Workday's new product head hopes he can make you like Workday

24:12And 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. 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, specifically when you talk about something which is not in the public domain. So we have public knowledge, right?

24:50And models are extremely good at compressing this knowledge. They're terrific. Who would have thought, right? You know, 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.

25:28You spoke about the 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's 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.

26:12you 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 it's 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 i i'm 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?

26:51An 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 for it to work, it needs to work 100 % of the time. And doing this in an enterprise context is heavy lifting. The second thing with 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, right?

27:24Because 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, right? 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 you know forge expense lines and try to trick you know the expense system you use AI models you know to also counter that right and as it turns out right you know AI models are terrific in spotting patterns that are generated versus you know done from a human And I'm sure you have filled out a capture request online in your life, a couple, right?

28:18So same idea, right? You know, you basically build protection using AI to you are not being mis - or abused by AI. So 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 other people. What are you thinking about how your team, the product org is structured and how you want to change it?

28:52That's an interesting question because you actually kind of trying to lead a witness here, right? You know, how you imagine to change it. You know, I'm not sure if I am. So, you know, Workday is a young company. Workday is 20 years old, right? You know, compare this with many other enterprise software companies. They are twice as old or even older, right? So Workday has a really strong technology foundation. Actually, what surprised me the most, Nile, 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.

29:32So you come back to the enterprise application domain of certain anticipations. And Workday really is incredible when it comes to its tech stack. You know, it's scalability, it's 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, you know, evolving us, you know, 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.

30:24I 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. You know, for instance, when you think about something like job applications, you know, something very natural, we all have an understanding, we all applied for jobs. I'm not sure if you did actually, but you know, I did in my life. And I think it resonates. Wait, when you got the job at Workday, did you have to apply in Workday? No, I did not, you know. So it's a good question. No, I did not actually.

31:03But you know, Workday is not my first job, right? I was a school grad once and I put an application into a system. And then through the interviewing process. And the big opportunity that we have by really innovating in Workday is now taking authentic AI 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 if they have a use of AI. Like I've said, 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?

31:51I just fell out of form. But there are industries, you know, 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, you know, actually sign up for a shift or, you know, basically, you know, joining a company is something which is either frictionless and you can do it through an intelligent experience, including your skill assessment, you know, on your mobile phone with a conversation, or you have to log on to a classical web application and fill out forms. I I mean, which one would you use?

32:28So, 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.

33:06Many of the great things that we are doing in the recruiting space and the contract intelligence place, 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, you know, application teams, which are basically building an old technology foundation, the AI foundation, our application server, and build the systems that, you know, you and I and any listener on this podcast would recognize as, oh, that's Workday, right?

33:43You know, that's, you know, the UI and the workflows around it. And then there's an infrastructure team, you know, as you would imagine, right, 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.

34:17Obviously, 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, right? 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 know, you're asking me a question about a time where I wasn't at work days. So, you know, there is, you know, I can't really speak to the, you know, the thinking that went into that. And judging from the 60 days or almost 70 days into my role, we are actually investing in AI.

34:55We 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.

35:29We'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 we are making as um any profit i hope as any professional software company out there you have a use of you know code assistance and um and wipe coding and you know i started my career in software engineering myself but i was a hands-on you know developer for many years and uh just seeing how much it helps and changes you know the quality and the results you you drive on a soft engineering side is amazing.

36:09With wide coding, actually, I think what it drives on the product management side is amazing that you can actually specify working prototypes and real interactions. You know, it's not just, you know, Figma anymore or PRD, 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. Foremost, because when I was a developer, You know, I made a fair amount of bugs when you, meaning like introducing issues, right, in the programs that you've write. 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.

36:50And 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, you know, I should have really got that. You know, I just didn't think the following, you know, conditions through. So, and AI helps in two ways, right? One is, you know, it's so good at test case generation that you just have way better verification. And secondly, the assisted coding, you know, generates high quality code, really not making many of the typical mistakes and anti-patterns that, you know, you just make, you know, as you're developing from a junior to a very senior software engineer.

37:25So we use all of that. That's super exciting. And yes, you know, Workday has a very powerful concept. It's called Everyday AI. It was the second thing, you know, that surprised me about Workday, right? And you're manually, you know, 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 then I joined the company, we were having an offsite just in week two. And it was, you know, basically a review of Everyday AI. And I was so amazed about how broadly Workday is applying AI.

37:59You know, I have talked to so many companies in my past that, you know, came to me in my previous role and said, well, how do we use AI, right? You know, what are the use cases? What works? And then, you know, come to workday, you know, ranging from employee self-service to contract intelligence in legal, you know, 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 what really delivers value and then quickly double down on those scenarios.

38:37So 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 first of all, I think, A, 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 the nature of the uncertainty, if you will, of future, depending on what your belief system is.

39:16But 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 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.

40:00And my decision process, you know, follows, you know, actually, I think a simple framework, right? One is trying to mitigate as much as possible human bias. We all have them, right? You know, they're so human, right? It's funny that I say that way. There's so much inherent 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? You know, we think about losses more significantly than potential wins, right?

40:37If 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 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, you know, 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.

41:17And 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, the 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, that, 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.

42:00And then thirdly, being time-bound, right? You know, 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, you know, the most and fiercest competitor that we have in life is status quo. You know, most of the time, you know, 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. You know, let's kick the can down the road because you are of the idea that, you know, if you just, you know, give it more time, you will come to a better solution.

42:46And the problem with that is that, you know, 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, right, having a really good decision framework that goes against, you know, insulates you from your own biases. 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.

43:32We need to take another quick break. We'll be right back.

43:47We'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.

44:13Let'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 CEOs 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 people who are spending money on it. And that means the software is all bad. Broadly, we don't use Workday here. We use UltiPro. I think UltiPro is bad. I'll just name all your competitors down the line. The users think the software is bad.

44:48Workday 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 bot? Using a colander, like a strainer. Okay. Right? And you're trying to carry water with a bucket with a listener. You see this everywhere, right?

45:23The 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. There's one that says I just hate this 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?

46:05What do we think, you know, generates value for the users of Workday? Okay, so when we say understand the details, right, I think it's really important, you know, in enterprise software to 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, you know, let's apply this framework, right?

46:42You know, the framework simply, you know, 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? You know, for instance, one big bias is, you know, you seek validation, right? You seek validation for what you believe to be true and you overemphasize on signals that reinforce that. And the first step is really going to the right level of detail and understand what is it at an action level that drives satisfaction or dissatisfaction in a piece of your application experience.

47:18And once you have that, as it turns out, 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. You're holding it wrong. Or thirdly, that category exists, believe it or not. Or, you know, thirdly, hey, actually, we have different ways or different expectations now of how you can use that. You know, for instance, when you talk about system interactions, right, the reality is that it's not, it's a dynamic environment.

47:55Meaning, when I look at, you know, I have two kids. They have different expectations of using a system than, you know, what I had. You know, I grew up and forms, it was kind of like, it was computer stuff, right? How cool is that, right? You have a web form. That's amazing. My daughter, she is mobile first and dare I say now 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?

48:32Like you said, I have to take information from here to there. Why is this complex, right? Why do you even have to, right? Is an AI model maybe 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.

49:10And that's your bureaucracy incarnate. We're literally shipping the org chart in the nature of this process. Okay, 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 are going to fill out. 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.

49:40I 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 the 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 it really connects to where we were starting, right? The whole question of what drives value with AI, right?

50:17And I fundamentally believe, right, with AI, as with any other technology, you know, 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 differentiated levels of accuracy and outcomes. We talked about, you know, autonomous cars and you said some work and some don't, right? So there is clearly a difference, right? You know, even though you could all say, well, I'm sure they make all use of AI, you know, somewhere, somehow. you know to do you know processing and trajectory protection and projection and so forth and that's exactly what we are focusing on with workday because you know some of the information like you have said right is important information right you know guess what you want to pay people you want to have internal mobility right you know we have in companies in many companies today a shortage of qualified labor right 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 you know just you know to pick a very simple example and now the question is what can you do by excellently applying ai to really revolutionize and improve these journeys you know and and there are clearly ways i was just you know speaking about recruiting right you can help my recruiters make better decisions they don't have to fill in the form anymore they don't have to make the assessment.

51:44The model helps them to identify the right candidate, you know, for the greater good of everyone. But 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 in the problem and 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.

52:16How 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. okay so because again right and you notice right i you know i i you know if you want to have commentary on the ongoing case you know you have to talk to the right person for it that 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 know you can apply ai very poorly and ai it's a It's a question of maturity.

53:07Since the course of the existence of machine learning, you know, 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 define it by the act of creating it. And as you move from immature AI to excellent AI, when we understand that the representation of data, the guardrails that we have to put around it, I think all of these biases, as you described it, that humans are susceptible to, just in a different way. But humans have emotions. Humans have irrational elements to them, right?

53:55We are not computers, and that is what makes humans great, right? But it also makes us, in many cases, poor decision makers. Right, but the AIs are trained on, you know, 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.

54:32And I think this is the important intersection related. 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.

55:11And I think if you do, again, 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? You know, 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, you know, we also have human limitations that, you know, we can't get passed on, right?

55:48And 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 codify it in a system, you can basically have the best possible decision-making on tap every day. And that's the power of it. You know, Takedra Maokana, the CEO of Waymo, has been on the show. And the thing that's – it's true. They work great in San Francisco. I think Austin and Phoenix now.

56:27They're rolling out. All warm weather cities. I asked her, will this thing work in Denver? And she was like, no, no, no, no, no. Right? Too hilly, too snowy. Can't do it. That's what I mean. Like we've designed systems in very narrow domains under essentially perfect conditions that we trust. And then you make it more complex. flex 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 narrow the domain, right? It's incredibly hard to make a car work everywhere.

56:59It's incredibly difficult to make a general AI that works on everything. And that's, again, that's the workday recipe, right? We are saying our claim is not that we're going to 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?

57:36People do not like using this software. How would you fix it for them today? That seems to be a really important point for you, Neelay. 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? 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.

58:07and 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, 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. You know, 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.

58:46And then secondly, when you asked me about my decision-making process, you know, 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 improvement, you know, for the ones, you know, who is articulating the need for improvement. I believe, honestly, in a relentless pursuit of the basics, when we say excellence, right, you know, how do you get excellence in anything? I think is, well, recognized importance every day. Secondly, apply the discipline rigorously, you know, every morning, you know, every afternoon before you go to bed to apply it.

59:23And 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 you know as i said right first of all so when you believing in its value otherwise you don't have the 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.

1:00:02I said I was going to ask this question two ways. So here's the second way. Do you use Workday at Workday? Yes. What are the five things that bother you most about using Workday at your job? I tell you, you know, can I start telling you what the biggest surprise was, you know, 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, you know, 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.

1:00:49Everything. And it is so different. And so I asked, you know, 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 those search you know find me you know my form for requesting a credit card you know searching it you know credit card and getting the form loaded and populated with you know you know the right fields already because you guess not you know my name right you know there is no surprise here and my employee idea and all of that and just making me basically select you know 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 you know and you know one of the workflows that people most do commonly is you know personal time off right um in uh workday everywhere you can do this by using slack or teams right you know with a chat So I bet many of the things that you were quoting too is people trying to do something which to them looks very simplistic.

1:02:38I 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, there's 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 because I wanted to know, because I believe in understanding the details. So 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.

1:03:17You'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. 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, PTO and PTO balance.

1:04:01This 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, you know, there are many that are not, you know. So, you know, like I said, right, how many things can you remember in a conversation like top of your head, you know, probably seven concepts, you know, 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 design a 3D scene without seeing it just through conversation?

1:04:40Of course not, right? Would you want to specify verbally that you want to select an element in the fourth layer? Probably not, right? It's way too complex. 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 threat because it's just overloading what you can memorize in your brain. So I think it's going to be a both end.

1:05:23But 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. Search 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 ask this question is because my favorite Slack room at our company is called Finance Support.

1:05:58And 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.

1:06:28Is that where we're headed? 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. I heard that OpenAI is using a lot of inference cycles because people are just being polite to the model, saying thank you and please. But the model is going to work for that. So what you are describing, I think, is just an amazing efficiency that people understand, hey, I don't have to write a fully specified sentence of 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.

1:07:02You 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.

1:07:40You 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 refreeing 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 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.

1:08:11But 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 open, you know, it doesn't hurt for sure. But it's not going to change how you run, you know, as a company.

1:08:45It's not going to, you know, 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 element 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.

1:09:28I 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 the cutter. Thank you for having me, Nile. It was a pleasure. I don't see why. I'm happy to come back on any time. Well, let's read the comments about a specific Workday feature request and see how you feel. But we'll have you back as soon as we can.

1:09:53I'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 TikTok and an Instagram. They're both at DecoderPod. They're a lot of fun.

1:10:17If you like Decoder, please share it with your friends and subscribe over to your podcasts. Decoder's production of The Verge and part of the Boxing Media Podcast Network. Our producers are Kate Cox and Nick Statt. Our editor is Ursa Wright. The Decoder music is by Breakmaster Cylinder. We'll see you next time.