The Cost of Building the Right Thing | AI, Speed & Discernment at ServiceNow

[BEEPING] Welcome, everyone, to another episode of Service Now Insights. I'm your host, Bobby Brill. Today, we're getting into something that is the most important conversation happening inside any company that builds software today. AI is making engineering teams dramatically more productive-- 10 times, even 100 times. And that changes everything, because the systems around engineering-- ones that make sure what we build is actually good, are racing to share the lead and the cost of building the wrong thing. Well, that's also climbing exponentially. Today, we're going to talk with three people here at Service Now who are tasked with winning this race, and they have three very distinct points of view. Gentlemen, before we get into it, please introduce yourselves. Hey, Bobby, thanks for having me today. David Hor, a group vice president digital content and design. I've been at Service Now for five years. Thanks for having me, Bobby. On and on nothing, I am the group vice president, heading up product research and insights to Service Now. I've been here since 2022, August. My name's Stanley Davis, I'm SVP of design. Next month will be one year for me. So David, you've been thinking harder about the productivity math on AI than just about anyone we've spoken to. But before we get into the tactics, what's your philosophy on AI? Talk about the lens you see all of this through. Like from the moment that I think it was like chat GPT2 burst onto the scene, I was like, this is something different. I've been through the internet boom, saw like crypto, all these things. But this is something that I saw. And I was like, this is really groundbreaking and game changing. And things have only accelerated since then. I think it unlocks huge amounts of potential. And one of the areas that I come out is from productivity. So I think that there is so much that we can do and so much that we're unable to do today. And with these new AI tools, it's gives us really a way to start to do stuff that was never before possible. When I say never before possible, probably economically viable is the way that it is able to reduce costs for us in many different ways opens up avenues for new things that we would never be able to do before. So I think about one of the big teams that I run is creating product documentation. And having humans understand what the product does and write all of that is a very, very expensive proposition. And so there's only limits to what we can do. Whereas with AI, not only can we do all of the documentation, but we can make it work for you. We can write it differently for Unund. We can make a version for Dunley. So those kind of things, I think, is just one example of where it's opening up new doors for what's possible. Now, Unund, you're the person in the room who consistently asks, what does all of this mean for the human on the other end? What does that look like from your side of this? So I have a perspective. So obviously to complete resident of the David, I think as a technology, what we're seeing is pretty unparalleled. It's very exciting, nerve-racking, and exciting, do you sing, how are we going to call it? You're going to-- you know, we're already starting to see one person, two member, three member startups, that scale from 0 to 100 million plus AR and scale with just three people. So you see that kind of stuff happening around you. It's just awesome to watch. And, Danley, you're a designer. You're the creative voice in this conversation. When you think about AI and creativity, where does it take you? As a voice creativity, I'll take you back to when I was a teenager. And I was using 3D Studio Max, which is a 3D creation software, pretty heavyweight software that was used for video game industry and movies. And I, as a teenager, spent hundreds of hours learning that interface to try and create something pretty basic, just to navigate around the system. And you fast forward 30 years and use mid-journey. And you don't have to know an interface anymore. You just prompt it what you imagine. And this amazing thing just becomes rendered. And you shortcut the need to try and wield a computer interface. And there's a saying that art imitates life and vice versa. And you mentioned science fiction. And it reminds me of the Marvel Universe, the Avengers, Iron Man, and Tony Stark's relationship with Jarvis. Where when Tony Stark is in the Iron Man suit, Jarvis is the connection point of all the things necessary to make Iron Man a superhero. And AI, to me, offers that possibility of human superpowers and lowering the barrier to entry of having to learn and wrestle with interfaces, drop down menus, buttons, all the CTAs, and just get to exactly what your creative mind is thinking without a whole lot of manipulation on the computer side. With art, it's meant for-- it's meant to elicit some type of emotional reaction. And for software products, it is meant to solve a problem. And I imagine-- and I'm seeing this today in our day-to-day work-- that as we've become faster with our productivity suite leveraging AI, we're spending less time manipulating the interface and more time obsessing the customer problem. And I suspect that most of us want to be in that zone, because you have more option value to create many more solutions than just the singular solution. It gives you more time and space to iterate and learn and get feedback from your customers. No, smitly, that's what we're collectively working towards just to solve those customer problems. And the customer has always been the North Star. How has that changed? Or has it changed? What AI has brought into this whole ecosystem, if you want to call it that, is it's really helped us accelerate the means to the end. So the whole process of building product, what it used to take weeks, months, you could do that in days, so that's super exciting. With that said, the whole concept of building products for users and customers hasn't changed. You're building a product to find a product where it can fit. You're building a product to solve a problem for a customer and end user. That aspect hasn't changed. And David, engineering teams are building at 10 times the speed, or even 100 times as they were two years ago. And they're building super fast. Now, you describe this as a survival problem for everyone around engineering. Walk us through that. So one of the things that I realized a couple of years ago is that one of the first areas that those big productivity gains are going to come is with engineers. So engineering is, I think, the leading industry in terms of AI adoption. And with that comes huge productivity gains. They can build anything. They can build way more, like 10 times to 100 times more than they could before. But just because they can build all that stuff doesn't mean it's going to be good. But where I was coming from is if they're building all of this new stuff, what are all the systems that are around that and all of the roles that you need around the building itself to make sure you're building the right thing and make sure that our customers can adopt it? So if I start with documentation, it's the number one scaled resource for customers and partners to be able to use service now. And if our engineers are twice as productive, or three times more productive, they could be putting out two or three times more product that needs documenting. And if we're already struggling to keep up with where we were, think how much more we're going to get in the hole without finding ways to scale that work. And so I think there's kind of like a survival that we need the AI for is like the product experiences are going to get worse, unless we can use AI in those supporting functions around. Another example, product leader Andrew Eng. I think he was chief scientist at Baidu, like Founder of Google Brain. He said recently, he's seen ratios of engineers to PMs changing from like 1 PM to 14 engineers to 1 to 7, to 1 to 1, to often he's got 2 PMs to every one engineer. The cost has dropped that much. But the cost of building has dropped that much. But the cost of building the wrong thing has climbed exponentially. And I think that applies to all of us. It's like, we use a research side, understanding the customer and what they needs are design, making sure we are building the right thing. And it works most effectively. That discernment is where I think the we need more productivity to keep up with the AI productivity. And Danley, how does that dovetail into the creative output? One of the mantras I followed through my career as a designer was designed the thing that was asked of me and then designed the thing I thought was better. And better meant that-- I had enough time of the day to actually do the work and oftentimes that meant very long hours and tinkering well into the night that understood the customer problem well. And I was only able to output around those constraints. And so in any given project, I might have a clear hypothesis and a number of concepts around the hypothesis. But I'm looking around the corner and I might see another customer problem. So that might orient me around a couple different hypotheses. And then I have a few more concepts to try and prove out product viability. But that takes time. These days with AI, rather than taking a week to create nine concepts for exploration and trying to find market viability, designers and create those nine concepts in an hour. And so it just speeds up the ideation process that was limited by bandwidth in the past. And it's not to say that those nine concepts or ten concepts are great. But to get to the good idea, I believe that you have to have a lot of bad ideas. And you go through the discernment of seeing something and saying, okay, that doesn't work really well because of these factors. But there's a there's a kernel of amazing in that one concept that I might want to pull into another concept. And AI gives us that ability. We still have to slow things down a bit. Like once you have all these options in front of you to pause and edit and curate. Some people call that taste. Some people call that judgment. And I think as an industry, we're starting to realize in this first chapter of AI software development that slowing down becomes part of the process. And through the slowing down of talking to your co workers, talking to customers, you're able to get the input and iterate. And then that allows you to speed up in the next cycle of software development, whereas maybe today and six months ago, we were just running, but not clear where we're running to. Everything you said about what we should do, I like 100% agree with that. Can we do those same things faster than we could yesterday with AI? Can we iterate with the customer real time? Can we provide the engineering team with daily demos and daily prototypes, like five different versions of it? And we can, right? We saw demos from the team today. So like I think we're all aligned on the work that needs to happen. The whole notion of slowing down is definitely relative. I'm not thinking of like months of indecision, a naval gazing. You know, maybe it's, you know, 15 minutes, just a line. But you know, it reminds me of the evolution of music from going analog to digital and the introduction of beat machines in the 80s and the rise of hip hop, where you're able to create beats and rhythms a lot faster. But there was a taste maker that was listening, maybe taking a beat, no pun intended, to find that right hook. Yeah. And even though all the mechanisms around that activity went faster, someone was taking the time to choose the right sample. Yeah. And that's, that's kind of what I mean by slow down that we come together to find the, the aspects of what we're building that we think are great. And then we go, I mean, like that's the one. Yes. Did it like the discernment? Yeah. 100% agree with that. You know, discernment is a word that keeps coming up in this AI conversation. But discernment at scale, that's a different problem. Service now has years of content and products and documentation and a lot of great assets. We've made a lot of really good stuff. Now David, I want to ask you, how does AI help unlock some of those opportunities to use all that we've got and the other sort of that question is, does it freak you out to use what we've got? The one of the initiatives I lead is like content governance. So we have a bunch of different teams across service now from, from marketing to the product teams to learning. We're all creating a lot of content. A lot of it is focused around how can we help our customers be productive and successful with service now? Now that content atrophies, it gets stale and it gets less useful over time. As our products move on, if the content is not moving on at the same rate, it becomes less useful. Right. And where we are now is like an AI consumer of that content is just as important as a human consumer of it. And where governance is important is if you're not managing that content life cycle and aging out the old stuff, you're going to end up with hallucinations and drift and all those kind of things. The good news is we can use AI for governance as well. I mean, it does sound a little bit like the Fox guarding the hen house. But as we talked about today, like we had an engineering lead come and talk to us and she was talking about the proliferation of artifacts. Like there is just so much stuff and how much time you need to take away from building to reviewing. Like you know, like constricts that you need to set aside time to review everything is being created. With human governance and things like that, you're never going to be able to do that because content is just exploding. So I do think we need to be using AI and then have, you know, rigorous structures in place to manage the content life cycle of these assets. So that we can get rid of them out of the systems to make sure that the AI continues to perform well. Maybe it feeds into quality as like the AI is only going to be as good as the signals it receives. So like you've got to feed it like if it's in databases, tables or unstructured content, you've got to make sure that's good. Otherwise, you're going to, you're going to lose use of trust because your quality declines. And David just said something about if you lose signal quality, you lose user trust. How do you think about trust from the human side? Trust starts with ensuring that we build a product that resonates. There has to be a hot moment. The first smile experience has to be awesome. Whenever the user needs the product has to work. And it has to be a performance. All those things create trust. Now, when you think about human AI or human automation models, there's always a concept of how do you build the AI to circumvent the limitations of the human. And when you create or leverage or capitalize AI systems to circumvent those limitations and build experiences that can bring the human AI system to a much better spot, that's in the long run builds trust. So governance, yes. But I think there are bigger aspects of how you build the product within the AI human system that are bigger predictors of trust. And Danlie, where have you seen AI surprise you or do something that you didn't quite expect? Something you didn't see coming. I example, I'll give in the context of good is that we have moved to a new UI. Tech stack leveraging some new frameworks and using AI to develop the code for those tech stacks have meant that we have more or less feature completeness in terms of how those UI components will work. Where in the olden days, humans had to define all of the capabilities and permutations of those UI components and oftentimes it didn't meet full desire ability, maybe of certain business use cases. So that work that's happened with our new products has been incredibly fast and that's been really amazing to see. What what concerns me without governance and when the benefits of governance to some degree of predictability and without governance being within our say design systems as an example, if AI, if teams are using AI to build a bunch of UI components. The issue of feature in completeness can happen much faster than when it was just humans creating components that blocked those features. And we're having to manage that now because we have a new AI powered design system. We have business units that are coming online to develop new products using those components, those business units have very bespoke cuspere needs and without the systems and the teams talking to one another and then with the power of vibe coding and AI software development. Now you have this huge rush of new artifacts and tech debt that you just push down the road. And there's this hidden cost of tech debt that happens where initially your the romanticized view of AI happens because you're seeing all these things happen a lot quickly a lot faster than it did in the past, but then you move downstream in a couple releases now you have all this hidden tech debt. So governance potentially can help us address some of those concerns and. And service now, as in I assume every company that's building software right now is trying to figure this out. And to David's point, if you have exponential growth in artifacts, but you still have the same number of people reviewing it, now you have a bottleneck. So we're all working through. How do we get past that? So last question for all of you. It's the only forward looking one. 18 months from now, I know that's the standard question. 18 months from now, when we get this right and better, the discernment, the governance, the human element, what does that actually look like? An aunt. The biggest thing for me is that we need to ensure that the human factor is not lost in translation. The human element is and should always be the central part of any man, machine system or human machine system. What is my wish and hope that we do not lose sight in this mad rush, if I may call it that important one, that the human always, always, always essential. Yeah. No, Dan Leigh, I have to be old hash for you at this point. I mean, you know, your fingers don't even have to do the work anymore. That's right. You know, and it speaks to what I hope we can see collectively in the next 18 months, which is that as I've spoken to customers and I think the, the listeners of this podcast could probably attest to this, we're all struggling with leveraging multiple complex software systems to get our work done. Whether it be unstructured or structured or licensed or personal, there's just a lot of very challenging and difficult navigating of systems to get work done today. And I believe that the promise of AI and the integration in our workflows will enable any person to get their work done without getting frustrated with the interface. Without pounding their fists on the table, without cussing out their computer, it's just the thing that they want to accomplish is as easy as speaking it as if you had a very intelligent wise colleague next to you that can will this to happen. And I suspect that will make the work lives of millions of people a lot better. Now David, I know your answer is going to be about where the rubber meets the road. So what's your closing thought? I think it's pretty easy. Customer value, right? We, my team exists so that our customers can be productive and successful with service now as products. So over the next 18 months, I want to see an increase in customer value that people are deriving because the more value they see, the more of our product we're going to buy, the more service now as share price goes, I'm happy I'll be. So there you have it. Customer value. And isn't that always the answer? And it runs to everything that we've talked about today. The philosophy, the speed, the discernment and of course the governance. I want to give a thank you to our guests. David Horr, a group vice president of digital content and design and a non-theranathon, group vice president of product research and insights. And to Dan Lee Davis, VP of design. For more information on what we talked about today and from past episodes, check out the show notes and hit subscribe so you never miss an episode of Service Now Insights. I'm Bobby Brill, thanks for listening.