ServiceNow, Inc. ($NOW)

Welcome to ServiceNow's Financial Analyst Day 2026. Thank you for joining us today. Before we begin, I want to remind everyone that today's event will be webcast and recorded for future playback. Information pertaining to our forward-looking statements and a reconciliation of our GAAP and non-GAAP results are available on our Investor Relations website at investors.servicenow.com. As you can see, we have an exciting agenda for you all. Bill and Nick will kick us off and discuss our vision and opportunity. Amit and team will present the Blueprint for Agentic business, including deeper dives into the key growth areas that unlock AI transformation. We'll have a 10-minute break, then Paul will go over our go-to-market strategy and lead a panel to showcase the tremendous value customers are getting from ServiceNow. Finally, Gina will close with a financial overview of the company's performance and outlook. So with that, let's get started. [Presentation]

Please welcome to the stage, Chairman and Chief Executive Officer, Bill McDermott; and Vice Chairman, Nick Tzitzon. It's always great to come out to a video where the Nick character is a useless corporate bureaucrat. You should tell them the real story . No, I'm not going to. Nice to see everybody. Anything you want to say before we dig into the content?

Just no place I'd rather be than right here right now with you, Nick and all of you, thank you very much for coming. We're going to give you a lot of insight on the company. The company is in great shape, and we're ready to roll. So let's get it started.

Sounds good. Well, why don't I bring up a few things one by one and ask you to comment on them. So we'll go first to this. I think very few people here in this room, Bill, on either side, on our side or on their side are interested in the past. But sometimes, it's worth reminding everybody where the company's come from. So when you look at the trajectory of the company over the past several years, what comes to mind when you look at this traffic?

Well, Churchill said, the further back you look, the further forward, you can see. We came in, in 2019, building on a great company terrific founder, very good CEOs, excellent board and good culture. And we said we want to be the defining enterprise software company in the 21st century and that we would be the first to get to $5 billion to get to $10 billion. And then many of you included weren't so sure we'd get to [ $15 billion in 2026, and we're blowing through $15 billion in 2026. And so the first thing I'd like to say is promises made, promises kept the fastest enterprise software company at scale to hit $15 billion in the time frame we did it and organically. Right now, this is the hottest brand in the enterprise. We're pursuing a gigantic TAM. We'll talk about that a little bit later. The tailwind is at our back. We have the products. You're going to see the products today. And you're going to see the best team in the industry today. We have the revenue and scale matters because it builds ecosystems and networks. We have the users and the loyalty of them, our attendance is up double digits. This is the biggest knowledge ever. You'll feel it, who's going to knowledge tomorrow? Great. You're going to love it. And the ecosystem, I mean, the show floor is just amazing. I encourage you to walk through it. You'll see the control tower. It's stunning, and we'll talk about that today. I said we were the platform of platforms in 2019. And now I'm telling you we're the AI of the AIs. So this is a company that has the loyalty of its customers. It has the inspiration of the most satisfied workforce in our industry, and it's the trusted brand. And I'm not the only 1 to say it. Fortune says it, Forbes say it. The customer says it with their wallet and you actually know it. So what better time has there ever been than right now for ServiceNow and ServiceNow shareholders.

You mentioned in the [indiscernible] Board. I see our Lead Director, Sue Bostrom is here; Paul Chamberlain is here from our Board, Larry Quinlan, is here from our Board. So it's great to see the Board...

Yes. I mean you've got a lead director, Sue Bostrom, Larry Quinlan, Paul Chamberlain. These are individuals that have been with me through thick and thin. So too is our founder. We still have our founder on the Board, which is awesome. And our Board is a really great board, very committed to the company. very inspired by what we're building and how we're executing. And hopefully, you feel that the entry point you're getting in at today is just like never going to happen again.

So let's shift gears. The portfolio has obviously evolved substantially in the years that you mentioned. This is the representation that customers will see at knowledge. When you look at this AI control tower for business reinvention, what are the important things for investors to understand?

Well, let's start with the AI control tower for business reinvention. So does GPS signals that will come from language models and other things. but there is only 1 air traffic control tower for business software in the enterprise at ServiceNow. And so what you'll see today, as you'll hear from our colleagues a lot about the Agentic front door with auto. So any channel you come into, you now have one single agentic experience with ServiceNow. So if you enjoy ChatGPT or you enjoy Claude or other language models that same simplicity is being brought to you for the enterprise. If you think about industry, we're in all the industries that are featured in this slide. Why that really matters is the moat and industry domain expertise is unmatched by ServiceNow in the enterprise. It also creates more loyalty and net new ACV opportunity, particularly in CRM, and we're going to talk quite a bit about that today. Autonomous workflows. This is super cool because now ServiceNow goes across the entire enterprise, east to west. So years ago, you knew us for IT. That's okay. We need to remind people that we are the ERP of IT we are the system of record of IT. And no place has greater permission to grow in this world of AI than IT. And of course, CRM on its way to being a couple of billion business. By the way, we have 6 of them. And security and risk. We're now in the biggest growth TAM I see in the next decade, especially when you think about the world's third largest economy is actually cybercrime. So when we made our bold moves, we knew what we were doing, and we'll cover that today. And naturally, when you look at the autonomy of workflows to be able to coalesce all of the clouds, all of the language models, all of the systems of record and all of the data sources into an autonomous workflow that can close an action out not give you a recommendation that's probabilistic, but a deterministic outcome achieved. That's where the world wants to go. And you're going to see something today on employee experience that is second to none. And also app development, not only is it a big business, but we all know, lots of code is getting written by us and by others, the more that AI is generated in the world the more it has to come through the ServiceNow platform. We are the gateway to the enterprise. So more AI is great for shareholders. So we're going to sense and that's any data we're going to decide that's any model. We love them all, and we have deals with all of them to either build software together, put it in our software or help them get into the enterprise with our unique attributes and then obviously act on any workflow. And it could be ours. It could be someone else's. It all comes through ServiceNow. So we welcome everybody. And to do this securely. And I think you're going to hear today that we're in the security business. And we think this is a gigantic opportunity for ServiceNow. And you say, "Well, why do you think that Bill?" Well, it's already $1.5 billion business, and we weren't trying real hard. And now you're going to have IT, IoT, any device, critical infrastructure, networks, devices, all coming from one platform that fuses IT and OT, the only one in the world. And I really think that any system is just like so stunning because, everybody else wants to, I'm reading an article say they want to shut the world out. They want to protect their moat. We're welcoming everybody in because we know we have the winning hand. So when you look at all the hyperscalers and all those systems of record that you know so well, they all integrate with ServiceNow. So we're still the platform of platforms, that is the foundation, and we're nice. There's no reason to waste time. having skirmishes because we want everybody coming into ServiceNow with their AI, and we're going to grow, grow, grow, and we got a bold ambition for 2030 we'll lay out there today.

So one of the things that Colin and his team have done, acknowledge is the customer's voice is really out there. And I know you're personally inspired by several of those stories. Any that you would pull out just for this crowd as a preview...

Well, I think FedEx to me is one that I'd really like to pull out. Raj will be on stage with me tomorrow, the CEO of FedEx. When you think about FedEx, it's a great company. And Fred Smith was a great innovator, an unbelievable entrepreneur. And he used to talk about the package itself and how it moves throughout the world is as important as what's in the package. So to think about FedEx teaming up with ServiceNow and Raj coming here on stage, knowing that they're moving 18 million packages a day through over 200 countries around the world and every key business process, things that would sound like core ERP to you is now running on our agentic platform. I mean that's pretty stunning. And then if you want to take something just really interesting, Chipotle is doing great. Everybody likes Chipotle. I like Chipotle anyway. And now they can change in their 4,000 locations, all their menus on the fly. They can be highly creative with their associates. And you're just changing everything to real-time enterprises. And so whether it's rethinking CRM or whether it's driving a new approach to supply chains, everything now is real-time business processes on the ServiceNow platform. So I think this control tower idea has now manifested itself into a complete portfolio of products.

Love that. Let me do a couple more just to set the stage. So you mentioned we're nice, and we've taken this posture in an environment where it seems like there's more coming into the enterprise than ever before. You always say trust is the ultimate human currency. What does that mean in practical terms?

Well, trust is the ultimate human currency. It's the one thing you can't trade on. You could trade on just about everything else in life, but not trust. You earn it and drops and you lose it in buckets. So we don't lose it. We want to win more and more every day. So I think we all know that the net present value of a loyal customer is every business' greatest asset, which is why I think you should all be excited that we have the highest retention rate in the enterprise. And we are continuing that push to make sure it stays that way. And we're transforming our company to make sure of it more on adoption go-lives and the use of AI. And that's really where that hockey stick formation is going to kick in big time. We're open. And when I say trust, open AI Anthropic, Google, NVIDIA, Microsoft, Amazon, we have deals with every one of them. They like us. We make a lot of money together. The language models are coming to us because they know what they do is very important and so do we. But they also know the context that we bring to the unique data position that we have in the enterprise and the process position and the relationships and the ecosystem is going to be a gateway for them to prosper and grow and bring their amazing intelligence to the enterprise, and we warmly welcome them. I also think you should know that we're getting really good at AI. And now we're even going to make a guarantee a total satisfaction guarantee on AI go lives in less than 100 days. Some of them could be a few [ dates ], but all of them are going to be less than 100 days. And we're going to make that commitment on stage. You're getting the first preview. We're excited about it. We have the forward deployed engineers, we have the customer excellence group and we have partners that are lined up and ready to roll with us to make that happen. And so we're going to make that offer and make that offer tomorrow on center stage. I think customers are going to like it a lot because the pipeline is huge. And if they see, we're going to now offer them something on the control tower. That's a pretty special offering as well to get them started, getting them using it, landing and expanding with is something you know we're good at I think you'll know that we're well on our way to being a truly gigantic enterprise software company. We're not slipping. We're growing. And I also want to make it clear that we are also using our own platform. So if you think about the positioning that we have right now, we have our own AI running on now, and we're achieving enormous productivity gains. What do I mean by that? Gena has already told you in earnings calls that it's been good for $0.5 billion and overachievement on the productivity curve. But I'm telling you that we took a couple of really smart moves with what I call tuck-ins based upon the size of our company and the fact that we never bought anything for revenue. If we did, we would have bought companies that you would have recognized a lot better with a lot more revenue. Now we bought the future. And the beauty of that is, we're going to leave this year with the exact headcount that we entered this year. And what you can take away from that is that our platform is resonating in the way we run our company, the fact that everybody has AI in their pocket to take care of customers, run their business and run our company. That's why 9 out of 10 customer cases are now managed by agents in our company. The same is true for HR and all the questions around running a business that used to be done by people are now being done by agents with people still in the process only when it's absolutely necessary or when it's a high-touch minute where you have to really touch an employee and make the heart of a human come through or in the case of a customer, a white glove treatment because they're exceptionally special. Other than that, the agents are doing the work.

Let's see if we can do 2 more in 2 minutes. So you've talked a lot about the growing opportunity. It's not controversial to say there's some skepticism about is the opportunity, in fact, bigger. I don't want to litigate the entire thing, but when you look at the prize in front of us, what is your takeaway about the trajectory ServiceNow is on?

When I started in my career at Xerox Corporation, it was a great CEO named David Kearns, and he always said, I can absolutely handle and empathize -- I can absolutely handle and empathize the folks that are a little skeptical, but not to cynics. And I think what's cool about people in this room and around the world, they like ServiceNow a lot, and they're rooting for ServiceNow. So you might have a certain skepticism, let me take that away from you real quick so we can get back on to track that we belong with the rules and rails of today's corporation. We have clarity around the TAM. I think we've taken you through this ride together from IT to multi workflows to an enterprise platform. Obviously, the AI platform we brought in and Now Assist across the enterprise. But now we took you some place very, very special. And I empathize with you because we waited 9 months for Moveworks to close the regulatory process. And then on the back of that, Veza came in okay? And Armis came in like within 3 days. So it's probably like, hey, what are they doing? Are they buying growth? What are they doing? No, we weren't. We were buying a ticket to a bright future. And so now you have an AI control tower for business reinvention where you have your agentic front door and you have your identity management. Now this is key. Does anybody here actually think that the working population of corporations around the world is going up? Well, it's not. It's flat. And the birth rates around the world are actually going down. And the good news is at that moment in time, here comes the agents. And here comes the robots to make the lives of people better and to increase the productivity of every company around the world, 2.2 billion agents in the next few years, robots, and we own the identity of not just our agents, but the agents that come from other companies in the flow of work, they'll come through our control tower. And then ultimately, we will coalesce all the clouds, all the language models, all the data, and we'll do it all in a highly secure IT and OT environment. You never heard that before because there's only one company in the world that's built to do it and it's ServiceNow. $600 billion TAM, we're going for it.

Maybe we'll have a robot moderate this conversation next year.

Maybe have a robotic CEO.

No, I don't think so. So let's leave it here. So I don't want you to steal Gina's thunder, but she did review this slide, so she knows we're going to show it. It's not about rhetoric, it's about results. We've heard that before. What's the promise we're making about the results trajectory of the company over the next several years.

Very clear. This is a sub revenue $30 billion plus commit between now and 2030. I want to be very clear. This is not the [ bill ambitious ] number. They wouldn't let me put that up there. Do you know what I mean? That is the one that you can say, okay, like the whole management team bottoms up across the board believes in that number. 60 plus is the revenue growth plus the free cash flow margin is going to be a 60-plus number. Right now, it's 56, it's going to 60-plus, and we're not taking our foot off the accelerator. We're going to grow the top line and you'll see the acceleration of that, and we're going to expand the margin profile of the company and, we're going to take down stock-based compensation down around the 10% mark in 2029. So you'll be dealing with a 50-plus GAAP company, GAAP. So I know what you want and we're going to give it to you. So bottom line, high level, we're going to double the company. We're going to be masterful on our execution across the world globally and through industry. The verticals are really good. We're actually also going to take a platform down market a bit because we have 90% of the Fortune 500 now. It's not a marketing slogan. I actually have people go through the math 9 out of 10 Fortune 500 now, fact, and so we know that we are going to expand with the Global 2000, but we're also going to take it down a little bit, where we could go into new markets in the upper mid-market and take care of business on folks that really aren't in our league. It's time for that now. And so we're going to run a very efficient ship. And when I started telling these stories to you a while ago, we were climbing a mountain and it was tough, and we are climbing and climbing and climbing. And there's 3 things that stand out in the core values of this company. Number one, we're hungry and humble. And we're hungrier than ever. The chip on our shoulder is tougher than it's ever been, yet there's a humility and a kindness and an openness about us that never will be in question. We're here to wow our customers. Customer satisfaction and loyalty is job one at ServiceNow. And ultimately, you're going to see a team today. When I tell the Board about this team, I tell them this is the absolute best team I've ever run with in my career, and I mean it. And so I don't have a second story. We got the best team in the business. So when as a team is the way we roll. And so what you're going to see today is unbelievable leadership. Amit, our great engineering leader. Obviously, the COO of the company as well, has done some extraordinary things in architecting the product portfolio. It's really amazing. It's so exciting. And [ Paul Fipps ], on the go-to-market side has engineered the AI control tower for business reinvention across the world. And Gina, obviously, is going to give you what she always does, right from the heart, the truth and the belief in this franchise. But I also want to call out the folks that report to these individuals and how great they are. Too many in name, but I do want to call out one in particular. When we acquired Moveworks, we also acquired a great management team. And instead of having a great leader like [ Bhavin ] report into somebody at ServiceNow, we actually said, we think you should be the boss and let the people at ServiceNow report into you. So we're really making account given big businesses to people we believe and trust. So we wouldn't have acquired it if it wasn't a cultural fit in the first place, oh, we didn't believe in the leaders and they believe in this mission as much as we do. So we now have a great team. We're going to win as a team and you're going to see the best days to ServiceNow are now and in the future.

He'll be back for questions, but that's a great way to that.

Thank you very much, everybody. Thank you.

Please welcome to the picture President, Chief Product Officer and Chief Operating Officer, Amit Zavery.

Great vision by Bill. I'm here to talk to you about how we're going to execute against it. It's great to be back to my -- for my second Financial Analyst Day since joining ServiceNow. During FAD last year, I shared our vision and plans for an enterprise agentic platform, and I'm really excited about to show you what we have accomplished so far. The last 12 months for us have been marked by innovation and growth. Our autonomous workforce of AI specialists are already delivering impact with customers like FedEx. EmployeeWorks launched just 2 months after acquisition of Moveworks, beat Q1 expectations by 5x. One of our data analytics products exceeded $100 million in ACV in its first full year of availability and data and analytics is on track to be a $1 billion business for us. The next evolution of our CRM products expanded into omnichannel, intake, sales order management and CPQ, which will help our CRM business cross $2 billion in our security and risk business crossed $1 billion and expanded further into our AI identity governance and OT cybersecurity, helping to differentiate our core. Our AI products are nearing $1 billion in ACV, and the momentum only continues. And the AI control Tower became a market-defining solution. So all this innovation and level execution enabled us to beat and raise our guidance in all the quarterly results last year. We also stayed true to our principles by continuing to expand our open ecosystem and the platform by partnering with partners across the tech stack and all industries. And we've taken also an AI-native approach to transforming every corner of our own business as well as building AI into every product. And we delivered a new conversational experience across our workflows, launched monthly releases and automatic upgrades as well as powered more innovation by using AI tools and AI-native approaches across all of our portfolio. But let's talk about today and fast forward to what we're going to be delivering as we go forward. Every analyst, including all of you probably are covering as you cover software is asking if AI is going to replace the platform or AI needs the platform. A recent headlines are answering that question better than I could. At Meta, for example, an AI agent exposed sensitive data, which is an enormous security incident with no external attacker. The own AI agent was the failure more here. An AI agent at PocketOS hit a credential error and delayered the entire production database and all the backups of customer data in just 9 seconds. And the industry has been trying to put Band-Aid on all these issues using agents and spanning more and more agents. But none of these stand-alone AI products can solve the core fundamental issues because none of them can govern the system as a whole. And there's an important distinction here to think through. Autonomous work is not just powered by one new innovation. It's really 2 essential capabilities coming together, working together to drive real outcomes. It's probabilistic AI, which is what generates the answers and deterministic execution is what runs the enterprises. And every enterprise needs both of those. That is what it means for AI to be an operating system with enterprise context. And thanks to our CMDB and context engine, we know why a particular decision was made. We can encode the real-time relationship between every asset, person, service dependency and policy. I know many will say nowadays, I'll just build it myself. I'll stitch together the frontier models, take some open source models as well because I've been told that the next best SaaS application is just a prompt away. But that idea falls apart when you test against 3 things. First, time to value is drastically underestimated. What should save a customer, time and effort only ends up costing them more. Second, the total cost of ownership is very complicated. They compare a build versus buy decision with the cost of API, typically and an engineered salary. But that misses the point. You have to add model selection and the constant updates, which happens with the [indiscernible] releases from the model companies, prompt engineering, security and compliance validation, managing new requirements by also not breaking the hundreds of systems, which you are already connected to, which run the business itself. And that's just the start. The third fail test for build versus buy is the security and governance gap. Autonomous AI agents that take action inside enterprise system without a harness will create an infinite risk surface. One compliance error from a homegrown agent would cost millions for a business. And we'll be also looking at research around this. customers building their own LLM-based solution, typically spend 5 to 10x more than using ServiceNow, depending on the complexity of the business they have and the different systems they have to connect. And all of the solution will take longer to build and it's usually less secure. So the ROI and the TCO is not even proven if you want to do it -- go down this path. And the ultimate trap of build versus buy is this. The building an app is definitely not the same as running a business. And that is why we made it possible for customers to both build and buy full solutions on our governed platform. At the core of that platform are 4 integrated pillars that create a complete sense, decide, act, secure loop for AI, which is built on a modern AI native stack. And this gives customers visibility context, intelligence, automation, automated actions, governance, all on 1 platform. The Gartner projects that 40% of agenetic AI projects will fail by 2027 but not because AI is incapable just because the AI is in governed. And that's what we are here to fix. Our platform is about more than helping customers, not just fail. It's about powering the success and growth. And we have proven that we can do this at scale. Today, ServiceNow runs 100 billion workflows and 7 trillion transactions annually, growing at 25% year-over-year. And that scale creates a flywheel. Every Action on our platform deepens our operational context and enriches our CMDB and context that makes our AI better. Our new UI has higher repeat usage, and we only grow more with the launch of auto. Unified AI experience, you will hear more about later. So basically, actions, which creates outcomes, outcomes which create new actions. The flywheel continues to accelerate. And every step makes our ITAM, ITOM, SPM, ITSM and all of our core products more valuable than ever, which is triggering more customer conversations and continue to expand our IT business as well. And humans usually need nowadays agents, -- agents definitely need guardrails. You get both through the platform we have delivered. And this is a durable advantage we built first in IT and now expanding even further into many new domains. And if customer is also feeling this compounding value in real time, I'll give you an example, like CVS has taken hundreds of millions of actions now on ServiceNow supporting our 170,000 colleagues [indiscernible] across 9,000 stores. Robinhood is deflecting 70% of employee requests before human intervention, saving over 2,000 hours a month. [ TridentCare ] achieved 96% scheduling automation with our AI-powered CRM, improving care for millions of patients while also increasing revenue. So the question becomes what does it take to enable and govern autonomous work for the enterprise. Today, my team will show you how it's done through the 4 pillars of sense, act, decide and secure. But the first, let's start with the AI platform that everything is built upon. John, over to you.

As Amit said, we're going to spend the next hour or so talking about 5 sections. I'm going to start with the ServiceNow AI platform. And we have reimagined and reinvented our platform. And you're going to see a lot today. You're going to see autonomous workers. You're going to see the power of AI control tower. You're going to see new AI native experiences, conversational experience. And we have reinvented our entire platform for the Agentic era. Now to be clear, we're still a system of action. We are the workflow platform. And things are evolving. We're seeing patterns in the market that I'm going to address as we go through this presentation. But what I want to talk about first is that we have reimagined the platform from the bottom up. And what that means is that we have recreated the AI stack, and it allows us to do things like innovate at the speed of innovation, which are at the speed of AI, which is very, very important because the expectations of our customers is changing rapidly. They want to see these innovations come very, very quickly and want new experiences very, very quickly. Now the things that you're going to see around multimodality and voice and new conversational experiences, those are all part of the native platform. They're not bolted on. And the first area that I want to dive into that Amit talked about is this idea of context. And the context is extremely important to any agentic system. It drives the AI. But The first, I want to play a video of Boris, who is the creator of Claude Code at Anthropic. And Boris is going to talk about how important context is to an AI system. And why the ServiceNow platform is uniquely positioned for context, could we roll the video, please?

Thank you, Boris, for joining me. Really excited to have a chance to have a conversation about all the work you've been doing, how we've been also partnering between ServiceNow and Anthropic?

Yes, Amit. I'm so excited to be here. There's so much to talk about.

The world is -- for enterprise customers is very fragmented.

That's right. I mean our average customer will have probably 300 different systems underneath.

And in different world versions.

Yes. I mean the business is just so complicated. There's all these different tools. There's all this different process and data. And so you need something like ServiceNow to organize and bring sanity. All the dependencies, all the data is just so complicated and you make it so simple. Customers can just work with one platform and then they don't have to consider all this complexity, you solve it for them.

Today, we run close to what is it 100 billion workflows on our system and around 7 trillion transactions. So we are collecting so much information on why somebody is doing something, what decisions were made. So when I use a large language model and tools like yours, and then combine that with the context engine and then the understanding of the whole enterprise workflow, it changes the outcome very well, very much for the enterprises.

If I'm doing something and I don't have the context, I'm not going to do a great job. If I'm just told go do the thing, but you don't have enough contacts -- with the model, it's just exactly the same thing. You want to give the model at task, you want to give it a tool to bring in the context that it needs. And it's just going to do such a great job at it. And ServiceNow is a really great way to bring in that context that it needs to do the job.

So as Boris said, context is the driver of AI. And we have a context engine inside the AI platform at ServiceNow. But what is context. Well, context is not data. Context is not a decision. It's not an outcome. Context is actually the history behind an outcome. It is the decisions and what made up those decisions that are important to context and that important to the AI system. That's what our context engine delivers. Now it doesn't just stop there, though, because there are new outcomes and new decisions that are created every day inside of our system. And so the context engine gets better over time and in turn, the entire agentic system at ServiceNow gets better. I'm going to talk a little bit about this earlier. And some of the industry would have you believe that every single process inside of the enterprise should be an LLM call. There's no need for structured workflows anymore. It's all about the LLM. Well, that's not very smart, it's not very efficient. And it is not the way that we want to drive our platform. Now you're going to see a lot on agentic AI and generative AI today because it's extremely powerful. It's extremely flexible. So things like our -- and our AI agents and the things that you're going to see today are based off of that. However, all the context in the world doesn't fix the predictability of AI, meaning the power comes from the idea that you could ask the same question twice and get 2 different answers. On the right-hand side is structured workflows. We've been doing this for decades. These are things like flows and approvals and catalog items. And they are what drive the enterprise today. And the trick here is not to offer one or the other. You need to offer both. And what we're going to do in the reimagined platform is to harmonize those 2 things together. So that agenetic systems and activities call into structured workflows and structured workflows can call into AI agents, and we bring them together in a way that nobody else can do to provide the most efficient, effective outcomes. Now as I was saying upfront, things are changing around us very, very rapidly. And our customers want broad access to our system. And we're seeing others in the industry, exposing their system of records through APIs and through MCP to allow for reading and writing of their system essentially becoming a database, a system of record. And while that's very exciting, that's not what we're going to do. We're going to expose the system of action. And we're going to do that by opening up this layer to the Claude's and the OpenAI's and the Gemini of the world. What is the system of action? Well, that's where the true power of our platform comes from. That is our workflows, our flows, our processes, our skills, the context engine, playbooks, all of those things that is the system of action that is ServiceNow. But we're not just going to [indiscernible] that. We're going to monetize it, and we're going to do that by introducing something called the action fabric. And there are a few things you should take away from this it is any protocol, any tool. So you can use your tool of choice and you can talk to the action fabric headlessly and kick off the autonomous work that is so powerful within our platform. And it's governed. So all of the business rules and everything that's happening, you can call into it and those actions are taken and workflows are triggered. But we're not going to stop there because we are now going to monetize that process. And as Amit said, we have a monetization model today for generative AI. It's called NowAssist. It's a $1 billion business. This will be $1.5 billion by the end of the year. And what we're going to do is plug directly into that system. So now that any time that an outside human being, machine, AI agent, third-party agent calls into the action fabric, we're going to [indiscernible] assist, the flywheel is going to spin faster. And this is a tremendous TAM opportunity for our company because now anything and any one and anybody can call into the action fabric and take advantage of what ServiceNow is known for automation. And what's going to end up happening is we're going to have this universal action layer, where all of these systems are calling directly into our action fabric and spinning our flywheel even faster than it does today. Now there are other things that are going on in the market and that we want to address with our platform, one of them is autonomous agents, these long-running agents. And they work on your desktop and they help you do things. They write code, they will handle your schedule. They will monitor your e-mail. They're great. They're assistance. And we wanted to build one of those and we did. And the first half was talking to CSOs and security, and they said, "Absolutely not." You're not going to be able to deploy those things, they're unsafe. They're not governable, we don't want them. So we started [ Project Ark ] with NVIDIA. Our partners and friends from NVIDIA, we got together and we said, well, how do we fix this? And what we ended up doing is using one of their technologies to secure these agents in essentially a sandbox mode. What that did was give us the ability to tell these agents what they can do, what they can't do, please don't delete my entire inbox, what systems they have access to. And it gave us the control to allow us CISO to say yes. But we are the automation company, so we wanted to expose our agent and many, many others. I think today, we just have signed a partnership with Anthropic for Co-work to talk directly to the action fabric. And what that does is it allows your assistant now not just to manage your calendar or plan a trip for you. But it can also ask for time off and kick off these headless workflows that are going on in the background. So I can do things like take time off and change my HR or my benefits and request a new laptop, all from these agents that are running on the desktop. Now there's going to be a lot of these agents. You might have 4 or 5 running on a desktop, you might have tens of thousands of desktops across your enterprise. So the last thing we wanted to do with Project Ark was plug it all back into the AI control tower. Each and every one of our agents is talking directly to AI Control Tower telling you exactly what actions is taking, what system is trying to get to and allowing somebody in the enterprise now to look holistically across the enterprise, not only at our agents that are running on the desktop, but at all agents, giving you that holistic view. Now the last piece of this puzzle for the action fabric is build anywhere. And again, our customers, the ecosystem, our partners are saying, "Look, we want to use the tool of our choice, but we love your platform." We want to build net new applications. We want to build primitives that run in your action fabric. And in turn, what that does is it makes us a system of action. It gives us a tremendous consumption opportunity across the board, and it drives the flywheel in ways that we couldn't have imagined before Action Fabric. And so what we want to do today is show you how easy it is to use your tool of choice and build net new applications, net new workflows on our platform. And to do that, Jithin is going to show us an awesome demo.

Thank you, John. Super excited to be here. Some of the incredible innovations John just spoke about, I really want to show you how it all comes together on a demo. AI and vibe coding has fundamentally changed how applications are built and how agents gets imagined into existence as autonomous specialists in the app. I'm a developer. My HR team, that's the job I'm going to do today in front of you. My HR team asked me for a brand-new addition to our employee benefits app. So typically, I have few tool choices. I could be using cursor, Codex, Gemini. Today, I'm in the mood for Claude Code. Here I am getting on to the next screen, which is the clock code, and what I'm about to do is in the cloud, I'm going to ask to help me build a pet insurance module for the Employee Benefits app. And the first thing I would do is in simple natural language, I'm going to say, add pet insurance enrollment to the Extra Care benefits app, which I just spoke about, tap. And what you're seeing right now is Claude is starting to get to work. You are seeing something go fluent. It's our ServiceNow's AI-ready platform language. We have open sourced our build agent skills and platform knowledge directly to agents like a Claude Code via our SDK. That means the whole thing, the UI, metadata, access controls, business rules, all of it. And the last screen you saw was the app is almost built by the Claude. Now I'm going to actually jump in and show you how it actually lives in our own native ID, which is actually ServiceNow studio. Now let's jump over right here. You can see on the top of the list, the benefits app. Let's go ahead and click and do it. And the moment, any application, which lands on our platform, like Amit was talking about and John was talking about, it elevates the app to an AI-native application. Automatically, when an application is built on our platform, our platform will actually recommend a set of agents who will live and breathe every day with the humans in the loop day-to-day 24/7. You can see our application has already recommended that we create an enrollment agent, right? Let's take a look at the agent. And in that -- you can see the enrollment agent, it's all set up. It can qualify, understand and manage the end-to-end enrollment with minimal human intervention. Insights like this are possible only with the CMDB, the context engine and the action graph, which John spoke about earlier. And you can see I'm already on my way to actually create an agent and every assist which is delivered in the app is a run time assist for the user. Imagine every application, having 3 to 4 agents living in it, every interaction is a monetization moment every day, 24/7 and now we'll jump into the actual building of the agent. Let's go ahead and build it. You can see with the click now the Claude Code with the ServiceNow Studio and the SDK skills, the agent is already getting built. And once the built complete, it's ready, it has its own roles, instructions, autonomy, it can actually sense, decide and act every day within the application, but we are not shipping it just blind. Let's make sure we run the right automated testing and scanning so that it's ready for production and overall. Now it's actually running through the scan process. It's going to come up with the readiness score. This is how our platform is built. It has run through its own evals, come up with the score, great score, 90%. Now I am ready to actually submit for deployment. Now what are you seeing is app engine management center. This is the product or the platform where every application goes through to make sure it has the right security controls and the access controls, everything built in. And by default, any app, which runs through any agent or an asset, AI asset would run through, it automatically gets registered as a part of the AI-controlled tower. Let's do -- this is where we do one more last review of the application. Everything looks good. And let me make sure I have the right readiness, which comes through with a release note and all of it you can see. I'm ready to now deploy the application right here. Now this is the actual app, which is live, ready for you to prompt. Now I'm going to switch my hat a little bit. I'm an employee, and I would like to actually understand what are my benefits from my pet insurance point of view. Right here, I'm going to actually prompt and type in. You can see it's pulling a massive amount of user awareness and context at the back end. It looks very simple, but in order to qualify and make sure this agent delivers the right accurate information, it's actually bringing in [ CMDB ] context engine every one of those process mining capability you can think of. The last point I have a 2-year-old Franky, which I want to make sure I can actually get the insurance set up, right? There you go. It's now going towards making sure I have my Franky getting access to the coverage, what it needs to be. beautiful, isn't it? So what you just saw is the actual agent is live in action. Every app built on our platform will get autonomous agent embedded within, and that's how we are fundamentally changing the way AI apps and agents are built within our platform. I'll say it one more time. It's like John shared earlier, it's about you can build in anywhere, any tools and any choice, what do you have run and govern in ServiceNow with the enterprise-grade controls and security. Every app sits with an autonomous agent embedded and running the AI assist meter at scale. So that's the end of my demo. And I would like to now bring in Gaurav, who's going to talk about the sense part of the overall value prop.

Thank you, John and Jithin. So you just saw why the ServiceNow AI platform is the system of action for autonomous work. And now I'm going to show you and talk to you about how we bring the sense and decide pillars to life on that same platform. We do that by helping our customers achieve 4 things. First, connect all their data wherever it lives; the second, control it with enterprise-grade governance; third, contextualize it with enterprise-wide intelligence; and then converge that contextualized intelligence right into the flow of work. And our customers are responding strongly to the strategy, making it one of the fastest-growing product businesses in ServiceNow history. Workflow Data Fabric now has over 4,000 customers, driving more than 3 billion monthly data transactions. And we've recently added consumption-based pricing and more than 700 customers have already consumed more than 0.5 billion credits. And then there's RaptorDB, where our new premium Pro SKU has seen explosive growth. We've gone from 0 to $100 million ACV in 5 quarters flat, with an ASP well north of $0.5 million. Okay. So let me step you through the 4 Cs of our strategy. First, Connect. Workflow Data Fabric is fundamentally architected for the agentic era. See, most data fabrics are primarily built for decision support for insights. Workflow Data Fabric, on the other hand, is built for insight and action with read and right capabilities, and it supports all types of data wherever it lives. And that's another crucial distinction. You see we embrace the system of record and the data platform choices our customers have already made. You Can, but you don't have to move the data into service now. I know the others who are playing for data gravity. But for us, what really, really matters is knowledge gravity. And workflow Data Fabric already offers 250-plus connectors and we are expanding our reach even further. With 100-plus new zero-copy connectors, so customers can access data wherever it resides, no replication required with full support for MCP clients. So our AI agents can work with any MCP-enabled source. And with auto for workflow Data Fabric, our customers can describe what they want in plain English and let AI build the new integration. But look, connected data is not the same as AI-ready data. Today, teams spend more time finding cleaning and preparing data rather than using it, slowing down AI adoption, limiting AI accuracy, and it's leading to some pretty frustrated data analysts. I can't imagine the AI agents are too thrilled about it either. So to ensure AI decision accuracy, you need data that is fully visible and governed throughout its entire life cycle. You need tight control of your data. And this truly needs to be nonnegotiable. So that's why we're introducing the ServiceNow data catalog, which delivers native metadata management, data lineage, privacy and ultimately, trust. So humans and AI agents alike can now instantly discover and use curated data products, safe in the knowledge that these data products have their enterprises seal of approved. That's great. But getting your data AI ready isn't a onetime activity. Enterprises have to keep it AI ready. And so to ensure the ongoing AI readiness of data, we'll be taking our data control capabilities even further with a complete AI-driven autonomous data governance solution. And we'll be delivering data quality, observability, enrichment and policy management, all unified inside the ServiceNow AI platform. We'll deliver this through a combination of ServiceNow products and partner products in our massive workflow data network, which now spans data quality, observability, MDM security and integration partner products because just like we do with the data lakes, we want to embrace and extend what our customers already have. And that's how our strategy is fundamentally different. We're going to take that a step further by introducing partner passport. So customers can procure and consume select partner products using ServiceNow consumption credits. On to contextualize. With more than 100 billion workflows a year running on our platform, no one is better able to understand our customers' business context than us. And as John mentioned, we bottle that magic up into something we're calling the context engine. So the platform of platforms, as Bill referred to, now has a living graph of graphs, a graph that brings together our knowledge, action, access, asset and decision graphs, all anchored on our powerful CMDB. Built right into this context engine is our market-leading analytical semantic layer. And we've used that as a foundation for a new product that we're announcing called autonomous data analytics. And so I think conversational analytics to guide the -- what happened, what will happen, what should I do type of decisions that need to be made by both humans and AI? And it's fully autonomous. So think embedded, think always on, AI analysts working tirelessly on your behalf, surfacing insights, interpreting enterprise-wide data in context, spotting outliers and issues, providing recommendations even taking action. And soon we'll be packaging this capability into autonomous data apps, easy button solutions to bring the power of this insight to action, capability right into our technology, customer and employee workflow areas. And so as an example of such a data app, customers will be able to combine product [indiscernible] support, contract engagement data from ServiceNow and let's say, Snowflake or Databricks, [ data Lakes ] and then identify churn risk to then trigger autonomous customer retention actions in ServiceNow immediately. Finally, converge. Today, enterprises typically analyze in 1 system, act in another. We decided to unify both at the database level with RaptorDB. We have RaptorDB standard, which is freely available to everyone and a premium version called RaptorDB Pro that delivers even greater scalability and performance through advanced database features. Now we're adding 2 important new capabilities to RaptorDB Pro based on feedback from our large customers as well as those in regulated industries. So the first is live archive, which is a cost-effective archival solution for ServiceNow that then also allows you to seamlessly query across hot and cold data; and second, live Connect, which allows you to point your existing BI tool against RaptorPro for real-time analytics with no ETL or data movement. Together, we feel these expand RaptorDB Pro's addressable market in our installed base by tenfold. So there you have it. 4 foundational capabilities to power and deeply differentiate ServiceNow's autonomous AI strategy by bringing data and intelligence right into the flow of work. Next, I'll hand it to Bhavin Shah to cover employee experience.

Thank you, Gaurav. I see a few familiar faces. I know some of us have connected over the years. I'm Bhavin, and I'm responsible for ServiceNow's employee experience products and AI Front Door. I want to cover the third part of this, chart here that you see or the fourth part, where we're talking about this employee experience and the acting upon different systems on behalf of employees. And essentially, what we're doing is building on the ServiceNow Data Fabric to drive this employee experience and drive values on top of that for our customers. When Moveworks was acquired, Bill, Amit and I got together, and we felt that we were uniquely positioned to take ownership of the AI Front Door for the entire workforce. The reason for that was by combining the Moveworks employee experience with the ServiceNow workflows and Data Fabric, we are creating a powerful front door for work across every system and every employee. Now we've been busy. My kids call it integration maxing at home. But we've rolled out Moveworks to every ServiceNow employee. We've launched the Front Door to called ServiceNow Employee Works, and we've integrated that Front Door into our new commercial model in just 4 months. So lots of activity, lots of work going on there. And we're moving fast because there's actually a gap in the market. Consumers are validating this in every conversation I'm having and in every deal now that we're winning. Now in the past 2.5 months alone, we've seen a 10x pipeline build for our go-to-market efforts, right? That means that demand is here, and we're now so ready to capture it given the 2 companies and what we both afford and it can produce together. And I'd say this, if there was an M&A award, I think Bill and Amit deserve it because both of our teams are on fire right now. The AI Front Door is also moving fast. And the frontier is moving fast as well, not just with the models but with enterprises, too. I'm sure you guys know this talking to customers, talking to different organizations. And to build an effective experience across all employees, you need the ability to execute. And this is really critical and hard to do. Now we started off with everyone being sort of captivated by AI Smart and generative capabilities. But then we transitioned in 2024 into what I'd call co-pilot chaos. And this is when every sort of functional AI was being introduced every platform, every offering with no real large-scale enterprise impact. When we saw these various studies come out, people were wondering where is the real value going to hit. And so today, what business leaders are looking for is what we characterize as enterprise AI. That means it works across the business, right? end to end, not just middle to middle, right, east to west, not just North to South. So I think the differentiation here is that ServiceNow and us together have been able to execute so fast because we can bring these capabilities end-to-end east to west, all to these enterprise customers. Now the thing to understand is that for an enterprise customer, a user isn't just a user, they're actually an employee. And this is where our differentiation goes even deeper. Employees have to navigate a complex journey, right, spanning peers, managers, executives, countless systems and unique business contacts, right? This is what it takes when you're running a large organization. And so these employees have a need that sort of expands beyond just the end users that a lot of the sort of offerings think of people as. Now we have a deep understanding of the workflows, the context and the action. And we're the only platform really that meets these demands of the enterprise with the employees in mind to get work done. And this is really resonating. In these meetings, we're having in the conversations and the deals we're winning, they understand that we can see this as a lens of actually an employee throughout the year, throughout their month or throughout a week. And in order to execute this end-to-end obviously requires another formula, right? The fully probabilistic YOLO model of personal AI isn't sufficient, right? We've tried that, we've talked about it earlier. And there's parts of the business that are not open for negotiation. They demand reliability, right? Think about payroll adjustment, HR investigation and escalation. These are all things that have to be done in a certain way based on the company's policies, based on the company's culture, based on how the company was created over the years that it's been around. And so not everything will be agentified immediately. And frankly, some processes, according to our customers, will never be agentified because they always want -- they always want to make sure there's a human in the loop. And so that need for human loop is something that ServiceNow does a really good job of unifying across for these organizations. Now my personal conviction of joining ServiceNow comes down to this, right? Personal AI gives you outputs. But EmployeeWorks and this new product that we've now rolled out really delivers outcomes end to end. And ServiceNow and like harnesses this and executes these plans in a way that gets work done for a company. Let me give you an example here, right? Sending you to this conference becomes less labor intensive for your company. And that is what real enterprise ROI is. Businesses want AI that can deliver outcomes similar to human labor. And to do that, you have to go end to end. You have to go across all of these systems very effectively. And so in a world where everyone's token maxing and that's seen as a flex, right? Enterprise AI is actually maximizing token efficiency. And that's on everyone's mind, as you know. And so what customers are really seeking is how can companies do this. And this is ultimately how they will pocket the benefits. Now this brings us to a unified experience. It's something that I spend every day working on thinking about the whole team is rallied around this. And with EmployeeWorks, we're able to deliver a seamless experience across the patchwork enterprise. We deeply understand the company, its employees. We bring together different platforms and workflows and that's what transformation for a real business comes down to. That's how we deliver real outcomes. But there's more. So if we actually have an AI marketplace that allows you to build deep into the ServiceNow platform but also along with thousands of prebuilt agents, popular apps like Workday, SAP, Coupa and more. And this actually lowers the cost of ownership, right? The old model of expensive implementations, specialized developers, long time lines, it's gone. The new model is vibe coding, customized experience in minutes. And so this is how AI scales, not by adding more tools, but by empowering more people to build on a governed platform. Let me give you an example here, CVS Health, Fortune 10 company, you guys know well, 220,000 active users, 2 million conversations, 0.25 million [indiscernible] calls and chats to IT and store service centers. This is real money to the bottom line, 40% year-over-year live Agent chat reduction. And so this is just 1 example. I'll use another one Honeywell, they're an industrial giant, 80% of the inbound requests and work, the deflection, if go to service desk has been handled by the AI. The human mediated workflows are actually happening 60x faster because the AI is doing the intermediation and we're seeing -- they're seeing about equivalent reduction in labor costs. Now in closing, like, I've been selling to this market for about 8 years to the ServiceNow installed base. And one thing that we've seen and that was revealed each time we would roll this out was that when you roll this out to all employees, there's actually an impact on the number of workflows and automations that get consumed because you lower the friction to get help. You lower the fiction to find what you need. You lowered the friction to do an action. And what that does is causes usage to climb. On top of that, what makes us really excited is that ServiceNow has 25 million active users on their employee center. And this becomes the installed base in which we're going to be building the future of EmployeeWorks. So I think it's going to be a really great year. And we have a lot of other exciting product announcements and releases coming up for the rest of this year. And so real quick, I just want to give you a quick preview into what you'll hear more tomorrow with regard to ServiceNow Auto. So you'll see this and you'll learn more about it at the keynote that we'll have in the morning. But ServiceNow Auto is really the combined intelligence of Moveworks, analysis coming together in a new unified experience. You'll be seeing this, obviously, at action shortly as we talk through it a little bit here. But also, I want you to understand that we'll be handing this off to Pat to tell you more about autonomous IT. And from there, we can show you some more. All right. Well, thank you very much.

Good afternoon, folks. And thank you for coming to this. Like I know we're hoping to educate, share some information here, but hopefully, I can add to that. Bhavin mentioned, I'm going to talk a bit about autonomous IT. I'm going to take a bit of a general statement up at the beginning. We're going to talk a little about just workforce automation, and then I'm going to dive more specifically on how we're applying that to the world of IT because that's kind of relevant to us because it's still the biggest part of our TAM right now. It's the biggest part of our current revenue base. This has been an automation company really since the get-go. I was one of the founders of the company. I worked with Fred Luddy in the early days. That was always his mental model. He wanted to build a general case workforce automation platform. And that's what's been driving value for our customers for 20 years. You take a process, you put it on service now. It's more efficient. And that efficiency and that productivity is we've been paying us for. That's the fundamental deal here. We make you more productive, if you pay us. There has been a big change in the tools we have available to solve that problem though. AI gives us a new tool in the toolbox to apply. We do think, though, that we have a bit of a different take on how to apply that to the world of workflows than the traditional enterprise. And I'll start by saying that we absolutely believe there's a value in what I'll call horizontal AI. Bhavin just talked to you a lot about our Moveworks and EmployeeWorks product. We bought a company here because we think it's a real value for our customers and for us. But fundamentally, horizontal AI is about interacting with you as a requester of services. It's your unified place to ask for things to get information, to kick things off to check on statuses, it makes you more productive as an employee. Behind the scenes though, you still have a variety of, what I'll call, vertical processes. If the thing you actually requested is a pallet of steel for a factory in Milwaukee, there is still a purchasing process, which goes through before that pallet of steel actually shows up in Milwaukee. Those vertical processes are where the actual black letter savings are for AI because that's where people have a job. I'm a purchasing specialist. I'm a sourcer, I'm going to work cases for a living. That's where the big value is for our customer base. It's in the verticals. If you can solve the verticals, it lets you get out of the game of reporting things and asking for things and interacting with things and into the game of actually solving a problem, actually resolving something executing a business process. So you're not reporting that your e-mail is broken. We actually fixed your e-mail for you. You're not asking for a pallet of steel. You may ask for some automation, we'll actually make sure the right steel from the right vendor show up at the right factory right day and you can build a car. Fundamentally, that's a business process. This, however, is hard. It's hard because most of these processes today are human mediated and many of them will probably remain human into the foreseeable future. Human beings take part in these processes and a lot of them span multiple systems as well. There's various steps, there are state changes, there's technology shifts in there. And our industry has tried to work around this complexity by frankly, throwing bodies at it. We will throw FTEs at your project, and we will try to wire up your purchasing process with some little sim here and some duct tape here and a little bit of bailing wire here, and we probably get it working. But we've done 1 of your many business processes at a big investment of FTEs. We fundamentally believe there's a better way to do this. We want to get out of the game of one-off bespoke process automation and into the game of giving our customers autonomous workers. This is a new paradigm. You saw Bill mentioned it. You're going to see all over the stages here. We are rolling out autonomous workers first inside our IT departments, but also inside other workflows and ultimately beyond ServiceNow. The idea behind an autonomous worker is very straightforward. It's just like a human being. You assign it work, it approves things. It produces the same audit rules. It follows the same logs. It lives in your user record. You don't have to do business process reengineering do one of these autonomous workers. It gets us out of the game of saving 5 minutes for a customer here and 10 minutes here and 20 minutes here and into the game of going to a customer and saying, "Hey, you've got 100 people doing that job. I can help you do it with 50 or maybe even 80, whatever the number is, it's less than 100. Let's talk." That's the real value behind the automation and the age of AI. If you look at where we're going more specifically, we have got a path to zero touch. We've got about 20 of these in the hopper, but 4 of these is we want to talk to you about today. Level 1 service desk specialist. I'll dig into this one a little bit more on 1 slide, so hold your questions. Junior IT operators, asset analysts and product managers for PMO. The idea is these are all jobs that our customers have that we feel like we can do some subset of that work with automation. So we can go to our customers and offer them that value. And the first one we have, which is this is live. This is not hypothetical. I've got 6 live pilot customers now. I've got about 50 customers in the hopper who want to get live with this very quickly. We've actually had to turn people away because the oversubscription is so high here. But these are autonomous IT specialists. You put them in your user record. If you've already bought service, now you assign into a team, just like [indiscernible] a new human being, and they do stuff. They answer questions. They take basic actions, they solve cases. And they do so in lieu of a human being, but they follow the same rules a human being wood. And this is important for our customers because it will help them get value and it will help them get more efficient and it will help them, frankly, save head count. Fundamentally, that is the game that they are in. It's important for us because that's how we grow as a company is we offer that value our customers. And fundamentally, it is important to all of us here because this is the productivity that AI is bringing beyond just the technology industry and into society as a whole. This is the promise of AI we will help make the overall economy more productive. We're going to start with IT, but it's not the end of it. With that said, enough of me talking about this. Let's bring Amy up, she can show it to you.

All right. Great. I'm already here. Thank you, Pat. It's fantastic. Okay. I'm incredibly excited to be here and share the product with you and share how -- what Bhavin and Pat talked about really come to life. And we're talking about both individual employee productivity of a team, which then transitions to the productivity of a team, which then goes into the productivity of entire workforce when you look at autonomous workers. So with that, I'll show you how this all works. Okay. So I'm going to start off here with EmployeeWorks. This is our new unified front door to work. And imagine, I'm an ITSM manager at [ Electry ] and I've got a lot on my mind as I start my day. First off, I just moved from California to Washington. I've been getting some physical therapy for my knee, and I don't know if I'm going to get covered for that in my new state. And if my current plan will do that for me. And I don't know who to ask, so it's kind of question that might stump us all at work. Do we go to our insurance provider, our benefits, our doctor, we don't know. So today, I'm going to go in and ask auto, what to do here. So I'm going to type in this prompt and see if I get coverage in Washington. So auto immediately gets to work. It's checking my plan, verifying that this life event qualifies me to change my benefits and it's summarizing the answer for me. But not just that, auto tells me what I need to do to kick off the process. And apparently, I have not changed my address yet. So I'm going to go ahead and put in my new address and enter that in, and now auto gets to work, updating my address across various systems at work like Workday, which just saved me a huge headache, didn't have to go in there to change my address. And it's also telling you everything I need to know to change my benefits based on the policy, based on the context of who I am and what's available to me. So I can assess it's a minimal change in coverage. This looks like it makes a ton of sense for me. So I'll go ahead and enroll in this plan. So auto can go ahead and finish the process for me. Everything is done. So in about a minute, I went from a complex benefits question to a completed solution, which is amazing. So it's not just about me though, I'm also thinking about my team at work and what they need. So I'm going to start a new conversation. And I want to see if there's anything I need to do to unblock my team, if there's anything kind of pending my approval. So auto can go through all the different requests and things sitting in multitudes of systems and pull that together for me at a glance, looking across any tickets that might be open or things that need my review. I see that summarize for me here and also prioritize, which is really helpful. And I can see that Alex is asking for a hardware refresh. And this is actually stalling his productivity because it's gone for quite a while without my action. So I'm going to dig into that and ask a little bit more before I approve this new computer, I want to understand what's going on here. So auto can bring up his request. And then I can go in and actually click into that request and have it serviced right here. This is a really cool part of our new AI experience. We can bring the information to you. You no longer need to hunt navigate, go other places. I can see everything I need to know like Alex's machine is clearly out of date. Everything is maxed out on it. I'm going to go ahead and approve this for Alex, Fantastic. Always feels really good to unblock the team. Now from there, there's another pending conversation, something that I've been asking about recently that has an update that I can jump back into. In this case, I have a couple of my engineers that provide VIP exec coverage and they have a shift that I need to give them specific access for. Now one of these team members, Jordan, I have to go in and actually review the access. But before I do that, I want to make sure that I can also revoke that access when their shift is over. So I'm going to ask a couple of questions here. When does the shift start? When will this access be revoked? Can we do that automatically? And auto goes to work one more time. And so the first support engineer is provisioned automatically. But the second one, auto can go through and provision and also make sure that their access will be revoked at the appropriate time, which is super cool. And so let's go in there. I'll go ahead and improve it. Everything looks great. Okay. So I just did stuff from my own personal productivity as well as the productivity of my team, which is fantastic. And it's a totally new employee experience, which is so exciting. So we're really excited to have this in our customers' hands and get that available to everyone. We have so much demand for it right now, which is super exciting. So next up, though, although that was a lot about employee productivity, we also know that sometimes despite everything we do, a team can get swamped. There's too many inbound requests. So you're going to go over to my service ops work desk and see how my team is performing overall and go into my service ops dashboard, so unfortunately, even though I did all that great work for my team, there's still some bad news here. Our backlog is up. CSAT is dropping down, and we're still overwhelmed by the volume of work coming in. And like Pat talked about, that's where an L1 IT service desk AI specialists can come in. I'm still learning about what this can do for my team. So I'll ask auto what I need to know about it. Auto, again, goes to work summarizing everything that this IT agent can do for my team. It's reviewing specialized capabilities. It's also projecting how many incidents could solve from a team. It brings up an entire profile right here on how well this specialist will perform, including the eval score, which gives me a high confidence that this will perform at a level that I need for my team. I can look through and see everything set up, including the skills that this AI agent will use and also escalation path. So in case it's a really complex issue, it can get routed to a human. So this looks pretty fantastic. And based on what it's going to do for the CSAT of my team, there's no question. I'm going to activate this AI specialists. All right. And just like that, it was added to my team. That's incredibly easy for any manager who's feeling swamped or that they don't have enough resources, they can activate these AI agents and add them to their team, no admin, no configuration, no deployment, just a few clicks and it's there. So we'll fast forward in time. And I come back to that same dashboard, and I see great. Everything is tracking on time now. We've got our AI specialists on the team. Things are progressing well, CSATs back up. But I also want to audit how this AI specialist is performing. So I'll go in and ask auto to give you an analysis on this AI specialist, goes through, it looks at all the past activity, it looks at the metrics, CSAT scores for this individual. And it pulls together again, an awesome comprehensive briefing for me. I can see that the specialist is handling 52% of all requests, CSAT of 4.6. This is fantastic. But I also want to audit exactly what it's doing in a particular incident. So I'll click into one, and I can see a full record here of exactly every step that, that AI specialist took. So I have no doubts about how it's doing this work, how it's resolving these incidents and the effectiveness it has, not only for my team, but for those that it's helping. Great. So everything is working really well. This combined team of both humans and AI specialists working together. This is truly the future of work, not just AI that assist but AI that acts and resolves delivering real business outcomes. So very excited to share that with you. Next up, John Ball will be joining us to talk about our innovation in CRM. Thank you.

So I'm going to be covering the fourth key step unlocking AI Transformation Act, and I'm going to do it through autonomous CRM. And so let's get straight to it and start by recapping just how far and fast we've come in CRM. In 2023, I was up here and telling you that we have become the fastest CRM player ever in the history of the industry to get to $1 billion in CCD. And now just 3 years later, we're going to blow through that -- double it and blow through $2 billion. So we've massively expanded our functional footprint to deliver awesome experiences across the entire customer life cycle. From leading opportunity management, to configure price quote and order management, all the way back to where we started in customer service and field service. And we're doing this at scale, managing over 1 billion cases per year, over 1 billion order and work order tasks. And at peak times in CPQ, we're configuring 100 times a second, every single second at peak times. We're recognized as a leader in CRM by analysts like Gartner, Forrester and IDC. And last, we have industry-specific IP that speeds time to value across verticals. And this growth and success is driven by our deep understanding of how to solve real challenges in delivering great customer experiences. I'll give you -- an folks, it's not about tracking interactions in data base. In service, you have to provide great omnichannel intake of requests and you have to make resolving those requests easy and efficient. In sales, you have to go beyond just tracking leads and opportunities, you have to make it fast and easy for sales reps to configure price and quote those opportunities. In all of that, you need workflow, powerful workflow. You need the ability to model the products and services the company sells as well as the types of requests, orders and changes, their customers are entitled to. Because without workflows and without the ability to model this declaratively, well, you're just writing a bunch of custom code. And a vibe coded app on top of a shaky foundation doesn't resolve the request. It just makes disappointment happen faster. So whether it's handling a warranty claim, disputing the Visa transaction, we're ordering a new telecom service. All of these examples require powerful deterministic workflow at the core. Now what AI does change is how customers, sales reps and customer service reps interact with these systems to get the job done. With conversational AI, you can talk and chat with the system using natural language. So that's cool, but it kind of reminds me of some great Elvis lyrics, a little less conversation, a little more action, please? Because you don't reach out to a contact center or a customer service center to have a conversation, you reach out because you want action taken to resolve your request. And understanding this point is crucial because the vast, vast majority of customer service requests are not how tos. The web and YouTube solve that. And I'll use a simple example to illustrate my point. So you want to change an existing order. This seems simple and straightforward, but to deliver this, you need to understand the intent and request order change. Then you need to understand all the specifics, is it a change of the delivery date or of the quantity or of the actual product being ordered. Conversational AI is great at capturing all those intents. But then workflow is required. If it's a change in quantity, do we have enough inventory or if it's a change in the product being ordered, you've got to rerun the CPU process to check for compatibility and then generate a new quote. That's CRM workflow logic, and that can't be solved with AI alone. And here's my most fundamental point. You have to get it right every single time. You're certainly not going to run refunds, disputes, orders or anything else mission-critical in the stochastic process that might hallucinate. In sale CRM, CPQ is a great example where AI can really turbocharge productivity. So imagine sending a draft quote to a prospect just minutes after a Zoom call with that prospect that is tailored to the specific requirements they described in the Zoom call. That's now possible with AI-powered CPQ. As long as you have headless and speed of thought, CPQ engine that enforces all the compatibility rules, bundling, discount policies, et cetera. Luckily, we do. Now this is not theoretical. We're live in production with sales, service and CPQ use cases with amazing customers like you see here, driving better customer experiences and millions of dollars of savings. And several of these customers are presenting here at knowledge, so you can hear their stores firsthand, not for me, from them. From NVIDIA, who reduced time to quote from 5 days to 5 minutes, it's just an amazing stat, 5 days; or Rossman, a large European retailer, who deployed our agenetic CRM for retail store support, saving a massive amount of time and allowing store associates to focus on the customer, which is the real value. Now there's no better way to understand is all than through a demo. So please welcome Chris Shutts, CEO and Founder of Logik.ai who now runs all of sales CRM. Chris, take it away.

Pretty complex generator. It's got dozens of options, dozens of different pricing rules, it's a pretty complex piece of equipment. And he wants to change his order. So what he's going to do is he's going to call into regenerators. And he's going to interact with one of our CRM voice agents and see if he can get his order change. So let's see how Marcus does. [Presentation]

Okay. So here you can see Marcus is interacting with our voice agent. She's asked him a couple of questions about his order. And behind the scenes, the AI agent is querying our order management system and finding possible orders that might be for Marcus that he placed a month ago. She then clarifies the correct order and then finds it. And let's see what Marcus wants to change on this order. [Presentation]

Okay. So what's happening here is the AI agent's interpreting what Marcus is saying in the large language model and then mapping its options on our configure, price, quote application. So this is what John was just talking about with this concept of speed of thought and 100% accuracy. So we've got an agent that's querying the option availability in CPQ and then making sure that all of these options fit together real time. In this case, Marcus needs an automated transfer switch and a maintenance plan and the agents making sure that all that will work to for his order. . [Presentation]

This is a pretty typical case for a complex, expensive order like this where there's a lot of back-and-forth dialogue with the customer, especially when you're doing order edits and order changes in manufacturing systems typically. So not only do we have the voice agent interacting with CPQ to make sure all the options fit together. The performance is really important. So we put a lot of work into our solving engine that runs inside of our CPQ app to make sure that, to John's point, we can get answers that are speed of thought so that Marcus has a good interaction with the AI agent real time. . [Presentation]

So now that we know exactly what Marcus wants, the AI agent can take all those options and then rerun the manufacturing bill of material rules because this is an engineered piece of equipment, so the bill of material is dynamic based on the options. She also needs to run all the pricing rules. And then perhaps, most importantly, figure out when they can actually produce it and manufacture it and she needs to give that information to Marcus before she can close out the order. . [Presentation]

So now that we have the product configuration, the bill of material correct, the sales bill of material that goes with this machine. Now the agent is taking all that information, putting it into a customer-facing document that she can then send to Marcus real time so he can confirm like billing address, shipping address, payment terms and things like that. [Presentation]

So now Marcus sees the e-mail on his phone. [Presentation]

Great. So now you can see Marcus in a couple of minutes was able to interact with the voice agent, and actually made some pretty complicated order management changes using the agent, interacting with our configure, price, quote and order management applications real time. So thank you.

All right. Great job as always, Chris. So hopefully, for those in the audience, that demo helped you understand how we're combining AI, data and workflows to really change the game in CRM. We're delivering better customer experiences, while improving our customers' cost structure, which is really something that every company on the planet wants. And so that's just a huge tailwind for business. And with that, it's time to pass to my colleague, John [indiscernible]. John?

Thank you so much, JB, and Chris. As JB mentioned, my name is John Aisien, and I'm responsible for our risk and security products. And what I'm here to do is to walk you through the fifth layer of the 5-layer cake that Amit and my colleagues have stepped you all through. I'd like to start by making a bit of a bold game. ServiceNow is a security leader. We spent the last 5 years already establishing significant preeminence in the governance, risk and compliance market, growing that business tremendously, nearly 4x greater growth over the last 5 years than the market itself is growing. My primary goal over the next 10 minutes is to essentially provide you with the proof points that back the claim that I'm making on the slide. Let's start by grounding you on the foundations. As Gina and team shared at the end of Q3 last year. Our Security and Risk business, a bit of an unsung hero within our portfolio, surpassed $1 billion of CACV at the end of Q3. We grew our business, security and risk organically, 40% in 2025 versus 2024. What has this growth been powered by 2 foundational anchors that have enabled us to achieve the results that we've achieved. First, our IT asset data gravity, right? For 20 years, we've been the preeminent even provider of IT asset insights and the workflows built on those assets that enable compounding in terms of the asset gravity that we have for a typical enterprise. You combine that with the east to west coverage that we have across so many buyer persona and user persona back office, human resources, supply chain, source to pay. I promise I won't go through all of them, but essentially 10 primary persona that we address across the enterprise. And if you believe like I do that all enterprise data is ultimately security and risk data. You could see how that provides us with both the right and the responsibility to achieve the preeminence that we've already. But what we realize is as we're building the next-generation architecture for this platform shift that we're going through, the agentic AI platform shift, we needed to add to the IT asset data gravity that we already have. So over the course of the last 2 quarters, as you probably have heard, we've got a bit busy inorganically. So one of the first things we did was acquire Armis. In 1 sentence, what is Armis to CISO. Armis Is a cyber asset graph. It enables us to take our IT asset dominance and provide a comparable view of that same data to the CISO while adding incremental attributes and asset types that we previously did not cover exceedingly well. Code, unbelievably powerful in this age of AI, OT, IoT, medical devices, but all of that is our cyber asset graph. You combine that with what we did in March by closing on our transaction of Veza and at its bare essence, Veza is an access graph. It's a way for a typical enterprise to gain insight into who and what has access to what. And then you build extremely high-value applications on top of that, a agentic or not, that is extracting insight from that data play. And of course, the third dimension in the multidimensional core that ServicesNow risk and security is our knowledge graph, essentially the enterprise context that can take the exact same set of assets and access in Deutsche Bank and that will generate a different outcome from the exact same set of assets and access in Allianz because the context of those organizations are different, different policies, procedures, rules, regulatory frameworks, people, et cetera. So it's this combination, if you want to leave with anything from this presentation, Our core in ServiceNow risk and security is powered by cyber assets, the things, access to those assets by humans and other things and enterprise context, all of this furnished across all of the platform building blocks that my colleagues have been talking about. And this combination is already meeting at the customer. We're not the only bright folks that have this insight. As an example, a global international financial services leader, whom I might add, as a representative in this room is using ServiceNow risk and security today has combined that with Armis, for capturing connected asset information across the building management systems and able to extract potential vulnerabilities from those BMSs, and essentially automatically remediate prior to any incident occur. This same customer also uses Veza to enable visibility and intelligence by both humans, systems and agents across the 50 or 60 AWS services that power a subset of the cloud estate. And it's a comparable story that we see across an international consumer packaged goods leader whose story I want to step through for brevity. What are the growth drivers, both current and future, that are powering this business. There's 4 that I'd love to leave you with. As we become a full-blown workflow to use the ServiceNow parlance, I expect my colleagues in finance to begin to share insights about this business over time. Here's 3 leading indicators that I want you to remember. One, how is this business doing organically? How are we growing usage as a key leading indicator to then growing ACV. That's one. Two, as many of you saw in our April 9 announcement around our AI native packaging, we did something super interesting. We enable the entirety of our customer and our partner base with the rights to AI control tower, the delivery mechanism for all of this IP. So one of the additional leading indicators that you should watch for is our effectiveness in building net new IP and driving our existing IP to market as an attach to AI control tower. The third, I've been in the cyber industry directly and indirectly for about 26 years was Brad. Remember him from 25 years ago. And one of the dreams of the CISO ecosystem through this time is collective defense, collective defense. The attackers are actually collaborating on the deep dark web, as you all know, the economic and the architectural prerequisites enabling the defenders to actually collaborate have been, shall we say, [indiscernible] I believe that the machine speed by which a agentic workload can identify code defects, chain them into vulnerabilities and do malfeasance with that requires a next-generation architecture. And the action fabric that Sig talked about earlier is the enabler for collective defense in the enterprise. And I'll describe why in a bit more detail in a minute. This is the architecture by which everything comes together. The data plane I described earlier, powered by assets, assets and knowledge manifested through the context engine and delivered through the action fabric to both ServiceNow risk and security workloads and third-party workloads. So imagine a circumstance where even though we're now a fully fledged provider of exposure management solutions post Armis, a customer is already using CrowdStrike for exposure management. They're using Microsoft for endpoint but they want incremental value arising from that 3-axis core that I described, powered by ServiceNow. We can serve that up in action Fabric and CrowdStrike agents Microsoft agents can take advantage of that derived insight for both decisioning and for action. That's the next-generation architecture that we're making with all of which spins meters for ServiceNow as we're delivering value to our customers. So to wrap our enterprise data intelligence layer that's unparalleled in its coverage of buyer and user persona in a typical enterprise requires us, provides us with a responsibility to enter this market in a material way in the way that we have and to become a participant in the primary table by which the next-generation architecture gets built as this platform shift becomes increasingly mainstream. Action Fabric is the collaboration layer, the enabling architectural building block that enables all custom risk and security workloads and third party [ ISV ] workloads to collaborate with ServiceNow workloads to reduce security -- increased security outcomes, increased risk outcomes and generate business outcomes as a result. And the last thing I'd like to wrap with, remember the role that Zero Trust played as an architectural North Star for the cloud world as workloads moved on mass outside of the firewall. A comparable next-generation architecture is needed for the agentic world, and I will [indiscernible] that the notion of permanent access privileges in systems is going the way of the Dodo. It just doesn't make any sense. And so we're going to play a role in making that vision a reality by taking the IP that we have, the IP that we've acquired and the people that we've assembled to contribute to this zero privilege architecture becoming a reality. So to provide you with some insight into why I believe this claim continues to have credence. I'm going to have [indiscernible], our AI platform product leader, demonstrate this through a simple demo scenario. [indiscernible]?

Thanks very much, John. You've seen a glimpse of what the AI control tower can do. But today, we're going to dive into the secure risk and compliance capabilities. So first off, let's look at CVS Health. They serve over 185 million people a year, and AI powers all of their operations with hundreds of models, agents, tools and prompts. The greatest unmanaged exposure for CVS' security team is the speed at which AI is increasing their attack surface. Today, I'm going to be the security admin in CVS' AI Center of Excellence focused on securing our AI investments. The AI control tower gives me a single view of our AI security posture. Now the moment I log in, I'm going to go ahead and check out the governance page, look at security and what I see is that AI asset security score has dropped overnight to 28%. Now I just need to figure out why. And sure enough, a new alert gets flagged up here that the Aetna Benefits AI agent has anomalous privileges. This agent helps millions of members understand their coverage in plain natural language. And it's at risk of leaking PII, member names, addresses, phone numbers, they can all be inadvertently linked -- leaked to other members. So let's go ahead and start the remediation process. I click remediate and immediately Armis' early warning flags agent vulnerabilities and active exploitations targeting health care. And while this agent was built with good intentions, Varonis detects it's gained elevated permissions and can share PII with other agents, requiring immediate action to stop a potential data leak. In addition, our asset intelligence here on the right shows how this information can be shared between other systems and agents. So going back to this over privileged state of the agent, I'm going to approve the recommended access permission adjustment and multiple things are going to happen simultaneously as a result. Let's go ahead and do that. Yes, please reduce my agent access. So we're going to temporarily disable the Aetna Benefits agent. We're going to work with Veza to remove permissions. We're going to update the AI control tower inventory and then the exposure record is automatically created in the unified security exposure management application created for my team to review when to briefing. Now this agent's elevated permissions and its connection to other critical assets were caught because it was under continuous monitoring. So I ask myself, what other risk vulnerability or active threat may be out there that we don't know about. Let's go take a look. So I can see here that I've already gotten my score up to 78%, which is exciting. But I can go ahead and use the power of Armis shadow AI detection and find that there are 3 other AI agents running in CVS Health's business units. This is shadow AI without any visibility or governance, each one could be the next compromised or over privileged agent. So I'm going to change these to manage, an AI control tower opens up 4 new use cases in [ USIM ] 1 for the original agent and 3 for the additional shadow AI agents that are asset discovery capabilities identified. I marked them. And once they're managed, now continuous security, governance and controls, monitoring happens across every managed agent. So here's what I just showed. We caught an agent with elevated permissions serving 37 million members, and we contained it with a full audit trail in a single operation. Number two, we uncovered shadow AI agents that no one previously knew even existed. And number three, we created incidents for all of these findings with audit-ready documentation. In this representative example, I am firmly the human in the loop as CVS' current policy for agentic execution requires. But the AI control tower can also operate fully autonomously from continuous monitoring to anomaly identification to remediation. And with that, we're going to hand it back over to Amit to take us home in the product section. Thank you. All right.

Thank you, Nansha. Thank you, John, and to all the speakers. That was just amazing, I think. I'm sure you can see how proud we all are of what we have built and our strong innovation road map. The world-leading analyst also make it clear that they agree. In the last few years, analyst recognition has grown from just in 6 categories to leading in 39 categories and that number continues to grow. And our recognition isn't just limited to our traditional products. Across the new categories, especially agentic AI, ServiceNow is now consistently recognized as a leader. And our future road map is also accelerating. That's partially because AI is not just empowering our customers to build and work faster. We also are leveraging this AI technology, speed up our innovation as well. What you see behind me is the breadth and depth of what's coming in 2026 alone. Across every area, we're shipping AI native capabilities and autonomous workflows to power our customers' agentic enterprises. And underneath all of this, is the platform is continuously getting stronger and more powerful every day. And the one key piece of our road map strategy is acquiring specific features and functions that accelerate how we deliver value to our customers. I know there are a lot of questions about our M&A and the investments we recently made. So let's talk about it directly. Bill also alluded to you earlier that we're making strategic decisions to acquire both tech and talent that strengthened our platform and also further differentiate our core. And we already have proof that, that is already working. You heard that, but in terms of the big bets we have made from Bhavin, Amy and John Ball in terms of how successful our products have grown and how well we are getting differentiated in the market because of those acquisitions getting integrated into our product portfolio. For us, this acquisition are not just about buying growth. They're about delivering the critical thesis to power our customer agentic capabilities and agentic enterprises. They fit right into our platform, making it stronger and more relevant. We've also taken the same AI-native mindset that have reimagined our products and our road map to evolve our commercial model as well. As many of you know, seat-based pricing does not reflect the value of AI. So we have moved beyond it. AI is now embedded in every tier of our products from foundation to advance to prime. Another shift is how we meet our value. The unit of value is the world that gets done in real time. And we now have hybrid as well as consumptive meters across all of our entire portfolio today. And this gives customers something predictable, like a subscription commitment at something which is flexible, so it scales with their actual adoption. And we're using different meters across the whole portfolio now. AI assist is, for example, for AI and data-related products, human and nonhuman identities for Veza, assets for ITAM and Armis. And over the last year, hybrid and consumptive meters accounted for more than 50% of our net new business, and that number will only grow. Innovation in our commercial model and our products also go hand-in-hand. For example, we're building an AI-native service management product designed for the mid-market. It will be entirely consumption-based and conversation first. This will be one of the -- or first of many ways we're taking the strength of our platform to entirely new customers through new channels as well. And with the shift towards autonomous workforce. We're also going beyond traditional software budgets and tapping into the labor market. Our customers can now hire manage the performance and retire digital equivalent of human workers for a fraction of the cost. And these autonomous workers can be added or removed on demand and are constantly expanding their skill sets as they learn more and more as part of our platform. And this is the hybrid workforce of the future, which we are now making it available today. Our AI specialists capture at least 6.5x more value while saving the customers over 80% compared to the cost of human fulfillers, which it replaces. And beyond the numbers and beyond the market recognition, at the end of the day, customers are the biggest proof point of our success. The enterprises that run the world have trusted us with their most critical operations. At 1 of the highest renewable rates in the industry today. And so we have covered a lot of innovation in this session today. So I want to come back to our 4 pillars of everything we do. Sense, decide, act, and secure. And of course, the AI platform that everything is built upon. Here's what we know to be true. We are in the amidst of the most significant enterprise transformation ever. Every company in the world will leverage autonomous work. The only question is how and who helps them get there safely. We have the platform, the architecture and the track record. We have the customer relationships, the partner ecosystem and the talent. We know how to execute it at speed and scale. And we have the commercial model, as you heard before, to capture the true value of AI. And we have done this before. We know what it takes to lead a market through a generational shift, not just participate in one. Thank you for joining us today. I hope you are as excited as I am for what's ahead. Now I know you guys noticed that we're running a little late, so we're going to take a short 5-minute break before Paul go through the go-to-market strategy. Thank you all. [Break]

please welcome President Global Customer Operations, Paul Fipps.

Okay. Welcome back the great innovation from Amit and the team that was fantastic to see. Now the last time when we were together, we laid out a strategic thesis, platform, industry and global scale. And today, that thesis is working. First, AI platform-led, AI data, workflow, all united on one enterprise platform, not theory, but real execution, customers are live, partners are building and the platform is delivering. Now just as important, deeper industry relevance, solving complex mission-critical challenges across financial services, health care, manufacturing, retail and public sector. And third, global expansion. We said we would invest with intention internationally. And today, Europe, APAC and Latin America are proving that strategy right. We have made real progress. But the bigger signal is where the market is going next. Across every boardroom, in every industry and every geography, the market is converging around 3 realities. Enterprises want to identify workflows. They don't just want to automate tasks. They want AI that can reason, that can decide and they can act. Critically, they want speed. They don't want value in 18 months. They want value now. And to do that, they need orchestration and control. One platform to govern AI across every model, every workflow and every function. This is no longer about experimenting with artificial intelligence. It is about operationalizing AI at scale securely. And at ServiceNow, we were made for this moment. So today, I'm going to focus on 4 ways that we are driving agentic growth. one, workflow agentification; two, autonomous implementation; three, AI-powered ecosystem; and four, our strategic expansion. So let's get started with agentification. Now let me show you what this looks like at 1 of the most advanced technology companies in the world, NVIDIA. So NVIDIA didn't come to us with a single point solution or challenge. They came to us because scaling AI inside the enterprise requires orchestrating multiple complex systems simultaneously. So let's take a look at all 4 areas. In field engineering, AI agents now triage and troubleshoot in minutes instead of hours, configure, price and quote. Those times, for some of the world's most complex AI infrastructure quotes, dropped from 5 days to 5 minutes. In customer success, AI is enabling proactive management at scale and in knowledge management, offering agents continuously creates and maintain critical documents. So here's the punchline. This is not isolated automation. This is platform-level agentic AI, multiple AI agents operating simultaneously across multiple business functions. And NVIDIA isn't the only place this is happening. Across our customers, our 4 deployed engineers, our elite 4 deployed engineers that you saw before are powering the now next AI program that I spoke about last year. They are doing the work of business reinvention, agentifying workflows, on-site, inside customer production environments. I'll give you an example. NTT Data. Our forward deployed engineers are shoulder to shoulder using AI to ensure 70,000 configuration items are fully under compliance. At PayPal, we're helping process trillions of payments faster. And at Robinhood, we're ensuring seamless onboarding as they scale headcount 26% year-over-year while integrating all of their acquisitions. Workflow agentification, that is only what ServiceNow can do. Let's turn to agentic implementations. Deployment of velocity is no longer a service differentiator. It is now a strategic growth lever. And we now have 2 paths: self-implementation AI-guided deployment built directly into the platform; four, services-led implementation, AI agents embedded in ServiceNow's delivery process. This dramatically compresses implementation time lines and time to value. So the result is some customers are going live up to 2x faster. Let's take the state of Hawaii. In one of the most regulated environments imaginable, we move from workshops to go-live readiness in just 6 weeks, 6 weeks. Historically, this could take many months. Now that's not incremental improvement, that's enterprise deployment velocity that's completely redefined. Now let's talk about one of our most important stakeholders, our partners, Partners are no longer just extending reach. They are accelerating implementations, they are expanding category adoption, and they are compounding platform value for ServiceNow. Here are some data points. Consulting and implementation, 34% year-over-year increase; managed service providers, 43% year-over-year increase; resellers, 35% year-over-year increase in sales certification growth. Why do I talk about that? Because it's a powerful increase in the number of sellers positioning ServiceNow with our customers across the globe. Our hyperscalers -- this year, we were a partner of the year in 5 categories with the hyperscalers. Microsoft's partner of the year for ISV innovation. Google's Partner of the year in not 1, not 2, not 3, but 4 different categories. And the first one was agentic innovation; second one, business applications, platform and then financial services. So hyperscalers are important partners for us because they accelerate revenue across all segments particularly net new logo acquisition. If you think about it, they give us access to millions of precommitted cloud buyers, faster procurement cycles and a co-sell motion that's at scale across the globe. And our tech partners and builders, we now have 2,500 applications in the App store for ServiceNow. These apps are built across CRM, risk and security, technology and employee works. Okay. Let's move to the fourth bullet and talk strategic expansion. This is not adjacency for adjacency's sake. This is disciplined category expansion from system of action to system of autonomous enterprise execution. Starting with ServiceNow EmployeeWorks, you heard earlier from Bob in, the power of Moveworks and ServiceNow completely unified. Let me give you a customer example. At a customer I talked to you recently, the CHR told me she said, "Look, I have mandated a 10% year-over-year reduction in operational costs through 2028." She said to get there, I have to have AI handle routine HR inquiries, employee self-service and ticket deflection. Now ServiceNow EmployeeWorks then became the conversational AI Front door for that project with auto agentifying the workflows in the background. Result, $55 million in projected annual cost savings 3.5 million productivity hours returned annually. That's business reinvention. Now the same is true for risk and security with Veza and Armis. Our customers now can see every asset, govern every identity, both human and nonhuman and secure all assets and agents through one platform. ServiceNow AI control tower is complete. Finally, our customers are no longer buying AI as separate solutions. And as many of you know, there's a lot of conversations in the marketplace around seat-based pricing versus consumption. But the future of enterprise monetization is not binary, it's hybrid. customers want the predictability of platform commitments with the flexibility of consumption where agentic scales and creates asymmetric value for them. ServiceNow's new commercial model ensures AI is built into every offering from day 1. And this model is designed exactly like our platform, predictability where it needs to be; flexibility where it should be and scalability where it matters. So when I back up and I just kind of take a step and look at the whole landscape, here's what I see. We help customers sense what's happening. We help customers decide with intelligence grounded in enterprise context. We help them act through a agentic workflows and we help them secure everything through AI control tower. So now I have the privilege, and I'm super excited to bring out 2 fantastic customers who are visionaries in their industries, Vishal Talwar, who is the EVP and CDIO at FedEx Corporation and the President of FedEx DataWorks; and Oliver de Wilde,, who is the Head of ServiceNow COE for Hitachi. Gentlemen? Right. Well, thank you both for joining us. Oliver, I'm going to start with you. So you're modernizing Hitachi's IT operations globally. Maybe you could just share with us what's so hard about deploying AI at the enterprise scale of Hitachi. I mean, it's a massive complex organization.

So it might be a bit of a controversial start, but the problem isn't just in the technology. That's not [indiscernible] the problem is in changing the organization. And when I talk about the organization, we're talking about the people, we're talking about the governance, and we're talking about the processes. So all of these parts really where companies struggle because they have to get all of those pieces in line and then the technology actually comes quite naturally and quite easily. You can always find very smart people to build something, to find a work around to make it work. We've had those problems in our deployments, and we worked through it, that was okay. But really, the harder part was getting all the stakeholders on board working out what processes you wanted to keep, what data you wanted to keep and how it needed to operate. So I'd say those are the harder things. The technology, actually, you guys have made it quite easy for us. The technology is there.

All that change management and basically the operational capabilities of the people. So Vishal, I mean, we all know FedEx. FedEx moves 18 million shipments per day across 220 countries and territories, an amazing operation, amazing company. The operational complexity is extraordinary. But from your perspective, what's hard about deploying AI at scale.

The stakes are just significantly higher. I mean if there's -- to put that in context, and you just said it, right? So we operate in 220 countries. We move out 80 million packages daily. And we manage that through multiples of tens of million of workflows. It's very different if you're playing a 1-person band or a 2-person band. You can sort of get the music right. But now if you're playing in orchestra, you have to get that right across 50, 100, take that at the scale of an enterprise. For every decision, every workflow, every task to be seamlessly coordinated, to be seamlessly executed, takes a lot of precision. And to have the right data available at the point of insight and action that it's needed, that data you can trust. And then try and bring the entire organization along to the point that Oliver just made around talent and change management. It's an entirely different operating mindset for the enterprise. You can't do that with a 1 or 2-person experiment on the side, you can't do that when you know that on the receiving end of your execution is life that is waiting for that parcel to get delivered on time. I mean health care is our largest segment. Aerospace is a pretty big vertical that we serve. These are high-stake businesses. You can't introduce risk in an environment. You have to be able to trust that anything that you're doing, whether it's AI or otherwise, you can introduce it responsibly across the breadth and the depth of those workflows. That's the hard part.

Great point. And you often refer to us as the digital backbone, which I think is a really great analogy. So Oliver, Hitachi and ServiceNow have been -- we've been collaborating for multiple years, particularly early on in the AI side. And together, we've seen some incredible outcomes from strong user satisfaction to rapid adoption. But what do you think has made that success possible?

So I think it's always been a partnership. I think we met sort of 4, 5 years ago. And I think the partnership started really there with customer success, and then we were invited to participate in the lighthouse program. And that was really where we and Hitachi Energy specifically, got to help design and build some of the components that we're now seeing today. And really, that allowed us help you shape it. It didn't come without its roadbumps. And there were definitely some technological challenges that we had to overcome, working with the customer success teams, working with the product teams and to actually get these things fixed and then deployed at scale because as Vishal just said, this isn't just about 1 or 2 people. We deployed it to 60,000 people overnight. And when we did that deployment last year, we saw like the real benefits instantly. We saw a 25% reduction at our Service Desk in people contacting you. We saw a 10x like increase in self-service on the service desk that just was not there before. So we saw like market changes, but actually being able to work with you and partner with you to help develop it and change it. And one of the biggest areas that we really, I think, helped with is on the whole volume creation. And this is, I think, something that we're seeing in elements of control tower, but really then the value sort of estimation and generation because it's something that I was always getting challenged by my CFO on is, how are you proving the investment that we're making in the time, in the licenses is actually returning a positive value for me. So being able now to see that and to quantify it. And you have all the data in the system, you can see what is being done faster, what's being done slower, what's even not being done at all. And that now, you can see it a lot more easily, we went through the spreadsheets, the power BIs, the manual ways of calculating and now we have tools and products to do it. So I think that part of the partnership, and I do treat it like a partnership has always been there, and it's really helped us become successful and see those sort of results that we've had.

I would say you really drove envision early on of a value-based, almost zero touch IT organization, incredible partnership.

It should be something that we could all strive towards. And we're definitely not there yet. We're not finished.

So Vishal, FedEx has a bold vision to make supply chain smarter with everyone. I mean this is one of the most exciting things you and I have done, I would say, over the past year, but maybe share with how your team is unlocking the new value with FedEx DataWorks, which I know you run as well and our strategic collaboration around AI-powered automation enterprise.

Enterprise. Sure, Paul. As we just outlined, FedEx operates 1 of the most complex supply chains in the world. And we've been doing that for about 50 years. And the one thing that we've come to acutely understand is the amount of fragmentation that exists in supply chains. And that amounts to about $1.8 trillion of value that's leaked annually because of that fragmentation. So what we want to do now through FedEx DataWorks, is make sure that we bring solutions to market that allows for that inefficiency to be trapped. We want to make sure that we will bring orchestration solutions that fill the void and connect all the elements of this fragmentation inside the supply chain. So starting with Source to Pay announcement that we just made, we want to make sure that the insights that we generate, the first-party data that FedEx network generates, which is about 2 petabytes of data, we want to release those into signals that is -- that benefits our customers so that they can go from a reactive to more predictive posture in the interventions that they want to drive inside their environment. source to pay is one example. We want to then extend that to building workflow solutions that allow our customers to more seamlessly orchestrate supply chains inside their enterprise. And that's where the partnership with Service Now and FedEx gets pretty exciting.

It's super exciting. You'll see more of that tomorrow with you and [ Raja ] on stage. So very excited to see that. Okay. So I think we're going to get to probably one of the harder questions here. And one thing we constantly hear from boards and CFOs right now. I know you all hear it is why can't we just build this ourselves. The whole build versus buy, particularly with all of the large language models. So Vishal, I'll start with you, what's the build versus buy conversation look like on the inside of FedEx?

Just because I can, doesn't mean I should. I mean for me, it boils down to that. I said the stakes are high. And it doesn't mean that we will not build stuff, you have to take a more nuanced approach. We are very keen on making sure that the core of our value chain, where we want to have the differentiation, where we want to go and help our customers to more deeply are areas where we will want to own IP and we will want to build. But I don't want to be known as the best HR system company in the world, the best IT system company in the world or the best finance system company. I will shamelessly take that from folks that have much more experience than FedEx will ever had in this enterprise and bring best practices inside and build additional backbone that you can help bring with speed and channel my energy instead into the core parts of our value chain. So for me, it boils down to just because I can build, I go back to one or two person band doesn't mean I should. There are areas where we will build and there are areas where we will partner and increase our speed to market.

That's a great answer. Just because I can doesn't mean I should. Oliver, how about you? I know I'm sure you've had these debates inside of Hitachi.

Of course, and look, I mean, Hitachi is an engineering company. We're not short of people who want to build stuff, which is a great thing. But I think as Vishal said, it's like just because you can build it doesn't it mean you should build it. And I think I completely agree with that sentiment, take the best bets from everywhere else. We're not a software sort of predominant house we build in Hitachi Energy. We build huge transformers, we build switchgear, we build trains. There's load of stuff that Hitachi builds. Let's keep building that. Let's keep building what we're good at. Let's buy what we have and what we don't build ourselves and then integrate it together. So I think it's maybe slightly less of a build versus buy common and actually more of a build versus orchestrate. I mean how you assemble this into your digital backbone or your digital core, whatever your company sort of refers to it. But how do you bring the pieces of the jigsaw together into something that then works for your organization and helps drive your organization forward. And you can buy things from ServiceNow, from Salesforce, from Microsoft, from Google, like Pick, whoever it is, but you've got to bring the best parts of that together for your enterprise. And every enterprise is different, your needs are different to my needs. I'm not shipping hundreds of thousands of packages around a day. But then we do have mission-critical infrastructure that operates in a different way. So we have different needs. So take the best parts that other people make and build it into your best solution.

Fantastic. I know we could -- I could probably keep asking you questions for the next 30 minutes. But I know you guys are both on a time. So thank you for joining us. Thanks for coming and sharing how we collaborate together with ServiceNow and FedEx and Hitachi and maybe everyone great round of applause for Vishal and for all of us.

Please welcome to the stage, President and Chief Financial Officer, Gina Mastantuono.

Thank you, paul. what incredible customers and hello, everyone. I've been really waiting backstage for a while to get out here, and I can't tell you how excited I am. So not only am I excited to be here but I'm really excited not only about where ServiceNow is today, but about the scale of the opportunity that we see in front of us. Listen, I know there's questions swirling out there in the market, and that's healthy. . Great companies to be able to answer hard questions such as will the growth of the foundational models come at the expense of budgets for the incumbents. We'll see compression shrink revenue do software stocks become only margin stories. These are questions I get all the time. In ServiceNow's case, the answer to each of these questions is an emphatic no. We are a unique platform company where the AI controls power for business reinvention. And as you've heard throughout today's presentation, ServiceNow is not a traditional SaaS company. where the orchestration layer AI agents run on, not software they replace or the AI operating system for the enterprise. We are truly in a category one where AI makes us both more competitive and more profitable simultaneously. We have a clear path to grow the top line and drive continued margin expansion to deliver durable shareholder value. We'll look at this next. But first, I want you to walk away remembering 3 things. One, our structural advantage is ours and ours alone. AI only reinforces it. The AI super cycle is a revenue tailwind for ServiceNow; two, ServiceNow is the AI platform enterprises are already buying -- it's not a bet on future potential but a flywheel that's already spinning. We're the best-in-class workflow and governance layer where enterprise AI value accrues. And three, margin expansion and AI growth are not at odds. They're the same story. AI-driven internal efficiencies, fund the innovation and bring focus to our growth investments. Let's dig deeper into the growth opportunity. Our core business is strong. It comes down to execution. As Bill talked earlier, this is a company that executes. In 2025, we grew 20% year-over-year to nearly $13 billion in subscription revenue. We're looking at a 5-year CAGR of 24%. And we added more than $2 billion in revenue in 2025 alone, which is more than the entirety of our subscription base in 2017. Now Assist ACV crossed $600 million last year, more than doubling year-over-year. That momentum carried into Q1 with ACV crossing $750 million. Now Assist isn't separate or distinct from our core workflows. They are our core workflows. This kind of organic innovation is a powerful growth catalyst for it, much like how AI fueled customer demand in the upgrade cycle from Standard to Pro. Our Better Together story continues to strengthen. In 2025, 91% of our net new ACV came from deals with 5 or more products, up from 86 the year before. This includes a 7.5x increase in Now Assist deals with 5 or more products. Customers are not experimenting with one AI solution in one corner of their business. They're deploying AI across the enterprise. Customers are going all in, in our core technology workflows, where we saw over 50% growth in deals with 5 or more tech products. Also, we have three key growth accelerants. Security and risk is the next growth vector for technology workflows. In 2025, net new ACV grew 40% year-on-year, and at less than 20% penetration from the base, there's plenty of room for expansion. With AI Control Tower, customers govern AI across the enterprise. Its ACV has quadrupled since launch. Armis and Veza further extend the TAM. CRM represents a massive market opportunity, crossing $1.8 billion in ACV in 2025. Sales CRM is leading the way with ACV more than doubling year-over-year. We win because of our single platform across the entire customer life cycle connected natively to service and operations. Data and analytics is a multiplier, which more than doubled net new ACV year-over-year. As you heard Gaurav say earlier, RaptorDB has already surpassed $100 million in ACV in just its first year. Every AI agent deployed and every custom workflow built pulls demand for data connectivity and performance. All of these growth vectors also have underlying tailwinds created by the proliferation of AI, data and assets. More agents deployed means more governance, more data connectivity, more platform usage. That's why half of our net new ACV has already shifted to non-seat-based pricing models as they catch those tailwinds. Those underlying units, including assets, infrastructure, platform usage are seeing significant growth. We don't count seats here. We count dollars. With the strength of AI adoption, we're also seeing a growing mix shift towards consumption. That's why we're democratizing access to AI with our new AI native packaging. Every new SKU has a bundle of tiered capabilities across the core product AI, Workflow Data Fabric, Moveworks and/or AI Control Tower. As our customers purchase those higher-value bundles, we expect to see an average price lift of 20% to 30%. This new packaging also unlocks customers' AI consumption journey earlier. Now at every level, consumption becomes an incremental growth driver as enterprises scale usage of Assist, connectors and the underlying assets being governed. Then as customers look for more advanced capabilities, they upgrade to Prime for our most premier offering. AI consumption is already showing up. Existing Now Assist customers who renewed in 2025 expanded their ACV by an average of over 3x. It's not just about Assist packs. Every cross-sell, every subscription purchased is adding Assist and is part of the consumption story. As I'm walking you throughout my go story, you may be asking, where will the budget come from to pay for these incremental consumption costs? AI spend is expanding budgets for ServiceNow. Customer conversations we're having today are focused on reducing labor costs to fund ServiceNow's advanced AI capabilities. Let's play this out. If you had a team of 20 support analysts today, the team would cost over $1 million annually. About 90% of that is labor, 2% is ServiceNow. Now what happens when you move into an agentic AI world, as enterprises look for efficiency, they'll naturally target their largest cost center, labor. ServiceNow's autonomous AI agents can resolve 75% of the team's work, reducing the necessary headcount to just 5. The customer wins. Their total cost to get that work done drops 65% and resolutions happen in a fraction of the time. At the same time, these 15 freed-up seats convert into 6.5x more in AI agent consumption, just like Amit talked about earlier. Even after accounting for license reduction, total ServiceNow spend grows over 5x. Lower cost for the customer, better experience, faster outcomes, that's the definition of a compelling value proposition, and it's driving the shift we're seeing today. AI consumption compounds as workloads get more complex. Generative AI laid the foundation with each task consuming 1 to 10 assists. Agentic AI deepens the value curve by completing multistep tasks, consuming significantly more tokens. Autonomous AI specialists represent the next step change, purpose built to perform specific job functions end-to-end with the two layers combined consuming over 15x the assists of generative AI. As AI solves more complex workloads, usage rates climb and so does the value we capture. This is just the beginning. Our autonomous workers will cover all corners of the enterprise as the platform scales. DocuSign is a customer exemplifying this journey from GenAI to their first agentic use case and, now, to zero-touch service desk, their first step towards autonomous workforce. Using ServiceNow, DocuSign has a target of autonomously handling 90% of all IT tickets so human agents can focus on the most critical work. They expect to save millions, and the opportunity is massive. DocuSign is realizing their vision of true workflow transformation and creating a playbook that they can replicate across their entire business. This is just one great customer example. You've already heard directly from FedEx and Hitachi earlier about their incredible AI journeys with ServiceNow. What does it mean for ServiceNow at a larger scale? Let's look at IT incident management, just one use case within ITSM. We see over 100 million incidents per month on the ServiceNow platform today. If 75% of those incidents can be processed by an autonomous workforce, this translates into a $3.5 billion ACV opportunity net of any seat licenses that go away. Multiply that across other ITM use cases and then across the entirety of our platform, we power 100 billion workflows and 7 trillion transactions annually. And you can see the incredible opportunity in front of us. How long does it take for a customer to realize autonomous outcomes at scale? With all new technologies in the enterprise, it takes time. But as you heard from Paul and others, we're finding every possible way to help our customers accelerate that journey. Let's look at an example. When a customer purchases agentic capabilities, they receive a generous Assist allocation, meaning Assist coverage tends to be relatively more limited in the first couple of years. What starts as a strategic deployment across ITSM, CSM, HRSD becomes a foundation for enterprise-wide AI transformation. By year 3, they're taking on more complex agentic use cases, inflecting consumption. Year-over-year AI ACV compounds, driven by the deepening adoption across the enterprise as the customer naturally graduates up the value chain to autonomous workflows. The result is a fundamentally different revenue model with fewer seats but far greater value. We anticipate by year 5, this AI customer would be spending 4.5x the initial Assist entitlement. These journeys have already begun. As Bill teased it earnings, we're raising our 2026 AI ACV target from $1 billion to $1.5 billion. The demand we're seeing is real. These are not features bolted on to existing products. They are built in. They're solutions with strong adoption and measurable outcomes already. When AI attaches to existing workflows, it doesn't just add revenue today. It makes the platform stickier and expand the ACV opportunity for each and every customer. This is a flywheel that's already spinning, not one just being built. And looking further out, by 2030, we expect 30% of our ACV from ServiceNow AI. When the unit economics work, when trust builds, when complexity scales, this is what happens. And I really just love that math. Okay. That should give you a sense of our growth story and trajectory. Now let's turn to profitability. AI is structurally expanding ServiceNow's margins. I'm going to repeat that. AI is structurally expanding ServiceNow's margins. I'm often asked whether AI inference costs will compress our gross margins. That framing doesn't apply to us. AI reasoning is less than 10% of our cost to serve. If inference costs rise, the margin impact remains modest. Customers aren't paying us for tokens. They're paying for a resolved outcome. Reasoning is one input. Workflow orchestration, governance, context, cross-system action, that's where the other 90% of the value and costs sit. We're differentiated and our pricing reflects the full platform, the CMDB, the workflow engine, the governance layer, the business service map, 20-plus years of operational context. That competitive positioning is what sets us apart from the stand-alone AI providers and why our gross margin profile holds. This allows ServiceNow AI to continue to ramp with subscription gross margins remaining above 80%. While our move to hyperscalers is impacting gross margins in the short term, the ROI on that strategy has paid off as net new ACV from public cloud partners nearly tripled year-over-year. We're also not just selling AI solutions. We're using them ourselves. AI is driving meaningful year-over-year gains in output from our fully ramped reps. We're growing the top line while getting more efficient with every sales dollar invested. We're also seeing an acceleration in the incremental savings from agentic AI flattening the hiring curve, with $200 million in savings in 2026, that's on top of $100 million that we saw in 2025, for a total of $300 million in expected annualized cost savings from agentic AI flowing to the bottom line in 2026. AI agents are doing 90% of the monotonous work. ServiceNow's own support and service operations have been rebuilt on our agentic AI. This margin expansion is structural, not cyclical. We are the proof of concept. Every customer is being shown what ServiceNow has already built at enterprise scale. All of this allows us to return to normalized margin expansion in 2027. We expect 100 basis points of non-GAAP operating margin expansion and 100 basis points of free cash flow margin expansion in 2027, inclusive of Armis. We can commit to this because operational discipline is a core muscle. And now AI is compounding that discipline with $300 million of hard savings flowing directly to the bottom line. The message is simple. We are not trading TAM expansion for margin expansion. The model enables both, and you'll see that in our numbers in 2027 and beyond. Turning to our long-term targets. I know you're all waiting for this. All day, we keep you here for this part, right? Okay. Bill gave you a little preview, but I've got a little surprise, so hold, wait, please. In 2021, we established a long-term target to achieve $15 billion in subscription revenue in 2026. Many was skeptical then. I see a few of you in the room. Fast forward to today, we're on track to beat that target by $0.5 billion organically. I know the guide is higher than $15.5 billion. Organically, we're beating that by $0.5 billion. Not many executive teams can say that about their long-term targets. I know you all know that, too. Our momentum puts us on pace to double that target in 2030. That's $30 billion plus in subscription revenue, and it's not blue sky scenario. It's what a durable platform growth story delivers. As you heard from Amit, though, we haven't been standing still. We've accelerated organic innovation to catch the tailwinds from emerging opportunities made possible by AI. We've expanded the TAM with recent acquisitions. And while we're not asking you to underwrite this upside today, we see a strong path to it, a higher 20% CAGR from our current 2026 guidance and $32 billion of subscription revenue in 2030. Pretty impressed that I got to say that number, right? But this is not blue sky. There's a road map of real defensible growth engines, and they are not heroic assumptions. Security, supercharged by our demand for AI Control Tower and the new TAM unlocked by our acquisitions of Armis and Veza, data becomes even more critical as enterprises deep in their AI investments. You heard that from customers today. AI has upended the market and we are taking share. Together, these three vectors compound at over 25% growth year-over-year through 2030. Most importantly, cutting across all of it is AI, agentic workflows, autonomous workers, unlocking value in ways that simply did not exist 2 years ago. And I would note this does prudently bake in a deceleration in some of our more mature products. In this environment, it's a show-me story. I get that. We get that. That's why we're ensuring your top line growth option with a commitment to continued strong profitability. Combining top line growth and profitability at a level a few companies achieve at any size, let alone ours, puts us on a trajectory of achieving the Rule of 60+ by 2030. This is what we're building, a business that delivers accelerating value for customers and shareholders simultaneously year after year. That means focusing on GAAP profitability as well. Two years ago, we committed to getting stock-based comp below 15% of revenue by 2026. We did it in 2025. We also told you that sub-10% is the longer-term destination. Today we'll tell you when. 2029. It's the same playbook: revenue scale, disciplined equity practices, a comp philosophy that allows us to attract the best talent but doesn't over-index on stock. Let me put a finer point on how we're returning capital to shareholders. We doubled our share repurchases in 2025. In Q1 alone, the $2 billion ASR represented nearly double the shares we repurchased in 2025, all in 1 quarter. The results? We expect to be dilution net neutral for 2026. We still have $4.2 billion in authorization remaining, so we have plenty of capacity to keep managing dilution going forward. We're always evaluating the best use of capital to maximize shareholder value. Our framework is clear. First, we reinvest in organic growth at high incremental returns, products like AI Control Tower, Now Assist, workflow Data Fabric show what strategic internal investment delivers. Second, we deploy capital into acquisitions of technology and talent with a focus on tuck-ins that open new TAMs or meaningfully accelerate our product road map. Third, we are committed to returning capital to shareholders, balanced against the significant growth opportunities we see ahead. We will continue to be thoughtful about that trade-off. With the sizable free cash flow generation that will come with our significant margin expansion, we'll also have tremendous flexibility as we think about capital allocation in the future. So let's end where we started. I know there's questions swirling in the software industry, and it's easier for some people to put us in a box with others. The fact is we are in a category of one. ServiceNow is the orchestration and governance platform that AI requires more of, not less. The AI supercycle is a revenue tailwind for ServiceNow. We showed you the math today. It works. The margin expansion is structural and we are living proof. Our own AI transformation is the strongest customer reference we have. We bring growth and profitability, two engines of shareholder value, both powered by AI. That's how you get to the Rule of 60+ at more than $30 billion in revenue. Thank you all for joining us today. And now we're going to welcome back Bill, Amit and Paul to the stage for Q&A. Just give us a minute.

Kirk Materne from Evercore ISI. Thanks for a great presentation, both the technology depth and the long-term vision. It was great to see. I think my question is somewhat maybe for everybody on there on the panel. But I think one of the debates going forward is going to be at sort of the orchestration or at the agent control plane. I think every big enterprise, the LMs all understand that a lot of the value might accrete to that layer. It's also super early days in terms of agent deployment for most big companies. So as investors, what should we watch for? What are the milestones that we can see from you all to know that you're hitting on that strategy? Because right now, I think it's sort of a land grab and I think everybody is asking the question. I think most people understand you have the right to win there, but what are the metrics, the KPIs we should be watching for to understand your strategies playing out?

Yes. No, thanks for the question. So I think the way I see it, there will be a lot of pieces being orchestrated by different parts of the technology providers. But the idea of an autonomous worker takes away this requirement to do individual work by each of the vendors. So what we're doing with autonomous worker is taking away the effort you have to put in to create individual agents, manage them yourself, figure out orchestration, the reasoning and all that thing, which is not really very conducive to a long-term way to manage a business. So the metric I would look at is how many people are now going to start adopting autonomous worker and how we see that kind of proliferation of AI specialist inside the enterprises so that they can now get away from their dealing with individual pieces themselves, but getting the full value of a solution. So what we're seeing now with the AI specialist, for example, what we introduced with 20 AI specialists, especially the L1 support specialist, we're seeing a lot of customers who don't want to do that building and managing and taking care of the spare parts themselves. They want to elevate that and get a full solution. So I think that, that will be a trend over the next few years versus what is happening now. Same thing happened with cloud. If you remember, everybody used to buy the stack. They don't want to build themselves. Then they mentioned they realized keeping and maintaining that stuff is not easy, dealing with changes in the technology is not easy. And a lot of them are going to hyperscaler, somebody who provided the full stack, and then you can build an application with it and you don't have to deal with all the different pieces. And I think the same thing will happen with us now. And that's the metric I'm watching for, and I'm seeing that already play out with a lot of the customer conversations we're having.

Obviously, very impressive to see all the progress you guys have been making and the targets coming out. I think seeing that ACV target of 30% coming from AI out just a few years is really amazing. But I guess on the other side of that is kind of implicitly it would suggest the non-AI components are going to be growing much slower. Back of the envelope I did was 10% or less CAGR during that same time period. I think I've had an argument to investors about how that's the wrong way of looking at this. But I'd love to hear from you all as you receive that question of like but what about the rest of the business and that seems to be growing slowly? Is that a worrisome sign? Help back me up on like why that's maybe the wrong way to ask the question?

You love that one.

I love that one. So AI is the core. You're exactly right. Customers want to buy products with AI built in. And that's why we introduced our AI native pricing and packaging. It's why even if people don't want to go full in automatically all the way up to Pro Plus, which is now Prime, they can start with foundation. They can taste it. They can start to get working because who really wants to buy any software today that doesn't have AI embedded in it? So it's 100% the wrong way to be thinking about it. And remember, you all remember when we first launched Pro, right? No one1 said, well, the core standard is declining and Pro is doing so well. They said, oh, my goodness, Pro adoption is fantastic. This is wonderful. And you get 25% price uplift. Show me more. That's exactly what's happening today. Only now, it's 30% on top of Pro and now we're embedding actually AI into even the foundational pieces so not everyone has to go full stack right away, but they can really start utilizing the AI, understanding the benefits. We firmly believe that once they start seeing the benefits in a small scale, they're going to much more rapidly proliferate and grow with us, and that's when the consumption wheel continues to fly. But you're already seeing pretty incredible growth in $750 million in Q1, up to $1.5 billion. This is remarkable growth and we are just getting started. And so it's wrong to think about, well, if AI is doing so well, it must mean the core is not. It means AI is pulling the core. And that's what we continue to see. We're driving all of our customers to be wanting to consume more AI. That is what the benefit of the ServiceNow Platform. It's going to help our customers, and we really see the value accrue over the next year, 2, 3, 5 years.

Alex Zukin with Wolfe Research. A couple of competitors out there are saying, hey, we're taking some share from ServiceNow. You have two targets out there, and I would say none of your competitors have the security angle. And it seems like the kind of variability of the upside is kind of partially driven by executing on this new I think, Bill, you called it the largest new TAM is cyber crime. So maybe just talk about how do you see the competitive narrative and the landscape evolving. And how does the security component of your portfolio drive that maybe higher target that you laid out there?

Go ahead, guys.

Maybe I'll start, Alex, on that one. I think on the competitive side, some of those competitors have been loud in the marketplace. We just don't see them in the competitive stack. Now it might be in a different segment that's much smaller than a ServiceNow customer, our target market. There might be some edge case areas. But I think for us, we're very focused on the segments that we serve and innovating and delivering great value for those segments. And any time that comes out, we dive into the research and look at the data and figure out what's working, are we missing something. So we're very cognizant of what they're talking about. I think on the security side, just from a pure customer market standpoint, we really think about Veza. I've never seen anything like it. Well, I shouldn't say that. I think CPQ is like it. I think when we bought Logik, it just started to really take off because it actually filled out an entire part of the stack with CRM, and John Ball and the team have done an amazing job there. I think with Veza, just the identity, securing human and nonhuman agents and understand the identity process has given us an incredible extension of AI Control Tower. So I tell the AI Control Tower complete. And then with Armis, every customer that we talk to around operational technology, where we're discovering, managing and securing operational technology assets and bringing that back into the CMDB, it is just like a very compelling story. And whatever industry I tell that story in, they need it because they don't have it right now. So I think it's a very close fit with ServiceNow, SecOps, vulnerability response. It's a beautiful extension.

I'll just add one thing. The other part which is also important is the data part. And so the stack we have built, having an AI platform, which, as you saw today, it's very deep, has very, very critical functionality required to run any kind of AI systems out there, including all the workflows we've been talking about. Now having a data platform which can bring all the information together to do the context engine work we talked about and be able to make decisions very quickly and predict a better outcome really drives our workflows and outcomes much better than anybody else can do today. And you bring security on top of that, where you ensure no various things will happen in your platform, really changes the game. When we go and talk to the customers, they don't have to bolt on all those things separately. They don't have to bring all these things from separate areas and manage all that stuff in a different environment. So when we bring security and data on the same platform and then you have workflows, the agentic workflows with autonomous worker, I don't think there's anything out there in the market today. There is no competitor who can do that. They can talk about pieces of it. So if you hear the noise out there, they're talking about pieces of technology in maybe mid-market or somewhere else. The enterprise play, there's nobody like us. And when I talked about earlier about bringing the same technology now to mid-market, that also takes that business away for them, right? So you'll see a lot more of our capabilities being delivered AI native, completely consumption driven, right, and conversational experience now delivered for mid-market with the AI native mindset and bringing ITSM and other capabilities to mid-market, which we didn't do before, that gives another opportunity we have not talked about earlier.

Keith Weiss from Morgan Stanley. Two questions. One real quick question on timing. Gina, you mentioned the Pro SKU, and we saw a really nice adoption of the Pro SKU.Can we expect Now Assist or Prime or I'm not quite sure what we're calling it now to ramp similarly to what we saw from the Pro SKU? Number one. And number two, more strategically, you guys are bringing agents to your customers. You're bringing the autonomous worker. You're bringing the AI specialist. But you also have to open up your platform for your customers to build their own agents or let other people bring their agents. So when we're thinking about like the 5x uplift from autonomous worker, what's the uplift when you just open up your platform and letting other people build agents on top of your platform like they're going to request?

I'll take the first one and then I'll hand it over to Amit. So on the first one, actually, we're conservatively building in a similar ramp with our AI that we had with Pro. I actually think -- and so far, it's actually been slightly accelerated to what we saw with Pro, but our numbers that I showed are building in the conservative estimate of similar penetration glide way as we saw with Pro.

Yes. On the agent stuff, I mean, I think there is the architectural evolution, which is happening where there are going to be agents interfacing into applications. They're not just going to be users or humans just interfacing. So having an ability for us to provide that access in a governed manner with monetization is the right way going forward. So I do expect more and more of that kind of use case is emerging. And what we have done is we've been very careful about how we expose it. So we have an MCP server. We allow agent-to-agent capabilities as well, communication, but we heard about Action Fabric. The idea is that we will wrap this thing with set of APIs which are governed but also monetizable. So we measure every Assist -- using the Assist kind of metric, measure any access and we meter that and people can burn down. So it makes our Assist more fungible, not just be able to do Now Assist kind of use cases but also now accessing data. But we can get to manage it and make sure that you register for it, you have what kind of requirements, the SLA, the security. So we're bringing all that stuff into this thing. And one of the announcements we did is with Anthropic be able to also do things with their Co-Work. But it's also in the governed and measured capability so that it doesn't allow people to just get access to it without any kind of permissions.

Arjun Bhatia with William Blair. Actually, I wanted to follow up on Keith's question about Action Fabric. And I'm curious if you worry that customers might push back that you're essentially introducing a gate for their data. And I'm curious how much of it is theirs versus yours. Or does this all not matter at all because the ROI is going to be high enough that customers are going to come out winning on top of this? And then one question for Gina. I'm curious when the pricing model evolution might take place such that most of your revenue, not net new ACV, is consumption or non-seat-based. I see that playing out throughout 2030.

Yes. I'll address that. I think we have been talking to customers about how they want to access our environment and what ways they want to -- one, what the volume they would need, what is the different kind of metering we would require for that and what would that metric cost would look like. And so far, it's been pretty well understood by them. They realized they used to access things but there was no guarantee of SLA. There was no way to know who was accessing and what security issues you would run into. So when we provide a much more governed platform, they seem to be very comfortable with it so far. And when we've been introducing this concept, we've talked to many customers already, and there's never been an issue in terms of worry about having this metric being delivered. And given that it gives a fungibility with the Now Assist, it also makes it much easier for them to think. It's not a separate thing which we're introducing,or creating a new kind of metric, which would be confusing otherwise.

On your question with respect to non-seat-based when we think net new ACV would be more -- listen, I was really thinking that you all would be pretty impressed that we're already at 50% so far. And by the way, it's been increasing pretty rapidly over the past couple of years as Now Assist has been driving a flywheel. And so we haven't given timelines for what we expect that to look like long term. I do expect the 50% will continue to increase. I don't think it will ever be 100%. I think some of our business will always be seat-based. And if you think about kind of the new AI native pricing and packaging, just by virtue of the initial subscription, you're getting a large chunk of assists in there. So there is consumption already baked into that initial seat. And so consumption will continue to be a bigger portion as we go forward.

I think it's probably also worth noting that nobody buys software from a major enterprise market leader because they have seeds or consumption or based on the value. It's always based on the value, and then it's how you back into the value and the way the customer is most interested in it. In our case, we have no problem with seats one way or the other because we have consumption. But it just so happens that our active users aren't going down because we go east to west. And so when you think about the 2019 ServiceNow, around $3.5 billion, we pretty much add a new ServiceNow every year.

And I was just -- sorry, were you finished?

No, I was just going to say, and the active users because now you've gone from IT to the employee, to the customer, to the creator, to the data, to the Control Tower, to the security, the number of human beings and machines and robots are all going to expand. So however we mix the formula, it's a great formula for shareholders. .

I was just going to add. The hybrid pricing model of subscription plus consumption has been really resonating with customers. They like a bit of predictability as well as if they exceed, being able to understand how much consumption is coming through. And so we'll stay on the forefront of where the customers and where the market is leading. And hopefully, what you're seeing in all of the announcements here is that we're always on the front foot of how customers are really thinking and how they're thinking about driving value from the platform.

I think this is such a hot and important topic. I think, Paul, you would agree that a lot of customers are getting a little bit surprised on the tokenization of models and how that is surprising their budget landscape, which is forcing them into more predictability with enterprise leaders like ourselves, where they want the seats. They want to be able to predict their budgets and they're getting highly surprised in some cases. So as Gina said, this hybrid model seems to be like the Goldilocks formula right now, but we're open to anything.

We're seeing a lot of vendors copy that now, right? I think it's becoming the industry standard where we introduced a couple of years ago.

Yes.

Exactly.

Brad Zelnick, Deutsche Bank. Really appreciate the very compelling presentation today. Bill, I wanted to follow up on your comments about now being the right time to go further down market. Why now? And why might it be different? Because in the past, it seemed the medium-sized enterprises didn't really value the platform the way that large enterprises did. And I'd be curious as well what your view is on AI readiness in kind of medium enterprise and, relatedly, what does this mean for partner leverage. Does this create an opportunity for a whole new set of partners?

Brad, first of all, thank you for your kind remarks. The team really put a lot into it, and I'm glad you saw the innovation today. Thank you so much. I'll give my color on it. And then, of course, Paul is very close to this each and every day. But we have a more complete story now than we've ever had before. And with AI, the autonomous implementation, because if you think about implementation risk and the time to get things off the shelf for mid-market customers who generally don't have the staffs of a large customer, if we can do that through autonomous implementation, all of a sudden, that's a much more attractive conversation. And I'm not suggesting that we're going too far down because we want to be where the money is and we want to be where the retention is. So it's not just the number of new logos you get. It's being thoughtful in getting the right one so they stay with you and you don't lose your retention leadership. But the offering can be lighter weight. It can be autonomously implemented. And when you think about all the things we have now, we have so many different ways to come into the mid-market customer that we didn't have 2019 to, let's say, even 2024. So I think we're a new company. We're transforming even as we roll. Paul, anything you want to add?

I think it's great, Bill. And I think during my presentation, I talked about autonomous built into the product. So Amit and his team are really innovating on self-implementation there, but then also leveraging AI on our services implementation standard, which we'll be launching tomorrow and really compressing that time to value. I mean, we see a massive opportunity. On the AI readiness part, what I would say is these mid-market companies, which I think is where you're going, Brad, in that segment of the business, I think the innovation that we've done over the past couple of years really enables them to be AI ready with their data. So things like RaptorDB, Pro, Workflow Data Fabric, all the things that the teams have innovated on that we didn't have even 2 years ago, now you can go in, you can kind of plug that in and get the data ready because we know AI success is going to be all about the data and grounding these models inside of that data. So the new innovation capability, combined with what Bill is talking about on the self-implementation and also just the autonomous implementation work, we think is ripe.

Brad, if I just could finish, just one further comment. We have built quite a network now in the ecosystem of resellers and partners that care a lot about the platform. And some of them were born for the mid-market. We have one that's a $23 billion market cap company that actually serves the mid-market with great expertise. So if you talk to a large-scale account executive that's managing General Motors, the likelihood of them calling on a $300 million mid-market company is pretty low. They're going to spend their time at GM. So it's important to have the channel and the indirect partnerships both from an integration and a sales perspective to make a lighter weight implementation, especially if it's autonomous, really rock for the mid-market. And frankly, we're getting a little annoyed. Alex brought up a question about the competition. I made a promise to myself this morning. I was only going to say nice things today and I wasn't going to get into it. But I have to just tell you, we've had some people say some things. A lot of people say things. But then when we do the research,on these things, we find out that it doesn't necessarily tie with what they said. So please be advised that what they say in a certain way is a complement because if we're the target, we must be the leader. The other thing is I want to thank some of you who sent me letters, how you always know what's really going on. And you send me letters, hey, Bill, can you believe this stat? Or, the other one is now copying Control Tower. Hey, Bill, can you believe? So we're always like one step ahead of them anyway. And so we'll come up with a new idea next month, and we'll always be one step ahead of them. But in the case of the smaller ones that have the loudest microphone, I think we've got some ideas for them. We want to meet them.

Gabriela Borges from Goldman Sachs. Back right. I wanted to ask Paul a follow-up to some of the case studies that you showed earlier, like the Level 1 ITSM case study. So it strikes me that the outputs of some of these case studies are really beautiful. But when we actually think about what a complex enterprise environment looks like, it can actually be pretty heterogeneous in practice. And you're coming out with this 100-day guarantee on ROI. So maybe I think, Paul, for you, and Amit, maybe, how are you able to bridge that gap? Tell us a little bit more about what the AI FDEs are doing in practice because it seems like there's a lot of technical milestones to go from garbage in, garbage out to something that looks like the case studies that you're showing us here today.

No, thank you. It's a great question. And the way we think about it is really kind of twofold. One is, and I talked about it a little bit during the presentation, but our 4 deployed engineers, truly led by John Aisien in the front of here, are truly elite. And they work with customers on really, I'll call them, the high-value workflows. So things like massive usage outages at a huge bank and the cost per outage is incredible. The volume is not that high but super critical and important to that bank. Then we think about, how do we actually agentify the existing workflows that they have? In some cases, customers want to redesign those entirely. You may have a process, like an incident management process if we're talking about ITSM, that you just had for years and you now want to redesign it, optimize it and then agentify it. So that's the high-volume part. And that actually the high-volume part drives a lot of the Assist consumption. And that asymmetric scale from a value standpoint that I talked about during the presentation, the high-volume use cases are where that comes from. Now the great news is the product team has innovated across the autonomous workforce now, which you saw a little bit of today, you'll see a lot more over the next 2 days. So we now have autonomous workers that you kind of plug in based on the products that you own, and they work side-by-side with the human workers. So autonomous workers actually help us with high-volume use cases much faster while the FDEs look at the high-value use cases. And so we're attacking it from both things. And the receptivity from the customers has just been incredible.

John, do you want to add anything on the FDE model we have? Today, I think the FDE model is only for very selected. What we do with FDE is to identify very high use cases, as Paul was mentioning, but also kind of help customers reimagine the business process for those complex use cases and understanding where the integrations are required, what changes you need to make. And then real product takes over and that lands up being what you implement and the FDEs usually move on. So it's not a continuous FDE engagement, like many other vendors have typically.

If I can add one more thing beyond on what Paul and Amit described. I think two other benefits of this FDE motion that we're seeing, and we're super excited to scale out these benefits into our field organization as we rearchitect and refine our go-to-market, but the two benefits are, a, kind of accelerating the innovation flywheel itself. Even when you go in with a completely comprehensive kind of basket of IP, you're going to find stuff that you didn't predict. Let me give you one example. Unfortunately, I can't use the customer by name. But we deployed a sort of investment management, user and portfolio management agentification in 12 weeks for a Northeast-based customer. That customer actually had their own AI guardrails, like no kidding. They actually had a customer implementation of AI guardrails. So now Assist Guardian did not support the ability to plug in a third-party guardrail in the same way we support any model or any identity, et cetera. So the FDE team literally extended Now Assist Guardian to support BYOG, so bring your own guardrails. And now as an example, Palo with the AI acquisition, which is essentially an AI guardrail, has plugged into that BYOG, extending TAM across both joint customers. And so I'd say it's a virtuous flywheel we're seeing where the FDEs are building that last mile, but adding it to core product, which, in turn, is lighting up incremental capabilities and many of them sort of partner-driven as we meet with the customer with that IP.

Samad Samana from Jefferies. If I could maybe dig into 30% AI ACV. That implies roughly like $9 billion, right, in 2030, plus or minus. If you think about unpacking that, how much of that is from the portfolio as it exists today versus what you think you have on the road map? And I guess the related question in that is how much does the change in the price game packaging influence that? And does that require any changes for the existing installed base when they come up for renewal? I know it's a several-part question, but I'm just trying to learn from Alex.

So a couple of things. So first and foremost, I want to be clear on the new pricing model. So Prime is basically the same pricing as Now Assist Pro Plus, right? So we're not increasing pricing in our most premier offering. The customers who are all in our Now Assist are not going to expect to see increased pricing. Where the increased pricing comes is when people in lower tiers and lower levels continue to adopt and grow their AI usage by bundling products or by going from standard to foundation, foundation to advance. And so what I'd say is that, obviously, the pricing model is baked into that 30%, but it's not a huge differential from where we are today. It will mean that it's really about penetration, how many more customers are driving upwards to those higher level pricing packages. And what I said earlier is that we're expecting a similar penetration trend as we saw from standard to Pro, which I think is actually quite conservative as you think going forward. So that was the first part of your question. You had a few in there. Is there something else that I need to answer?

Sorry. I think you kind of answered it already. But just for customers that renew, it sounds like if they're already on Pro or Pro Plus, it would be the same migration no matter what, but it doesn't require any changes. That's all I was kind of curious about, that there wouldn't be any, on renewal, any impact.

No.

Probably this came across, but just in case, there are no options at ServiceNow that are not AI. It's just degrees of the offering and how advanced it becomes with the autonomous prime. But everything is AI-enabled. So there's only one ServiceNow, and it's an AI ServiceNow. So I think you probably already knew that. But just in case customers have asked that question, we only have AI.

But to be clear, we're not counting every single dollar of revenue as AI as some others are.

Right.

We are only counting that incremental. We are being very consistent with how we've always been and how we've always treated it. There's AI in everything, but base subscriptions, we're not counting in that AI revenue. That's why it's only 30% and not 100%.

It's Tyler Radke from Citi. Continuing on the theme of multipart questions, I'll try to keep it to two. But Bill, for you, just on M&A given that's been a huge topic I guess, first, clarification. There's no M&A in those $30 billion and $32 billion targets. But philosophically, how do you think about what you have today? Do you need to do similar size or larger deals compared to Armis and Veza and whatnot? And then, Gina, I think this is the first time we've seen two different kind of scenarios for the revenue. As we think about 2030, like why is there two? Can you help us understand kind of the differences between the upside and the base case?

You're lucky I didn't give you the real, real upside numbers. That would have really confused you. I'll let Bill start.

I think we're trying to be respectful of this environment we're in, and I appreciate everything you guys got to deal with. But we're stronger than ever and feeling fantastic about the company. As it relates to M&A, first of all, I think you should know in the $30 billion and the $32 billion or however many 30s, we did not put large-scale M&A in there. So we typically do these little tuck-ins or aqui-hires and very small companies for us. I recognize it was new for you to think about the Moveworks and the Vezas and the Armis. But believe me, if you had a management team that doesn't have the courage to do smart stuff, that's the ones you short. And when we did Armis, it was at a time when the market was most confused as to what was going. And our conviction never wavered down and it still isn't wavering now. We know we got our version of Instagram. So I want you to know, like we put a lot of thought into all those things. And all those leaders that were running those companies as independent companies, and you saw Chris and the sensation he brings to us with CPQ or Bhavan on Moveworks or Tarun on Veza, all of these leaders Yevgeny on Armis, are running big pieces of ServiceNow, and they wanted to be here in this culture to build this masterpiece. So that's real important to us. And it was never about the revenue, and none of it hit the revenue line in our last report. So let's just make sure we square up on that. Right now, our position is organic. It has always been organic. Those were very unique opportunities to get us to a $600 billion TAM. If you asked us, do we think we have what we need to achieve what we said we would take? The answer is absolutely. And I don't think there's anybody on this stage that does not believe that. Very clear.

Great. And on the question on the $30 billion to $32 billion, given the uncertainty in the market, we felt that it was prudent to give a range in not just one number. We feel highly confident in that $32 billion but also are taking into account just market sentiment at the moment and wanted to assure you that there's a real strong glide path. And we presented it to you, 25% plus CAGR on our growth engines, really driving to that $32 billion But if you're a little hesitant in this market, you want to tie your number to $30 billion, I'm okay with that, too.

And that will actually conclude our Q&A session today and our webcast. We invite all of you to join our executives at Chika for a drinks reception after that, and you can ask them any additional questions there. Thank you.

Thank you. Thanks, everyone.

Thank you, everybody. .

Thank you.