing that. I think that’s a common thread. If you look at the AI companies that are really working.

Carl Eschenbach: It’s fun because I told Roelof when I saw him last week, I’m like, “See? I told you. PLG is good, but it’s not the panacea that everyone thinks. You are still going to need people like me someday.”

Pat Grady: Yes.

Carl Eschenbach: And they do, right? So I just thought I wanted to talk about—as far as pricing goes, there are multiple ways people are pricing AI—and then I’ll speak specifically about agents. Let me back up two years ago when copilots came to market. A lot of our peers, a lot of my peers and competitors immediately went to market and they basically said to their customers, “We now have a copilot. And because we have a copilot, we’re going to raise our pricing 20 percent.” And we made a decision, and I specifically said, “I do not want to do that. I don’t think it’s the right thing. Copilots are going to become part of the core foundation of all products. And it should be built in, not bolted on, and it should be the value our customers get and receive by paying us a subscription fee on an annual basis. We have to innovate.”

Pat Grady: Yeah.

Carl Eschenbach: To be honest, we got beat up quite a bit. “Why aren’t you doing this? You have copilots. You should be monetizing.” And I said, “Because it’s the right thing to do.” When we bring true value with our agents in the future, and we can show you a strong ROI or a value equation, then you’ll pay us. Fast forward to today. We price our AI solutions in one of three ways. Number one, we price it based on seats. So we are a seat-based company for the most part. And if we have an agent or an AI technology that provides value to all of the employee population, then we will do an uplift based on that agent or that technology.

Pat Grady: Uplift per seat.

Carl Eschenbach: Per seat.

Pat Grady: Okay.

Carl Eschenbach: The second would be based on an actual agent itself. Like, if I can come to you, Pat, and say, “We’re rolling out a payroll agent,” and you think about how many payroll administrators you have today. But we bring a role based agent into the market—I’m making this up—and we’re gonna say it’s $50,000 a year for this agent. Today, you have a payroll agent or a payroll employee over here looking at everything. All they’re doing is administration, and that costs $200,000. You’ll say, “Hmm, I’ll do this.” So you can do it based on the role.

Pat Grady: Do some of those cannibalize the seat-based model of core Workday? Meaning if I now have a payroll agent, I need to hire fewer payroll people, and I would have been paying for a seat in Workday for each of those payroll people. Does the agent business cannibalize the sort of original seat-based business?

Carl Eschenbach: I don’t think so.

Pat Grady: Okay.

Carl Eschenbach: Because what AI should allow that payroll administrator to do is go off and do more high value tasks and drive more strategic outcomes for the company, as opposed to just being replaced. If you think about what we do today—you, me, everybody—we spend the majority of our day working with technology. Sitting at our desk, working on a phone, we’re working with technology. The power of AI is going to happen when we transform that equation and the technology works for us.

Pat Grady: Yes.

Carl Eschenbach: And we don’t even know it’s happening. Right now, we’re interfacing with all of these chatbots and all of these different LLMs out there that the consumer—but in the enterprise, what’s going to happen and the value is going to happen when these agents and AI technologies start to work on our behalf. We don’t do those tasks and we go off and do other things. So we’re going to flip the equation around.

So that’s where I think the value comes in for existing employees to go get new skills. Just like the AI momentum could be called a revolution, I think we’re going to go through a skills revolution with the existing workforce to train them to go do other things. And if you look at the history of time, people, like all of us, all employees, they’re very pliable, they’re adaptable. And what do we always figure out to do? How to leverage technology. Technology is the single biggest source of productivity gains in the history of time.

Pat Grady: For sure.

Carl Eschenbach: Right? That leads to GDP growth, that leads to company growth. The other thing we need to work on is this narrative that’s out there. As soon as you go in and you talk to the enterprise—a CEO, CFO, CIO, whatever it may be—it immediately goes to an ROI conversation. Like, “This is going to save me a boatload of money.” And in some cases it is. That being said, if you only focus on the benefit of the ROI, what happens is immediately, if you’re an employee, you start to think, “Man, my job’s being replaced.” We need to flip that narrative as well. We need to start to think about how do we use this technology to drive ROI, to get dollars to reinvest in the business to drive growth. We need to talk about AI from a growth value proposition, not just an ROI value proposition. And that’s how you get the employees and agents and AI to peacefully coexist in the enterprise and really drive growth for companies.

Pat Grady: Yeah.

Carl Eschenbach: Sorry, we sidebarred. Let me come back. There’s another way to monetize it and how I’m thinking about it.

Pat Grady: Okay, so we got—first off, we have uplift on all the seats. Secondly, we have price per agent.

Carl Eschenbach: Yeah, think about that value-based pricing, right? And then the third is consumption based.

Pat Grady: Okay.

Carl Eschenbach: Meaning how many times is something—like, I was talking to Bret Taylor at Sierra, right? They do it based on consumption and outcomes, right? And as you know, Pat, being on their board, we’re thinking about the same things. How many times—back to the initial question—are people hitting us to get access to our data? So we can base on consumption.

Pat Grady: Okay.

Carl Eschenbach: So when all of these agents come out in the world and all of these agents say, “Wow, I need to get access to the data to be registered or whatever it is. We got to get access to Workday.” Okay, great. Please do so. But we’re going to charge for that access going forward, as opposed to just that open set of public APIs.

Pat Grady: So in that case, it’s a platform business, and it’s sort of metered, almost metered consumption model.

Carl Eschenbach: Right. And then you can price it. A lot of people today are going out and saying, “Here, buy so many—you know, so much consumption.” And then as you do it, you burn it down over time, right? That’s a big model that’s out there.

Pat Grady: Yes.

Carl Eschenbach: And we’re going to do all the above.

How does an insanely productive human being use AI?

Pat Grady: You mentioned AI for productivity. Okay. You are easily top three most productive human beings I’ve ever met. Possibly top one. Probably top one, honestly, but you’re certainly top three. As an insanely productive human being, how are you using AI to make yourself more productive? Do you have any favorite products? Do you have any favorite workflows? Like what …?

Sonya Huang: Studio Ghibli pictures?

Pat Grady: Yeah. Any great Studio Ghibli pictures? Yeah. What are you doing with AI to make yourself more productive?

Carl Eschenbach: Yeah, so inside of Workday—and then I’ll talk about how I use it, too. Inside of Workday, we’ve rolled out a whole bunch of productivity tools around AI. We have Slack AI. We have Zoom AI. We use Gemini all the time. Gemini is something I use all the time. So an example, I’m preparing for earnings every 90 days, which is so much fun. Please join me if you guys want.

Pat Grady: [laughs]

Carl Eschenbach: All the analyst reports come out, right? And you go, how do you summarize them? How do you—it’s amazing. You just throw it in Gemini and—boom! And you save hours of reading. That’s one example. Or you’re on a Zoom call that you missed. Like, how does that get summarized back to you? I use those type of things all of the time.

Pat Grady: Yeah.

Carl Eschenbach: And it just makes me that much more productive, clearly. One of the things we’re doing inside of Workday—and I think it’s first of its kind at scale—we’re launching this month something called everyday AI, where all 20,000 employees have to go through training on AI and how to learn how to use all of these tools to drive personal productivity. And through that personal productivity gain, Workday gets gain.

Pat Grady: Yeah. Yeah.

Carl Eschenbach: Right? So we—and they have to test out. There’s an entire curriculum. It’s a really interesting way to get people excited about AI, how to engage with it and leverage the technology as opposed to be afraid of it.

Pat Grady: Mm-hmm.

Carl Eschenbach: And we think it’s one way to drive, if you will, that peaceful coexistence between the technology and the humans to drive overall gains for the company. That’s another example of how the whole company has to do it. And I’m launching it in two weeks.

Pat Grady: Very cool.

What are the agents?

Sonya Huang: Can you tell us more about the agents that you’re bringing to market? So you mentioned the payroll agent. What other agents should folks expect from you?

Carl Eschenbach: Yeah. So the most successful agent we have in the market, which is selling unbelievably well, we bought a company at this time last year, about a year ago called HiredScore. And they have what I would describe as a recruiter agent.

Sonya Huang: Hmm.

Carl Eschenbach: So if you think about recruiting, Sonya, in the HR function, it is—talent acquisition or recruiting is probably one of the most expensive functions you have in HR. This recruiter agent we brought to market now, it’s been in market about three quarters and it’s growing rapidly, growing 50 percent quarter over quarter right now. We can give it to a recruiting organization or talent acquisition organization, and they, in a very short matter of time, can see a 50 percent increase in recruiter productivity.

Sonya Huang: Hmm.

Carl Eschenbach: Just think, you get hundreds of resumes for a job. Today’s someone searching—now it basically can do that in seconds and say, “Here’s the top two or three you should go after.” But it’s more than just recruiter productivity. It also has proved to accelerate time-to-hire by 20, 30, up to 40 percent.

Pat Grady: Hmm.

Carl Eschenbach: So your recruiters are more productive, and you can accelerate your time to hire. Then once you get them inside the building, we have two other AI products. One is called Talent Optimization, and the other is a talent mobility agent. And when you implement them, we have seen upwards of 40 percent reduction in attrition. And the reason for that is for the employee, it takes a look at all your skills, your background and your experience, and for the manager, you say, “I have this project or outcome I’m trying to achieve,” and it starts to do the matching between the two. So what it drives is an increased level of internal mobility with your workforce, which drives down attrition because people feel like they’re getting new opportunities. So that’s like a full lifecycle of agents, from recruiting to onboarding to talent optimization to talent mobility, and all that we can monetize because people can actually see. You see a reduction of 50 percent of, you know, need for additional recruiters. That’s pretty powerful.

Pat Grady: Yes. Can I ask you a question on—you mentioned this idea of matching talent to other opportunities inside of their company.

Carl Eschenbach: Yeah.

Pat Grady: One of the ideas that we’ve heard from a few different companies at this point is this idea that thanks to AI, the matching problem that is here is a job to be done, here is the right person to do the job, thanks to AI, that matching problem is now possible. Historically, it wasn’t because you’d have to collapse the dimensionality of that problem to things like, what is the title? What is the geo? What is the pay scale? Which really doesn’t tell you whether or not this person is going to do a good job. And so I’m curious, are you a believer in the ability of AI to actually match the right talent to the right opportunity? And if so, is that a big theme for Workday going forward? We heard a glimpse of it with the internal product. Is there more of that to come?

Carl Eschenbach: Absolutely. And it all becomes completely or highly dependent on skills. It’s all about skills. It’s not about pedigree or college or anything else. Like, you know, let me give you one statistic and then I’ll come back to say how we do it. Accenture, one of our biggest partners out there, who’s a very reputable, as you know, system integrator, global system integrator. Today, their new hires, 30 to 35 percent of their new hires do not have a college degree.

Pat Grady: Hmm.

Carl Eschenbach: It’s pretty amazing. Because what they’ve done, they’re probably one of the most advanced companies I’ve worked with that looks at skills and job outcomes or job recs they’re trying to fill, and then they use AI to match the two, right? And that’s what our product does internally. Let’s say you have 10,000 employees. Because we are the system of record, we know all the skills you have. And you have this, if you will, database of all the skills of your employees, so now when you want to try to hire someone, you first look inside and say, “This is what I’m trying to fill, this job. These are the projects this person’s going to work on, and these are the type of skills that are required.”

Pat Grady: Mm-hmm.

Carl Eschenbach: Then you look at your own 10,000-employee population and say, “Wow, here’s all the skills Billy or Susie have,” and you do that internal matching. And so as we move to a skills-based-outcome world, this becomes more and more valuable inside of companies for people not to just hire based on pedigree is how we like to think about it.

Sonya Huang: Carl, I remember when I interviewed for Sequoia, you were very memorable.

Pat Grady: [laughs]

Sonya Huang: I mean, so much of that interaction, though, is just the human, “Can I see myself working with this person, being inspired by being around them every day?” Do you think that AI can actually—agents can disrupt the actual interviewing process and, you know, the process of actually selecting the right candidate for the job in terms of some of the human judgment elements?

Carl Eschenbach: It’s a great question because you know me, I like doing this with my partners like you. It is all about the human touch and human connection in the relationship you establish with the other person. That’s really what’s important at the end of the day. But that’s also why we always talk about—and you talk about probably the same thing. There’s going to be a human in the loop in all of this, especially in hiring. I personally would not accept someone into our company if it was 100 percent done purely based on skills and skills requirement. There has to be a human in the loop all the time, I think. Yeah.

And I know we talk about where AI is going in the future, and it will have emotions, it will have feelings, it will have empathy. I’m trying to get there with everyone, but I still think this is required ultimately when you’re going to hire a human. In fact, one of the things I think that’s going to change—and here’s an example of where humans will not just necessarily be replaced by AI, but since AI is going to have such a profound impact on human productivity, it’s going to free us up to move from a world of technology transformation to a skills transformation by people like us now going and picking up skills that I actually think we’ve lost in the workforce, right? Those life skills, those people skills you’re talking about. How do you learn to network? How do you learn to give feedback if you’re a manager? How do you collaborate? How do you lead with empathy and feelings? We lost a lot of that during COVID, and now we got to get that back because people come to the office, how do we educate and train and mentor the younger people? You can’t do it all the time over Zoom. There has to be a human connection to make that happen. So we’re now talking about how do we use the AI transformation of the workforce to transform skills that we think are missing in the enterprise, to bring the two together ultimately to drive better outcomes for both employees and companies.

How AI will change work for the better

Pat Grady: This is one of the optimistic views of AI that I think we very much believe in. And we hear similar things. You know, Winston at Harvey talks about how the legal profession is going to kind of go back to what it was 50 years ago, where it’s less about processing transactions and it’s more about human connection and the advice and the consigliery role that people can play. You know, Christopher O’Donnell at Day talks about how because AI-generated CRM does a lot of the drudgery for you, you can actually focus on the human connection, too. And so I think the optimistic view, and sort of what I’m hoping for out of the world of AI is technology—to your point earlier on how we work for technology and it’s going to work for us, technology kind of fades into the background because AI is doing a lot of the work, and it sort of frees you to have the human connection be more front and center.

Carl Eschenbach: Yeah. Well said, Pat. That’s why I think we move from a world where we’re working with technology to the technology working for us. And today it’s front and center when we talk about how we’re using AI, but the power of AI is these models learn and they start to do things autonomously on your behalf. So you don’t even have to do it, so you go do new things. And the last thing I’d say is—this is factual. Technology enables change. That’s all it does. It enables change. To drive change, it still takes people, no matter what.

Pat Grady: Well put.

Carl Eschenbach: People are going to be involved with AI. They have to implement it, they have to embrace it, they have to teach people how to use it and they have to teach people new skills. Technology only enables change, it doesn’t drive it. It still takes humans.

Pat Grady: So this is one of the ironic things. I completely agree with the point, and maybe a different way to frame it. One of the ironic things about this agentic economy, agency is the thing that is uniquely human, right? The ability to determine what is it that I want to achieve here. That is still a uniquely human thing. You can dispatch an AI agent to go reason through the steps and figure out how to get there, but deciding what question should I ask, what is the objective, you know, that’s still a uniquely human thing.

I think another framework would be, you know, Google sort of commoditized knowledge. OpenAI is sort of commoditizing intelligence. Agency is the thing on top. Intelligence is making use of that knowledge. Agency is determining to what end. And that’s the thing that is still uniquely human. And I think the argument in favor of AI is it gives you superpowers to go do that.

Carl Eschenbach: Yeah, I think it’s well put. Again, especially in the enterprise, Pat, I think there’s so much opportunity. I think AI will drive a step function change in human productivity, no doubt. No question about it. I think how we get there, there’s different views, there’s different opinions, right? And I think we at Workday, and myself for sure, think it’s going to be a peaceful coexistence of humans and technology working together, just like we’ve done in the past major tectonic shifts. I remember when cloud evolved and came about, everyone said—everyone’s going to—the CIO of the organization are not going to need any more people. I don’t know. There’s probably more people than ever working in the CIO’s organization. People say developers aren’t going to be needed. I’m not sure that that’s going to be true. We’ll need less junior developers, but really architects and senior developers are still going to do the final work and approve it or not, back to Sonya’s point.

Sonya Huang: We’re not going to have Carl vibes-coding the code base?

Carl Eschenbach: Yeah.

Pat Grady: I would love to see Carl vibes-coding.

Carl Eschenbach: That would be a problem. There are some things I can do and there’s a lot I can’t. That’s one I can’t.

Transitioning to Workday

Pat Grady: So we talked about transformation a bit, and how people are going to transform and AI is going to transform the workplace. The timing of your transition into the CEO role at Workday was pretty interesting because it was January of 2023, I believe. And so we’re just a couple months post-ChatGPT moment.

Carl Eschenbach: Yeah.

Pat Grady: And so you came into a company that was at that point 17 years old, 18 years old, something like that. A big company, already many billions of revenue and tens of thousands of people. And from the outside looking in, it seems like you have driven transformation on at least two vectors. You know, one is driving just efficiency and velocity and just kind of basic cleaning up the business, getting it in better shape, sort of transformation. And then the second, of course, is the AI transformation. And so the question is, because I think there are a lot of people looking at transformation over the next few years, what did you do to drive transformation? What was effective for you? What were some of the—maybe what was a crucible moment or two that you had to sort of bust through to actually drive change in an organization with that much scale?

Carl Eschenbach: Yeah. No, listen. It is a big company when I joined, and it’s one of the most successful, obviously, software companies of our generation, if not ever. And it wasn’t Carl who drove any transformation, it was a team that drove it with me, right? And to get their support was probably what I had to do and work on most.

Let me start with what hasn’t changed and what will never change because I think it’s very special and it will never be transformed. When Aneel and Dave, the founders of Workday, who worked together, and Dave was the founder of PeopleSoft, when they sat down to start this company 20 years ago, Pat, the first thing they did is they said, “What kind of company do we want to build, and what is the core foundation of the company and how we’re going to scale from this day forward?”

And the first thing they did is they sat down and they wrote values. They wrote six values of the company. And to this day we still talk about those six values as the foundation for the company. We focus on our people first, our customers, two Is, integrity and innovation. And the last two is we like to have fun, and the sixth one is profitability. And profitability is the sixth one for a reason, because if we do the first five right, we’re going to be profitable.

And even today, that’s all I talk about when I’m in front of the company or in front of customers. We talk about how important a values-based company is. That being said, our culture is strong because of those values. But culture, while the values will never change, culture does change as companies scale and grow. And part of our transformation is to think culturally how do we actually become a bigger company? How do we run things more effectively, more efficiently, and drive a deeper level of operational rigor? How do we transform a lot of work that’s been done? Because probably my experience and background, I can’t change who I am or where I came from on the go-to-market side, how we’ve segmented the market, how we’ve gone deeper into verticals, how we’ve expanded and built a true partner ecosystem.

I now say I don’t want to build just an ecosystem around Workday. I want to build an entire economy. Our partners are now not only deploying us, but they’re all now taking us to market. They’re selling on our behalf, they’re being rewarded, and most importantly, they’re innovating like crazy on top of the Workday platform. We’re unique that we’re both a platform company and an application company at the same time. And the platform is starting to resonate as we open up the aperture—some of the things we talked about earlier—to allow people get access to the platform. They’re innovating on top of us. And then they build it once a partner can, sell it to our marketplace and sell it to our 11,000 customers, right? Build it once, sell it to many. So we’ve done a lot on that side.

And then on the technology side, yeah, I stepped into this. Everyone knew about AI. We knew AI was gonna become a big part of a company like Workday. It always has been on the roadmap. In fact, you know, if you talk to Aneel, who I think is one of the most thoughtful, mindful and smartest enterprise software guy I ever met, like, he’s been doing AI and machine learning in the platform for a decade, right? That’s what he said. So this is just the next evolution. I didn’t know stepping into this this was going to happen this quick. And I think one of the things that we’ve done in some of the transformation and crucible things we’re doing is moving with speed and velocity at scale.

Pat Grady: Yeah.

Carl Eschenbach: Like, we’re moving much faster where we have a sense of urgency. Because we think we’re uniquely positioned because of the data set we have, we think we got to move quick before anyone tries to come and disrupt us. So we’re focused on disrupting ourself. And I think that’s been a lot of fun. Unfortunately, to support all of these growth initiatives and these transformations that we’re going through, one of the hardest things I’ve ever had to do in my career is a sizable transformation of our people where we did a restructuring earlier this year, about eight percent of our workforce, about 1,650 people. And listen, that was the hardest thing I’ve ever had to do in my career. When it’s on your watch and your time to do that at a values-based company is not easy. So getting all the leadership and my partner Aneel and the board to support that was a heavy lift.

But the way we did it was incredible. The empathy we showed, the caring, the loving, the embracing of our workmates that were leaving us, the packages and severance we gave them, was done in such a way that I think people felt as good as they could going through this type of a transformation. And now we freed up all of these OPEX dollars to go invest in these transformations on both go-to market and the product side. It’s been an incredible—it’ll be two and a half years here in a couple months. It’s been a great experience, and I’m grateful for the opportunity. I feel a sense of pride to continue to endure this company well beyond my time here, and feels like days there’s a lot of pressure if I’m not lying, but I have an amazing set of people that I get to serve alongside that believe in our mission, believe in the product, believe in the technology, and believe we’re actually having an impact on the world because we manage more employees than anyone out there today. That’s pretty fun.

Pat Grady: Pretty cool. So if the first 20 years of Workday were sort of helping usher your customers through this on prem-to-cloud transition, in the next 20 years—I know this is oversimplified. For the next 20 years, you’re helping to usher your companies into this world of AI. Does the nature of the moats that you build as a business change when you go into the AI world? Or said differently, you’ve had this wonderful moat historically, which is being a core system of record that a lot of other applications sort of orbit around. Does the nature of the moat for the next 20 years change with AI, or is it kind of more of the same, it’s just the AI version thereof?

Carl Eschenbach: I think the moat remains the data. When I think about data, data is the new UI UX for AI. Think about that. Data is the new UI UX for AI, right? Garbage in, garbage out. Highly curated data with context in great outcomes with agents. So I think it’s both. I think our moat is what allows us to build the next set of moats, and that is role-based agents.

Pat Grady: Yeah.

Carl Eschenbach: And if we can bring role-based agents—because if you think a lot of today what you see in agents, a lot of them are just super copilots. They’re task agents. They call them agents, but they’re doing a task.

Pat Grady: Yes.

Carl Eschenbach: And it’s automating some repetitive task, right? Think of RPA on steroids almost, right? When you go into a world where we leverage the moat of data and we have the context of the data and we understand the skills humans have, think about now building role-based agents that doesn’t do a task, it picks up skills. And because we know that, I think that’s going to become our next big moat along with the data we have as we transform how people work alongside of agents, and not just use them to solve a repetitive task. So I think as the world goes into the future, because of that moat I think people do trust us to help them navigate the unknown of AI to help them take advantage of it in the future. Specifically, obviously, we’re not a consumer company, but in the enterprise.

Lightning round

Pat Grady: Should we jump into lighting rounds?

Carl Eschenbach: Uh oh, let’s do it.

Pat Grady: [laughs]

Sonya Huang: I’m going to surprise you.

Pat Grady: Go for it.

Carl Eschenbach: Where’s the lightning? Come on.

Sonya Huang: Which of your beloved Sequoia partners do you miss the most?

Carl Eschenbach: Oh, wow!

Pat Grady: [laughs]

Carl Eschenbach: I got it. Ready?

Pat Grady: Yes.

Carl Eschenbach: Emily.

Pat Grady: There you go! Nailed it. Nailed it.

Sonya Huang: Emily Fraker, I hope you’re listening.

Pat Grady: That’s the best answer you could have possibly given, because I don’t think anybody will take offense at that answer.

Carl Eschenbach: Listen, I love all my partners. Listen, as I said earlier, Sonya, I’m so grateful you gave the old operator a chance to come in. And I think some people were nervous about me coming in, but I dove in. I learned. Thank you to you two, especially. I’d attempt to write those damn memos and I’d be like, “All right, that’s enough. Let me get some help.” And you guys would just—I remember the red lines coming back. I’m like, “Damn it. I thought that was really good,” until I gave it to you two. But I hope you saw that I came in with no ego and came in as an apprentice, and I dove in and learned and cold called prospects. It was like I went back 30 years of my career banging the phones, and I didn’t think I was any different than anyone else. And hopefully you saw that.

Pat Grady: It was amazing. It was an inspiration to all of us. This is a thing that I don’t think people appreciate is, like, people—I think people assume that, oh, well, Carl is such an amazing operator, you plop him down in a venture capital firm, of course he’s going to be successful. I don’t think what people realize is the degree to which you embrace the beginner’s mindset and, like, truly learn the job from scratch. And that was …

Carl Eschenbach: It was hard.

Pat Grady: … truly inspirational to everybody.

Carl Eschenbach: But I had people like you and Doug and Roelof and Jim, and you name all the—every little partner’s, like, everyone—like, I remember coming out of partner meetings and I’d say, “Get in this conference room. What’s a DAO? What’s a MAO? What’s this?” And it’s like, what are you guys talking about, right? Then you learn it more. And yeah, it was amazing. That’s why I still appreciate being around all of you. Yeah.

Pat Grady: All right, next question. I think that it’s safe to say you would be a consensus pick for most people’s Mount Rushmore of CEOs.

Carl Eschenbach: No. God no, Pat.

Pat Grady: Who is on your Mount Rushmore of CEOs? Who are the four CEOs that you really look up to and admire?

Carl Eschenbach: Wow. One would definitely be top of mind comes Joe Tucci. Just an incredible human being, incredible leader. People will run through walls for him, as I would. He was pretty special. I don’t know. I haven’t thought about that, Pat. I don’t know him well, but I’m super impressed with what Satya’s done.

Pat Grady: Yeah.

Carl Eschenbach: He’s pretty impressive. Watching come up from the technology side and his success, I thought has been pretty impressive. Geez, I don’t want to call anyone out because then I’ll miss someone.

Pat Grady: [laughs] You could just leave the fourth spot open and say, “Hey, you know who you are.”

Carl Eschenbach: Yeah, that’s actually a really good question, Pat, I must admit. I don’t know if I have anyone top of mind, but I really get impressed with people who find a way to have longevity as a CEO, and that are very pliable or adaptable as things change in the world, right? They don’t just kind of root themselves into what they were or how they used to lead or what they used to think, but they think about how to transform themselves. Just like we talked about business transformation, how do you personally transform yourself to operate in whatever era you’re in? I think people who have a long career as a CEO is something I’m always impressed with as well. I don’t know. And the fourth one, you know, there’s many. I’ll use your line. There’s so many of them out there, I can’t even think of answering only one.

Pat Grady: Fair enough.

Carl Eschenbach: [laughs]

Sonya Huang: Favorite AI app that you use in your personal life? What do you do with your wife and your kids with AI?

Carl Eschenbach: No, I use Gemini probably because it’s accessible. I mean, I use ChatGPT, right? I definitely use them a lot, just asking questions. Yeah, that’s what I use probably more than anything else. Just a generic, you know …

Sonya Huang: I’m going to start sending you some AI songs.

Carl Eschenbach: Yeah. Yeah, there you go. I think my kids use some. They send me funny stuff and I’m like, “What the hell are you guys doing?” But no, I just use more of the business tools around AI to try to help me become more productive and get more hours in the day. Yeah.

Pat Grady: For any founders who are listening, who are in the process of growing into being CEOs, if you had to pick one piece of advice to give them, what would the one piece of advice be?

Carl Eschenbach: Hmm. Yeah, quite simply, it would be to focus on the success of others, not focus on your success.

Pat Grady: That’s very well put. All right. Thank you, Carl.

Carl Eschenbach: Thank you, partners, for having me.

Sonya Huang: Thank you, Carl.

Carl Eschenbach: It’s been an honor and pleasure to be here with you. It’s great to be inside the Sequoia building again. Thank you.

Pat Grady: It’s great to have you back.

Carl Eschenbach: Thank you.