Workday's new product head hopes he can make you like Workday
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Hello and welcome to Decoder. I'm Nilay Patel, Editor-in-Chief at The Verge, and Decoder is my show about big ideas and other problems. Today, I'm talking with Garrett Kazmaier, the brand new President of Product and Technology at enterprise software company Workday.

Decoder listeners probably know the name Workday. A lot of companies use its platform to run HR and finance, which the suits have started bumbling up into a phrase they call human capital management. I invite you to have whatever feelings about that you want. Anyhow, if you've been applying to jobs lately, you have almost certainly run into Workday, and you are almost certainly frustrated with it.

I mean, I'll just say this from the jump. It's rare that enterprise software executives come on this show because it's a guarantee I will ask them why everyone hates enterprise software and what they're doing to fix it. Workday is no exception. Last year, Business Insider published an article literally titled Everybody Hates Workday. Now, Garrett's new on the job, maybe a little bit braver than most. And to his credit, he came on Decoder and he took the heat.

We spent a lot of time talking about what enterprise software really is, what it does, and why all of these tools have a reputation for being so deeply frustrating for so many people.

As you'll hear, the heart of this conversation is the major tension that exists between the idea that software is a tool that accomplishes some work and the idea that using a software tool is the work itself. And making that even more complex, everyone experiences software like Workday in totally different ways across a company. I mean, just think about it. For most people at work, Workday is just a database or a series of forms that are required to fill out to file expenses or log a performance review.

For the people who actually work in HR and finance, using Workday is a huge part of their actual job. It might be their only job. And then for the C-suite, who make a lot of decisions using data generated by tools like Workday, they might never actually use the software at all. Instead, just looking at reports other people generate from the information in the database. This is tricky stuff, and as most companies manage those tensions and balance those trade-offs, well, user experience falls to the bottom of the list, which is why most of this software is so bad.

You will not be totally surprised to hear that Garrett's solutions to a lot of these issues is to use AI. After all, Workday now calls itself an AI platform.

So I really wanted to know what role Garrett thinks AI will play at work, and if simply letting an AI fill out all those forms for people might make things better, or alternatively, just result in bad data all through the database. And I really wanted to know how comfortable he was letting AI make decisions about finance and HR, because AI systems can have a lot of bias built into them. That's something lots of companies, including Workday, have already faced lawsuits over.

Look, I told you, there's a reason enterprise software executives don't often come on the show. So credit to Garrett for sticking in there. I think you'll like this one. Okay, Garrett Kazmaier, Workday's President of Product and Technology. Here we go. ♪

Gary Kazmaier, you're the president of product and technology at Workday. Welcome to Decoder. Thank you for having me, Nila. Excited to be here. I'm excited to talk to you for a variety of reasons. One, enterprise software executives don't often want to come on the show because I just asked them about the nature of enterprise software. So you're very brave. Thank you for coming on. And then second, you're the new guy. You just started in March. So you don't have to defend all the stuff that other people did. You can just be honest about it.

Exactly. And hey, maybe you just don't invite so many of enterprise executives. So I don't know. Maybe I can lure some more in. My threat is always that we all just use the software together live, but it's an audio podcast. Don't worry. We're not going to do that today. Sorry.

Workday is one of those pieces of software that maybe everybody encounters in the course of their career. You apply to a job. Workday is the interface. You're at a job. It's your finance system. It's human capital management, is I think what we call it now. You're doing your performance reviews. How do you think about Workday? What is this thing? Yeah, it's that incredible system that helps organizations on the one side manage their people and manage their money, which is great. Well, two of the most important assets our corporation is built upon.

But I think more importantly, like what you have said, right, when you think about everyday work experience, it's the systems that, you know, everyone touches, everyone interacts with. And I think makes a huge difference in having, you know, a great work experience and, you know, ultimately a great personal development and building a great career. So, yeah, it's a system of work and it's very exciting to be here. There's a lot of...

Companies that want to describe themselves as the backbone of how you might do work.

We had web service companies come on, Squarespace, come on and say, we're the operating system for small businesses, right? You book the class or you book the auto mechanic and then we'll do the billing and finance. Workday is also expressed like that in some way, right? You've got people, you've got money, they're moving through your system, they're spending the money, you're tracking what the people are doing and if they're performing well. Do you want the big picture of we're running the entire business in Workday?

I think that's kind of an archaic way to think about systems and people because enterprise software is an ecosystem. Organizations are large and touch many domains and people and money is important, but there is also customer, right? There is service. There are so many things, right? There are so many things that makes a company and there are so many things that make a work experience that

I would argue that, you know, back in the old days, right, when you look into the legacy enterprise software systems, right, that they had this idea of that perfectly walled garden and, hey, there's going to be one door, you know, you enter in the morning and you're going to stay in that door and, you know,

we're going to give you what that system has to offer and that's it right and i think today's reality is that it is not reflective of what makes a great enterprise software stack you know you you have a multitude of vendors different offering different capabilities and and you have to compose them together to reflect you know what really is important to your company and then secondly i think it also

It's about the work experience, right? You know, from people bringing their own devices to they bringing in their own AI models, you know, more often than not, or their own AI coding experiences, meaning that

They also have, you know, way more agency about the systems they use and the systems they expect to use, right? So you have, you know, collaboration and productivity, and that's something very specialized. And you have enterprise systems for all sorts of purposes. And I actually think it's about an intelligent ecosystem and being part of, I would describe it as an enterprise software fabric, if you will, where it is really important that vendors, you know, like Workday work with other vendors in the industry and

build that system so that customers can use them in orchestration without having that kind of ridiculous idea, right? You get everything from one and you have to be happy with that. I mean, how would that work?

I mean, you have a long career in enterprise software, but that is a trend across every enterprise business product that I've ever encountered where you start with one part of the business and then the line everyone uses is we want to be the operating system for your business. We want to take everything. And it sounds like you're just totally against that.

I think I'm totally for a vibrant software ecosystem. And if you think about it, I think it starts with an operating system. I think that's a great metaphor, but it's also something that I think we have to evolve. Back in the old days when we said operating system, it was this monolithic piece and everything had to run on it. And then came along web and suddenly, well, what you had on your operating system was

was not the only thing that you could use when you were on your device, right? Because you could access web services and online services and suddenly

I would say, when we say operating system, what is the operating system? You might say, well, it's a specific software platform and only the things that run on it are allowed. And I would say, well, I guess the operating system today is the browser and everything which relies on HTTP is part of my operating system ecosystem. And in the AI world, you might say it's

It's an operating system that is defined by MCP, Modern Context Protocol, right? And we have an orchestration of agents, right? So I do think, you know, operating systems are important because they define actually how an ecosystem works. They define standards and they define very important shared interests, you know, security being one of them, right? So those are all things that

no one would want to give up upon. But I don't think they are single source, single vendor monolithic pieces anymore that just create one experience. I would say it's a dated way of thinking about enterprise software actually. There's a real push and pull here and there's a reason I'm starting in this esoteric space.

I think a lot about what work is. Like, what are we all doing? And in the age where, you know, there's a big push and pull between remote work and in-office work and what those experiences are, so much of our jobs just every day is using software. You and I are talking right now through a piece of software called Riverside that is, like, quite cranky, but, like,

On one very basic level, my job is just using Riverside a few times a week. And the things I can do at my job are limited in some ways by Riverside. They're enabled in huge ways by the software existing. And you see that with every kind of enterprise software.

Right. Workday, there are enormous numbers of HR and finance professionals who show up to work every day. And what they do is they use Workday in one way or another. There's executives who receive reports from Workday and their job is just evaluating the information Workday has compiled for them and then making some decisions on it.

How do you see the role of the software there? Because what it looks like and how it works and what it's for is all pretty dependent on the fact that some people's jobs are just using the software. It's an interesting way to think about a job, right? Because what you just said, right, when you said it's my job to use Riverside, I actually thought maybe that's not true. You know, maybe your job is, you know, asking powerful questions and talk to many peoples and creating a show that engages, you know, listeners, right?

And Riverside, the software piece that you have just mentioned, that is just something that allows you to do your job really well. I think the same applies when we think about Workday. People, they have important jobs to do. They try to hire great candidates. Once they hire great candidates, they try to onboard them and train them in the way our company works.

They want to build thriving organizations that let people have a really good work experience. They want to manage performance. They want to reward and recognize people.

And those are the jobs right on the HR side. And in the finance side, right, it's as simple, right? You ship products, you want to write bills and you have to pay bills and you want to create a compliant profit and loss statement and you want to be financially responsible and viable in the long term and manage your cash position and so forth. And those are the jobs, right? And

Now, basically the question is, what do you have available in terms of tools and software that allows you to do a job in the best possible way? And that's the core of Workday, right? Workday says that for the jobs that you have, which are software independent, which are emerging from the very core thing that you do as your value creation, we're going to give you the best services and products to do everything related to people and money.

I think it's a very important focus to get this straight because sometimes I do think people get confused, specifically in technology. AI is a wonderful example of that, by the way, right? Because now some people think,

maybe the job is AI. Maybe that's my job, to do something with AI. And there is a certain thrill and excitement that goes with that. But ultimately, there is a reason when you look at studies. For instance, Stanford has that AI index survey. It's a beautiful 400-page read on the state of the art with AI. And part of that survey is

enterprise leaders get asked about their returns on their AI investment, right? And the vast majority said, you know, AI gave them less than 5% top-line increase and less than 5% bottom-line efficiency. You wonder, right, with all of that investment, how can that be? And I think ultimately it's because there is a confusion that some people think maybe my job is AI, but actually it's not. You know, the job is

What you are trying to do for your business and AI may be a powerful way for you to do this job better. And for software vendors, they're not at work there. It's the same, right? How can we help people manage their money and people better through AI and being focused on the real job versus on the technical means that facilitates a certain way of doing that job?

I saw a similar survey from IBM where they surveyed CEOs, and the CEOs, the results were only 25% of the AI projects had returned on the investment. So we're in this place where everyone's spending the money. You guys are spending the money on AI. We're going to talk about that. And no one knows why. I'm curious. I'm sure you have an answer. But I see that piece. Then there's the other piece of AI.

The reason people are investing so much money in it is because maybe the AI can use the software or maybe the AI will fill out the forms or do the, you know, this is the big promise. The AI will do the boring stuff. Workday, I think for a lot of people is expressed as the boring stuff in their job, right? They're throwing out expense reports or whatever. And you have, or you have all the way up to agentic AI, which actually doing stuff and making decisions. How do you see that interaction, right? Is AI going to use more and more of Workday for people?

You know, first of all, I would say workday is the exciting stuff. You have to. I appreciate that you have to say that workday is the exciting stuff. Because, you know, I think, you know, sometimes, you know, when you say, you know, what's the exciting stuff, right? I actually think, you know, I was...

Well, you know, like you said, I worked in enterprise software, you know, in my entire career, but actually all of the enterprise software businesses that I was a part of, they're people businesses, you know, like a hundred percent, right? You know, you drive all of your work through and with people and teams, right? So managing that,

And growing and managing people, that's an exciting part of the job. And Workday is that system in which you make that happen. So this is why I'm saying it's the exciting stuff. For me, Workday is more than doing a PTO request. Can I ask you about this? Managing people is the exciting part of the job, sure. Somebody who manages people on their days, I agree. On their days, I disagree. I don't think about the software as the management part.

Right. And maybe it's just I work in a creative field and our conversations about management are very different. But there's not a place where I'm like, I'm going to use this software and that will accomplish a management task. It's much more I've accomplished the management tasks and now I need to record it in the software just so I remember what happened. We have a record of it. But like there's a confusion there. Right. I think.

Whether or not what happens in the database is real life is maybe the central confusion of the entire tech industry across the board. It might be the central confusion at the highest levels of our government right now. But how do you see Workday closing that gap, if at all? Is it even possible to close that gap?

I totally think it's possible to close this gap. I see us having closed that gap and increasingly closed that gap in certain domains. But I completely agree with you. Work is so complex and it happens in so many ways. And like I said earlier, it's not that it's all happening in one system only. And it's an ecosystem. So I completely agree with that point. And some of the work is even offline, like you said, talking to someone, as simple as that.

But there are domains, you know, for instance, like in the recruiting space, you know, for instance, how do I build recruiting campaigns? How do I interview people? When I interview people, how do I select, you know, candidates that are, you know, best fit for the job on something more profound than pedigree? But, you know, skills, for instance, right, and skills that they have shown or skills that I can infer, those are...

This is work where work genuinely happens to a significant degree in a system like Workday. So it may not be happening everywhere. I agree with that. And maybe it shouldn't even happen for everything everywhere. But there is a significant portion where professionals

actually do their job right in these systems. And that's why I think it's so important to get them right. I haven't had to use Workday in a long time. So to prepare for this interview, I watched a lot of Workday training videos on YouTube. Amazing ecosystem of Workday training videos on YouTube, I have to say. And it just occurred to me as I was watching some of this stuff that Workday is expressed to people as a database, like very sort of openly. It's a database or maybe a spreadsheet in some other of the interfaces. And

You know, people applying to a job experience it as a series of forms to be filled out. Again, when I'm talking about like you use the software to accomplish the task, a lot of the task is making sure the database has the right information in it. That runs sort of headlong into AI, right? Now you've got people using AI to generate the information for the database, or you've got an AI system that's going to look at a receipt and figure out what it is and put it in the right fields. And there's a real, there's like a new kind of data risk there.

Right. Where you're definitely going to get all the fields filled in. There's one thing generatively I can do. It's fill in the fields with little effort. But it might be hallucinations. It might be garbage. It might be worse than if a human didn't fill out the field. How are you thinking about that risk? What you describe is a question of maturity, actually, of maturity in both ways, both technology and use. You know, AI is undeniably dangerous.

the most profound change in technology. I think we are just living through, you know, the beginning of a renaissance of what AI can deliver, right? You know, there's in any domain, material science, medicine, enterprise software being just one of them, people are solving problems in incredible ways, you know, with the use of AI, right? And I, as a person, right, you know, between you and I,

Humankind has big challenges, very big challenges. And I think it's a wonderful opportunity to have broken one of these big technology boundaries on reasoning and judgment and knowledge compression and being able to use that on virtually any domain that exists. Right.

That is incredible. But what you just spoke about is the flip side of that. It's new. The reality is that we have intuitions about it. We have an intuition that a computer program is right because it's deterministic. So there is an intuition that goes alongside when we use a computer. When you talk to a person,

That is different, right? Because we know that we have biases, we get stuff wrong, and there's a different type of intuition that we have than what we expect a computer program to do. And now we have these generated models. And like you have said, they are probabilistic, right? They're not deterministic in many ways. And even very subtle things change their behavior and things that we wouldn't expect them to do, right? It was just, I think, last week or the week before this,

outcry of the latest OpenAI model. And, you know, I think it was called the sycophancy of it, right? Yeah, it was too nice. Yeah, because it was too nice, right? And it was actually a few small changes which dramatically have changed, you know, the behavior of the model, making it too nice and not really helpful anymore. So what you just spoke about, right, when we think about AI specifically in enterprise context, I think the big point is that

AI gets you to a lot of results really quickly. It's so powerful. In many ways, it is really shallow. But in the enterprise space, for instance, here today, we take one of these very powerful models, we give it a really good prompt that is reflective of a business problem, the model gives us something, and it looks roughly good. And we say, oh yeah, we got it solved. We are happy about that. But the reality is when you actually want to make it work with

accuracy expectations that you need to have in order to be used in a professional context or that someone makes mission-critical decisions upon. Or in cases, which is very important, where someone cannot correct the model in a direct way. Coding is a good example. Why is coding successful? The model gives you something back in a modality that you understand as a coder, right? You know what the model is doing. You see the code. You have a generalization

chance to lean in. That's why it took off so quickly, right? Because you basically spoke the same language with the model and there was a big corrective. And in an enterprise system, what you have said, right, you've got to fill out this complex form, you know, for maybe a process that you don't even fully understand yourself, right? And it has language which is legal and compliant that, you know, is alien to you, right? And the model is doing something. How do you know? We need to take a quick break. We'll be right back.

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We're back with Garrett Kazmaier, Workday's president of product and technology. Before the break, we were discussing how software fits into the nature of modern work and what that might mean for the relationship people have with platforms like Workday. Now I want to ask Garrett the decoder questions, especially because he's only been at the company for a few months. He's got really fresh eyes when it comes to our big themes around structure and decision making. I would be a little more reductive. I would describe the problem here as garbage in, garbage out.

Right. The promise of so much enterprise software, particularly HR or finance software, is you just had more information. You would get better decisions if everyone would just fill out all of the fields. Right. Like if only everybody would just fill out the software correctly, we would have better perfect real time information about the business and then we would make better decisions.

And what AI is giving, maybe not built into Workday, but just in general, now people with a ChatGPT app on their phone can now definitely fill out all of the fields. 100%. You already see it in job applications, right? People are applying to 1,000x more jobs than they were before because they can just fire a copy into these job applications from ChatGPT. And now the systems are overwhelmed with irrelevant information.

And so you've got garbage in. How do you solve this problem in the context of having to turn that into actionable information? It's two points, right? And I do want to go back to quickly on what I would describe as the challenge of shallow enterprise IP before we get to what you just said about how do we cope with now the new behaviors that are emerging with the use of AI. I would actually challenge what you have said, right? Models get it wrong a lot.

you know, specifically when you talk about something which is not in the public domain. So we have public knowledge, right?

And models are extremely good at compressing this knowledge. They're terrific. Who would have thought, right? I think everyone was surprised in 2022 when it really broke through and how powerful it would become with actually a very simplistic mathematical model. That's the mind boggling insight, right? The math are simple. It's just applied at volume and it produces these incredible results. But you have now these PhD level models.

coming into an enterprise context and showing up for the first day at work. They know nothing about the intrinsics of an enterprise, their proprietary data. You spoke about a database. This is nothing which is represented in the knowledge base of that model. And yes, some of it is emergent, meaning that without really being trained with it, they are reasonably good at doing something with it. But the bigger point being is they get it wrong a lot. So one side of when you ask what work they're doing is

Actually taking these models and making them do something valuable in an enterprise context, you know, just something as simple and it connects to what you just spoke about, making sure you're selecting the right candidates for a job. That is a non-trivial task because you need to understand job requirements, behaviors and job applications. You need to basically train and specialize a model

And not only a model, actually a system, a set of models to do that with high accuracy of your reviewing contracts, right? Contracts are in many times using language terms and have implications, which again are not existing in the public domain. So you have to train a model to basically understand enterprise contracts and apply them in a system like Workday. So I think that's big, right? And right now in San Francisco, we have autonomous cars driving around here.

I think that's an important insight, right? For something to be used in a mission-critical

domain, it needs to work all the time, right? An autonomous car is not viable if it only works 99%, right? Would you use it? I wouldn't use it. It needs to work 100% of the time. There's a lot of Tesla owners out there who have made a different decision than you. Well, I'm not even going there, right? I know a trick question when I hear it, so I'm not even going there. But the way it works needs to work 100% of the time. And doing this in an enterprise context is heavy lifting. The second thing you have said is that, of course, with AI,

behavior changes in helpful and unhelpful ways. I use AI a lot to research and it's awesome because I have all of this intelligence on tap available to do research. And it's also being used in unhelpful ways, like you said, for creating content spam and unhelpful data signals which overflow systems. The good thing, though, is that this is always a balance. There's a constant balance between

misuse, abuse, and protection. Meaning, what is the antidote to what you have just described? Is that if bots generate applications or forge expense lines and try to trick the expense system,

you use AI models to also counter that. And as it turns out, AI models are terrific in spotting patterns that are generated versus done from a human. I'm sure you have filled out a capture request online in your life a couple. So same idea. You basically build protection using AI to

you are not being mis- or abused by AIs. I think that's, you know, it's the same meta theme of increasing maturity and using AI systems and working with AI actors, both inside and outside of a company. I want to ask you the decoder questions and I want to try to put all this together. You've only been at work a few months. You probably, you know, where the bathrooms are in the office, I'm hoping by now. You've met a lot of people. What are you thinking about how your team, the product org is structured and how you want to change it?

That's an interesting question because you actually kind of need to witness here how you imagine to change it. I'm not sure if I am. So Workday is a young company. Workday is 20 years old. Compare this with many other enterprise software companies, they are twice as old or even older.

Workday has a really strong technology foundation. Actually, what surprised me the most, Neelay, when I got into Workday is how good its technology foundation is. Because, you know, you come and I joined from a cloud provider before. I worked at Google in the past four years before I joined Workday. So you come back to the enterprise application domain with certain anticipations and Workday...

really is incredible when it comes to its tech stack, its scalability, its elasticity. I mean, it was a cloud-first system from the get-go, so it really has a great foundation to stand on. And when I think about evolving us, going into the future, it's pretty much aligned with what you have said, right? It's maturing enterprise systems around the use of AI. My take is the following, right? Today, you see a lot of bolt-on AI,

Meaning you see a lot of legacy system and they just get an AI overlay and you see integration vendors basically they dealt with all of this complexity and now AI comes to the scene and they say, well, let's just slap AI over it and we call it automation. I just want to go back to autonomous cars, right? If you have AI, the opportunity is to purposefully build with AI.

To change how a job to be done, where we started, actually gets completely innovated and revolutionized. For instance, when you think about something like job applications, something very natural, we all have an understanding, we all applied for jobs. I'm not sure if you did, actually, but I did in my life. When you got the job at Workday, did you have to apply in Workday?

No, I did not. So it's a good question. No, I did not actually. But Workday is not my first job. I was a school grad once and I put an application into a system and went through the interviewing process. And the big opportunity that we have by really innovating in Workday is now taking a gen TKI models and not just

driving API automation of an old process that was defined by, if you will, human constraints to a new way of doing it with the heavy use of AI. Like I've said, right, you know, for instance, tailored onboarding experiences, tailored experiences for job applicants and targeting who we approach and how are they feeling through that. You just said, right, you know, why does it matter how I apply to a job, right? I just fill out a form, but

There are industries, for instance, where you have high numbers of frontline workers in retail and hospitality, where you have thousands, tens of thousands, hundreds of thousands of applicants a year. And for them, actually sign up for a shift or basically joining a company is something which is either frictionless and you can do it through an intelligent experience, including your skill assessment on your mobile phone with a conversation.

Or you have to log on to a classical web application and fill out forms. I mean, which one would you use? So, right, I think there are real opportunities of how we can really change the way how these processes are done from what it used to be, human and document-driven, to proactive and AI-led. When you think about those opportunities and how your team is organized, how do you map those things together? How's your team actually structured? How's Workday structured?

We have an AI team today, which is, again, I think a testament to Workday being a young company and being very forward-leaning when it comes to AI. So there is a great AI organization that is part of my product organization, which is basically driving the Workday ML and AI platform. Many of the great things that we are doing in the recruiting space, in the contract intelligence space, self-service, agent system of record,

This is all being driven by that group. And as I've indicated, one of the key innovation pillars that we have. And we have our application domains. We have our office of the CHRO, office of finance, our industries. And those are application teams which are basically building our technology foundation, the AI foundation, our application server, and build applications.

The systems that you and I and any listener on this podcast would recognize as, oh, that's Workday, right? The UI and the workflows around it. And then there's an infrastructure team, as you would imagine, which is basically running our

deployments into the various cloud providers. I mean, we are running on AWS and we are running on Google Cloud. And as you can imagine, right, you know, this is infrastructure and a pipeline that also needs to be built and maintained. And those are in a nutshell are the groups, AI, our applications from HR, finance and industries and our infrastructure team.

Right before you joined Workday, had some big layoffs. I think it was 1,750 people were let go. Obviously, those weren't your decisions. As you came into the company, do you think, oh, I need to hire up? Or those cuts, one of the justifications were we need to invest in AI. Yeah.

Tell me about that balance. Did you see, okay, we need fewer engineers because the ones we have are using a bunch of AI tools? Or did you see we actually need to go hire a bunch of AI engineers? Yeah, you're asking me a question about a time where I wasn't at work days. So I can't really speak to the thinking that went into that, judging from the 60 days or almost 70 days into my role.

We are actually investing in AI. We are investing across our application suite. And I think in a bigger picture, like you have said, yes, the work of software engineering is changing with the use of AI and with the application of AI, meaning that we build skills inside of Workday to effectively use AI. And we are hiring for people that bring that expertise into the company. So both end. I wouldn't think, though, that this is in any way different or special from what the overall industry is doing.

Yeah, I mean, we've seen so many companies, including companies we have interviewed on the show, like Duolingo, say, okay, we're all in on AI. We're through the testing and experimentation phase. The way we're running this company is now formally changing because of AI. And we expect AI to appear in all the things we do. Are you all the way there? Yeah, you know, we are making, as any professional software company out there, heavy use of, you know, code assistance and wipe coding software.

And, you know, I started my career in software engineering myself, right? I was a hands-on, you know, developer for many years and just seeing how much it helps and changes, you know, the quality and the results you drive on the soft engineering side is amazing. With wipe coding, actually, I think what it tries on the product management side is amazing that you can actually specify working prototypes and real interactions. You know, it's...

not just Figma anymore or POD, which is great, right? Because you give so much more fidelity to your ideas. So yes, we make heavy use of that and I truly believe it delivers real returns.

For most, because when I was a developer, you know, I made a fair amount of bugs. Meaning like introducing issues right in the programs that you've written. And, you know, I hope that, you know, every software developer out there is, you know, having the same moral integrity to say, yeah, you know, bugs happen. And what I saw, what convinced me the most of assisted coding is that actually...

most of the bugs that you create on hindsight are, yeah, I should have really got that. I just didn't think the following conditions through. And AI helps in two ways. One is it's so good at test case generation that you just have way better verification. And secondly,

The assisted coding generates high-quality code, really not making many of the typical mistakes and anti-patterns that you just make as you're developing from a junior to a very senior software engineer. So we use all of that. That's super exciting. And yes, Workday has...

a very powerful concept. It's called Everyday AI. What's the second thing that surprised me about Workday? Manule, you're asking me these questions. I'm not trying to make advertisement for Workday here, but you're just asking it. Workday had this program, Everyday AI, or has the program. And when I joined the company, we were having an offsite just in week two. And it was basically a review of Everyday AI.

And I was so amazed about how broadly work is applying AI. I have talked to so many companies

in my past that came to me in my previous role and said, well, how do we use AI? What are the use cases? What works? And then come to Workday, ranging from employee self-service to contract intelligence in legal, basically both front and back office are making heavy use of AI models, AI applications and AI systems. And I think it was a very smart decision of the company to say,

let's experiment very broadly in every function. Let's find, you know, what really delivers value and then quickly double down on those scenarios. And so I think Workday is incredibly mature when it comes to applying AI for itself. The other big decoder question I ask everybody, how do you make decisions? What's your framework? I believe in understanding the details. You can ask the question in another way around, right? Why do decisions go wrong in the first place, right? And, yeah,

First of all, I think we have to recognize that there is a certain element of uncertainty in every decision that anyone takes, meaning you can make a really good decision and you can have a bad outcome. And you can make a horrible decision and you just get lucky. And that's a nature of the uncertainty, if you will, of future, depending on what your belief system is. But I think it's hard to predict the future and there is always an element of chance and probability. So I think that's something we have to recognize.

about being it. And that tells us something, right? That tells us that the one thing I can influence in decision-making actually is having a really robust process that in average, if you will, or, you know, in a great end count,

produces significantly more good outcomes than bad outcomes. So I'm basically trying to address the uncertainty by having a very robust framework and process to get to a high quality decision process, because I know that statistically that will drive to high quality decisions and outcome, but you can't make all of them right. And my decision process follows actually, I think, a simple framework. One is trying to mitigate as much as possible human bias.

We all have them, right? They're so human, right? It's funny that I say that, right? There's so much ingrained in our nature. There is this great book, I think, by Daniel Kahneman, Thinking Fast and Thinking Slow. I'm sure you've come across it, right? Which talks about all of the biases that we have in our brain functions. And there are just some typical repeat patterns, right? For instance, that we have loss aversion, right? We think about losses, right?

more significantly than potential wins, right? If I give you $10 and if I then take it away again, right, you're not the same happy as you are right now. You are more unhappy. I made you unhappy, right? Even though you're exactly the same. It's just the loss feels heavier, right? And it biases decision-making. There is a bias in preferences, right? There's so many things that

you can address by one, having a very structured decision-making process, right? Going through all of the alternatives, listing them out, writing them down, actually, you know, being explicit about them and thinking with pen and paper, if you will, because it helps you to bust all of these biases that you have. And then second of all, in the decision-making process,

actually engaging the right set of people to come to an unbiased decision itself, right? The right balance of expertise. And thirdly, understanding the details matter. You know, abstractions are helpful, yes. And, you know, there is a certain element where it is not adding value anymore to go even deeper, right? You know, we would all agree, right? You don't need to understand quantum mechanics to know how to throw a ball, right? You know, there are helpful abstractions.

But in decision making on leadership and on businesses, you really do want to go to the right level of detail to truly understand the dynamics of what's going on. And then thirdly, at being time bound, right? My father has this great saying that is with me a lot. And he says, you need to have the courage to take the second best solution. And what he means with that is that the most

and fiercest competitor that we have in life is status quo. Most of the time, we are not taking a decision one way or another. Most of the time, we simply decide to not decide. And let's analyze more. Let's find even more data. Let's kick the can down the road because you are of the idea that if you just give it more time, you will come to a better solution. And the problem with that is that you're actually...

passing the point where progress is more important than 1% more accuracy in the decision that you take. So if you put those four things together, having a really good decision framework that goes against or insulates you from your own biases, and secondly, having the right mixture of experts around you to make sure you are

really having the key influential or the key voices representative, thirdly, understanding the details so you can make an informed decision and fourth, when the clock runs out, you go. And you need to have to have the courage for the second best solution sometimes. We need to take another quick break. We'll be right back. ♪

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We're back with Workday President of Product and Technology, Garrett Kazmaier. For the break, I was asking Garrett the decoder questions about structure and decision-making. So now I really want to put his answers into practice. Because what I really want to know about Workday is how someone new to the job like Garrett might go about making it better, especially when its reputation among regular users and the reputation of so many enterprise apps like it is that Workday is just a total pain to use.

Let's put this into practice. Workday, like all enterprise software, suffers from a disconnect between the customer and the user. Every piece of enterprise software has this problem. It's CIOs and CEOs and COOs who buy this stuff, and then there are employees who use it. And that means there's not a great feedback loop between the experiences of the people using the software and the people who are spending money on it. And that means the software is all bad. Like, broadly, I

We don't use Workday here. We use AltyPro. I think AltyPro is bad. Like, I'll just name all your competitors down the line. The users think the software is bad. Workday has a particular reputation here. Business Insider literally published a piece in 2024 called Everyone Hates Workday. And the quotes are brutal. Here's one from an AI company. There's a copy director at an AI company. The quote is, using Workday is like constantly being bot smacked by bureaucracy incarnate.

Getting somebody onboarded using Workday is like trying to get water from your sink to your stove using a colander. That's bad. Using a what? Using a colander, like a strainer.

Okay. Right. And you're trying to carry water with a bucket. You see this everywhere, right? The interface as expressed is bad. People do not like using the software. There's another quote from that same piece. Everything is so non-intuitive. Even the simplest tasks leave me scratching my head. I, there's one that says, I just hate the software. Great. That's every piece of enterprise software. Workday in particular has this headline, has this reputation. You can see it on Reddit. You can see it in the comments of the training videos I was watching on YouTube.

How do you fix that problem? I think a part of that is, A, you know, really understanding what you just articulated. You know, as I said, right, you know, understand the details, right? You know, understand truly the details to make sure you are taking good decisions when you specifically about what do we invest time in? What do we think, you know, generates value for the users of Workday? So when we say understand the details, right, I think it's really important, you know, in enterprise software to understand

go through the workflows in detail and, you know, sometimes for yourself, you know, sometimes by observation, sometimes by interview and really have firsthand experience about what it is, right, that people do and how does it feel and what makes it good and what can be improved upon it. And I think to your point, right, that's,

It sounds so benign, right? But you said, let's apply this framework, right? The framework simply states that, right? Well, do you understand the problem really when someone is saying this is great or someone is saying this is bad? What do they truly mean, right? Because we all have our biases, right? For instance, one big bias is you seek validation, right? You seek validation for what you believe to be true and you overemphasize on signals that

that reinforce that. And the first step is really going to the right level of detail and understand what is it at the action level that drives satisfaction or dissatisfaction in a piece of your application experience. And once you have that, right, you know, as it turns out, right, once you understand that,

usually the framework becomes almost obvious to say, well, this is something that really should be different. Or this is something that actually works the way it's supposed to be. It's just not communicated. It's miscommunicated, right? You are holding it wrong. Or thirdly, that category exists, believe it or not. Or thirdly, hey, actually, we have different ways or different expectations now of how you can use that. For instance, when you talk about system interactions, right?

The reality is that it's a dynamic environment. Meaning when I look at, you know, I have two kids, they have different expectations of using a system than what I had. I grew up and forms, it was kind of like computer stuff, right? How cool is that? You have a web form. That's amazing.

My daughter, you know, she is mobile first and dare I say, you know, AI first. And she's eight years old, right? So that's the amazing part of it. A different intuition. So when I say Workday and AI specifically, right, it's now, hey, how can we make this conversational? Or how can we make it so you don't even have to specify some of this information anymore? Like you said, I have to take information from here to there. How...

Why is this complex, right? Why do you even have to, right? Because an AI model may be proficient and can we make it proficient? So it just automates that. This is why I wanted to start with, let's talk about what the software actually is, right? If the goal of the enterprise software, when I see a quote that says, this software is bureaucracy incarnate, what I imagine that means is a bunch of people at a company had a bunch of priorities and they all got expressed in a form.

Right. Everybody wants another piece of data from whatever process is happening. And we're just going to put another field in the form and then everyone can get their data. And that's your bureaucracy incarnate. Right. We're literally shipping the org chart in the nature of this process. OK, so now we're going to say AI is going to fix it. We're going to fill in all the forms as fast as possible just by talking and all the forms go to that. And then that looks like the risk.

Like there you have exactly the risk. Maybe the AI is just going to say 20% more nicer things because the model's wrong because 4.0 got a little too nice one day. Maybe it's just going to make some stuff up because it thinks that's what you want to hear. Maybe it's going to mishear the person.

I hear a lot from a lot of companies that AI is the new user interface, right? All the way down to Eddie Q on the stand in the Google trial yesterday said maybe 10 years from now, you won't even have an iPhone because AI will replace it all. Like that's where we are as an industry. And then I look at this very simple problem of for a lot of people, workday is filling out a database. Filling out the database with AI might mean the database is full of bad information.

But no one else has solved the problem in any other way. Yeah, I think this really connects to where we were starting, right? The whole question of what drives value with AI, right? And I fundamentally believe with AI, as with any other technology, you can apply it superficially or you can apply it in an excellent way. And applying AI in an excellent way actually meaning getting AI to work

differentiated levels of accuracy and outcomes. We talked about autonomous cars and you said some work and some don't, right? So there is clearly a difference, right? Even though you could all say, well, I'm sure they make all use of AI somewhere, somehow to do processing and trajectory projection and so forth. And that's exactly what we are focusing on with Workday because some of the information, like you have said, is important information, right? Guess what?

You want to pay people. You want to have internal mobility, right? We have in companies, in many companies today, a shortage of qualified labor for the work that they want to get done. And they have people sometimes inside the company who could do it or even people outside of the company that they could activate for doing it. Just to pick a very simple example. And now the question is, what can you do by excellently applying AI technology

to really revolutionize and improve these journeys. And there are clearly ways, I was just speaking about recruiting, that you can help recruiters make better decisions. They don't have to fill in the form anymore. They don't have to make the assessment. The model helps them to identify the right candidate.

Well, let me ask you about that. You inherit literally the biases of the models, right? You inherit literally the capabilities of the model. Right now, there's a lawsuit against Workday saying that the tools are biased against workers and applicants, particularly black workers and applicants over the age of 40. That might be the problem in the model. That might be the problem in how you've expressed the model. It might just be how people are using the model. But now you're saying you're going to help make these decisions and you have this liability. How do you fix that?

First of all, I cannot possibly comment on any ongoing lawsuit, but in general- But the lawsuit exists, right? You know it exists. This is the problem in depending on the AI.

the AI might make errors of this magnitude. I give you Garrett's opinion. Again, and you know this, if you want to have commentary on the ongoing case, you have to talk to the right person for it. That's not who I am, but I can speak to you in general about AI.

I think AI actually helps us to get unbiased. And the same principle applies, right? You can apply AI very poorly. And AI, it's a question of maturity. Since the course of the existence of machine learning, people learned that if you have the wrong training data and you're lacking guardrails,

The model just expresses what was given the model during its training phase. You basically, you define it, you know, by the act of creating it. And as, you know, you move from immature AI to excellent AI, right? When we understand that, you know, the representation of data, the guardrails that we have to put around it,

I think all of these biases, as you described, that humans are susceptible to, just in a different way. But humans have emotions. Humans have irrational elements to them. We are not computers, and that is what makes humans great. But it also makes us, in many cases, poor decision makers. But the AIs are trained on the corpus of their training material is biased human information.

And this is why you need to get... How do you take that and then turn that into a thing that unbiases us? Yeah, exactly. Especially in these contexts. I mean, Amazon had to stop using AI screening tools that were imposing bias into their hiring process. Can you measure it? Can you say, okay, we're good enough? You know, you do it by getting the recipe right. Meaning you're getting the training recipe right, you're getting the guardrails right. And I think this is the important intersection you laid out.

In order to get AI right, you have to look at it holistically, meaning you have to understand the domain. You know, you have to understand what is the judgment that a model applies. You have to understand what training data you need to provide for it. And you need to provide

the guardrails that you have to basically put as check-ins, balances around it, so it stays in its defined parameters. And once you do that, right, that's the power of AI and machine learning models. They will consistently work at the same level of quality, right? But it's the responsibility, right, to create that system around it. And I think if you do, again, you know, my opinion, if you do that,

it works in an incredibly powerful way. And let's just go back to something we all experience in San Francisco every day, autonomous cars. That's a great example, right? Because so many things can go wrong, right? And now we are at a stage where they work reliably. And the lesson is, if you design a system the right way,

If you think about it holistically, you can actually make it work all the time better than a human driver would because we also have human limitations that we can't get passed on. And what is every good recruiting team doing? What is every good performance review team doing at the beginning of a review session? Let's unbias ourselves. Let's call out the seven biases. Let's talk about them so we free ourselves from that being applied.

There are teams who are good at it, teams who are not as good at it. But if you pick codified in a system, right, you can basically have the best possible decision making on tap every day. And that's the power of it. You know, I took a job at Malacana, the CEO of Waymo has been on the show. And the thing that struck, it's true that they work great in San Francisco. I think Austin and Phoenix now they're rolling out all warm weather cities. I asked her, will this thing work in Denver? And she was like, no, no, no.

Right? Too hilly, too snowy, can't do it. That's what I mean. We've designed systems in very narrow domains under essentially perfect conditions that we trust, and then you make it more complex, and then it's just like, no, we can't do it yet. Maybe we'll get there one day, but we can't do it yet. I think you're completely right, and that's exactly what I mean, right? You make it work by narrowing the domain, right? Yeah.

It's incredibly hard to make a car work everywhere. It's incredibly difficult to make a general AI that works on everything. And that's, again, that's the workday thing

recipe right we are saying you know our claim is not that we're gonna make an artificial general intelligence that solves any problem we do exactly what you said right it's narrow down the domain on something that we really understand that you understand perfectly and let's design a system that basically solves that part of the enterprise ecosystem

When you think about the complaints people have for Workday today, I want to ask you two different questions. One, what are the top five ones that you want to fix, right? People do not like using this software. How would you fix it for them today? That seems to be a really important point for you, Nealey. As I said, you were brave. This is why enterprise executives don't come on the show, because that's honestly what the listeners want me to ask. How are you going to fix my problems today, right? It's not just feature requests. It's the holistic experience of using enterprise software is bad. How would you fix it today?

you know, I think, you know, my conviction, right, you know, down to the bones of my body, if you will, I'm a product person, you know, I love well-designed products and I seek them out for myself and I aspire to build them. You know, I think when we say about building a product, right, and, you know, I was talking earlier, like, you know, are you holding it the right way? Beautiful design is, you know, how it works. It's not just how it looks, you know, design is

how it works in everyday use, you know, from a coffee machine to an enterprise software system. And I think the recipe is for all of them the same. A,

recognizing that this is a big deal. This is not something that just falls off at the end. That's something you have to carefully research, design, and invest for to make it work. And then secondly, when you asked me about my decision-making process, really understanding those details, right? What works well and what doesn't. If something doesn't work well, what's the best way to improve it and to make a tangible change

improvement for the ones who is articulating the need for improvement. I believe honestly in a relentless pursuit of the basics. When we say excellence, how do you get excellence in anything? I think it's, well, recognize the importance every day. Secondly, apply the discipline rigorously every morning, every afternoon before you go to bed to apply it. And if you do this consistently enough over a period of time,

you will see huge differences. The problem with all of these things is, right, you cannot go from here to there in a step function change, you know, from here to tomorrow, right? Because actually what you're saying is that how do you achieve excellence in something which is non-trivial? And as I said, right, first of all, it's only believing in its value. Otherwise, you won't have the strength to see it through. And then secondly,

applying the basics of that discipline every single day rigorously. And over a period of time, you will see amazing returns. I said I was going to ask this question two ways. So here's the second one. Do you use Workday at Workday? Yes. What are the five things that bother you most about using Workday at your job?

Can I tell you what the biggest surprise was, first of all, when I came to Workday? I have used Workday before. Sure. And the Workday at Workday looks so different. I mean, this is one of the issues, right? Because your customers deploy it. Exactly. And I was coming into Workday and I said,

I am surprised because the system that I have just used for everything, from entire onboarding, from benefits enrollment to corporate credit card, to learning my team, to any sort of approvals, to org review, I've done it all in Workday. Everything. And it is so different. And so I asked, well, explain this to me, right? Because honestly, in my previous Workday experience, I had struggled with a couple of things here.

This is different. And they said, you know, basically what you said, yeah, you know, it's a real issue that, you know, we have customers who configure and deploy the system and are not updating the system to any of the improvements we have done over the recent past. When you say, what do I want to change immediately, right? Go on a campaign and actually make sure that

The quality of the experience, and I'm not saying there's nothing to be improved anymore, but the many, many things that I have experienced firsthand myself are 10 times better from what I personally have seen before, making sure that all of this flows through the user. What are some of the things? Be specific. Search. Find me my form for...

requesting a credit card, searching it, credit card, and getting the form loaded and populated with the right fields already, because you're guessing that you know my name, right? There is no surprise here. And my employee ID and all of that, and just making me basically select the credit card example, what is the limit, and if I want express delivery and send, something like that, or

So search experience, one terrific example, or the assistant experience, right? And one of the workflows that people most do commonly is personal time off, right? In Workday everywhere, you can do this by using Slack or Teams with a chatbot, right? So I bet many of the things that you were quoting too is people trying to do something which to them looks very simplistic. I just want to know my PTO balance and put in a request.

Why do we even have to log on to that system? So when I talked about the difference in expectations, right, that you just expect it to happen in your collaboration suite. Well, with Workday everywhere, that works. And I was surprised. Before you asked, I didn't request PTO in my first 60 days, but I was trying it out, okay, because I wanted to know, because I believe in understanding the details. So, yeah.

As I said, one of the things I want to do first is making sure all of this flows through to everyone because I think there is so much goodness that people are not yet getting to. You're hinting at something here, and this will be the last question because it's just a big idea that I keep coming back to in all these conversations about AI and software and how we use it, that eventually...

The interface will just be natural language, right? There's the small step you're talking about, which is go to the customers and help them deploy Workday more beautifully and make it make more sense for people and use all the tools. And great. And then there's the big one, which is you're just going to talk to it and it's going to do some stuff. How far away do you think we're from that? I think there is a whole lot of tasks where people use forms to approximate conversations where conversational is clearly a better paradigm.

like a self-service type of request, like I said, in a PTO and PTO balance. This is more like a self-service scenario which I can easily specify and it's automating that and conversational is a good way of exposing it. And there are some others that fall in the same category, but turns out there are many that are not. So like I said, how many things can you remember in a conversation? Like top of your head, probably seven concepts max at a time.

there are some application domains where you have a way more complex context and state model right there you know just say something as simplistically would you could you design a 3d scene without seeing it just through conversation of course not right would you want to specify verbally that you want to select an element in the you know fourth layer probably not right it's way too complex you know pointing and clicking is way more efficient so

I argue the case that conversational will be a key part of what everyday experience is for a certain set of problems that just very nicely fit into it.

And there is a larger set of problems. They are the state model and the context are so dense that you cannot possibly conceive them in a conversational thread because it's just overloading what you can memorize in your brain. So I think it's going to be a both end. But I think every scenario where you're going to ask something, can you do this for me? Can you find that information for me? It's more...

simplistic in terms of the information retrieval. George is a great example of that, by the way. Yeah, I think that's going to be completely replaced with a conversational interface because why not? Essentially, you have a request response paradigm with some refinement in the middle for which a conversation turns out to be is the best way of facilitating that. One of the reasons I asked this question is because my favorite Slack room at our company is called Finance Support, and it's staffed mostly by bots.

And the people who are new to it come in and they ask very nice questions in full sentences. And the people who use it every day just shout nouns into the void. I'm looking at it right now. It just says there's someone who just says extra April expenses. One person literally just typed the word credit card and then entered into an entire flow with a spot, right? Like they're just, it's basically a command line, right? And we've just recapitulated the command line with a more conversational interface where people have realized that the keywords will just do the job.

Is that where we're headed? We're just, we're just doing command line.

I don't think so, you know, but I do think what you're describing actually is a good thing. You know, I heard that OpenAI is using a lot of, you know, inference cycles because people are just being polite to the model saying thank you and please and write, you know, but the model is going to work right for that. So what you are describing, I think, is just, you know, an amazing efficiency that people understand, hey, I don't have to write a fully specified sentence of, you know, punctuation. I can just keyword it in and the system is going to do it for me.

And I think it's because for some of the work, this is just very efficient. You know, this is why are you really typing a URL in your browser and writing it full out? Or I don't, right? I rely on autocomplete and search to do the job for me, right? That's a perfectly fine way of accomplishing the job. But I do think, like I've said, right, there is...

a certain set of problems, information retrieval, simple workflows, that this is just a very nice way of doing it. That command line is good for a reason, to your point, an intelligent command line is very powerful, but there's a way bigger set of tasks and jobs where you wouldn't command line it, right? You need to see what you're dealing with. You have many elements that are in relationship with each other. Just take something as simplistic as a contract and finance, right? Because you just said finance, right?

and you have a context with multiple payment terms that are dependent on each other, and you're reframing a contract, right? So you do need to see those pieces and how they interact with each other to make sense out of them. And an AI is going to help you, right, in identifying them and telling you, right, you know, this is a red line in a contract that you have to pay attention to. This is a payment term that you may want to leverage. But if you want to modify that contract, if you want to rearrange it,

Yeah, you want to see the piece that you're working with, right? And you have this across so many domains that I think AI is going to change UI and we have AIs that are designed or UIs that are designed with AI in mind.

I don't think that today some vendors are telling you you're going to have a chatbot and a workflow engine, and that's going to be great. And it's a good story to tell because we have all of this stuff built and having a chatbot over it doesn't hurt for sure, but it's not going to change how you run as a company. It's not going to be a transformative outcome for you. But if you design applications with AI collaboration in mind from the get-go, payroll, benefits election, right? If you want to elect your benefits,

You may want to see options. You may want to have multiple options compared to each other. There are going to be things where we can decide we're going to render UIs into chatbots or we're going to render AI next to UIs, but it's not going to lose those elements of interactivity where you just

need to break beyond textual input and output. Well, Garrett, I feel like we could talk about the future of AI and how it changes workplace interface forever. I have to say, by the way, thank you. It is true. Not many enterprise executives are brave enough to come on the show and answer the question. So I appreciate it. Thank you so much for being on Decoder.

Thank you for having me, Nilei. It was a pleasure. I don't see why I'm happy to come back on anytime. Well, let's read the comments about the specific Workday feature request and see how you feel, but we'll have you back as soon as we can.

I'd like to thank Garrett for taking the time to join Decoder and really invite any other enterprise software executive brave enough to be on the show to join me as well. And thank you for listening. I hope you enjoyed it. If you'd like to let us know what you thought about this episode or really anything else, drop us a line. You can email us at decoder at theverge.com. We really do read all the emails, even the ones that include brand new suggestions for our taglines.

You can also hit me up directly on threads and blue sky, and we have a Tik TOK and an Instagram. They're both at Dakota pod. They're a lot of fun. If you'd like to code or please share it with your friends and subscribe or your podcast. Dakota's production verge and part of the boxing podcast network. Our producers are Kate Cox, Nick stat. Our editor is first to write the decoder. Music is by break master cylinder. We'll see you next time.

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