Welcome to ServiceNow's Financial Analyst Day 2026. Thank you for joining us today. Before we begin, I wanna 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. With that, let's get started. [Presentation] [Presentation] [Presentation] [Presentation] [Presentation] [Presentation] [Presentation] [Presentation] Take a seat, Nick. On a chair. Easiest if everybody just moves down one. No, okay. Okay. Please welcome to the stage Chairman and Chief Executive Officer, Bill McDermott, and Vice Chairman, Nick Tzitzon. Wow, nice turnout. Thank you. It's always great to come out to a video where the Nick character is a useless corporate bureaucrat. Well, you should tell them the real story. No, I'm not going to. Nice to see everybody. Anything you wanna say before we dig into the content? 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's in great shape, and we're ready to roll. Let's get started. Sounds good. Well, why don't I bring up a few things one by one and ask you to comment on them? Sure. 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. Sometimes it's worth reminding everybody where the company's come from. When you look at the trajectory of the company over the past several years, what comes to mind when you look at this graphic? 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. We said we wanna be the defining enterprise software company of the 21st century, and that we would be the first to get to five, to get to 10. Many of you included weren't so sure we'd get to 15 in 2026, and we're blowing through 15 in 2026. 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 timeframe 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 gonna see the products today, and you're gonna 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, I hope. Who's going to Knowledge tomorrow? Great. You're gonna love it. The ecosystem, I mean, the show floor is just amazing. I encourage you to walk through it. You'll see the AI 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. This is a company that has the loyalty of its customers. It has the inspiration of the most satisfied workforce in our industry, it's the trusted brand. I'm not the only one to say it. Fortune says it. Forbes says it. The customer says it with their wallet, you actually know it. What better time has there ever been than right now for ServiceNow and ServiceNow shareholders? You mentioned an aligned board. I see our lead director, Sue Bostrom, is here. Paul Chamberlain is here from our board. Larry Quinlan is here from our board. It's great to see the board join us. Yeah. I mean, you know, you got a lead director, Sue Bostrom, Larry Quinlan, Paul Chamberlain. These are individuals that have been with me through thick and thin. So too has our founder. We still have our founder on the board, which is awesome. Our board is a really great board. Very committed to the company, very inspired by what we're building and how we're executing. These are individuals that have been with me through thick and thin. So too has our founder. We still have our founder on the board, which is awesome. Our board is a really great board. Very committed to the company, very inspired by what we're building and how we're executing. Hopefully you feel that the entry point you're getting in at today is just, like, never gonna happen again. 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? Let's start with the AI Control Tower for business reinvention. You know, there's GPS signals that'll come from language models and other things, but there is only one air traffic control tower for business software in the enterprise, that's ServiceNow. What you'll see today is you'll hear from our colleagues a lot about the agentic front door with ATO. Any channel you come into, you now have one single agentic experience with ServiceNow. 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. That really matters is the moat in 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 gonna talk quite a bit about that today. Autonomous workflows, this is super cool because now ServiceNow goes across the entire enterprise, east to west. Years ago, you knew us for IT. You know, 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. Of course, CRM on its way to being a $2 billion business. By the way, we have six of them. Security and risk, we're now in the biggest growth tangent I see in the next decade, especially when you think about the world's 3rd largest economy is actually cybercrime. When we made our bold moves, we knew what we were doing, and we'll cover that today. 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. You're gonna see something today on employee experience that is second to none, and also app development. You know, 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. More AI is great for shareholders. 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. We welcome everybody. 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. You say, "Well, why do you think that, Bill?" Well, it's already a $1.5 billion, and we weren't trying real hard. Now you're gonna 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. I really think that, you know, any system is just like so stunning because I'm reading articles saying they wanna shut the world out. They wanna protect their moat. We're welcoming everybody in because we know we have the winning hand. When you look at all the hyperscalers and all those systems of record that you know so well, they all integrate with ServiceNow. We're still the platform of platforms. That is the foundation. 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 gonna grow, grow. We got a bold ambition for 2030 we'll lay out there today. One of the things that Colin and his team have done brilliantly at Knowledge is the customer's voice is really out there. Yeah. 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, you know, FedEx to me is one that I'd really like to pull out. You know, 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. 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. Then if you wanna take something just really interesting, Chipotle is doing great. Everybody likes Chipotle. I like Chipotle anyway. Now they can change in their 4,000 locations all their menus on the fly. They can be highly creative with their associates. You're just changing everything to real-time enterprises. 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. 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. You mentioned we're nice. Yes. 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? Yeah. Well, trust is the ultimate human currency. It's the one thing you can trade on. You can trade on just about everything else in life, but not trust. You earn it in drops, and you lose it in buckets. We don't lose it. We wanna win more and more every day. 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. We're transforming our company to make sure of it. More on adoption, go lives, and the use of AI, that's really where that hockey stick formation is gonna kick in big time. We're open, and when I say, you know, trust, OpenAI, 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. 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 days, all of them are going to be less than 100 days, 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. 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. If they see we're going to now offer them something on the AI Control Tower, that's a pretty special offering as well to get them started, getting them using it, landing and expanding with it, 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. I also wanna make it clear that we are also using our own platform. If you think about the positioning that we have right now, we have our own AI running on Now on Now, and we're achieving enormous productivity gains. What do I mean by that? Gina has already told you in earnings calls that it's been good for $500 million in overachievement on the productivity curve. 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. No, we bought the future. The beauty of that is we're gonna leave this year with the exact headcount that we entered this year. What you can take away from that is that our platform is resonating in the way that 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 nine out of 10 customer cases are now managed by agents in our company. The same is true for HR. 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 two more in two minutes. Sure. You've talked a lot about the growing opportunity. Yeah. It's not controversial to say there's some skepticism about is the opportunity in fact bigger. Mm-hmm. I don't want you to litigate the entire thing. Yeah. 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 the Xerox Corporation, there 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 the cynics." 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. You might have a certain skepticism. Let me take that away from you real quick, so we can get back on the track that we belong on 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. Now we took you someplace very, very special. I empathize with you because we waited nine months for Moveworks to close the regulatory process. On the back of that, Veza came in, okay? Armis came in, like within three days. 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. 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. The birth rates around the world are actually going down. 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. 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. 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. Maybe have a robotic CEO. No, I don't think so. Let's leave it here. I don't want you to steal Gina's thunder, but she did review this slide, so she knows we're gonna show it. It's not about rhetoric, it's about results. Yeah. We've heard that before. Mm-hmm. What's the promise we're making about the results trajectory of the company over the next several years? Okay. Very clear. This is a sub-revenue $30 billion+ 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. 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 billion+ is the revenue growth, plus the free cash flow margin is going to be a $60 billion+ number. Right now it's $56 billion, it's going to $60 billion+. 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 gonna expand the margin profile of the company and we're gonna take down stock-based compensation down around the 10% mark in 2029. You'll be dealing with a $50 billion+ GAAP company. GAAP, G-A-A-P. I know what you want, and we're gonna give it to you. Bottom line, high level, we're gonna double the company. We're gonna be masterful in our execution across the world, globally and through industry. The verticals are really good. We're actually also gonna take the platform down market a bit because we have 90% of the Fortune 500 now. It's not a marketing slogan. I actually had people go through the math, nine out of 10 Fortune 500 now. Fact. We know that we're gonna expand with the Global 2000, but we're also gonna take it down a little bit where we can 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. We're gonna run a very efficient ship. 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. There's three 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. Ultimately, you're gonna 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. So I don't have a second story. We got the best team in the business. Win as a team is the way we roll. What you're gonna 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. Paul Fipps on the go-to-market side has engineered the AI Control Tower for business reinvention across the world. Gina obviously is gonna give you what she always does, right from the heart, the truth and the belief in this franchise. I also wanna call out, you know, the folks that report to these individuals and how great they are. Too many in name, but I do wanna call out one in particular. When we acquired Moveworks, we also acquired a great management team. Instead of having a great leader like Bhavin report into somebody at ServiceNow, we actually said, "We think you should be the boss. Let the people at ServiceNow report into you." We're really making it count, giving big businesses to people we believe in and trust. We wouldn't have acquired it if it wasn't a cultural fit in the first place, or we didn't believe in the leaders. They believe in this mission as much as we do. We now have a great team. We're gonna win as a team, and you're gonna see the best days for ServiceNow are now and in the future. You'll be back for questions, but that's a great way to start. Thank you so much. Thank you very much, everybody. Thank you. Thank you very much. Maybe this way, right? Please welcome to the stage President, Chief Product Officer, and Chief Operating Officer, Amit Zavery. Great, great vision by Bill. I'm here to talk to you about how we're gonna execute against it. It's great to be back 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 two months after the 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 analytics is on track to be a billion-dollar 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 ACV. Our security and risk business crossed $1 billion and expanded further into AI identity governance and OT cybersecurity, helping to differentiate our core. Our AI products are nearing $1 billion ACV, the momentum only continues. The AI Control Tower became a market-defining solution. All this innovation and elite-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 in all industries. We've taken also an AI-native approach to transforming every corner of our own business, as well as building AI into every product. 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. 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. Recent headlines are answering that question better than I could. At Meta, for example, an AI agent exposed sensitive data, which is enormous security incident with no external attacker. Their own AI agent was the failure mode here. An AI agent at PocketOS hit a credential error and deleted the entire production database and all the backups of customer data in just nine seconds attacker. Their own AI agent was the failure mode here. An AI agent at PocketOS hit a credential error and deleted the entire production database and all the backups of customer data in just nine seconds. The industry has been trying to put Band-Aid on all these issues using agents and spawning more and more agents. None of these standalone AI products can solve the core fundamental issues because none of them can govern the system as a whole. There's an important distinction here to think through. Autonomous work is not just powered by one new innovation. It's really two 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 enterprises. Every enterprise needs both of those. That is what it means for AI to be an operating system with enterprise context. 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 that 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. That idea falls apart when you test against three 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 an API, typically, and an engineer's salary. That misses the point. You have to add model selection and the constant updates which happens with the new and new releases from the model companies. Prompt engineering, security and compliance validation, managing new requirements, while also not breaking the hundreds of systems which you are already connected to, which run the business itself. That's just the start. Go back, please. 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 infinite risk surface. One compliance error from a homegrown agent could cost millions. For a business. We've been also looking at research around this. Customers building their own LLM-based solution will typically spend 5-10 times more than using ServiceNow, depending on the complexity of the business they have and the different systems they have to connect. All of the solution will take longer to build, and it's usually less secure. The ROI and the TCO is not even proven if you want to do it, go down this path. The ultimate trap of build versus buy is this. You know, building an app is definitely not the same as running a business. 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 four integrated pillars that create a complete sense, decide, act, secure loop for AI, which is built on a modern AI-native stack. This gives customers visibility, context, intelligence, automated action, governance, all on one platform. Gartner projects that 40% of agentic AI projects will fail by 2027. Not because AI is incapable, just because AI isn't governed. 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. 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. 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 will only grow more with the launch of ATO, our unified AI experience you will hear more about later. Basically, actions which creates outcomes which create new actions. The flywheel continues to accelerate. 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. Humans usually needs nowadays agents, and agents definitely need guardrails. You get both through the platform we have delivered. This is a durable advantage we built first in IT and now expanding even further into many new domains. Our customers are 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 over 170,000 colleagues of theirs 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. 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 four pillars of sense, act, decide, and secure. First, let's start with the AI platform that everything is built upon. Jon, over to you. Thank you, Amit. As Amit said, we're going to spend the next hour or so talking about five sections. I'm going to start with the ServiceNow AI Platform. We have reimagined and reinvented our platform. 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 experiences. 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. Things are evolving. We're seeing patterns in the market that I'm going to address as we go through this presentation. What I want to talk about first is that we have reimagined the platform from the bottom- up. What that means is that we have recreated the AI stack, it allows us to do things like innovate at the speed of AI. Is very, very important because the expectations of our customers is changing rapidly. They wanna see these innovations come very, very quickly. They want new experiences very, very quickly. 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. The first area that I wanna dive into that Amit talked about is this idea of context. Context is extremely important to any agentic system. It drives the AI. First, I wanna play a video of Boris, who is the creator of Claude Code at Anthropic. Boris is gonna 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. Yeah, 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. Yeah, I mean, the businesses are so complicated. There's all these different tools. There's all this different process and data. You need something like ServiceNow to organize and bring sanity to all of it. All the dependencies, all the data is just so complicated, and you make it so simple. Mm-hmm. 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. Organize and bring sanity to all of it. All the dependencies, all the data is just so complicated, and you make it so simple. Mm-hmm. 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. We are collecting so much information on why somebody's doing something, what decisions were made. 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 gonna do a great job. Mm-hmm. If I'm just told, "Go do the thing," but you don't have enough context. With a model, it's just exactly the same thing. You want to give the model a 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. ServiceNow is a really great way to bring in that context that it needs to do the job. As Boris said, context is the driver of AI, and we have a Context Engine inside the AI Platform at ServiceNow. 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 then 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. The Context Engine gets better over time. In turn, the entire agentic system at ServiceNow gets better. Amit talked a little bit about this earlier. Some in 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. That's not very smart. It's not very efficient, and it is not the way that we want to drive our platform forward. You're gonna see a lot on agentic AI and generative AI today because it's extremely powerful. It's extremely flexible. Things like our AI and our AI agents, and the things that you're gonna see today are based off of that. 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 two 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. They are what drive the enterprise today. The trick here is not to offer one or the other. You need to offer both. What we're going to do in the reimagined platform is to harmonize those two things together so that agentic 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. Our customers want broad access to our system. 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. While that's very exciting, that's not what we're going to do. We're gonna expose the system of action. We're gonna do that by opening up this layer to the Claude and the OpenAI and the Gemini of the world. What is a 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. We're not just gonna expose that. We're gonna monetize it, and we're gonna do that by introducing something called the Action Fabric. There are a few things you should take away from this. It is any protocol, any tool. You can use your tool of choice, and you can talk to the Action Fabric endlessly and kick off the autonomous work that is so powerful within our platform. It's governed. All of the business rules and everything that's happening, you can call into it, and those actions are taken and workflows are triggered. We're not going to stop there because we are now going to monetize that process. As Amit said, we have a monetization model today for generative AI. It's called Now Assist. It's a billion-dollar business. It'll be a billion and a half dollars by the end of the year. What we're going to do is plug directly into that system. Any time that an outside human being, machine, AI agent, third-party agent calls into the Action Fabric, we're gonna Now Assist. The flywheel's gonna spin faster. This is a tremendous TAM opportunity for our company because anything, and anyone, and anybody can call into the Action Fabric and take advantage of what ServiceNow is known for, automation. What's gonna end up happening is we're gonna 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. 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 email. They're great. They're assistants. We wanted to build one of those, and we did. The first stop was talking to CISOs in security, and they said, "Absolutely not. You're not gonna be able to deploy those things. They're unsafe. They're not governable. We don't want them." We started Project Ark with NVIDIA, our partners and friends from NVIDIA. We got together, and we said, "Well, how do we fix this?" 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. It gave us the control to allow a CISO to say yes. We are the automation company, so we wanted to expose our agent and many, many others. I think today we just assigned a partnership with Anthropic for Copilot to talk directly to the Action Fabric. What that does is it allows your assistant now not just to manage your calendar or to plan a trip for you. It can also ask for time off and kick off these headless workflows that are going on in the background. 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. There's going to be a lot of these agents. You might have four or five running on a desktop. You might have tens of thousands of desktops across your enterprise. 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 it exactly what actions it's taking, what systems it's 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. The last piece of this puzzle for the Action Fabric is build anywhere. 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 wanna build net new applications. We wanna build primitives that run in your Action Fabric. 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. I asked it would run through, it automatically gets registered as a part of the AI Control Tower. This is where we do one more last review of the application. Everything looks good. Let me make sure I have the right readiness, which comes through with the release note and all of it you can see. I'm ready to now deploy the application right here. This is the actual app which is live, ready for you to prompt. I'm gonna switch my hat a little bit. I'm an employee. I would like to actually understand what are my benefits from my pet insurance point of view. Right here, I'm gonna actually prompt and type in. You can see it's pulling in massive amount of user awareness and context at the back end. 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 two-year-old Frankie, which I wanna make sure I can actually get the insurance set up. Right. There you go. It's now going towards making sure I have my Frankie getting access to the coverage what it needs to be. Beautiful, isn't it? What you just saw is the actual agent is live in action. Every app built on our platform will get autonomous agent embedded within, 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 Jon shared earlier. It's about you can build in anywhere, any tools, and any choice what you have, run and govern in ServiceNow with the enterprise-grade controls and security. Every app ships with an autonomous agent embedded and running the AI assist meter at scale. 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. Thank you, Jon and Jithin. You just saw why the ServiceNow AI Platform is the system of action for autonomous work. Now I'm gonna 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 four things. First, connect all their data wherever it lives. Second, control it with enterprise-grade governance. Third, contextualize it with enterprise-wide intelligence. Then converge that contextualized intelligence right into the flow of work. Our customers are responding strongly to this 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. We've recently added consumption-based pricing, and more than 700 customers have already consumed more than 0.5 billion credits. Then there's RaptorDB, where our new premium pro SKU has seen explosive growth. We've gone from 0 to $100 million ACV in five quarters flat with an ASP well north of a $500,000. Let me step you through the four Cs of our strategy. First, connect. Workflow Data Fabric is fundamentally architected for the agentic era. 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 write capabilities, and it supports all types of data wherever it lives. That's another crucial distinction. 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 ServiceNow. You know, I know there are others who are playing for data gravity. For us, what really, really matters is knowledge gravity. Workflow Data Fabric already offers 250 plus connectors, and we're 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. 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. It's slowing down AI adoption, it's 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. 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 non-negotiable. That's why we're introducing the ServiceNow Data Catalog, which delivers native metadata management, data lineage, privacy, and ultimately trust. Humans and AI agents alike can now instantly discover and use curated data products, safe in the knowledge that these data products have their enterprise's seal of approval. That's great, getting your data AI ready isn't a one-time activity. Enterprises have to keep it AI ready. 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. 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. Just like we do with the data lakes, we want to embrace and extend what our customers already have. That's how our strategy is fundamentally different. We're gonna 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. As Jon mentioned, we've bottled that magic up into something we're calling the Context Engine. You know, 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. We've used that as a foundation for a new product that we're announcing called Autonomous Data Analytics. Think conversational analytics to guide the, you know, what happened, what will happen, what should I do type of decisions that need to be made by both humans and AI. It's fully autonomous. 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. 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. As an example of such a data app, customers will be able to combine product usage, 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 one 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 two important new capabilities to RaptorDB Pro based on feedback from our large customers, as well as those in regulated industries. 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. Second, Live Connect, which allows you to point your existing BI tool against RaptorDB Pro for real-time analytics with no ETL or data movement. Together, we feel these expand RaptorDB Pro's addressable market in our install base by 10-fold. There you have it. Four 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, I'm responsible for ServiceNow's employee experience products and AI front door. I wanna cover the third, or, you know, the third part of this, you know, 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. Essentially what we're doing is building on the Workflow Data Fabric to drive this employee experience and drive values on top of that for customers. When Moveworks was acquired, you know, 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 were creating a powerful front door for work across every system and every employee. We've been busy. You know, my kids call it integration maxing at home. We've rolled out Moveworks to every ServiceNow employee. We've launched the front door to called ServiceNow EmployeeWorks. We've integrated that front door into our new commercial model in just four months. Lots of activity, lots of work going on there. 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. In the past two and a half months alone, we've seen a 10x pipeline build through our go-to-market efforts, right. That means its demand is here, we're now so ready to capture it, given the two companies and what we both afford and can produce together. 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. You know, 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. 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 smarts and generative capabilities. We transitioned in 2024 into what I'd call copilot chaos. This is when every sort of functional AI was being introduced, every platform, every offering, with no real large-scale enterprise impact. We saw these very studies come out. People were wondering, you know, where's the real value gonna hit? 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? right? East to west, not just north to south. 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. The thing to understand is that for an enterprise customer, a user isn't just a user, they're actually an employee. 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 context, right? This is what it takes when you're running a large organization. These employees, you know, 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. We're the only platform really that meets these demands of the enterprise with the employees in mind to get work done. 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 through the lens of actually an employee throughout their year, throughout their month, throughout their week. 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. There's parts of the business that are not open for negotiation. They demand reliability, right? Think about a payroll adjustment, HR investigation, an escalation. These are all things that have to be done 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. Not everything will be agentified immediately. Frankly, some processes, according to our customers, will never be agentified because they always wanna make sure there's a human in the loop. That need for a human in the 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. EmployeeWorks and this new product that we've now rolled out really delivers outcomes end-to-end. ServiceNow 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. To do that, you have to go end-to-end. You have to go across all of these systems very effectively. 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. What customers are really seeking is how can companies do this, and this is ultimately how they will pocket the benefits. This brings us to a unified experience. This is something that I spend every day working on, thinking about. The whole team is rallied around this. 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. There's more. We actually have an AI marketplace that allows you to build deep into the ServiceNow platform, but also along with thousands of pre-built agents for popular apps like Workday, SAP, Coupa, and more. This actually lowers the cost of ownership, right? The old model of expensive implementation, specialized developers, long timelines, is gone. The new model is vibe coding, customized experience in minutes. 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, a quarter million fewer 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. This is just one example. I'll use another one. Honeywell, they're an industrial giant. You know, 80% of the inbound requests and work, the deflection, if you will, to the 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, you know, they're seeing about, you know, equivalent reduction in labor costs. Now, in closing, look, I've been selling to this market for about eight years, to the ServiceNow install 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 friction to find what you need. You lower 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 install base in which we're gonna be building the future of employee work. I think it's gonna be a really great year. We have a lot of other exciting product announcements and releases coming up for the rest of this year. Real quick, I just wanna give you a quick preview into what you'll hear more tomorrow with regards to ServiceNow Auto. You'll see this, and you'll learn more about it at the keynote, that we'll have in the morning. ServiceNow Auto is really the combined intelligence of Moveworks and Now Assist coming together in a new unified experience. You'll be seeing this obviously in action shortly as we talk through it a little bit here. 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. Like, I know we're hoping to educate, share some information here, but hopefully, I can add to that. Bhavin mentioned I'm gonna talk a bit about autonomous IT. I'm gonna take a bit of a general statement up at the beginning. We're gonna talk a little about just workforce automation, and then I'm gonna dive more specifically into how we're applying that to the world of IT 'cause that's kinda relevant to us 'cause it's still the biggest part of our TAM right now, at least 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. That's what's been driving value for our customers for 20 years. You take a process, you put it on ServiceNow, it's more efficient. That efficiency and that productivity is what people are paying us for. That's the fundamental deal here. We make you more productive, 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. 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 'cause we think it's a real value for our customers and for us. 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 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. You're not reporting that your email's broken, we actually fixed your email for you. You're not asking for a pallet of steel. You may ask for it, some automation will actually make sure the right steel from the right vendor shows up at the right factory on the 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 mediated 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's 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 FDEs at your project, and we will try to wire up your purchasing process with some little shim here and some duct tape here and a little bit of bailing wire here, and we probably get it working. We've done one of your many business processes at a big investment of FDEs. We fundamentally believe there's a better way to do this. We wanna 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 mention it. You're gonna 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. Excuse me. The idea behind an autonomous worker is very straightforward. It's just like a human being. You assign it work, it approves things, it approves the same audit rules. It follows the same logs. It lives in your user record. You don't have to do business process re-engineering to do one of these autonomous workers. It gets us out of the game of saving five minutes for a customer here, 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 in the age of AI. If you look at where we're going more specifically, we have got a path to zero touch. You will we've got about 20 of these in the hopper, but four of these we wanna talk to you about today. Level one service desk specialists. I'll dig into this one a little bit more in one 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. The first one we have, which is, this is live, this is not hypothetical. I've got six live pilot customers now. I've got about 50 customers in the hopper who wanna get live with this very quickly. We've actually had to turn people away because the oversubscription is so high here. These are autonomous IT specialists. You put them in your user record. If you've already bought ServiceNow, you assign them to a team just like you would onboard 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 would. 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 headcount. Fundamentally, that is the game they are in. It's important for us because that's how we grow as a company, is we offer that value to our customers. 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 gonna 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. She's already here. I'm already here. Woo. Brilliant. Thank you, Pat. That's fantastic. I am incredibly excited to be here and share the product with you and share how, you know, what Bhavin and Pat talked about really come to life. We're talking about both individual employee productivity, which then transitions to the productivity of a team, which then goes into the productivity of an entire workforce when you look at autonomous workers. With that, I'll show you how this all works. I'm gonna start off here with EmployeeWorks. This is our new unified front door to work. Imagine I'm an ITSM manager at Electri, 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 gonna get covered for that in my new state and if my current plan will do that for me. I don't know who to ask. Like, it's the 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. Today I'm gonna go in and ask ATO what to do here. I'm gonna type in this prompt and see if I get coverage in Washington. ATO 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. Not just that, ATO tells me what I need to do to kick off the process. Apparently, I have not changed my address yet, so I'm gonna go ahead and put in my new address and enter that in. Now ATO 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 and change my address. It's also telling me 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. 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. ATO can go ahead and finish the process for me. Everything's done. In about one minute, I went from a complex benefits question to a completed solution, which is amazing. It's not just about me, though. I'm also thinking about my team at work and what they need. I'm gonna start a new conversation, and I wanna see if there's anything I need to do to unblock my team, if there's anything kind of pending my approval. ATO 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 summarized for me here and also prioritized, 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. Yikes. I'm gonna dig into that and ask a little bit more before I approve this new computer. I want to understand what's going on here. ATO can bring up his request, and then I can go in and actually click into that request and have it surfaced 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's maxed out on it. I'm going to go ahead and approve this for Alex. Fantastic. Always feels really good to unblock the team. From 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 executive 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. Before I do that, I wanna make sure that I can also revoke that access when their shift is over. I'm gonna ask a couple questions here. When does the shift start? When will this access be revoked? Can we do that automatically? ATO goes to work one more time. The first support engineer is provisioned automatically, but the second one, ATO can go through and provision and also make sure that their access will be revoked at the appropriate time, which is super cool. Let's go in there. I'll go ahead and approve it. Everything looks great. Okay. I just did stuff for my own personal productivity as well as the productivity of my team, which is fantastic. It's a totally new employee experience, which is so exciting. 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. 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. I'm going to go over to my service ops work desk and see how my team's performing overall and go into my service ops dashboard. Unfortunately, even though I did all that great for 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. Like Pat talked about, that's where an L1 IT service desk AI specialist can come in. I'm still learning about what this can do for my team, so I'll ask ATO what I need to know about it. ATO 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 this could solve for my team. Brings up an entire profile right here on how well this AI specialist will perform, including the eval score, which gives me 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 paths. In case there's a really complex issue, it can get routed to a human. This looks pretty fantastic, and based on what it's gonna do for the CSAT of my team, there's no question I'm gonna activate this AI specialist. All right. 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. We'll fast-forward in time, and I come back to that same dashboard, and I see, great, everything's tracking on time now. We've got our AI specialist on the team. Things are progressing well. CSAT's back up. I also want to audit how this AI specialist is performing. I'll go in and ask ATO to give me an analysis on this AI specialist. Goes through, it looks at all the past activity, looks at the metrics, CSAT scores for this individual. 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. I also want to audit exactly what it's doing in a particular incident. I'll click into one, and I can see a full record here of exactly every step that that AI specialist took. 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. Everything's 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 assists, but AI that acts and resolves, delivering real business outcomes. Very excited to share that with you. Next up, John Ball will be joining us to talk about our innovation in CRM. Thank you. Good to see everyone again this year. I'm going to be covering the fourth key step to unlocking AI transformation, and I'm going to do it through autonomous CRM. 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 had become the fastest CRM player ever in the history of the industry to get to $1 billion in CACV. Just three years later, we're going to blow through that, double it and blow through $2 billion. We've massively expanded our functional footprint to deliver awesome experiences across the entire customer life cycle, from lead and opportunity management to Configure, Price, Quote and order management, all the way back to where we started in customer service and field service. 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. Last, we have industry-specific IP that speeds time to value across multiple verticals. 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 a hint, folks, it's not about tracking interactions in a database. In service, you have to provide great omni-channel 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 a company sells, as well as the types of requests, orders, and changes their customers are entitled to. Without workflows and without the ability to model this declaratively, well, you're just writing a bunch of custom code. A Vibe-coded app on top of a shaky foundation doesn't resolve the request. It just makes disappointment happen faster. Whether it's handling a warranty claim, disputing a Visa transaction, or ordering a new telecom service, all of these examples require powerful deterministic workflow at the core. 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. 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 customer service center to have a conversation. You reach out because you want action taken to resolve your request. Understanding this point is crucial because the vast majority of customer service requests are not how-tos. The web and YouTube solve that. I'll use a simple example to illustrate my point. Say you want to change an existing order. This seems simple and straightforward, but to deliver this, you need to understand the intent, request order change. 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, workflow is required. If it's a change in quantity, do we have enough inventory? If it's a change in the product being ordered, you've got to rerun the CPQ process to check for compatibility and then generate a new quote. That's CRM workflow logic, that can't be solved with AI alone. Here's my most fundamental point. You're certainly not gonna run refunds, disputes, orders, or anything else mission-critical in a stochastic process that might hallucinate. In Sales CRM, CPQ is a great example where AI can really turbocharge productivity. 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 is 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. This is not theoretical. We are live in production with sales, service, and CPQ use cases with amazing customers like you see here, driving better customer experiences and $ millions of savings. Several of these customers are presenting here at Knowledge, so you can hear their stories firsthand, not from me, from them. From NVIDIA, who reduced time to quote from five days to five minutes, it is just an amazing stat, five days to five minutes. Rossmann, a large European retailer, who deployed our agentic CRM for retail store support, saving a massive amount of time and allowing store associates to focus on the customer, which is their real job. Now, there's no better way to understand this all than through a demo. Please welcome Chris Shutts, CEO and Founder of Logik.ai, who now runs all of sales CRM for us. Chris, take it away. Thank you, John. Hi, everyone. For this demo, we have a customer, his name's Marcus. Oh. He bought a generator a month ago. It's a 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 What he's gonna do is he's gonna call into Electri Generators, and he's gonna interact with one of our CRM voice agents and see if he can get his order changed. Let's see how Marcus does. [Presentation] [Presentation] [Presentation] [Presentation] [Presentation] [Presentation] [Presentation] Here you can see Marcus has interacted with our voice agent. She's asked him a couple questions about his order, and behind the scenes, the AI agent's 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] [Presentation] [Presentation] What's happening here is the AI agent's interpreting what Marcus is saying in the large language model and then mapping it to options in our Configure, Price, Quote application. This is what John was just talking about with his concept of speed of thought and 100% accuracy. 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 agent's making sure that all that'll work for his order. [Presentation] [Presentation] [Presentation] Now this is a pretty typical case for a complex, expensive order like this, where there's a lot of back-and-forth dialogue with a customer, especially when you're doing order edits and order changes in manufacturing systems, typically. Not only do we have the voice agent interacting with CPQ to make sure all the options fit together, the performance is really important, 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 AI, with the AI agent real time. [Presentation] Now that we know exactly what Marcus wants, the AI agent can take all those options and rerun the manufacturing bill of material rules, 'cause 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] [Presentation] [Presentation] [Presentation] 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's taking all that information, putting it into a customer-facing document that she can then send to Marcus real time, he can confirm, like, billing address, shipping address, payment terms, and things like that. [Presentation] Now Marcus has the email on his phone. [Presentation] [Presentation] [Presentation] [Presentation] Great. Now you can see Marcus, in a couple minutes, was able to interact with a voice agent and actually make some pretty complicated order management changes using the agent interacting with our Configure, Price, Q uote and order management applications real time. Thank you. All righty. Great job as always, Chris. 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 customer's cost structure, which is really something that every company on the planet wants. That's just a huge tailwind for our business. With that, it's time to pass to my colleague, John Aisien. John should be coming up here. Take it away. Sir. I'm gonna use a clicker. There's the clicker. Thank you so much. Great job. Thanks so much, J.B. and Chris. As J.B. mentioned, my name is John Aisien. I'm responsible for our risk and security products. What I'm here to do is to walk you through the fifth layer of a five-layer cake that Amit and my colleagues have stepped you all through. I'd like to start by making a bit of a bold claim. ServiceNow is a security leader. We spent the last five years already establishing significant preeminence in the governance, risk, and compliance market, growing that business tremendously, nearly four times greater growth over the last five years than the market itself is growing. My primary goal of 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? Two 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. Essentially 10 primary persona that we address across the enterprise. 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 achieved. 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. Over the course of the last two quarters, as you probably have heard, we've been a bit busy inorganically. One of the first things we did was acquire Armis. In one sentence, what is Armis to a 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. 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. You build extremely high value applications on top of that, agentic or not, that is extracting insight from that data plane. Of course, the third dimension in the multidimensional core that ServiceNow 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 both organizations are different. Different policies, procedures, rules, regulatory frameworks, people, et cetera. It's this combination, if you wanna 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. 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 has 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 then able to extract potential vulnerabilities from those BMSs and essentially automatically remediate prior to any incident occurring. 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. It's a comparable story that we see across an international consumer packaged goods leader whose story I won't step through for brevity. What are the growth drivers, both current and future, that are powering this business? There's four 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 three 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 9th announcement around our AI native packaging, we did something super interesting. We enabled the entirety of our customer and our partner base with the rights to AI Control Tower, the delivery mechanism for all of this IP. 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. Where's Brad? Remember him from 25 years ago. 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, lacking. I believe that the machine speed by which agentic workloads can identify code defects, chain them into vulnerabilities, and do malfeasance with that requires a next generation architecture. The Action Fabric that Sig talked about earlier is the enabler for collective defense in the enterprise all up. 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, access, and knowledge, manifested through the Context Engine and delivered through the Action Fabric to both ServiceNow risk and security workloads and third-party workloads. Imagine a circumstance where even though we're now a fully-fledged provider of exposure management solutions post-Armis, a customer's already using CrowdStrike for exposure management. They're using Microsoft for endpoint, but they want incremental value arising from that three-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 real, all of which spins meters for ServiceNow as we're delivering value to our customers. 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, increase security outcomes, increase risk outcomes, and generate business outcomes as a result. 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 en masse outside of the firewall. A comparable next generation architecture is needed for the agentic world, and I will posit that the notion of permanent access privileges in systems is going the way of the dodo. It just doesn't make any sense. 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. To provide you with some insight into why I believe this claim continues to have credence, I'm going to have Nenshad Bardoliwalla, our AI Platform product leader, demonstrate this through a simple demo scenario. Nenshad. Thanks very much, John. You've seen a glimpse of what the AI Control Tower can do, but today we're gonna dive into the secure risk and compliance capabilities. 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's security team is the speed at which AI is increasing their attack surface. Today, I'm gonna be the security admin in CVS's AI Center of Excellence, focused on securing our AI investments. The AI Control Tower gives me a single view of our AI security posture. The moment I log in, I'm gonna 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%. I just need to figure out why. 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. Let's go ahead and start the remediation process. I click Remediate, and immediately, Armis' early warning flags agent vulnerabilities and active exploitations targeting healthcare. 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. Going back to this overprivileged state of the agent, I'm gonna approve the recommended access permission adjustment, multiple things are gonna happen simultaneously as a result. Let's go ahead and do that. Yes, please reduce my agent access. We're gonna temporarily disable the Aetna benefits agent. We're gonna work with Veza to remove permissions. We're gonna update the AI Control Tower inventory, the exposure record is automatically created in the unified security exposure management application, created for my team to review when debriefing. This agent's elevated permissions and its connection to other critical assets were caught because it was under continuous monitoring. 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. I can see here that I've already gotten my score up to 78%, which is exciting. I can go ahead and use the power of Armis's Shadow AI detection and find that there are three 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 overprivileged agent. I'm gonna change these to managed. An AI Control Tower opens up four new use cases in use SIEM, one for the original agent and three for the additional Shadow AI agents that our asset discovery capabilities identified. I mark them, and once they're managed, now continuous security, governance, and controls monitoring happens across every managed agent. 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. 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's current policy for agentic execution requires. The AI Control Tower can also operate fully autonomously, from continuous monitoring to anomaly identification to remediation. With that, we are going to hand it back over to Amit to take us home in the product section. Thank you. All right. Thank you, Nenshad. 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 roadmap. The world-leading analysts also make it clear that they agree. In the last few years, analyst recognition has grown from just in six categories to leading in 39 categories, and that number continues to grow. 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. Our future roadmap is also accelerating. That's partially because AI is not just empowering our customers to build and work faster. We also are leveraging the same AI technology to 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. Underneath all of this is the platform is continuously getting stronger and more powerful every day. The one key piece of our roadmap 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, let's talk about it directly. Bill also alluded to it earlier that we're making strategic decisions to acquire both tech and talent that strengthen our platform and also further differentiate our core. We already have proof that that is already working. You heard that in terms of the big bets we have made from Bhavin Shah, 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, these acquisitions are not just about buying growth. They're about delivering the critical pieces 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 has reimagined our products and our roadmap to evolve our commercial model as well. As many of you know, seed-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 advanced to Prime. Another shift is how we meter value. The unit of value is the work that gets done in real time. We now have hybrid as well as consumptive meters across all of our entire portfolio today. This gives customers something predictable, like a subscription commitment, and something which is flexible, so it scales with their actual adoption. We're using different meters across the whole portfolio now. Now Assist, for example, for AI and data-related products, human and non-human identities for Veza, assets for ITAM and Armis. 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 the one of the first of many ways we're taking the strengths of a platform to entirely new customers through new channels as well. With a shift towards autonomous workforce, we are 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. 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 a platform. This is the hybrid workforce of the future, which we are now making it available today. Our AI specialists capture at least six and a half times more value while saving the customers over 80% compared to the cost of human fulfillers, which it replaces. Beyond the numbers and beyond the market recognition, at the end of the day, customers are the biggest proof points of our success. The enterprises that run the world have trusted us with their most critical operations at one of the highest renewable rates in the industry today. We have covered a lot of innovation in the session today. I want to come back to our four 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 midst 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. We have the commercial model, as you heard before, to capture the true value of AI. 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. I know you guys noticed that we're running a little late, so we're gonna take a short five minutes 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. All the great innovation from Am and the team, that was fantastic to see. The last time when we were together, we laid out a strategic thesis. Platform, industry, and global scale. 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. Just as important, deeper industry relevance. Solving complex mission-critical challenges across financial services, healthcare, manufacturing, retail, and public sector. 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. The bigger signal is where the market is going next. Across every boardroom, in every industry, and every geography, the market is converging around three realities. Enterprises want to agentify workflows. They don't just wanna automate tasks. They want AI that can reason, that can decide, and that can act. Critically, they want speed. They don't want value in 18 months, they want value now. 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. At ServiceNow, we were made for this moment. Today I'm gonna focus on four ways that we are driving agentic growth. One, workflow agentification. Two, autonomous implementation. Three, AI-powered ecosystem. Four, our strategic expansion. Let's get started with agentification. Now let me show you what this looks like at one of the most advanced technology companies in the world, NVIDIA. 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. Let's take a look at all four 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 five days to five minutes. In customer success, AI is enabling proactive management at scale, and in knowledge management, authoring agents continuously create and maintain critical documents. Here's the punchline. This is not isolated automation. This is platform-level agentic AI. Multiple AI agents operating simultaneously across multiple business functions. NVIDIA isn't the only place this is happening. Across our customers, our forward-deployed engineers, our elite forward-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. 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 velocity is no longer a services differentiator. It is now a strategic growth lever. We now have two paths. Self-implementation, AI-guided deployment built directly into the platform. Or services-led implementation, AI agents embedded in ServiceNow's delivery process. This dramatically compresses implementation timelines and time to value. 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 moved from workshops to go-live readiness in just six weeks. Six weeks. Historically, this could take many months. That's not incremental improvement. That's enterprise deployment velocity that's completely redefined. 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's 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 partner of the year in five categories with the hyperscalers. Microsoft's partner of the year for ISV innovation. Google's partner of the year in not one, not two, not three, but four different categories. The first one was agentic innovation. Second one, business applications, platform, and then financial services. 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 pre-committed cloud buyers, faster procurement cycles, and a co-sell motion that's at scale across the globe. 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 EmployeeWorks. 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 Bhavin. The power of Moveworks and ServiceNow completely unified. Let me give you a customer example. At a customer I talked to recently, the CHRO told me, she said, "Look, I have mandated a 10% year-over-year reduction in operational cost through 2028." She said, "To get there, I have to have AI handle routine HR inquiries, employee self-service, and ticket deflection." ServiceNow EmployeeWorks then became the conversational AI front door for that project, with ATO agentifying the workflows in the background. Result: $55 million in projected annual cost savings, three and a half million productivity hours returned annually. That's business reinvention. 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 non-human, and secure all assets and agents through one platform. The 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. 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 one. This model is designed exactly like our platform. Predictability where it needs to be, flexibility where it should be, and scalability where it matters. 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 agentic workflows, and we help them secure everything through AI Control Tower. Now I have the privilege, and I'm super excited to bring out two 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. Oliver de Wilde, who is the head of ServiceNow COE for Hitachi. Gentlemen. All right. Well, thank you both for joining us. Oliver, I'm gonna start with you. You're modernizing Hitachi's IT operations globally. Maybe you could just share with us, you know, what's so hard about deploying AI at the enterprise scale of Hitachi? I mean, it's a massive, complex organization. It might be a bit of a controversial start, but the problem isn't just in the technology. That's not the hard part. The problem is in changing the organization. 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. All of these parts really is 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 workaround to make it work. We've had those problems in our deployments, and we worked through them. That was okay. 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. I'd say those are the harder things. The technology, actually, you guys have made it quite easy for us. The technology's there. Yeah. All that change management and basically the operational capabilities of the people. Absolutely. Really matter when you're on that scale. Vishal, I mean, we all know FedEx. FedEx moves 18 million shipments per day across 220 countries and territories. I mean, it's an amazing operation, amazing company. The operational complexity is extraordinary. From your perspective, what's hard about deploying AI at scale? The stakes are just significantly higher. I mean, to put that in context, you just said it, right? We operate in 220 countries. We move about 18 million packages daily. We manage that through multiples of tens of millions of workflows. It's very different if you're playing a one-person band or a two-person band. You can sort of get the music right. Now, if you're playing an 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. 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 one or two-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, healthcare 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. That's a great point. You often refer to us as the digital backbone, which I think is a really great analogy. Oliver, Hitachi and ServiceNow have been collaborating for multiple years, particularly early on in the AI side. Together we've seen some incredible outcomes from a strong user satisfaction to rapid adoption. What do you think has made that success possible? I think it's always been a partnership. I think we met sort of four or five years ago. I think the partnership started really there with customer success. Then we were invited to participate in the Lighthouse program. 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. Really that allowed us to help you shape it. It didn't come without its road bumps. There were definitely some technological challenges that we had to overcome, working with the customer success teams, working with the product teams, to actually get these things fixed, then deployed at scale. 'Cause as Vishal just said, this isn't just about one or two people. We deployed it to 60,000 people overnight. 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 it. We saw a 10 x, like, increase in self-service on the service desk that just was not there before. We saw, like, marked changes. 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 value creation. This is, I think, something that we're seeing in elements of Control Tower, but really then the value sort of estimation and generation. It's something that I was always getting challenged by my CFO on is, how are you proving that the investment that we're making in the time and in the licenses is actually returning a positive value for me? 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. That now you can see it a lot more easily. We went through the spreadsheets, the Power BI, the manual ways of calculating it, now we have tools and products to do it. 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 a vision early on of a value-based, almost zero-touch, 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. Vishal, FedEx has a bold vision to make supply chains 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. Maybe just share with how your team's unlocking the new value with FedEx Dataworks, which I know you run as well, and our strategic collaboration around AI-powered automation. So. Across the enterprise. Sure, Paul. As we just outlined, FedEx operates one of the most complex supply chains in the world. We've been doing that for about 50 years. 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. What we wanna do now through FedEx Dataworks is make sure that we bring solutions to market that allows for that inefficiency to be trapped. We wanna make sure that we bring orchestration solutions that fill the void and connect all the elements of this fragmentation inside the supply chain. Starting with Source-to-Pay Operations 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 PB of data, we want to release those into signals that benefits our customers so that they can go from a reactive to more a predictive posture in the interventions that they want to drive inside their environment. Source-to-Pay Operations 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 ServiceNow and FedEx gets pretty exciting. It's super exciting. You'll see more of that tomorrow, with you and Raj on stage, very excited to see that. Okay, I think we're gonna get to probably one of the harder questions here. 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?" You know, the whole build versus buy, particularly with, you know, all of the large language models. Vishal, I'll start with you. What's the build versus buy conversation look like on the inside of FedEx? You know, just because I can doesn't mean I should. I mean, for me it boils down to that the stakes are high. You know, 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 do more deeply, are areas where we will want to own IP and we will want to build. 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 in the world. I will shamelessly take that from folks that have much more experience than FedEx will ever have in this enterprise and bring best practices inside and build the digital backbone that you can help bring with speed, and channel my energy instead into the core parts of our value chain. 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", so. Thank you. Oliver, how about you? I know I'm sure you've had these debates inside of Hitachi. Of course. Look, I mean, Hitachi's an engineering company. We're not short of people who want to build stuff, which is a great thing. I think as Vishal said, it's just like, just because you can build it doesn't mean that you should, doesn't mean you should build it. I think I completely agree with that sentiment. Take the best bits 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 loads of stuff that Hitachi builds. Let's keep building that. Let's keep building what we're good at. Let's buy what we haven't, what we don't build ourselves, and then integrate it together. I think it's maybe slightly less of a build versus buy comment, and actually more of a build versus orchestrate, and how you assemble this into your digital backbone or your digital core, whatever your company sort of refers to it as. How do you bring the pieces of the jigsaw together into something that then works for your organization and helps drive your organization forwards? 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. Every enterprise is different. Your needs are different to my needs. I'm not shipping hundreds of thousands of packages around a day. Then we do have mission-critical infrastructure that operates in a different way. We have different needs. Take the best parts that other people make and build it into your best solution. Fantastic. I know, I could probably keep asking you questions for the next 30 minutes, but I know you guys are both on a time schedule. Thank you for joining us. Thanks for coming and sharing how we collaborate together with ServiceNow and FedEx and Hitachi. Maybe everyone give a great round of applause for Vishal and for Oliver. Thank you. Thank you. Thank you. Thank you very much. Thanks. Please welcome to the stage President and Chief Financial Officer, Gina Mastantuono. Thank you. Thank you, Paul. What incredible customers. Hello everyone. I've been really waiting backstage for a while to get out here. I can't tell you how excited I am. 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. That's healthy. Great companies should be able to answer hard questions such as, "Will the growth of the foundational models come at the expense of budgets for the incumbents? Will seat 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. We're the AI Control Tower for business reinvention. As you've heard throughout today's presentation, ServiceNow is not a traditional SaaS company. We're the orchestration layer AI agents run on, not software they replace. We're the AI operating system for the enterprise. We are truly in a category of 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 three 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. 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 five-year CAGR of 24%, and we added $2 billion, 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 five or more products, up from 86 the year before. This includes a 7.5x increase in Now Assist deals with five 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 five or more tech products. 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. 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 end 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 our AI Control Tower. As our customers purchase those higher value bundles, we expect to see an average price lift of 20%-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. 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 Assists and is part of the consumption story. As I'm walking you throughout my ghost 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. 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 five. 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, those 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 workflows get more complex. Generative AI laid the foundation, with each task consuming 1-10 assists. Agentic AI deepens the value curve by completing multi-step 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 workflows, 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 gen AI 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 translate into a $3.5 billion ACV opportunity, net of any seat licenses that go away. Multiply that across other ITSM use cases, then across the entirety of our platform, we power 100 billion workflows and 7 trillion transactions annually. You can see the incredible opportunity in front of us. How long does it take to a customer to realize autonomous outcomes at scale? With all new technologies in the enterprise, it takes time. As you heard from Paul and others, we're finding every possible way to help our customers accelerate that journey. Let's look at example. When a customer purchases agentic capabilities, they receive a generous Assist allocation, meaning Assist overage 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 three, 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 five, this AI customer would be spending 4.5x the initial assist entitlement. These journeys have already begun. As Bill teased at 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 onto 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 expands the ACV opportunity for each and every customer. This is a flywheel that's already spinning, not one just being built. 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. 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 gonna 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 cost sit. We're differentiated, our pricing reflects the full platform, the CMDB, the workflow engine, the governance layer, the business service map, 20+ years of operational context. That competitive positioning is what sets us apart from the standalone 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 on margin expansion in 2027, inclusive of Armis. We can commit to this because operational discipline is a core muscle. 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. 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. It's all day we keep you here for this part, right? Okay. Bill gave you a little preview. I've got a little surprise. Hold. Wait, please. In 2021, we established a long-term target to achieve $15 billion in subscription revenue in 2026. Many were skeptical then. I see a few of you in the room. Fast-forward to today, we're on track to beat that target by $500 million organically. I know the guide is higher than $15.5 billion. Organically, we're beating that by $500 million. 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. 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. 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, and he didn't take it? Yeah. This is not blue sky. There's a roadmap 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 deepen their AI investments. You heard that from customers today. AI has upended the CRM 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 two years ago. 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 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're telling 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 one quarter. The result, we expect to be dilution net neutral for 2026. We still have $4.2 billion in authorization remaining, 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 roadmap. 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. 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 super cycle 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. Now we're gonna welcome back Bill, Amit, and Paul to the stage for Q&A. Just give us a minute. For the Q&A session, we will have microphones in the audience. Please raise your hand and a microphone will be brought to you. When called on, please state your name and company, then ask your question. Thank you. Thank you, everyone. All right. All right. Okay. Go here? Here. We going over here? Okay. Thanks everybody. Kirk Materne from Evercore ISI. Thanks for a great presentation, both the technology depth and the long-term visions. It was great to see. I think my question is somewhat maybe for everybody on the panel, but, you know, I think one of the debates going forward's gonna be at sort of the orchestration or at the agent control plane. I think every big enterprise, the LLMs 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. 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 everybody, it's sort of a land grab, and I think everybody's 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 strategy's playing out? Wanna start? Yeah. Yeah. No, thanks for the question. I think the way I see it, there will be a lot of pieces being orchestrated by different parts of the technology providers. The idea of an autonomous worker takes away this requirement to do individual work by each of the vendors. What we're doing with autonomous worker is taking away the effort you had 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. The metric I would look at is how many people are now gonna 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 the dealing with individual pieces themself, but getting the full value of a solution. What we're seeing now with AI specialists, for example, what we've introduced with 20 AI specialists, especially the L1 support and specialist, we're seeing a lot of customers don't want to do that building and managing and taking care of the spare parts themself. They want to elevate that and get a full solution. I think that that will be a trend over the next few years versus the that's happening now. Same thing happened with cloud. If you remember, everybody used to get the LAMP stack. They would in order to build themselves. Eventually they realized keeping and maintaining that stuff is not easy. Dealing with changes in the technology is not easy. They land up going to hyperscaler, somebody who provided the full stack. You can build an application with it, and you don't have to deal with all the different pieces. I think the same thing will happen with us now. That's what the metric I'm watching for, and I'm seeing that already play out with a lot of the customer conversations I'm having. Excited crowd. Thank you. Obviously very impressive to see all the progress you guys have been making, and the targets coming out. You know, I think seeing that, you know, ACV target of 30%, you know, coming from AI out just a few years is really amazing. I guess on the other side of that is kind of implicitly it would suggest the non-AI components are gonna be growing much slower. You know, back of the envelope, I did was, you know, 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. 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?" You know, help back me up on like why that's, you know, maybe the wrong way to ask the question. You love that one. I love that one. 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 ProPlus, which is now Prime, they can start with Foundation. They can taste it. They can start to about working. Who really wants to buy any software today that doesn't have AI embedded in it? It's 100% the wrong way to be thinking about it. Remember, you all remember when we first launched Pro, right? No one 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. 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 gonna much more rapidly proliferate and grow with us, and that's when the consumption wheel continues to fly. You're already seeing pretty incredible growth in, you know, $750 million in Q1 up to $1.5 billion. This is remarkable growth, and we are just getting started. 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 is going to help our customers when we really see the value accrue over the next year, two, three, five years. Hey, guys. Alex Zukin with Wolfe Research. Wonderful presentation, obviously. Would expect nothing less. A couple of competitors out there 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. It seems like the kinda variability of the upside is kinda partially driven by executing on this new, I think, Bill, you called it the largest new TAM is cybercrime. Maybe just talk about how do you see the competitive narrative and landscape evolving, and how does the security component of your portfolio drive that maybe higher target that you laid out there? Go ahead, guys. Yep. Maybe I'll start, Alex, with 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. It might be in a different segment that's, you know, much smaller than a ServiceNow customer. That's our target market. There might be some edge case areas. I think for us, we're very focused on the segments that we serve and innovating and delivering great value for those segments. I think, and, you know, anytime that comes out, we dive into the research and look at the data and figure out what's working. Are we missing something? We're very cognizant of what they're talking about. I think on the security side, just from a pure customer and go-to-market standpoint, you know, we really think about Veza is. I've never seen anything like it. I mean, 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 'cause 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, the securing, human and non-human agents and understanding the identity across that has given us an incredible extension of AI Control Tower. I talk about AI Control Tower complete. 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. Whatever industry I tell that story in, they need it because they don't have it right now. I think it's a really close fit with ServiceNow, SecOps, vulnerability response. It's a beautiful extension like we talked about earlier. I'll just add one thing. The other part which is also important is the data part. The stack we have built, having an AI Platform, which as you saw today, is 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. You bring security on top of that where you ensure no nefarious things will happen in your platform, really changes the game when you go and talk to the customers. They don't have to bolt on all those things separately. They don't have to bring all the signals from separate areas and manage all that stuff in a different environment. When we bring security and data on the same platform, and then your workflows, the agentic workflows with autonomous worker, it's really, I don't think there's anything out there in the market today. There's no competitor who can do that. They can talk about pieces of it. If you hear the noise out there, they're talking about pieces of technology in maybe mid-market or somewhere else. In the enterprise place, there's nobody like us. What we, when I talked about earlier about bringing the same technology now to mid-market, that also takes that business away from them, right? You'll see a lot more of our capabilities being delivered AI native, completely consumption driven, right, and conversational experience now be delivered for AI native for mid-market with AI native mindset and bringing ITSM and other capabilities to mid-market we didn't do before. That gives another opportunity we have not talked about earlier. Keith, can you just say your name? Excellent. Thank you guys for taking the question. Keith Weiss from Morgan Stanley. Thank you for a great presentation. 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, not quite sure what we're calling it now? Mm-hmm. T o ramp similarly to what we saw from the Pro SKU, number one? Number two, more strategically, you guys are bringing agents to your customers. You're bringing the autonomous worker. You're bringing the AI specialist. You also have to open up your platform for your customers to build their own agents or let other people bring their agents. When we're thinking about, like, the 5x uplift from autonomous worker, what's the uplift when you're just opening up your platform and letting other people build agents on top of your platform like they're gonna request? I'll take the first one, then I'll hand over to Amit. I'll take the. On the first one, actually, we're conservatively building in similar ramp with 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. Yeah. 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. Having an ability for us to provide that access in a governed manner with monetization is the right way going forward. I do expect more and more of that kind of that kind of use cases emerging. What we have done is we've been very careful about how we expose it. 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. We measure every assist. You know, using the assist kind of metric, measure any access, and we meter that, and people can burn down. It makes our assist more fungible, not just be able to do Now Assist kind of use cases, but also now accessing data. We can get to manage it and make sure that you register for it, you have what kind of requirements, the SLA, the security. We're bringing all that stuff into this thing, and the one of the announcements we did is with Anthropic to be able to also do things with their Claude Code. It's also in the governed and measured and measure capability so that it doesn't allow people to just get access to it without any kind of permissions. They should probably turn up the mic. It's hard to hear them when they ask a question. Great. Thank you for taking the questions. Arjun Bhatia with William Blair. Actually, I wanted to follow up on Keith's question about Action Fabric. I'm curious if you worry that customers might push back that you're essentially introducing a gate for their data. I'm curious how much of it is theirs versus yours, or does this all not matter at all because the ROI is gonna be high enough that, you know, they're gonna come out, customers are gonna come out winning on top of this? 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. How do you see that playing out throughout 2030? Thank you. I will 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 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. So far it has been pretty well understood by them. They realize 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. When we provide a much more governed platform, they seem to be very comfortable with it so far. When we have been introducing this concept, we have talked to many customers already, and there has never been an issue in terms of worry about having this kind of metric being delivered. 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. By the way, it's been increasing pretty rapidly over the past couple of years as Now Assist has been driving a flywheel. 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. 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. There is consumption already baked into that initial seat. 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 seats or consumption or based on the value. It's always based on the value. Then it's how you back into the value in 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. So if you think about the 2019 ServiceNow, around $3.5 billion, we pretty much add a new ServiceNow every year. Sorry, were you finished? No, I was just gonna say, 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. However we mix the formula, it's a great formula for shareholders. I was just gonna 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. We'll stay on the forefront of where the customers and where the market is leading. 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 about how they're thinking about deriving value from the platform. Since 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. As Gina said, this hybrid model seems to be like the Goldilocks formula right now. We're open to anything. You're seeing a lot of vendors copy that now, right? Right. I think it's becoming the industry standard. Yes What we introduced last year. Yes. A couple of years ago. Exactly. Thanks very much. Brad Zelnick, Deutsche Bank. Really appreciate a 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? Why might it be different? Because in the past it seemed medium-sized enterprises didn't really value the platform the way that large enterprises did. I'd be curious as well what your view is on AI readiness in kind of, you know, medium enterprise. Relatedly, what is, what does this mean for partner leverage? Does this create an opportunity for a whole new set of partners? Thank you. 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. We have a more complete story now than we've ever had before. 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. I'm not suggesting that we're going too far down, because we wanna be where the money is, and we wanna be where the retention is. It's not just the number of new logos you get, it's being thoughtful and getting the right ones so they stay with you and you don't lose your retention leadership. 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. 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. I think during my presentation I talked about autonomous built into the product. Amit and his team are really innovating on self-implementation there. 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, you know, 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 for with their data. Things like RaptorDB Pro, Workflow Data Fabric, all the things that the teams have innovated on that we didn't have even two years ago, now you can go in, you can kind of plug that in, you can get the data ready, 'cause we, as we know, AI success is gonna be all about the data and grounding these models inside of that data. The new innovation capabilities combined with what Bill's talking about on the self-implementation, but also just the autonomous implementation work, we think is ripe. Brad, if I just could finish just with one further comment. There, 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. If you talk to a large-scale account executive that's, you know, managing General Motors, the likelihood of them calling on a $300 million mid-market company is pretty low. They're gonna spend their time at GM. 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. Frankly, we're getting a little annoyed. You know, Alex brought up a question about the competition. I made a promise to myself this morning. I was only gonna say nice things today, and I wasn't gonna get into it. But, you know, 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. Please be advised that what they say in a certain way is a compliment 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, that, or the other one is now copying Control Tower? Hey Bill, can you believe?" 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. In the case of the smaller ones that have the loudest microphone, I think we've got some ideas for them. We wanna meet them. Hi, good afternoon. 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 one ITSM case study. It strikes me that the outputs for 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. 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. Yeah. 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. Thank you. Yeah. No, thank you. It's a great question. The way we think about it is really, you know, kind of twofold. One is, I talked about it a little bit during the presentation. Our four deployed engineers, truly led by John Aisien in the front row here, are truly elite. They work with customers on really the high value. I'll call them high value workflows. Things like massive usage outages at a huge bank. The cost per outage is incredible. The volume's not that high, super critical, important to that bank. We think about how do we actually agentify the existing workflows that they have? In some cases, customers wanna redesign those entirely. You may have a process, like an incident management process if we're talking about ITSM, that you've just had for years and you now wanna redesign it, optimize it, and then agentify it. That's the high volume part, and that actually, the high volume part, drives a lot of the Assist consumption and drives that asymmetric scale from a value standpoint that I talked about during the presentation. The high volume use cases are where that comes from. 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. 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. Autonomous workers actually help us drive the high value, high volume use cases much faster while the FDEs look at the high value use cases. We're attacking it from both angles, and the receptivity from the customers has just been incredible. Interesting. John, you want to add anything on the FDE model we have? What we do with FDE is to identify very high value use cases, as Paul was mentioning, but also kind of help customers to reimagine the business process for those complex use cases, and understanding where the integrations are required, what changes you need to make, and then really the product takes over and then lands up being the what you implement and the FDEs usually move on. It's not a continuous FDE engagement like many other vendors have typically. Actually, Text me that. I apologize. If I can add one thing. Maybe you have a mic for him? Oh. Okay. Here's a. Excellent. Thank you. If I can add one more thing, beyond 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 re-architect and refine our go to market. The two benefits are, A, kind of accelerating the innovation flywheel itself. Like, even when you go in with a completely comprehensive kind of basket of IP, you're gonna 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 an implementation, a custom implementation of AI guardrails. Now Assist Guardian did not support the ability to plug in a third-party guardrail in the same way we support any model, any identity, et cetera. The FDE team literally extended Now Assist Guardian to support BYOG, so bring your own guardrails. Now, as an example, Palo, with their Protect AI acquisition, which is essentially an AI guardrail, has plugged into that BYOG, extending TAM across both joint customers and prospects. It's a virtuous flywheel that we're seeing where the FDEs are building that last mile, but adding it to core product, which is, in turn is lighting up incremental capabilities, in many of them sort of partner driven as we meet at the customer with that IP. Great. Hi, Samad Samana from Jefferies. Thank you for spending all this time with us today. It's great to hear from you all as always. If I could maybe dig into the 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 roadmap? I guess the related question in that is how much does the change in the pricing packaging influence that? Does that require any changes for the existing install base when they come up for renewal? I know it's a several part question, but I'm just trying to learn from Alex. A couple things. First and foremost, I want to be clear. On the new pricing model, Prime is basically the same pricing as Now Assist Pro Plus, right? We're not increasing pricing in our most premier offering. The customers who are all in 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 to Advanced. What I'd say is that obviously the pricing model is baked into that 30%, but it's not a huge, 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? 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. That was the first part of your question. You had a few in there. Is there something else that I need to answer? The second one was. I think you kind of answered it already, just for customers that renew, it sounds like if they're already on Pro or Pro Plus it would be the same or, like, migration no matter what. It doesn't require any changes. That's all I was kinda curious about. Correct. That there wouldn't be any. That's right. On renewal, any impact. No. I 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. Everything is AI enabled, so there's only one ServiceNow, and it's an AI ServiceNow. I think you probably already knew that, but just in case customers have asked that question, we only have AI. 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, base subscriptions we're not counting in that AI revenue. That's why it's only 30% and not 100%. Yeah. Hey, it's Tyler Radke from Citi. Thanks for doing this. Continuing on the theme of multi-part questions, I'll try to keep it to two. 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. 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? Gina, I think this is the first time we've seen two different kind of scenarios for the revenue. You know, as we think about 2030, 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 upside number. That would've really confused you. Yeah. I'll let Bill start. I think we're trying to be respectful of this environment we're in. Exactly. I appreciate everything you guys gotta deal with. 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. You know, we typically do these little tuck-ins or acquihires, in very small companies for us. I recognize it was new for you to think about, you know, the Moveworks and the Veza and the Armis. Believe me, if you had a management team that doesn't have the courage to do smart stuff, that's the ones you short. When we did Armis, it was at a time when the market was most confused as to what was going on, and our conviction never wavered then and it still isn't wavering now. We know we got our version of Instagram. 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 Bhavin 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. 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. 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 today, the answer is absolutely. I don't think there's anybody on this stage that does not believe that. Very clear. Agreed. On the question on the $30 billion-$32 billion, given the uncertainty in the market, we felt that it was prudent to give a range and 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% + CAGR on our growth engines really driving to that $32 billion. If you're a little hesitant in this market, you wanna tie your number to $30 billion, I'm okay with that too. That will actually conclude our Q&A session today and our webcast. We invite all of you to join our executives at Chica 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.