ServiceNow President & CPO on Why AI Kills the Companies That Don't Transform | Amit Zavery | E305

Any company who wakes up and says, 'I am the end-to-end orchestrator,' I think they're dead. There's no clear ROI in many cases. So you end up failing more often because you're doing the spare-part approach versus doing a very thoughtful platform-first mindset. Our AI business is going to be $1.5 billion this year. That plan was $1 billion. All of our products have become AI-native. So if you don't transform, you will get killed. A lot of leaders will say, 'Oh yeah, we have to put all this together. It'll take us three months or six months or nine months.' Until then, nobody does any AI, or nobody does cloud, or nobody does this, and that is where the companies are going to die. But going and saying, 'Please onboard an employee and give them access to 15 different applications based on their role, and then ship them a new laptop based on where they live, plus make sure the benefits are up to date' — that is taking action. Now the systems behind the scenes have to do that heavy lift. That's what we're solving for.

Thank you. Thanks for having me, Carlos. Very excited for the conversation.

Yeah, it's pretty straightforward if you think about it, right? So the chief product officer basically is responsible for defining the product strategy, building the products — so product engineering as well as making sure that we're delivering the best core capability to our customers. And the COO role is how do you operationalize all these things. So how do you really deliver value to customers? How do you make sure the company is operating at a very good scale and efficiently, as well as delivering this capability at a much faster pace. So combining strategy, product delivery to execution gives us the ability to now look at it end to end, all the way from engineering to customers at one place. So it gets less fragmented, less handoffs, and you have one person kind of overlooking it so that we can make decisions faster. So that's the goal with this role: is to really move fast and do things perfectly for customers.

Yeah. No, I think it's a unique thing here, and I appreciate how Bill, who is our CEO, thinks about it. And it gives us the ability to — and we've seen a lot of the innovation we've done over the last 18, 24 months has been that way because we've been able to now bring efficiency but also alignment across the organization, right? So you have product, engineering, experience, and then you have operations, which allows us to really do a great job, I think.

So, ServiceNow, we are at $15 billion in revenue, growing at 20-plus percent. Our free cash flow margin is around 35–37%. Operating margins are around 32%. So it's a pretty scaled business growing very fast — one of the fastest-growing software companies in the world today. And we were the fastest to $1 billion, $5 billion, $10 billion, and now $15 billion. So we're very proud of that fact. In terms of the size of the company, we are around 29,000 employees, and in product and engineering, the R&D team is around 15,000 people. So most of our investment in the company is to make sure we build the best products for our customers — more than half of our investment goes there. And the rest of the things, of course, go-to-market is important for us to really work with the customers to make them successful and adopt our products, and then all the supporting functions around it. But our goal always has been: it's a very product-led, very engineering-driven culture inside the company, but customer-first mindset.

Yeah, I think — see, with any transformation or technology shift, people start to wonder what it means for any company which has been around in that space for some time. So this shift with AI, I think the investor community is still trying to understand what it means: is it a tailwind or a headwind for companies like ours? And if they don't understand the change yet, they're putting every software company in the same bucket. But I think we believe we are a very different kind of platform company than other software providers, which are much more vertical stacks. So we do have a lot of tailwind. Our AI business is going to be $1.5 billion this year and growing at a phenomenal rate, right? Our plan was $1 billion. We already increased our plan to $1.5 billion. All of our products have become AI-native. So I think it takes time for the market, the investor community, to understand those nuances and differences: whether we are an early company or are we remaining legacy. So if you don't transform, you will get killed. But if you transform and build the right things and do the right things — we have a lot of domain, a lot of customer base — if we give them a lot of value from AI, they don't go anywhere else. And that's what we're starting to see with our growth. If you look at our growth, it has been very, very good and accelerating, and we're seeing a lot of new businesses emerge inside ServiceNow as well. So I think it's just timing, I would say, for investors to understand the differences between different providers. And I think it's starting for them to see there's also rotations happening with what's happening with infrastructure, what's happening with the technologies out there, with semiconductors and memory. So there's a lot of confusion in the market today with AI. So I think it's a matter of time, from my perspective, but we feel good about our long-term strategy. We feel good about our traction, and I don't really worry too much about short-term changes in our stock prices. Of course, we want to continue to be appreciated for our growth and everything, but that's for investors to decide. All we can do is control what we can, and we're doing the best we can there.

Yeah, I think the market cap does not directly impact — I mean, of course, we want to listen to our investors. They are a very big stakeholder for us when we make decisions. So we have a fiduciary duty to deliver returns for our investors. So we do always keep on looking at it: where can we be more efficient, how can we get more growth, how do we convey and communicate our messages about what we're doing and how we differentiate it, how do we show our traction in the market. Some of the investments I'm doing around new opportunities, which are going to be big roadmap opportunities for us, accelerate our time — right, in the security space, data space, what we're doing in the CRM space — are all driven to create more value to our customers, but also at the end for our investors. So we always think about that on a regular basis to make sure that our investors remain happy, they get great returns, but we also do the right thing for the long term as well. The short term you can have these ups and downs, but long-term the trajectory has to be great, and the trajectory has always been good, both from a financial metric, innovation metric, as well as return to investors from the financial metric as well, right, for them.

Yeah, I think — see, as a product leader, you always have to first have a perspective of where the long-term opportunity is, build things which are going to be sustainable. So my goal with anything we decide is sustainable growth. Don't do short-term things and then you have to redo everything every time, and our customers will be unhappy, engineers and product leaders will be unhappy, and eventually you're not going to get the results. So I always want to balance — no doubt, investor sentiment and feedback from the shareholders and others — but we also have to have conviction about what we do. You have to feel confident about where the market is going and what your differentiation is going to be, and how you're going to build a lot of value in products for customers to be really excited about buying, using, and continuing to expand the usage. So short-term is good to kind of pay attention to, but you cannot lose sight of long-term. You have to product development. Even though people say that it's easy and fast to build products now because software has become easier to build, it's not about the speed of engineering. It's always the real quality comes from understanding what to build and delivering that in a much more valuable and usable way. And that's really what I focus on from my team perspective, and ensure that we're getting constant feedback from our customers, our users, our partners, our ecosystem, and then have a gut feel. You have to know what's happening and where you will make the biggest investments and how are you going to monetize those things long-term.

Yeah, I think — see, the basics are still the same, right? Building the best products and customers loving your products, you're going to win. You have to get that right. You cannot build a sustainable business on substandard products or features which are not going to get value. So those are basic capabilities and basic things. You just feel transformation going on. Any new technology comes in, you still have to do the basics right. So you have to have a very good team, people who understand how to build very scalable, secured, performant, but very nicely usable products. You have to really ensure that you have customer relationships so that you understand what they're doing, how they're doing things. You have to get input from various different things, different environments, so that you can make the right decision. So those basics remain the same. I think every transformation has introduced new challenges, right? When we went from, say, client-server to more of a distributed architecture, web-based architecture — the technology transformation was huge. You had to rewrite software, you rebuild a lot of things. How people use your products changed quite a lot. Browser-based interfaces were very different than what you had with the thick clients, and the data residing in a distributed environment — it was very different. So it was a big technology shift. Commercially, it didn't change much when we moved from, say, web-based to cloud-based architectures where things are hosted in a cloud, they're running, and you're operating those products yourself as an engineer. So your technology changed, the role changed, but also the commercial model started changing. People were doing more subscription-based pricing versus perpetual license pricing. So suddenly you're doing technology transformation and a commercial transformation that you had to kind of figure out: how to make that change for your customers, how do you change your sales teams, how you change the incentives, and then how do you really monitor and meter all those things because you're also charging for number of users and things like that. When we're now moving to the world of AI, it's going to a much more consumption-driven architecture. The architecture is very different with AI, of course, right? You're using different large language models, you're using technologies for reasoning, you're bringing that into deterministic workflow. So the tech stack is changing quite a lot. But commercially also, people are now saying, 'You know what, I want to do usage-based pricing. I want to do consumption. When I use it, I want to pay you.' So commercially, instead of subscription, now you're going to a more consumption-driven pricing. So again, every technology shift brings different nuances that require the kind of people, the way you work, where you operate, where you sell, where you build — all changes around you. So basics might be the same in terms of you want to build the best products, you want to get customer satisfaction, and you want to do long-term thinking. But how you do it is changing every time, and the speed is very different now, right? What is happening with AI — the compression in time is very high. You're doing changes on a regular basis versus maybe six-month plans. You're delivering things much more regularly, and you have less time to really do long-term features. You're building things much faster now. So I think that's the change, and I think you have to be cognizant of it and deliver to those kinds of thinking now.

Yeah, I think that what we've seen — and when we talked to a lot of our customers, and this report was based on a lot of interviews and analysis — you find variations in terms of how customers are approaching the AI journey. A lot of them are like, 'You know what, everybody should go and do something with AI,' without providing some kind of prescription or some kind of guidance. So the projects are all over the map for companies, right? Some of them are getting some success, some projects are failing. There's no clear ROI in many cases, but also there's no coherent way of thinking and providing expertise across the company organization. And then there becomes a very scattered approach, but also very much like, I would say, a spare-part approach. You're bringing technologies, pieces of technologies, and trying to cobble it together and make it work. And then given the underlying technologies changing so fast, it's very hard to keep up for most of the organizations. So you end up failing more often because you're doing the spare-part approach versus doing a very thoughtful platform-first kind of mindset. So the pace setters — people who have been very successful, companies which we've seen — is that they're taking a little more holistic approach. They're providing a little more structure to the company. They're providing help and expertise as needed, and they're picking the projects which are going to be much more value-add as well as have the right kind of investment and right kind of people. Because AI is not just a technology shift, it's also a cultural shift inside the companies, and you have to approach this thing together as an organization and have everything behind it. You can't just say go and do partial things. So don't do spare parts, don't do pieces of technology, buy a platform, work with people who understand this, identify the right projects, and make sure you put full focus on it to regularly introspect what is working, what is not working, and fixing those things on a regular basis. So having that mindset are the ones who are seeing the pace setters. They have really differentiated themselves by putting that energy behind this AI transformation enterprise-wide, and then putting all the energy with the right kind of partners to make it all successful, and fixing things which are not working very quickly so people don't just linger around. And those are the pace setters we have seen really work out very well.

Yes. I think data has always been fragmented, hard to manage, and I think data will always remain fragmented. I think this idea that everything will come together in one place — it's not going to happen in any enterprise. It's very difficult, and because systems are built, things are running everywhere, data is distributed. The best way to fix the data gap is to really have a very good, thoughtful understanding first of what your landscape is, where is what, who owns it, what the systems are. So getting that kind of understanding is a starting point. And the second thing, which is most valuable, is really providing a very good integration layer — a data stack which gives you the ability to do, without moving everything around, a federated sense to make decisions. So either through zero-copy adapters or through different data integrators, integration technologies, providing that layer and creating a semantic layer on top of it. Once you have a semantic layer with data distributed across multiple different domains and silos, you can now have a virtual view of everything without having to rationalize everything, move everything around, which is very painful, time-consuming, and it never works because things get fragmented very fast again. So this semantic layer with a knowledge graph and then a context graph gives you a lot more insight and ability to do insight-to-action while the data is being distributed. So the data gap has to be fixed that way instead of re-engineering everything every time.

Yeah, as I said, this idea of having a technology which allows you to connect the systems and the data — coming from system of record, coming from data warehouses, coming from file systems — all need to be available for you to make decisions. And what we have built now, we have something we call Workflow Data Fabric. The Workflow Data Fabric is basically a connectivity layer with some semantic capability to understand where your data is, how you integrate it together, but also create a semantic layer for doing insight-to-action — has been the stack we provide to our customers. And that has worked very well for our customers because they are able to now move very fast to get insights from all these different systems out there without having to do a lot of heavy lifting. If you ask every data team to go rewrite everything, move everything around, it fails. And that's why we created this Workflow Data Fabric as a technology offering with the connectivity layer, the semantic layer, and on top of that we're building the context graph. And that has unlocked a lot of value because you are now understanding a lot of the insights from all these systems.

Yeah, I completely agree. So see, the thing is, access has to be now provided in multiple ways. The idea that everybody will go through a user interface is a fallacy. It is not going to work in the future. It's already starting to break apart. So you have to provide — assuming the humans are going to use your systems, agents are going to use your systems, copilots of the world are going to use your systems — and you need to make sure you provide access in multiple ways: access through a user experience layer directly, but also through MCP server for that access, A2A for agent-to-agent integration, cloud code or co-work or cloud co-work or Gemini Enterprise or Microsoft Copilot — all of these things will have to have the ability to take information you require to do your day job. If a human is interacting with it to do the job, or agents are interacting with it to do a workflow or anything like that, that has to be available. So the way we're architecting our platform today, and we have this layer called Action Fabric, is to abstract out interfaces to make it easy for anyone to interface into our products, into our technology, into our data. Of course, beyond just accessing, the biggest change which we're bringing in is also being able to instruct the systems to do action on your behalf, not just give you information. That's where the unlock happens. Getting your information — say, 'Hey, tell me what my latest forecast is' — very simple. But going and saying, 'Please onboard an employee and give them access to 15 different applications based on their role, and then ship them a new laptop based on where they live, plus make sure the benefits are up to date because they're a new employee' — that is taking action. That is the complicated part. So even through MCP server or through any agent, they can give you the instruction. Now the systems behind the scenes have to do that heavy lift, and that's what we're solving for: other than just giving you access, can I also now take action?