Welcome to ServiceNow's Financial Analyst Day 2026. Thank you for joining us today. Before we begin, I want to remind everyone that today's event will be webcast and recorded for future playback. Information pertaining to our forward-looking statements and a reconciliation of our GAAP and non-GAAP results are available on our investor relations website at investors.servicenow.com.
As you can see, we have an exciting agenda for you all. Bill and Nick will kick us off and discuss our vision and opportunity. Amit and team will present the blueprint for agentic business, including deeper dives into the key growth areas that unlock AI transformation. We'll have a 10-minute break. Then Paul will go over our go-to-market strategy and lead a panel to showcase the tremendous value customers are getting from ServiceNow. Finally, Gina will close with a financial overview of the company's performance and outlook. So with that, let's get started.
Companies everywhere bought into AI, yet most still aren't seeing the return. Billions invested, but nothing's working together, making it hard for people to get work done.
That was not me.
Correct, Nick. How does a ServiceNow AI platform break down the walls?
Who are you talking to?
The camera, Nick. Connect in any workflow, any AI, any data source. Through everything and everyone, finally new to get up in every corner of your company. We can resolve cases across departments and deliver for customers.
We can run HR workflows and improve employee experiences. And with the AI control, we can finally see and manage all our AI.
ServiceNow is the one platform that lets you connect and control everything so you can put AI to work for your people. Take a seat, Nick, on a chair.
Easiest if everybody just moves down one. No. Okay. Yeah. Okay.
Please welcome to the stage Chairman and Chief Executive Officer Bill McDermott and Vice Chairman Nick Sturiale.
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 want to say before we dig into the content?
Just no place I'd rather be than right here right now with you, Nick, and all of you. Thank you very much for coming. We're going to give you a lot of insight on the company. The company's in great shape and we're ready to roll. So, let's get it started.
Sounds good. Well, why don't I bring up a few things one by one and ask you to comment on them. So, we'll go first to this. I think very few people here in this room, Bill, on either side, on our side or on their side, are interested in the past, but sometimes it's worth reminding everybody where the company's come from. So when you look at the trajectory of the company over the past several years, what comes to mind when you look at this graphic?
Well, Churchill said the further back you look, the further forward you can see. We came in in 2019 building on a great company, terrific founder, very good CEOs, excellent board, and good culture. And we said we want to be the defining enterprise software company of the 21st century and that we would be the first to get to five, to get to 10. And then many of you included weren't so sure we'd get to 15 in 2026. And we're blowing through 15 in 2026. So the first thing I'd like to say is promises made, promises kept. The fastest enterprise software company at scale to hit 15 billion in the time frame we did it and organically. Right now this is the hottest brand in the enterprise. We're pursuing a gigantic gem. We'll talk about that a little bit later. The tailwind is at our back. We have the products. You're going to see the products today and you're going to see the best team in the industry today. We have the revenue and scale matters because it builds ecosystems and networks. We have the users and the loyalty of them. Our attendance is up double digits. This is the biggest Knowledge ever. You'll feel it. I hope. Who's going to Knowledge tomorrow? Great. You're going to love it. And the ecosystem. I mean the show floor is just amazing. I encourage you to walk through it. You'll see the control tower. It's stunning and we'll talk about that today. I said we were the platform of platforms in 2019 and now I'm telling you we're the AI of the AIs. So this is a company that has the loyalty of its customers. It has the inspiration of the most satisfied workforce in our industry. And it's the trusted brand. And I'm not the only one to say it. Fortune says it, Forbes say it, the customer says it with their wallet and you actually know it. So, what better time has there ever been than right now for ServiceNow and ServiceNow shareholders?
You mentioned 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. So, it's great to see the board showing up.
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 is our founder. We still have our founder on the board, which is awesome. And our board is a really great board, very committed to the company, very inspired by what we're building and how we're executing. And hopefully you feel that the entry point you're getting in at today is just like never going to happen again.
So let's shift gears. The portfolio has obviously evolved substantially in the years that you mentioned. This is the representation that customers will see at Knowledge. When you look at this AI control tower for business reinvention, what are the important things for investors to understand?
Well, let's start with the AI control tower for business reinvention. So 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 at ServiceNow. And so what you'll see today is you'll hear from our colleagues a lot about the agentic front door with Assist. So any channel you come into you now have one single agentic experience with ServiceNow. So if you enjoy ChatGPT or you enjoy Claude or other language models that same simplicity is being brought to you for the enterprise. If you think about industry we're in all the industries that are featured in this slide. Why that really matters is the moat 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 going to talk quite a bit about that today. Autonomous workflows. This is super cool because now ServiceNow goes across the entire enterprise east to west. So years ago you knew us for IT. 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 and of course CRM on its way to being a couple of billion business. Oh, by the way, we have six of them. And security and risk, we're now in the biggest growth TAM I see in the next decade. Especially when you think about the world's third largest economy is actually cyber crime. So when we made our bold moves, we knew what we were doing and we'll cover that today. And naturally when you look at the autonomy of workflows to be able to coalesce all of the clouds, all of the language models, all of the systems of record and all of the data sources into an autonomous workflow that can close an action out, not give you a recommendation that's probabilistic, but a deterministic outcome achieved. That's where the world wants to go and you're going to see something today on employee experience that is second to none and also app development. 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. So more AI is great for shareholders. So we're going to sense and that's any data. We're going to decide that's any model. We love them all and we have deals with all of them to either build software together, put it in our software or help them get into the enterprise with our unique attributes and then obviously act on any workflow and it could be ours, it could be someone else's. It all comes through ServiceNow. So, we welcome everybody and to do this securely and I think you're going to hear today that we're in the security business and we think this is a gigantic opportunity for ServiceNow. You say, 'Well, why do you think that, Bill?' Well, it's already a billion and a half business and we weren't trying real hard. And now you can have it, IoT, any device, critical infrastructure, networks, devices, all coming from one platform that fuses it and OT, the only one in the world. And I really think that, you know, any system is just like so stunning because everybody else wants to, I'm reading articles say they want to shut the world out. They want to protect their moat. We're welcoming everybody in because we know we have the winning hand. So when you look at all the hyperscalers and all those systems of record that you know so well, they all integrate with ServiceNow. So we're still the platform of platforms. That is the foundation. And we're nice. There's no reason to waste time having skirmishes because we want everybody coming into ServiceNow with their AI. And we're going to grow, grow, grow. And we got a bold ambition for 2030. We'll lay out there today.
So, one of the things that Colin and his team have done brilliantly acknowledge is the customer's voice is really out there. And I know you're personally inspired by several of those stories. Any that you would pull out just for this crowd as a preview?
Well, I think, 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. And you think about FedEx, it's a great company. And Fred Smith was a great innovator, an unbelievable entrepreneur. And he used to talk about the package itself and how it moves throughout the world is as important as what's in the package. So to think about FedEx teaming up with ServiceNow and Raj coming here on stage knowing that they're moving 18 million packages a day through over 200 countries around the world and every key business process things that would sound like core ERP to you is now running on our agentic platform. I mean that's pretty stunning. And then if you want to take something just really interesting, Chipotle is doing great. Everybody likes Chipotle. I like Chipotle anyway. And now they can change in their 4,000 locations all their menus on the fly. They can be highly creative with their associates. And you're just changing everything to real time enterprises. And so whether it's rethinking CRM or whether it's driving a new approach to supply chains, everything now is real time business processes on the ServiceNow platform. So I think this control tower idea has now manifested itself into a complete portfolio of products.
Love that. Let me do a couple more just to set the stage. So you mentioned we're nice. And we've taken this posture in an environment where it seems like there's more coming into the enterprise than ever before. You always say trust is the ultimate human currency. What does that mean in practical terms?
Yeah. Well, trust is the ultimate human currency. It's the one thing you can't 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. So, we don't lose it. We want to win more and more every day. So I think we all know that the net present value of a loyal customer is every business's greatest asset which is why I think you should all be excited that we have the highest retention rate in the enterprise and we are continuing that push to make sure it stays that way and we're transforming our company to make sure of it more on adoption go lives and the use of AI and that's really where that hockey stick formation is going to kick in big time. We're open and when I say 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. But they also know the context that we bring to the unique data position that we have in the enterprise and the process position and the relationships and the ecosystem is going to be a gateway for them to prosper and grow and bring their amazing intelligence to the enterprise and we warmly welcome them. I also think you should know that we're getting really good at AI and now we're even going to make a guarantee, a total satisfaction guarantee on AI go lives in less than 100 days. Some of them could be a few days, but all of them going to be less than 100 days. And we're going to make that commitment on stage. You're getting the first preview. We're excited about it. We have the forward deployed engineers. We have the customer excellence group and we have partners that are lined up and ready to roll with us to make that happen. And so we're going to make that offer and make that offer tomorrow on center stage. I think customers are going to like it a lot because the pipeline is huge. And if they see we're going to now offer them something on the control tower that's a pretty special offering as well to get them started getting them using it, landing and expanding with 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. And I also want to make it clear that we are also using our own platform. So if you think about the positioning that we have right now, we have our own AI running on Now 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 a half a billion in overachievement on the productivity curve. But I'm telling you that we took a couple of really smart moves with what I call tuck-ins based upon the size of our company and the fact that we never bought anything for revenue. If we did, we would have bought companies that you would have recognized a lot better with a lot more revenue. You know, we bought the future. And the beauty of that is we're going to leave this year with the exact headcount that we entered this year. And what you can take away from that is that our platform is resonating in the way we run our company. The fact that everybody has AI in their pocket to take care of customers, run their business, and run our company. That's why nine out of 10 customer cases are now managed by agents in our company. The same is true for HR. And all the questions around running a business that used to be done by people are now being done by agents with people still in the process only when it's absolutely necessary or when it's a high-touch minute where you have to really touch an employee and make the heart of a human come through or in the case of a customer a white glove treatment because they're exceptionally special. Other than that, the agents are doing the work.
Let's see if we can do two more in two minutes. So you've talked a lot about the growing opportunity. It's not controversial to say there's some skepticism about is the opportunity in fact bigger. I don't want you to litigate the entire thing, but when you look at the prize in front of us, what is your takeaway about the trajectory ServiceNow is on?
When I started in my career at Xerox Corporation, it was a great CEO named David Kearns and he always said, 'I can absolutely handle and empathize the folks that are a little skeptical, but not the cynics.' And I think what's cool about people in this room and around the world, they like ServiceNow a lot and they're rooting for ServiceNow. So, you might have a certain skepticism. Let me take that away from you real quick so we can get back on 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 and we brought in the Now Assist across the enterprise but now we took you someplace very very special and I empathize with you because we waited 9 months for Moveworks to close the regulatory process and then on the back of that Viz.ai came in okay and Armis came in like within 3 days so it's probably like, hey, what are they doing? Are they buying growth? What are they doing? No, we weren't. We were buying a ticket to a bright future. And so now you have an AI control tower for business reinvention where you have your agent front door and you have your identity management. Now, this is key. Does anybody here actually think that the working population of corporations around the world is going up? Well, it's not. It's flat. And the birth rates around the world are actually going down. And the good news is at that moment in time, here comes the agents and here comes the robots to make the lives of people better and to increase the productivity of every company around the world. 2.2 billion agents in the next few years. Robots. And we own the identity of not just our agents but the agents that come from other companies in the flow of work. They'll come through our control tower. And then ultimately we will coalesce all the clouds, all the language models, all the data and we'll do it all in a highly secure IT and OT environment. You never heard that before because there's only one company in the world that's built to do it and it's ServiceNow. 600 billion. TAM, we're going for it.
Maybe we'll have a robot moderate this conversation next year.
Maybe maybe have a robotic CEO.
No, I don't think so. So, let's leave it here. So, I don't want you to steal Gina's thunder, but you did review this slide, so she knows we're going to show it. It's not about rhetoric, it's about results. We've heard that before. What's the promise we're making about the results trajectory of the company over the next?
Okay, very clear. This is a sub-revenue 30 billion plus commit between now and 2030. I want to be very clear. This is not the Bill ambitious number. They wouldn't let me put that up there. You know what I mean? That is the one that you can say okay like the whole management team bottoms up across the board believes in that number. 60 plus is the revenue growth plus the free cash flow margin is going to be a 60 plus number. Right now it's 56. It's going to 60 plus. And we're not taking our foot off the accelerator. We're going to grow the top line and you'll see the acceleration of that and we're going to expand the margin profile of the company and we're going to take down stock-based compensation down around the 10% mark in 2029. So you'll be dealing with a 50 plus GAAP company. GAAP, G-A-A-P. So I know what you want and we're going to give it to you. So bottom line, high level, we're going to double the company. We're going to be masterful on our execution across the world globally and through industry. The verticals are really good. We're actually also going to take the platform down market a bit because we have 90% of the Fortune 500 now. It's not a marketing slogan. I actually have people go through the math. Nine out of 10 Fortune 500 now. Fact. And so, we know that we're going to expand with the Global 2000, but we're also going to 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. And so, we're going to run a very efficient ship. And when I started telling these stories to you a while ago, we were climbing a mountain and it was tough. And we are climbing and climbing and climbing. And there's 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. And ultimately, you're going to see a team today. When I tell the board about this team, I tell them this is the absolute best team I've ever run with in my career. And I mean it. And so I don't have a second story. We got the best team in the business. So win as a team is the way we roll. And so what you're going to see today is unbelievable leadership. Amit our great engineering leader obviously the COO of the company as well has done some extraordinary things in architecting the product portfolio it's really amazing it's so exciting and Paul Fipps on the go-to-market side has engineered the AI control tower for business reinvention across the world and Gina obviously is going to give you what she always does right from the heart the truth and the belief in this franchise but I also want to call out, you know, the folks that report to these individuals and how great they are. Too many in name, but I do want to call out one in particular. When we acquired Moveworks, we also acquired a great management team. And instead of having a great leader like Bhavin report into somebody at ServiceNow, we actually said, 'We think you should be the boss and let the people at ServiceNow report into you.' So we're really making account giving big businesses to people we believe in and trust. So 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 and they believe in this mission as much as we do. So we now have a great team. We're going to win as a team and you're going to see the best days of ServiceNow are now and in the future.
He'll be back for questions. That's a great way to start.
Thank you very much everybody. Thank you. Thank you very much. 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 going to execute against it. It's great to be back for my second Financial Analyst Day since joining ServiceNow. During SAD last year, I shared our vision and plans for 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. Employee Works 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 omni-channel intake, sales order management and CPQ which will help our CRM business cross 2 billion in ACV and our security and risk business crossed $1 billion and expanded further into our AI identity governance and OT cyber security helping to differentiate our core. Our AI products are nearing $1 billion ACV and the momentum only continues and the AI control tower became a market-defining solution. So 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 all industries. And we've taken also an AI native approach to transforming every corner of our own business as well as building AI into every product. And we delivered a new conversational experience across our workflows, launched monthly releases and automatic upgrades as well as powered more innovation by using AI tools and AI native approaches across all of our portfolio. But let's talk about today and fast forward to what we're going to be delivering as we go forward. Every analyst including all of you probably are covering as you cover software is asking if AI is going to replace the platform or AI needs the platform. The 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. The own AI agent was the failure mode. An AI agent at Postgres hit a credential error and deleted the entire production database and all the backups of customer data in just 9 seconds. And the industry has been trying to put band-aids on all these issues using agents and spawning more and more agents. But none of these standalone AI products can solve the core fundamental issues because none of them can govern the system as a whole. And there's an important distinction here to think through. Autonomous work is not just powered by one new innovation. It's really two essential capabilities coming together, working together to drive real outcomes. This probabilistic AI which is what generates the answers and deterministic execution is what runs the enterprises and every enterprise needs both of those. And that is what it means for AI to be an operating system with enterprise context. And thanks to our CMDB and context engine, we know why a particular decision was made. We can encode the real-time relationship between every asset, person, service, dependency, and policy. I know many will say nowadays 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. But 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 salary. But that misses the point. You have to add model selection and the constant updates which happens with the 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 already connected to which run the business itself. And 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 systems without a harness will create infinite risk surface. One compliance error from a homegrown agent could cost millions for a business. And we've been also looking at research around this. Customers building their own LLM-based solution will typically spend five to 10 times more than using ServiceNow depending on the complexity of the business they have and the different systems they have to connect and all of the solution will take longer to build and it's usually less secure. So the ROI and the TCO is not even proven if you want to go down this path. And the ultimate trap of build versus buy is this. You know, building an app is definitely not the same as running a business. And that is why we made it possible for customers to both build and buy full solutions on our governed platform. At the core of the 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 and this gives customers visibility, context, intelligence, automated actions, governance all on one platform. Gartner projects that 40% of agentic AI projects will fail by 2027. But not because AI is incapable, just because AI isn't governed. And that's what we are here to fix. Our platform is about more than helping customers not just fail. It's about powering the success and growth. And we have proven that we can do this at scale. Today, ServiceNow runs 100 billion workflows and 7 trillion transactions annually, growing at 25% year-over-year. And that scale creates a flywheel. Every action on our platform deepens our operational context and enriches our CMDB and context that makes our AI better. Our new UI has higher repeat usage and we will only grow more with the launch of Assist. Our unified AI experience you will hear more about later. So basically actions which creates outcomes, outcomes which create new actions. The flywheel continues to accelerate and every step makes our ITAM, ITM, SPM, ITSM and all of our core products more valuable than ever which is triggering more customer conversations and continue to expand our IT business as well and humans usually needs nowadays agents and agents definitely need guardrails. You get both through the platform we have delivered and this is a durable advantage we built first in IT and now expanding even further into many new domains and a customer is also feeling this compounding value in real time. I 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. Trident Care achieved 96% scheduling automation with our AI-powered CRM, improving care for millions of patients while also increasing revenue. So the question becomes, what does it take to enable and govern autonomous work for the enterprise? Today my team will show you how it's done through the four pillars of sense, act, decide, and secure. But first let's start with the AI platform that everything is built upon. John, over to you.
Thank you. 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 and we have re-imagined and reinvented our platform and you're going to see a lot today. You're going to see autonomous workers. You're going to see the power of AI control tower. You're going to see new AI native experiences, conversational experiences. And we have reinvented our entire platform for the agent era. Now to be clear, we're still a system of action. We are the workflow platform and things are evolving. We're seeing patterns in the market that I'm going to address as we go through this presentation. But what I want to talk about first is that we have reimagined the platform from the bottom up. And what that means is that we've recreated the AI stack and it allows us to do things like innovate at the speed of AI which is very very important because the expectations of our customers is changing rapidly. They want to see these innovations come very very quickly. They want new experiences very very quickly. Now the things that you're going to see around multimodality and voice and new conversational experiences, those are all part of the native platform. They're not bolted on. And the first area that I want to dive into that Amit talked about is this idea of context. And context is extremely important to any agentic system. It drives the AI. But first, I want to play a video of Boris who is the creator of Claude Code at Anthropic. And Boris is going to talk about how important context is to an AI system and why the ServiceNow platform is uniquely positioned for context. Could we roll the video, please?
Thank you Boris for joining me. Really excited to have a chance to have a conversation about all the work you've been doing. How we've been also partnering between ServiceNow and Anthropic.
Yeah. I'm so excited to be here. There's so much to talk about and the world for enterprise customers is very fragmented.
That's right. I mean average customer will have probably 300 different systems underneath and in different versions.
Yeah. I mean the business is just so complicated. There's all these different tools. There's all this different process and data and so you need something like ServiceNow to organize and bring scan to all but all the dependencies all the data is so complicated and you make it so simple customers can just work with one platform and then they don't have to consider all this complexity you solve it for them.
Today we run close to what is it 100 billion workflows on our system and around 7 trillion transactions so we are collecting so much information on why somebody's doing something what decisions were made so when I use a large language model and tools like yours and then combine that with the context engine and then the understanding of the whole enterprise workflow, it changes the outcome very well, very much for the enterprises. If I'm doing something and I don't have the context, I'm not going to do a great job.
If I'm just told go do the thing, but if you don't have enough context and 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 and ServiceNow is a really great way to bring in that context that it needs to do the job.
So as Boris said context is the driver of AI and we have a context engine inside the AI platform at ServiceNow. But what is context? Well, context is not data. Context is not a decision. It's not an outcome. Context is actually the history behind an outcome. It is the decisions and what made up those decisions that are important to context and 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. And so the context engine gets better over time and in turn the entire agentic system at ServiceNow gets better. Amit talked a little bit about this earlier. And someone 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. Well, that's not very smart. It's not very efficient and it is not the way that we want to drive our platform forward. Now, you're going to see a lot on agentic AI, and generative AI today because it's extremely powerful. It's extremely flexible. So, things like our AI agents and the things that you're going to see today are based off of that. However, all the context in the world doesn't fix the predictability of AI. Meaning the power comes from the idea that you could ask the same question twice and get 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 and they are what drive the enterprise today. And the trick here is not to offer one or the other. You need to offer both. And what we're going to do in the reimagined platform is to harmonize those 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 up front, things are changing around us very, very rapidly and our customers want broad access to our system and we're seeing others in the industry exposing their system of records through APIs and through MCP to allow for reading and writing of their system essentially becoming a database, a system of records. And while that's very exciting, that's not what we're going to do. We're going to expose the system of action and we're going to do that by opening up this layer to the clouds and the OpenAIs and the Geminis of the world. What is the system of action? Well, that's where the true power of our platform comes from. That is our workflows, our flows, our processes, our skills, the context engine, playbooks, all of those things. That is the system of action that is ServiceNow. But we're not just going to expose that. We're going to monetize it. And we're going to do that by introducing something called the action fabric. And there are a few things you should take away from this. It is any protocol, any tool. So you can use your tool of choice and you can talk to the action fabric headlessly and kick off the autonomous work that is so powerful within our platform and it's governed. So all of the business rules and everything that's happening you can call into it and those actions are taken and workflows are triggered. But we're not going to stop there because we are now going to monetize that process. And as Amit said, we have a monetization model today for Generative AI. It's called Now Assist. It's a billion dollar business. It'll be a billion half dollars by the end of the year. And what we're going to do is plug directly into that system. So now that any time that an outside human being, machine, AI agent, third party agent calls into the action fabric, we're going to burn Assist. The flywheel is going to spin faster. And this is a tremendous TAM opportunity for our company because now anything and anyone and anybody can call into the action fabric and take advantage of what ServiceNow is known for, automation. And what's going to end up happening is we're going to have this universal action layer where all of these systems are calling directly into our action fabric and spinning our flywheel even faster than it does today. Now there are other things that are going on in the market and that we want to address with our platform. One of them is autonomous agents. These long running agents and they work on your desktop and they help you do things. They write code. They will handle your schedule. They will monitor your email. They're great. They're assistants. And we wanted to build one of those. And we did. And the first stop was talking to CISOs and security and they said, 'Absolutely not. You're not going to be able to deploy those things. They're unsafe. They're not governable. We don't want them.' So, we started Project Arc with Nvidia. Our partners and friends from Nvidia, we got together and we said, 'Well, how do we fix this?' And what we ended up doing is using one of their technologies to secure these agents in essentially a sandbox mode. What that did was give us the ability to tell these agents what they can do, what they can't do. Please don't delete my entire inbox. What systems they have access to and it gave us the control to allow a CISO to say yes. But we are the automation company. So we wanted to expose our agent and many many others. I think today we just signed a partnership with Anthropic for Claude Code to talk directly to the action fabric. And what that does is it allows your assistant now not just to manage your calendar or plan a trip for you, but it can also ask for time off and kick off these headless workflows that are going on in the background. So, I can do things like take time off and change my HR or my benefits and request a new laptop all from these agents that are running on the desktop. Now, there's going to be a lot of these agents. You might have four or five running on a desktop. You might have tens of thousands of desktops across your enterprise. So, the last thing we wanted to do with Project Arc was plug it all back into the AI control tower. And 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. Now, the last piece of this puzzle for the action fabric is build anywhere. And again, our customers, the ecosystem, our partners are saying, 'Look, we want to use the tool of our choice, but we love your platform. We want to build net new applications. We want to build primitives that run in your action fabric.' And in turn, what that does is it makes us a system of action. It gives us a tremendous consumption opportunity across the board and it drives the flywheel in ways that we couldn't have imagined before action fabric. And so what we want to do today is show you how easy it is to use your tool of choice and build net new applications, net new workflows on our platform. And to do that, Jithan's going to show us an awesome demo.
Thank you, John. Super excited to be here. Some of the incredible innovations John just spoke about, I really want to show you how it all comes together on a demo. AI and vibe coding has fundamentally changed how applications are built and how agents gets imagined into existence as autonomous specialists in an app. I'm a developer. My HR team, that's the job I'm going to do today in front of you. My HR team asked me for a brand new addition to our employee benefits app. So typically I have few tool choices. I could be using Cursor, Codex, Gemini. Today I'm in the mood for Claude Code. Here I am getting on to the next screen which is the Claude Code. And what I'm about to do is in the cloud I'm going to ask to help me build a pet insurance module for the employee benefits app. And the first thing I would do is in simple natural language, I'm going to say add pet insurance enrollment to the extra care benefits app which I just spoke about. Tap. And what you're seeing right now is Claude is starting to get to work. You are seeing something called Fluent. It's our ServiceNow's AI-ready platform language. We have open-sourced our build agent skills and platform knowledge directly to agents like Claude Code via our SDK. That means the whole thing, the UI, metadata, access controls, business rules, all of it. And the last screen you saw was the app is almost built by the Claude. Now I'm going to actually jump in and show you how it actually lives in our own native IDE which is actually ServiceNow Studio. Now let's jump over right here. You can see on the top of the list the benefits app. Let's go ahead and click into it. And the moment any application which lands on our platform like Amit was talking about and John was talking about it elevates the app to an AI native application. Automatically when an application is built on our platform, our platform will actually recommend a set of agents who will live and breathe every day with the humans in the loop day-to-day 24/7. You can see our application has already recommended that we create an enrollment agent, right? Let's take a look at the agent. And in that, you can see the enrollment agent is all set up. It can qualify, understand and manages the end-to-end enrollment with minimal human intervention. Insights like this are possible only with the CMDB, the context engine, and the action graph which John spoke about earlier. And you can see I'm already on my way to actually create an agent. And every assist which is delivered in the app is a runtime assist for the user. Imagine every application having three to four agents living in it. Every interaction is a monetization moment every day 24/7. And now we'll jump into the actual building of the agent. Let's go ahead and build it. You can see with the click now the Claude Code with the ServiceNow Studio and the SDK skills the agent is already getting built and once the build complete it's ready. It has its own roles, instructions, autonomy. It can actually sense, decide and act every day within the application, but we are not shipping it just blind. Let's make sure we run the right automated testing and scanning so that it's ready for production. And overall, now it's actually running through the scan process. It's going to come up with a readiness score. This is how our
The platform is built, it has run through its own evals, and come up with a score. Great score, 90%. Now I am ready to actually submit for deployment. What you're seeing is App Engine Management Center. This is the product or the platform where every application goes through to make sure it has the right security controls and the access controls, everything built in. By default, any app or AI asset which runs through it automatically gets registered as a part of the AI control. This is where we do one last review of the application. Everything looks good, and 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 deploy the application right here. Now, this is the actual app which is live and ready for you to prompt.
I'm going to switch my hat a little bit. I'm an employee and I would like to understand what are my benefits from my pet insurance point of view. Right here, I'm going to prompt and type in. You can see it's pulling in a massive amount of user awareness and context at the back end. It looks very simple, but in order to qualify and make sure this agent delivers the right accurate information, it's actually bringing in CMDB context, engine, every one of those process mining capabilities. You can think of the last point, I have a 2-year-old Frankie which I want to make sure I can actually get the insurance set up. Right there you go, it's now going towards making sure I have my Frankie getting access to the coverage what it needs to be. Beautiful, isn't it? So what you just saw is the actual agent is live in action. Every app built on our platform will get an autonomous agent embedded within. And that's how we are fundamentally changing the way AI apps and agents are built within our platform. I'll say it one more time. It's like John shared earlier. It's about you can build in anywhere, any tools, and any choice what 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. So that's the end of my demo and I would like to now bring in Gor who's going to talk about the sense part of the overall value problem. Thank you.
Thank you, John and Amit. So, you just saw why the ServiceNow AI platform is the system of action for autonomous work. And now I'm going to show you and talk to you about how we bring the sense and decide pillars to life on that same platform. We do that by helping our customers achieve four things. First, connect all their data wherever it lives. The second, control it with enterprise-grade governance. Third, contextualize it with enterprise-wide intelligence. And then converge that contextualized intelligence right into the flow of work. And our customers are responding strongly to the strategy, making it one of the fastest growing product businesses in ServiceNow history. Workflow Data Fabric now has over 4,000 customers driving more than 3 billion monthly data transactions and we've recently added consumption-based pricing and more than 700 customers have already consumed more than half a billion credits. And then there's RaptorDB where our new premium pro SKU has seen explosive growth. We've gone from zero to 100 million ACV in five quarters flat with an ASP well north of half a million dollars.
Okay. So let me step you through the four C's of our strategy. First, connect. Workflow Data Fabric is fundamentally architected for the agentic era. See, most data fabrics are primarily built for decision support, for insights. Workflow Data Fabric on the other hand is built for insight and action with read and write capabilities and it supports all types of data wherever it lives. And that's another crucial distinction. You see, we embrace the system of record and the data platform choices our customers have already made. You can, but you don't have to move the data into ServiceNow. I know there are others who are playing for data gravity, but for us, what really matters is knowledge gravity. And Virtual 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.
But look, connected data is not the same as AI-ready data. Today, teams spend more time finding, cleaning, and preparing data rather than using it. It's slowing down AI adoption. It's limiting AI accuracy. And it's leading to some pretty frustrated data analysts. And I can't imagine the AI agents are too thrilled about it either. So to ensure AI decision accuracy, you need data that is fully visible and governed throughout its entire lifecycle. You need tight control of your data. And this truly needs to be non-negotiable. So that's why we're introducing the ServiceNow Data Catalog, which delivers native metadata management, data lineage, privacy, and ultimately trust. So humans and AI agents alike can now instantly discover and use curated data products, safe in the knowledge that these data products have their enterprise's seal of approval.
That's great, but getting your data AI-ready isn't a one-time activity. Enterprises have to keep it AI-ready. And so to ensure the ongoing AI readiness of data, we'll be taking our data control capabilities even further with a complete AI-driven autonomous data governance solution. And we'll be delivering data quality, observability, enrichment, and policy management all unified inside the ServiceNow AI platform. We'll deliver this through a combination of ServiceNow products and partner products in our massive Workflow Data Network which now spans data quality, observability, MDM, security and integration partner products because just like we do with the data links, we want to embrace and extend what our customers already have and that's how our strategy is fundamentally different. We're going to take that a step further by introducing Partner Passport so customers can procure and consume select partner products using ServiceNow consumption credits.
On to contextualize. With more than 100 billion workflows a year running on our platform, no one is better able to understand our customers' business context than us. And as John mentioned, we bottle that magic up into something we're calling the Context Engine. So the platform of platforms as Bill referred to now has a living graph of graphs. A graph that brings together our knowledge, action, access, asset, and decision graphs all anchored on our powerful CMDB. Built right into this Context Engine is our market-leading analytical semantic layer and we've used that as a foundation for a new product that we're announcing called Autonomous Data Analytics. And so think conversational analytics to guide the what happened, what will happen, what should I do type of decisions that need to be made by both humans and AI and it's fully autonomous. So think embedded, think always-on. AI analysts working tirelessly on your behalf, surfacing insights, interpreting enterprise-wide data in context, spotting outliers and issues, providing recommendations, even taking action.
And soon we'll be packaging this capability into Autonomous Data Apps, easy button solutions to bring the power of this insight-to-action capability right into our technology, customer, and employee workflow areas. And so as an example of such a data app, customers will be able to combine product usage, support, contract, engagement data from ServiceNow and let's say Snowflake or Databricks, 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. So the first is Live Archive which is a cost-effective archival solution for ServiceNow that then also allows you to seamlessly query across hot and cold data and second, Live Connect which allows you to point your existing BI tool against 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. So there you have it. Core 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, Gor. I see a few familiar faces. I know some of us have connected over the years. I'm Bhavin and I'm responsible for ServiceNow's employee experience products and AI front door. I want to cover the third or the fourth part of this chart here that you see where we're talking about this employee experience and acting upon different systems on behalf of employees. And essentially what we're doing is building on the ServiceNow data fabric to drive this employee experience and drive values on top of that for customers. When Moveworks was acquired, Bill 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.
Now, we've been busy. My kids call it integration maxing at home, but we've rolled out Moveworks to every ServiceNow employee. We've launched the front door called ServiceNow Employee Works and we've integrated that front door into our new commercial model in just 4 months. So, lots of activity, lots of work going on there. And we're moving fast because there's actually a gap in the market. Customers are validating this in every conversation I'm having and in every deal now that we're winning. In the past 2 and a half months alone, we've seen a 10x pipeline build for our go-to-market efforts. That means that demand is here and we're now so ready to capture it given the two companies and what we both afford and can produce together. And I'd say this, if there was an M&A award, I think Bill and I deserve it because both of our teams are on fire right now.
The AI front door is also moving fast and the frontier is moving fast as well, not just with the models, but with enterprises too. I'm sure you guys know this talking to customers, talking to different organizations. And to build an effective experience across all employees, you need the ability to execute. And this is really critical and hard to do. We started off with everyone being sort of captivated by AI smarts and generative capabilities. But then we transitioned in 2024 into what I'd call co-pilot chaos. And this is when every sort of functional AI was being introduced, every platform, every offering with no real large-scale enterprise impact. We saw these various studies come out. People were wondering where's the real value going to head? And so today, what business leaders are looking for is what we characterize as enterprise AI. That means it works across the business, end to end, not just middle to middle, east to west, not just north to south. So I think the differentiation here is that ServiceNow and us together have been able to execute so fast because we can bring these capabilities end to end, east to west, all to these enterprise customers.
Now the thing to understand is that for an enterprise customer, a user isn't just a user, they're actually an employee and this is where our differentiation goes even deeper. Employees have to navigate a complex journey, spanning peers, managers, executives, countless systems, and unique business contexts. This is what it takes when you're running a large organization. And so these employees have a need that expands beyond just the end user that a lot of the sort of offerings think of people as. Now, we have a deep understanding of the workflows, the context, and the action. And we're the only platform really that meets these demands of the enterprise with the employees in mind to get work done. And this is really resonating in the 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.
And in order to execute this end to end obviously requires another formula. The fully probabilistic yolo model of personal AI isn't sufficient. We've tried that, we've talked about it earlier and there's parts of the business that are not open for negotiation, they demand reliability. 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. And so not everything will be identified immediately and frankly some processes according to our customers will never be identified because they always want to make sure there's a human in the loop. And so 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. Personal AI gives you outputs but Employee Works and this new product that we've now rolled out really delivers outcomes end to end and ServiceNow harnesses this and executes these plans in a way that gets work done for a company.
Let me give you an example here. Sending you to this conference becomes less labor intensive for your company. And that is what real enterprise ROI is. Businesses want AI that can deliver outcomes similar to human labor. And to do that, you have to go end to end. You have to go across all of these systems very effectively. And so in a world where everyone's token maxing and that's seen as a flex, enterprise AI is actually maximizing token efficiency. And that's on everyone's mind. And so what customers are really seeking is how can companies do this and this is ultimately how they will pocket the benefits.
Now this brings us to a unified experience. This is something I spend every day working on, thinking about, the whole team is rallied around this and with Employee Works we're able to deliver a seamless experience across the patchwork enterprise. We deeply understand the company, its employees. We bring together different platforms and workflows and that's what transformation for a real business comes down to. That's how we deliver real outcomes. But there's more. So if you 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. And this actually lowers the cost of ownership. The old model of expensive implementation, specialized developers, long timelines is gone. The new model is vibe coding, customized experience in minutes. And so this is how AI scales. Not by adding more tools, but by empowering more people to build on a governed platform.
Let me give you an example here. CVS Health, Fortune 10 company, does 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. And so this is just one example. I'll use another one. Honeywell, they're an industrial giant. 80% of the inbound requests and work, the deflection, if you will, the service test is being handled by the AI. The human mediated workflows are actually happening 60x faster because the AI is doing the intermediation and they're seeing about equivalent reduction in labor costs.
Now, in closing, 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 on which we're going to be building the future of employee work. So I think it's going to be a really great year and we have a lot of other exciting product announcements and releases coming up for the rest of this year. And so real quick I just want to give you a quick preview into what you'll hear more tomorrow with regards to ServiceNow Auto.
So you'll see this and you'll learn more about it at the keynote that we'll have in the morning, but ServiceNow Auto is really the combined intelligence of Moveworks and Analytics 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, but also I want you to understand that we'll be handing this off to Pat to tell you more about autonomous IT and from there we can show you some more. All right. Well, thank you very much.
Good afternoon, folks. And thank you for coming to this. I know we're hoping to educate, share some information here, but hopefully I can add to that. Bhavin mentioned, I'm going to talk a bit about autonomous IT. I'm going to take a bit of a general statement up at the beginning. We're going to talk a little about just workforce automation and then I'm going to dive more specifically in how we're applying that to the world of IT because that's kind of relevant to us because it's still the biggest part of our TAM right now. 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. And 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. And 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. And I'll start by saying that we absolutely believe there's a value in what I'll call horizontal AI. Bhavin just talked to you a lot about our Moveworks Employee Works product. We bought a company here because we think it's a real value for our customers and for us. But fundamentally horizontal AI is about interacting with you as a requester of services. It's your unified place to ask for things, to get information, to kick things off, to check on statuses. It makes you more productive as an employee. Behind the scenes though you still have a variety of what I'll call vertical processes. If the thing you actually requested is a pallet of steel for a factory in Milwaukee, there is still a purchasing process which goes through before that pallet of steel actually shows up in Milwaukee. Those vertical processes are where the actual black letter savings are for AI because that's where people have a job. I'm a purchasing specialist. I'm a sorcerer. 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. So, you're not reporting that your email is broken. They actually fixed your email for you. You're not asking for a pallet of steel. You may ask for it, but 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 FTEs 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. But we've done one of your many business processes at a big investment of FTEs.
We fundamentally believe there's a better way to do this. We want to get out of the game of one-off bespoke process automation and into the game of giving our customers autonomous workers. This is a new paradigm. You saw Bill mention it. You're going to see it all over the stages here. We are rolling out autonomous workers first inside our IT departments but also inside other workflows and ultimately beyond ServiceNow. The idea behind an autonomous worker is very straightforward. It's just like a human being. You assign it work. It approves things. It produces the same audit rules. It follows the same logs. It lives in your user record. You don't have to do business process re-engineering to do one of these autonomous workers. It gets us out of the game of saving five minutes for a customer here and 10 minutes here and 20 minutes here and into the game of going to a customer and saying, 'Hey, you've got 100 people doing that job. I can help you do it with 50 or maybe even 80, whatever the number is. It's less than 100. Let's talk.' That's the real value behind the automation 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 see about 20 of these in the hopper, but four of these I want to 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. And 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 want to get live with this very quickly. We've actually had to turn people away because the over-subscription is so high here. But 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 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. And this is important for our customers because it will help them get value and it will help them get more efficient and it will help them frankly save 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. And fundamentally it is important to all of us here because this is the productivity that AI is bringing beyond just the technology industry and into society as a whole. This is the promise of AI. We will help make the overall economy more productive. We're going to start with it, but it's not the end of it. With that said, enough of me talking about this. Let's bring Amy up. She can show it to you.
All right. Great. I'm already here. Thank you, Pat. That's fantastic. Okay. I'm incredibly excited to be here and share the product with you and share how what Bhavin and Pat talked about really come to life and we're talking about both individual employee productivity which then transitions to the productivity of a team which then goes into the productivity of entire workforce when you look at autonomous workers. So with that I'll show you how this all works.
Okay. So, I'm going to start off here with Employee Works. This is our new unified front door to work. And 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 going to get covered for that in my new state and if my current plan will do that for me. And I don't know who to ask. 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. So today I'm going to go in and ask Otto what to do here. So I'm going to type in this prompt and see if I get coverage in Washington. So Otto immediately gets to work. It's checking my plan, verifying that this life event qualifies me to change my benefits, and it's summarizing the answer for me. But not just that, Otto tells me what I need to do to kick off the process. And apparently, I have not changed my address yet. So, I'm going to go ahead and put in my new address and enter that in.
And now, Otto gets to work, updating my address across various systems at work, like Workday, which just saved me a huge headache. Didn't have to go in there to change my address. And it's also telling me everything I need to know to change my benefits based on the policy, based on the context of who I am, what's available to me. So, I can assess it's a minimal change in coverage. This looks like it makes a ton of sense for me. So, I'll go ahead and enroll in this plan. So, Otto, go ahead and finish the process for me. Everything's done. So, in about a minute, I went from a complex benefits question to a completed solution, which is amazing. So, it's not just about me, though. I'm also thinking about my team at work and what they need. So, I'm going to start a new conversation, and I want to see if there's anything I need to do to unblock my team, if there's anything kind of pending my approval. So Otto 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. So I'm going to dig into that and ask a little bit more before I approve this new computer. I want to understand what's going on here. So Otto 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.
Now, from there, there's another pending conversation, something that I've been asking about recently that has an update that I can jump back into. In this case, I have a couple of my engineers that provide VIP exec coverage and they have a shift that I need to give them specific access for. Now one of these team members, Jordan, I have to go in and actually review the access. But before I do that, I want to make sure that I can also revoke that access when their shift is over. So I'm going to ask a couple questions here. When does the shift start? When will this access be revoked? Can we do that automatically? And Otto goes to work one more time. And so the first support engineer is provisioned automatically. But the second one Otto can go through and provision and also make sure that their access will be revoked at the appropriate time, which is super cool. And so let's go in there. I'll go ahead and approve it. Everything looks great. Okay, so I just did stuff for my own personal productivity as well as the productivity of my team, which is fantastic. And it's a totally new employee experience, which is so exciting. So, we're really excited to have this in our customers' hands and get that available to everyone as we have so much demand for it right now, which is super exciting.
So, next up though, although that was a lot about employee productivity, we also know that sometimes despite everything we do, a team can get swamped. There's too many inbound requests. So, 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. So, unfortunately, even though I did all that great work for my team, there's still some bad news here. Our backlog is up. CSAT is dropping down. And we're still overwhelmed by the volume of work coming in. And like Pat talked about, that's where an L1 IT service desk AI specialist can come in. I'm still learning about what this can do for my team. So, I'll ask Otto what I need to know about it. Otto 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 path so in case there's a really complex issue it can get routed to a human. So, this looks pretty fantastic and based on what it's going to do for the CSAT of my team, there's no question I'm going to activate this AI specialist.
All right. And just like that, it was added to my team. That's incredibly easy for any manager who's feeling swamped or that they don't have enough resources. They can activate these AI agents and add them to their team. No admin, no configuration, no deployment, just a few clicks and it's there. So, we'll fast forward in time and I come back to that same dashboard and I see great, everything's tracking on time now. We've got our AI specialist on the team. Things are progressing well. CSAT's back up. But I also want to audit how this AI specialist is performing. So, I'll go in and ask Otto to give me an analysis on this AI specialist. Goes through, looks at all the past activity, looks at the metrics, CSAT scores for this individual, and it pulls together again an awesome comprehensive briefing for me. I can see that the specialist is handling 52% of all requests, CSAT of 4.6. This is fantastic, but I also want to audit exactly what it's doing in a particular instance. So, I'll click into one, and I can see a full record here of exactly every step that that AI specialist took. So I have no doubts about how it's doing this work, how it's resolving these incidents and the effectiveness it has not only for my team but for those that it's helping.
Great. So everything'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. So very excited to share that with you. Next up, John Ball will be joining us to talk about our innovation in CRM. Thank you.
Good to see everyone again this year. So, I'm going to be covering the fourth key step to unlocking AI transformation, act, and I'm going to do it through autonomous CRM. And so, let's get straight to it and start by recapping just how far and fast we've come in CRM. In 2023, I was up here and telling you that we have become the fastest CRM player ever in the history of the industry to get to a billion dollars in ACV. And now, just three years later, we're going to blow through that, double it, and blow through two billion. So, we've massively expanded our functional footprint to deliver awesome experiences across the entire customer lifecycle 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. And we're doing this at scale, managing over a billion cases per year, over a billion order and work order tasks. And at peak times in CPQ, we're configuring 100 times a second every single second. At peak times, we're recognized as a leader in CRM by analysts like Gartner, Forrester, and IDC. And last, we have industry-specific IP that speeds time to value across multiple verticals.
And this growth and success is driven by our deep understanding of how to solve real challenges in delivering great customer experiences. I'll give you a hint, folks. It's not about tracking interactions in the 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 because without workflows and without the ability to model this declaratively, you're just writing a bunch of custom code and a vibe coded app on top of a shaky foundation doesn't resolve the request. It just makes disappointment happen faster. So whether it's handling a warranty claim, disputing a Visa transaction, or ordering a new telecom service, all of these examples require powerful deterministic workflow at the core.
Now, what AI does change is how customers, sales reps, and customer service reps interact with these systems to get the job done. With conversational AI, you can talk and chat with the system using natural language. So that's cool, but it kind of reminds me of some great Elvis lyrics. A little less conversation, a little more action, please. Because you don't reach out to a contact center or a customer service center to have a conversation. You reach out because you want action taken to resolve your request. And understanding this point is crucial because the vast majority of customer service requests are not how-tos. The web and YouTube solve that. And I'll use a simple example to illustrate my point. 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. Then you need to understand all the specifics. Is it a change of the delivery date or of the quantity or of the actual product being ordered? Conversational AI is great at capturing all those intents, but then workflow is required. If it's a change in quantity, do we have enough inventory? Or if it's a change in the product being ordered, you've got to rerun the CPQ process to check for compatibility and then generate a new quote. That's CRM workflow logic. And that can't be solved with AI alone.
And here's my most fundamental point. You have to get it right every single time. You're certainly not going to run refunds, disputes, orders, or anything else mission-critical in a stochastic process that might hallucinate. In sales CRM, CPQ is a great example where AI can really turbocharge productivity. So, imagine sending a draft quote to a prospect just minutes after a Zoom call with that prospect that is tailored to the specific requirements they described in the Zoom call. That's now possible with AI-powered CPQ as long as you have a headless and speed-of-thought CPQ engine that enforces all the compatibility rules, bundling, discount policies, etc. Luckily, we do.
Now, this is not theoretical. We're live in production with sales, service, and CPQ use cases with amazing customers like you see here, driving better customer experiences and millions of dollars of savings. And several of these customers are presenting here at Knowledge. So you can hear their stories firsthand, not from me, from them. From Nvidia, who reduced time to quote from 5 days to 5 minutes. It's just an amazing stat, 5 days to 5 minutes. Or 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. So, please welcome Chris Schots, CEO and founder of Logic.ai, who now runs all sales CRM for us. Chris, take it away.
Pretty complex generator. It's got dozens of options, dozens of different pricing rules. It's a pretty complex piece of equipment and he wants to change his order. So, what he's going to do is he's going to call into Electric Generators and he's going to interact with one of our CRM voice agents and see if he can get his order changed. So, let's see how Marcus does.
Hello, this is Jennifer from Electric Generators. How may I help you today?
I placed an order a month ago and I think I need to make a change.
Sure. What is the name on the order?
Thank you. I see a few orders. Is it the most recent order placed on March 15th?
I see order 1034 for a 10 kW generator. We are about to start the manufacturing process. What can I help you with on this order?
Okay, so 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 is querying our order management system and finding possible orders that might be for Marcus that he placed a month ago. She then clarifies the correct order and then finds it. And let's see what Marcus wants to change on this.
I'd like to change the order. I'm not home often. I want to add an automatic transfer switch and remove the manual transfer switch. I'd also like to add a maintenance plan so it's always ready.
Okay, sounds good. Let me see what available options we have for that unit. It looks like we have the automated transfer switch and we have additional options for the maintenance plan. For a 10 kilowatt machine, we recommend the gold plan. This plan covers all the recommended preventative maintenance and includes a quarterly check by a technician in person. Shall I go ahead and update your order?
Okay, so what's happening here is the AI agent is interpreting what Marcus is saying in the large language model and then mapping it to options in our configure price quote application. So this is what John was just talking about with this concept of speed of thought and 100% accuracy. So we've got an agent that's querying the option availability in CPQ and then making sure that all of these options fit together real time. In this case, Marcus needs an automated transfer switch and a maintenance plan and the agent's making sure that all that'll work for his order.
Hi Marcus, your order has been modified.
Great. I also just realized that I need to add a 220 volt circuit.
Sure. One minute while I check machine compatibility.
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, so not only do we have the voice agent interacting with CPQ to make sure all the options fit together, the performance is really important. So, we put a lot of work into our solving engine that runs inside of our CPQ app to make sure that to John's point, we can get answers that are speed of thought so that Marcus has a good interaction with the AI agent real time.
Yes, it looks like we do offer a 220 volt circuit for this machine. Let me put you on a brief hold while I finish making the configuration changes.
So now that we know exactly what Marcus wants, the AI agent can take all those options and then rerun the manufacturing bill of material rules because this is an engineered piece of equipment. So the bill of material is dynamic based on the options. She also needs to run all the pricing rules and then perhaps most importantly figure out when they can actually produce it and manufacture it. And she needs to give that information to Marcus before she can close out the order.
Hi, Marcus. Your new order total is $13,526, an increase of $2,000. Delivery will now be May 15th. Would you like me to email you the full proposal now?
So now that we have the product configuration, the bill of material correct, the sales bill of material that goes with this machine, now the agent's taking all that information, putting it into a customer-facing document that she can then send to Marcus real time so we can confirm like billing address, shipping address, payment terms, and things like that.
Great. I just sent you the updated proposal. Let me know when you receive it.
So now Marcus has the email on his phone.
Okay, I received it. It looks good. Can we make the changes?
Sure. One minute while I make the order changes we discussed.
Okay, you're all set, Marcus. You should receive a confirmation once order management is done processing in the next few hours. Is there anything else I can help you with?
