ServiceNow, Inc. (NOW) Earnings Call Transcript & Summary

Okay. I think we are live. Welcome back, everybody. Brad Zelnick, Deutsche Bank Software Equity Research. And for this session, we are delighted to be hosting ServiceNow. None other than the company's President, Chief Product Officer and Chief Operating Officer, Amit Zavery. Amit, thank you so much for joining.

Thanks for having me, Brad. Great. Thank you, everyone.

Let's settle in. Exciting times for sure, exciting times for ServiceNow. Format of this presentation will be a fireside chat. I got a bunch of questions, I think that will hopefully leave us a lot smarter on all things, ServiceNow and get to hear about your perspective on the industry, which you've been around, I don't want to date you, but -- or age you, but you've got great perspective.

Thank you.

Maybe Amit, just to kick us off, can you level set everyone why ServiceNow is the AI platform of choice for the enterprise. How should we think about ServiceNow as a company? Is it a SaaS platform, an infrastructure software company, maybe none of these things? And what role will ServiceNow play in an AI-first future?

Thanks, Brad, for having me. I think as you rightly point out, ServiceNow does many things. But the core foundation for ServiceNow has been the AI platform or a platform of choice for automating a business process, right? So we are very good at connecting various systems together inside an enterprise and making sure that you can really operate your business east to west and end-to-end. And ensure that your business is efficient, it's predictable as well as giving you the ability to efficiently predict how you can run and operate the things, right? So ServiceNow has been very good at going inside every enterprise and helping them rethink their business processes, operationally make it efficient, but also understand the complexity underneath the covers. So you can abstract out of this complexity at the business process level so you can get enterprise efficiency and enterprise automation. So that's how you should think about it. The platform, the way ServiceNow has built the product offering is really this one platform mindset. And the one platform means is that it has one common data model, one unified experience as well as ability to introduce a lot of new capabilities in the same technology stack, right? So that you can keep on modernizing without having to go and go to different platforms every time. So that's how we've kind of incorporated AI as well. So now once you have this platform, you can do many things. And the big things people have been doing, of course, is automating the business process around different pieces of their functional areas, be it IT, HR, finance, supply chain, customers, and that's where the scale comes in from ServiceNow. So every customer once they get started with one thing, they can keep on adding more and more of these workflows on top of the same platform or build their own workflows. And that gives us a lot of differentiation in the market as well as the customers appreciate the simplicity, but also the flexibility of growth coming out of the same kind of end-to-end platform as well. It's very hard to, as you said, it's like where do you put us? We are really this enterprise OS. Think of us as something which is running and operating a business end-to-end, east to west, north to south across every department you want and then working across all the things you might have invested in and modernizing it as you go along without having to keep on redoing everything.

The notion of an enterprise OS is extremely powerful and has really well served the company from its inception all the way to today. And Amit, you yourself are relatively new to the leadership team. What's the opportunity ahead that attracted you to ServiceNow? And how do you foresee making the greatest impact from here?

Yes. So I've been at ServiceNow around 10 months, but I've been a big follower of ServiceNow for many, many years. One of the things which attracted me to ServiceNow, one, I think if you look at AI, and I was doing a lot of work at Google Cloud in terms of building out a platform at GCP and then adding AI and making sure that customers can benefit from the large language model innovation happening. The part which was missing in many of these providers, including where I was at Google, was the ability to really now do it at an enterprise level to automate the business processes. And I saw the value of that capability really resonate in terms of what ServiceNow was doing. ServiceNow was at this place where, as I said, they were enterprise OS. LLM helps automate a lot of these processes, but the enterprise context is provided by ServiceNow. The marrying of AI with ServiceNow, 20 years of foundational information about running a business was very compelling to me. So when Bill called and when I talked to him about it, he had a great vision, he wanted to really grow this platform and solve many more problems than ever before. Meeting with Fred, the idea of humble and hungry really resonated with me as well. So it got me excited. And I have seen no other company able to do this one platform build-out, which can remain modern and give you that flexibility other than ServiceNow. So that got me excited and interested. And I've seen this play out. Every customer I speak to who are using ServiceNow or looking to use ServiceNow are really looking at business transformation. And it's not about just IT transformation or technology transfer, it's a business transformation play. And the opportunity now for us to go to customers and do this with AI and turbocharging that automation was very, very compelling. So what I see going forward, I think there's a lot of opportunities for us. And we've seen that play out already since I've joined. You saw the expansion we did. We're doing in our CRM, expansion we're doing in data and analytics, security and risk, what we're doing with our core business workflow. So we're starting to expand our TAM significantly because we have the foundational pieces and this idea of managing complex orchestration and complex workflow really gives us the ability to play in many areas and have a seat at the table because we can solve these complex problems for us. So what I expect now is to be really the AI business transformation platform, delivering value to customers more and more significantly and changing the game, we are basically the AI platform today for every company out there who are looking to automate end-to-end. There's nobody else doing that today. And I would expect to kind of continue investing, innovating and differentiating in that area, so we keep on growing in that space.

A lot of companies are talking about it ServiceNow is actually doing it.

We have customers live and their production in this area, and this multi-Agentic workflows are really happening on ServiceNow.

So just maybe shifting back to Q2 results. ServiceNow differentiated itself from peers by once again demonstrating elite level execution. Why do you think the company has been able to execute so well despite recent macro turbulence, especially given volatility in one of your fastest-growing verticals, U.S. federal?

ServiceNow has been made for this moment of automation and efficiency. And if you look at what's happening in every company, federal department and all, there's a lot of focus in terms of how do you become more efficient? How do you get better productivity? How do you really connect different systems together and get value out of it. And for 20 years, ServiceNow is building these workflows. And once you infuse it and build it on top of AI and you add data to it, we change the game, right? And that's what resonates with every department and every company -- every enterprise I speak to. And the foundational pieces we have built out, and for example, we're working with a large department in Federal government with the Department of Defense area, who are now standardizing on ServiceNow because they -- what they're getting out of it is visibility across all these things, then the trust and safety required. In enterprise, a big difference, as you know, or anybody who's running complex systems, is very different than what you do in consumer space. You have to have understanding of what compliance requirements are, what trust needed is, what is the security elements of it? And how do you put it as part of the platform and not something on the side. So what we're bringing to this conversation is really resonating and giving us this opportunity to really solve those complex problems and think of this at enterprise-grade level versus pieces of technology or spare parts, which somebody has to compile and bring it together. And I was talking to you earlier about like how people can build a car with a lot of these pieces, but does it drive? Does it meet your standards? Does it really be able to maintainable? And that's what we're doing now with these complex AI systems and making it solution-oriented so that they can adopt it and then can, of course, make it usable in their environment, but a lot of things already come that way. And that has been the thing which we've seen with our results over the last many years, including Q2, and it continues to resonate with what people are now looking at with AI as well.

Makes perfect sense. Maybe if I can dig a little bit more on the U.S. federal opportunity. It seems the underlying intent of DOGE deeply aligns with ServiceNow, the value proposition of the underlying platform. When do you think we reach an inflection point where these agencies start to lean in further and ultimately drive re-acceleration in federal for ServiceNow?

Yes. I think we're starting to see that already happen, right? I mean there is a lot of standardization happening with ServiceNow in the Federal agency. Of course, I think the mission you see with DOGE and the U.S. federal government is to drive efficiency, improve how they operate and do more with less in some cases. and we are made for that. So I think we're seeing this department by department. You've seen that as being one of the large verticals for us for sure. But even if you see the results we're producing for these agencies, they are starting to use that as a standard for what they want to get out of software. And our usage levels are going up quite drastically. There is a lot of value creation for all these departments as well. And we connect with various things they might be investing in already. So we've become this central nervous system connecting. And over time, you will see that kind of play out more and more because right now, there's a lot of rethink happening inside a lot of these government agencies. So they might be reevaluating other pieces of the technology and how they want to connect it. But we still remain the core element as part of their overall infrastructure and the platform investment they're making.

That makes sense. As I think about the heritage of the company, ServiceNow is no doubt a core system of record for the enterprise. And in the age of AI, there's competition from other players to be the central orchestrator of AI agents. What gives ServiceNow the right to be the control tower for AI?

Yes. I think as you point out, ServiceNow has been always this central or enterprise OS. We've been always built around this idea that we will connect various things together, be it any system, any kind of cloud capabilities out there, any AI system as well as any kind of personas and with any kind of data model underneath the covers, any data which might be coming from various things. So the right to play for us is very clear, right? One, we orchestrate all these business processes for years, for 20-plus years. AI is a natural extension of what we were doing. And even turbochargers is really a tailwind for us to really get us even more opportunities and more conversations with our customers because a lot of them are struggling to understand where to start, how to use AI, what use cases make sense, how to really go production with these things, and we have solved that problem. So the right for us is really this idea of AI controlled tower because you need to watch over some of these things. And every company -- every CIO or C-level execs I speak to, they're worried about proliferation of random technologies inside the company, especially AI. And they have no control, no visibility as well as no trust as well as no way to kind of ensure it's secure. What we do, we have two ways of looking at it. One, we build this product called AI Control tower which just like what we do today with CMDB, which has been kind of the gold standard for every enterprise to know what assets they have inside the company, be it hardware, software, operating system, databases, VMs or even machines. And now they look at the Wi-Fi controls and everything else, we track that for an enterprise. Nowadays I want to add AI models, AI systems, everything into the same CMDB because a natural extension. If the software is an asset, an AI agent is an asset, so they want to put it in there, and we've been able to extend that very easily. So whenever I talk to anybody inside an enterprise, they said, oh, this is something we have to do. It sells easily because they want that control. We have 100-plus customers now in like less than 2 months. We hit the year number in 2 months for what we expected with this product because everybody is struggling to understand how to control this. And we bring this trust and safety and compliance into the conversation. And second, given that we have this one platform story, our AI capabilities, the Agentic and the multi-Agentic workflows is part of the same cloud orchestration platform. So the orchestration work we're doing today with humans or processes are naturally extended to now AI agents. And we work across, as I said, an open standard heterogeneous way. So AI agents could be ours, third parties. Bespoke people might have built them their own. And then we use a reasoning engine to figure out what you need to do for a task, but that's the orchestration, the brain is us because we've taken the 20 years of workflow data and applied that to the orchestration engine. So this is what you need to do for employee on boarding or this is how you resolve a customer issue related to shipping. These are the 7, 8, 9 things you need to do inside this company. And then you break that task out, get that thing done and finish the task. The action-oriented aspect of it, gives us the ability to be the system of action across all these layers and be the orchestrator. And with AI Control power, we're getting the ability to now manage and ensure safety, compliance, the life cycle of AI. And that gets people going because now we've been able to get so many customers production and get value versus not knowing what's going on inside the enterprise. So that's the thing we're bringing into all those conversations.

Your advantage seems very clear when you articulate it that way. Maybe just as we think about AI, while it's very exciting, investors at least have expected more meaningful direct revenue benefit for software companies to date, not just ServiceNow. And we know thousands of customers are piloting apps, but it seems like meaningful enterprise-wide adoption has been a little bit more elusive. What do you think ultimately catalyzes proof of concepts into broad-based production?

No, I think there's a lot of experimentation going on, no doubt. And I think it depends on how you take this thing on. The AI definitely creates a lot of value. There's a lot of opportunity to really improve how you efficiently run your business, how you do things in a better way. But how do you combine deterministic as well as kind of dynamic thing to be more predictable. And that's what the problem we have solved with our Agentic and multi-Agentic workflows and orchestration engine, where we can connect various systems together and take them in a production environment versus giving you pieces of it. So a lot of the things which have failed and typically, when I talk to a lot of the groups who are trying to implement AI, there's a lot of excitement to do it in yourself, DIY. Because they think you can get some pieces of AI technologies and put it together and somehow you'll build an app, which looks nice, but then you realize not maintainable. It doesn't understand enterprise context. It doesn't have understanding of trust, safety, compliance and bringing all these various pieces together, which are changing at a different rate is very hard to keep up with. So the failure rates I've seen has been much higher in those areas where people want to just take spare parts and build something out versus where we have been successful, and we have -- again, if you look at our numbers with Now Assist last -- whenever we announced publicly, $250 million in annual contract value and on target for $1 billion next year. For AI specifically is because we are talking about production quality, real-life systems, which customers have gotten a value out of. So you look at -- we're working with many companies, maybe standard Charter is using -- bank is using AI control tower with our AI agents, orchestration engine with RaptorDB as a full production environment for running and operating the AI systems. Because now they can now have some control, but they're also getting a solution. So the success for any company has to be oriented on like what can you get, which is enterprise grade and defense context because a lot of context in enterprise is not in documents. And people might think that I can read a document and I can figure out how business runs in a particular department, never happens. Otherwise, all enterprise systems would have been very perfect for many years. We've been -- I've been in enterprise software for 35 years, and I realize every time people talk about, oh, I can solve this legacy thing or change these things just because I've got new technology, never has happened. You have to coexist with existing environments. And what I think will -- what is happening is that context is missing because a lot of context is in humans' brains. A lot of people who work in the department and they change quite frequently. So that consumer-grade AI doesn't work in many cases because the context as well as the enterprise capabilities are missing. So what we are doing differently is building the solution, solving the end-to-end problem and understanding how to integrate with various complex systems which might exist and improving that condition. And that's where the success rate is much higher for us with implementations we have done with our customers versus a lot of the reports you see in the market about failures, which are more those kind of side projects, somebody is experimenting without the knowledge or the experience in many areas.

It almost feels like ServiceNow has an unfair advantage. I don't want to get too far ahead of ourselves. But...

I think the experience we've had for years and we've solved this problem, we can bring that to help customers basically.

That makes sense. And just on a related thread, AI and production at scale requires having a data strategy and a data estate that's AI ready, if you will. What can ServiceNow do to help customers lacking a robust data strategy, realize the promise of AI?

No, you're 100% right. I think the data and getting the data strategy right really is a very critical part of an AI, if you want AI to be successful. And I think the way we've been thinking about it, that's why we launched something called Workflow Data Fabric. Our data product in there connecting various systems using zero copy architecture where you don't have to move things around, but I still get the context and the metadata associated with all the systems that might have could be running in Snowflake, Databricks or other data warehouses like Oracle, Teradata, BigQuery, other things like that. And we provide that ability to have context and metadata-driven ways to analyze and bring it at a business process level versus a data level. So a business process where you can do insight to action across all the segregated and fragmented data sources and compiling the metadata, and we bought this company, data.world for data cataloging and knowledge graph and things like that, allowing that lineage and the data understanding to improve your processes as well as give you actionable insights, has been the game changer. So we have to solve that, and it's something which -- since I've joined, we've been pushing aggressively and that business has been really doing well because customers are seeing value of doing that at a business process level versus just integrating data sources, which is what most of the companies do today. Like they take various data sources, they say, all we'll consolidate into one more data lake. And then you again have fragmentation, then you create another integration. It becomes very, very unattainable. The changes we are bringing in is like still allow you to keep your existing systems, but make it much more work productively to look at it from the business process and enterprise level versus individual data level. And that, I think, has a big important step to take for AI.