The Blueprint for Agentic Business | ServiceNow Knowledge 2026 Day 2 Keynote

Hello again and welcome back to the main stage. This is Knowledge 2026. Remember day one of Knowledge? Feels like it was only yesterday. That's because it was. So, let's keep the learnings coming.

To lay out the ServiceNow blueprint for agentic business. Please welcome to the stage Amit Zavery.

Thank you, Amit. Thank you. All right. AI has fundamentally changed how we build applications. Let's take vibe coding as an example. It's about using your natural language and your intuition to generate code. But let's go further than just code generation. Let's have a look at how we can build full applications with the right workflows, data, and governed logic. Now I am a developer from CVS and the business have just approached me and they would like to add a new benefit for pet insurance to their benefits application. As a developer, I want to work in the coding tool of my choice. And in this scenario, I'm in Claude Code. So I'm going to quickly enable the plugin for Fluent at ServiceNow and use a very natural language prompt to add pet insurance to the benefits application. Let's get this off and running.

And this right here is Fluent in action. This is ServiceNow's modern coding framework where we have open-sourced our build skills and platform knowledge so that you can vibe code a ServiceNow application in your coding agent of choice. But what you will notice is that this application is built with the right data model from the very beginning. Right components and data model. This application has even been ported into a ServiceNow sandbox environment, isolated away from production and ready for a governed deployment process. So I think on that note, let's jump into ServiceNow Studio. And in the corner, we can see our Extra Care benefits application was just updated and I'm getting a number of AI recommendations for how we can improve this application.

Now step one was to build the application, but step two is to make it autonomous. So the platform is recommending that we build an enrollment AI agent. It would understand an employee's eligibility, manage the end-to-end enrollment process without any human intervention. I think it sounds like a great idea. So let's go ahead and use Build Agent to create this custom AI agent for our application. This again is taking the app from just responding into a mode of acting autonomously just in time. Now our AI agent is ready. It has the right instructions, skills, and roles to act autonomously. However, we aren't done quite yet because we need to test it and make sure that this is actually production ready. So I've run a few automated test scripts and we have a readiness score of around 90%. So a couple of changes which will move into the next release, but I think we're on track to submit the deployment request. So as we review our release notes, as I like to say, let's get this baby up to prod and deploy this application so that our CVS employees can benefit from the pet insurance benefit.

Now, I have a dog at home, so I want to learn more about this pet insurance benefit. Let's see it in action. It looks like the AI agent for eligibility is checking whether I'm eligible. And I am. Fantastic. So my little Chihuahua Frankie, let's get him added to the pet insurance right there. There you have it, everyone. We have changed the game for app development. And at CVS, their apps don't wait. They sense, decide, act, and they are governed on the ServiceNow platform. Amit, on that note, back over to you.

Thank you. So at ServiceNow, we believe that for work to be truly autonomous, you need nothing short of a complete sensory nervous system. And this sensory system must connect all your data wherever it lives, control it with enterprise-grade governance, contextualize it with enterprise-wide intelligence, and then converge that contextualized intelligence right into the flow of work.

So first, Connect. Workflow Data Fabric is fundamentally architected for the agentic era, connecting all your data wherever it lives and allowing humans and AI to treat a vast patchwork of enterprise data as if it were one. See, most data fabrics are primarily built for decision support, for insights. Workflow Data Fabric is built for insight and action, with read and write capabilities and support for all types of data, structured or unstructured, internal or external, static or streaming, still or sparkling.

And that's another crucial distinction. We embrace the system of record and data platform choices you've already made. You can, but you don't have to move the data into ServiceNow. Look, I know there are others who are playing for data gravity, but for us, what really, really matters is knowledge gravity. We even have a conference with that name. 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. With Auto for Workflow Data Fabric, so customers can describe what they want in plain English and let AI build the integration for them.

But look, connected data is not the same as AI-ready data. To ensure AI decision accuracy, you need data that's fully visible and governed throughout its entire lifecycle. You need tight control of data. And this needs to be non-negotiable. And so that's why we're introducing ServiceNow Data Catalog, which delivers native metadata management, data lineage, privacy, and ultimately trust, so that humans and AI agents can now instantly discover and use curated data products, safe in the knowledge that these data products have your enterprise's seal of approval.

That's great. But getting data AI-ready isn't a one-time activity. Enterprises have to keep it AI-ready. And so to ensure ongoing AI readiness of data, we're taking our data control capabilities even further with a complete autonomous data governance capability. And so we'll be delivering data quality, observability, enrichment, policy management, all unified inside the ServiceNow AI platform. And we'll offer this through a combination of ServiceNow and partner products in our massive workflow data network.

On to Contextualize. We've connected the data. We've put it under control. But to really drive trusted decisions and actions, AI agents must have clear line of sight to business context. Public LLMs are blind to this, but ServiceNow isn't. With 100 billion workflows a year running on our platform, no one is better able to understand our customers' business context than us. And so we bottled that magic up into Context Engine. So our platform of platforms, as Bill likes to call it, now has a graph of graphs. And that graph brings together our knowledge, action, access, asset, and decision graphs, all anchored on our powerful CMDB.

And right into that Context Engine is built our market-leading semantic layer that we have used as a foundation for a new solution that we are announcing called Autonomous Data Analytics. This is big. So think conversational and natural language analytics to guide the what happened, what will happen, and what should I do types of decisions that AI and humans have to make every day. And so it's fully autonomous. You know, think an embedded, always-on AI analyst working tirelessly on your behalf, surfacing insights, interpreting enterprise-wide data in context, spotting issues, detecting outliers, providing recommendations, even taking action.

And finally, Converge. Today, enterprises 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 RaptorDB Pro that delivers even greater performance and scalability. And today I'm excited to share two important new capabilities we're adding to RaptorDB Pro. Live Archive, a cost-effective archival solution for ServiceNow that allows you to then seamlessly query across hot and cold data. And Live Connect, which lets you point your existing BI tool against RaptorDB Pro for real-time analytics with no ETL or data movement.

So there you have it, the four C's of our sensory system. And if you'd like to learn more about them, please join me at Solution Stage A at 1 p.m. today. And now to tell us more about the Decide component of the AI control tower, it's over to you, Nshad.

Sense is something my wife says I don't have enough of, but gives AI live access to data across the entire enterprise. But seeing and understanding data are two very different things. The large language models powering AI today are extraordinary at understanding language and reasoning through complex problems. They were trained on the internet. So they're incredibly good at finding patterns in text, code, knowledge, and the fundamental substrate of the internet, cat videos. But what they're missing is the context of how your specific business actually operates, leading to the unfulfilled promise of autonomy.

For example, when a server goes down at a financial services company and my one desire is to have it restored, most AI models will believe when I say that I want it that way. Context Engine will tell me why I want it that way. The heartache when 14 downstream services broke the last time we did it that way. And whether the risk team is going to tell me it ain't nothing but a mistake to do it that way again.

We told you Backstreet is back. That's because context is different from data. It's the history of every decision your business has made. How that decision got made and what happened next. The more decisions, the more history, the more context. So the real differentiator is whether AI has the business context to make decisions that you would trust it to make on your behalf in the middle of a critical workflow.

This is where ServiceNow's 20 years of modeling enterprise operations becomes a compound advantage. We've been mapping the extensive relationships between people and processes and assets and services and policies across every touchpoint, building the enterprise intelligence that AI now gets to operate on. This includes our knowledge graph. It includes the CMDB relationships, the cyber asset graph, and the decision graph.

Context Engine unifies all of that into a single organizational layer. It takes a snapshot of every factor involved in a decision, including what was happening, what action was taken, and what outcome resulted. Once AI has full business context, inference becomes proactive. And because every decision, every action, and every outcome feeds back into the system, it gets smarter over time. That's the difference between a model that can reason generally and a system that reasons about your specific business and improves every time it acts.

Now that we know what Context Engine does, I'm going to turn it over to the one person who can make Context Engine larger than life. Please give it up for Shardi Patel.

Thank you, Nshad. We will see you back on stage tomorrow at Backstreet Boys. All right. Now, we all know that AI is only as smart as the context that it has. And most AI is pretty good at suggesting, but with Context Engine, it goes beyond suggesting to taking action that's both accurate and precise. Let me show you what I mean. I'm going to step into the role of an HR business partner. And someone on my team that I support, Sarah Johnson, wants to make a lateral move in the company. All I'm going to do is type one sentence here into Auto. I need to process a role transfer for Sarah Johnson from finance to business operations effective Monday at 10 a.m. I'm going to step away and let Context Engine handle the rest.

Now the first thing that's going to happen is User Graph is going to pull together a complete picture of Sarah, starting with her role, her tenure, and her location, but also more importantly, systems that she's supposed to access. Now four years in finance for Sarah means four years of accumulated access to things like Workday and ADP payroll and her finance SharePoint. User Graph also knows what she's going to need on the other side. SAP, Teams, and her new business operations SharePoint. So User Graph has basically mapped who Sarah is today and who she needs to be on Monday at 10 a.m. and fed all of that over into Context Engine.

Next, Knowledge Graph is going to surface the right rules and policies to keep this transfer moving forward. It's showing me that access to Sarah's finance systems has to get turned off first because that is company policy. And every removal needs to be documented and logged for SOX compliance. And for every system on Sarah's list, both old and new, Knowledge Graph has surfaced the right playbooks for provisioning and deprovisioning access.

Okay, so we just saw with User Graph that that's showing us the systems that Sarah's supposed to access. But with our Security Graph, this is going to show us the systems that Sarah can actually reach. And as we all know, those two things are not always the same. So Security Graph is mapping every permission path for every connected system that Sarah has. And yikes, her blast radius is pretty significant because she still unintentionally has access to things like executive compensation data and board reporting files. But Context Engine has already mapped all of it for removal, and her access is cleanly removed and fully documented.

All right, so we're on to our fourth and final graph here, which is Decision Graph. And this is where the decades of work running through the ServiceNow platform are really going to pay off. It is taking all of that institutional history and turning it into intelligence that's calibrated to how your business operates. So what do I mean here? Decision Graph has analyzed 23 prior finance to business operations transfers in my organization, and it's picking up on something that most managers wouldn't even think to check. It's showing me here that employees who complete two specific courses, Business Operations Fundamentals and SAP Reporting Essentials, onboard and ramp 50% faster than those who don't. So of course, I'm going to enroll Sarah today.

I want to pause here for a second because this is so powerful. Decision Graph isn't just picking up on prior patterns. It is proactively solving problems for me that I didn't even know I had. All right, let's fast forward. It's Monday at 10:00 a.m. and Sarah is walking into her new role and everything just works. Her old access is gone, her new tools are provisioned, and she is well on her way to becoming a smooth business operator.

Now, I know we just went over quite a few graphs, four in fact, but no single one of these graphs was going to give us enough information to make this transfer happen. It's only when we can reason across all of that simultaneously, your people, your policies, your CMDB, your prior decision-making history. That is when we get intelligence that can actually run your business, and that's Context Engine. And nobody is going to give your AI more business context to work with than ServiceNow. Now to share how our platform becomes even more powerful with our partners, I'd like to welcome back Amit on stage.

Thank you for having me here.

Yeah, first of all, we are so delighted to be partnering with ServiceNow, and I've always felt that the level of synergy that our two platforms bring together to make things frictionless for our customers is unparalleled. I think the market has moved from the stage of asking, you know, whether should we use AI and how do we use AI, to, you know, adopting AI at scale. And scale is generally misconstrued, where people sometimes think it's about volume. It's not about volume. It's about adapting AI not just as a feature in an existing workflow, but to reimagine workflows with AI, you know, as a core architecture to it. And that's where I think our partnership is bringing that, you know, agentic enterprise vision to life. And we are doing that in three ways with you.

Number one is everybody's talking about Google's full-stack AI capability, and we are bringing that full-stack capability to the ServiceNow ecosystem. And that is from our hypercomputer infrastructure to our models to our agent platforms to the agentic data cloud. All of these capabilities are plugged in, and the connection is not just a company-to-company one, you know, pipe connection. We are connecting it at every level of the product stack so that you will be able to seamlessly integrate our models into your workflows. The data platforms talk seamlessly. Not, you know, while there are agent-to-agent protocols, those are thin pipes. But if you really want to run a workflow seamlessly, almost like a first-party experience, and that's the core. When we talked, and, you know, Bill and I talked, we always talk about how do we give a first-party experience to our customers, and that is the intention that we are doing at that level.

The second thing is the unified governance. There's a seamless integration between the Gemini Enterprise agent platform and your AI control tower. What do I mean by it? When somebody creates an agent and registers that agent, that registered agent automatically gets registered in Gemini Enterprise and it shows up in the console of Gemini Enterprise. The second thing is on Gemini Enterprise, somebody can directly create a ServiceNow agent and it's registered on both places so that there is seamless governance that organizations can have, especially the CIO, CTO cohort that's sitting here have agent sprawl as a challenge. And the third is we are breaking the data barrier. So we are directly connecting our agentic data cloud with, you know, your data platform so that there is seamless integration for thick context. Everybody is worried about long context. There is also a concept of thick context. You need to be able to process so much data real time to be able to give the experience. So that's how we are doing that.

Yeah, I think the fundamental challenge that AI is solving is something called context blindness that gets created when either a customer is interacting with an enterprise or the field technician is working on a problem. And the reason for it, and let's take the example whether somebody's calling a support and, you know, they are switching channels from chat to voice to video, or they had a conversation and they are coming back after one hour. You know, there is a lot of context and all the emotion that was discussed during that call or that interface is lost. And the reason for it is all traditional systems record the end state of a transaction. It records what somebody ended up buying or what was the end resolution of a product. It does not track the entire context or the traversal that takes to the end point. AI's ability is to track and record the entire traversal so that you understand the emotion, so that you don't feel like you are repeating. That is what, you know, we are trying to solve through these two innovations that we're creating.

The first one is for customer experience. You know, we have a product called Gemini Enterprise for Customer Experience. What we are now doing is that it is going to create and capture and hold the context across all these channels. But it is going to be directly integrated with, you know, your knowledge graphs so that when somebody is talking, say for example, you are working with your, you know, calling your telco and you're saying while you're traveling abroad from the airport, you're asking for certain changes, more data, certain changes in your plan and so on and so forth. And now you have to board and you cut the call and you log in to your app after the flight takes off. Usually this context is lost, right? And now what we are doing is that entire context is maintained and it is mapped with the knowledge graph on ServiceNow so that the actual information is pulled from ServiceNow and the action is taken on a button click. So that's the integration from a product level. Gemini Enterprise for Customer Experience is going to be directly integrated. The knowledge engine for that is going to be ServiceNow for a lot of aspects for customers where the data is already there in ServiceNow. And we also feel there's a lot more use cases where that information should actually be in ServiceNow. So we are going to push that from that perspective.

On field service, this is a huge problem because it's the perfect example of a lose-lose proposition where an inoperable equipment is the biggest, you know, dissatisfaction for a customer and a truck roll is the most expensive thing for an enterprise. And as a result, first touch resolution is the most important metric that everybody needs to have. But, you know, all the time when a technician goes in, there's almost like a physician kind of a scenario. The most experienced person is able to do the resolution, but anybody other than that results in multiple times, you know, the field technician needs to visit. Now what we are doing is we are integrating Gemini Live reasoning with, you know, audiovisual context with ServiceNow, which means that somebody could, you know, make AI watch the equipment performing. It can hear some sound, you know, a noise getting created before a break, and there could be a smell that is coming from some burning or something that the technician could talk and activate this. What does that do? Step two, that activates the sub-agents in ServiceNow with skills exposed for whether that HVAC system or some other equipment and actually provides the wiring diagrams or cited repair, you know, solutions from the experts so that you could solve the problem. And step number three is a lot of time is wasted on, you know, after-service records management. So all of that could be done by Gemini into ServiceNow directly. And also in certain cases using Nano Banana, you can create new diagrams and manuals and so on and so forth that could be created. So this would create a seamless experience, but it solves a problem that nobody wants to have at the field.

So, we've talked about how the platform senses data and how Context Engine helps AI decide with full business understanding. Now we get to my favorite part. What happens when AI actually does the work? Taking action has always been at the core of ServiceNow. We are the system of action. And now we've extended that DNA to the agentic business with AI that executes. We've gone autonomous. That means every capability I'm about to show you runs inside the policies, the permissions, and the governance your organization already has in place. So you get full visibility, full audit trail, and full control. That's winning all day long.

So let's begin where we started with a solution that still runs the enterprise today. IT service management. This is where most of you began your journey with us and we thank you for that. It's where CMDB was born. It's where workflows first connected with real business services and where our platform earned its reputation as the system of action. AI agents use CMDB and our knowledge base to diagnose issues, determine root cause, and handle IT requests from first touch to resolution autonomously before the business even knows there's an issue. And as a CIO, I am so here for that. I live it every single day. At ServiceNow, 90% of our IT support requests are handled autonomously. That's what it means to go from roadmap to reality. And yes, it is live in production today. Thank you, team. Love. Yes, teamwork.

And to learn more about autonomous IT, head to the spotlight session right after this keynote. The team will be there to take care of you. And what's even more exciting is what works in IT, it works in every other corner of your business.

Thank you, Kelly. Thank you for that amazing setup. My name is Nikki Patel, and I'm joined on stage by the lovely Miss Britney, who's going to help me drive the demo. Now, as the product lead who led the team to build the first autonomous worker, I am thrilled to be here on stage to show you how this is going to transform and elevate your teams. Now, let's go ahead and explore what that means for you and your teams. For the purposes of this demo, I'm an IT service desk manager at CVS Health. Now I start my day by going to my homepage and I can already see the L1 IT service desk AI specialist I onboarded yesterday in action. And for the first time, the tedious work is getting done even before I realize it exists. Common tickets that used to sit there for days, if not hours, all resolved. Though I'm noticing something else. Software access incidents are spiking fast. They're piling in. The queues are piling in, and all the capacity I had left over is getting quickly absorbed. Now, this is usually the moment my team starts to lose control and fall behind. But this time, it doesn't. The AI adviser steps in. The AI adviser proposes we add the software asset management AI specialist to the team, purpose-built to absorb this type of demand. Now, it's continuously scanning my instance, my environment, and tells me exactly where to add the next AI specialist. I didn't have to go looking for capacity. It showed up right when I needed it. Now, as you can see, the AI adviser explains the role of the software asset management AI specialist, Sam, and why it matters to me based on the context and data it's mined. Now, when access requests spike, the bottleneck is familiar. There's no clear view of available licenses. And let's not forget about the arduous process of manual fulfillment. But the software asset management AI specialist removes that friction. Though, what if I don't want it to handle all of it? What if I want to be in control? And in the words of Nshad, what if I want it that way? So, every AI specialist is fully configurable. I define what it can and can't do. And these configurations directly shape its behavior. No prompt rewriting, no instruction tuning required. I craft the experience I want. And the AI specialist adapts. So, every interaction feels natural and consistent with the way my team operates. Now, how does this AI specialist actually do its job and how is this different than a single AI agent? I know we've been throwing a lot of terms at y'all during this entire keynote. So, let's go ahead and take a moment to define what this means. Now, an AI agent executes specific actions. This is where you want a human in the loop, and it requires a lot of creative judgment. You only want AI agents to be doing specific actions. Now, an AI specialist delivers an end-to-end outcome. It's a coordinated team of AI agents, each trained for a specific job, working together as one team member. And they're not acting in silos. They're collectively running within your workflows, your policies, and your rules. Now, imagine having to go build all that yourself. As you can see, anyone can spin up an AI agent these days. But what's hard is making it reliable, governed, and consistent every time it acts on your business. A system of agents coordinating decisions, passing context, enforcing policy, and staying in sync as everything around you and your business keeps evolving and changing. The complexity doesn't stay flat, it compounds. But ServiceNow has already solved for me what's hardest to get right. They've built, trained, tested, and are governing this AI specialist for the job out of the box, designed to operate in my instance on my data and my policies. And just like that, it's onboarded. So now we have two AI specialists working together. We have the L1 IT AI specialist resolving common L1 incidents, and then the software asset management AI specialist resolving software access and licensing. So let's go ahead and see what that looks like in action. The moment the incident arrives, the L1 IT AI specialist starts triaging it, but it quickly realizes this isn't an incident. This is an actual software access request. So what it does is it creates the request and routes it directly to Sam the AI specialist. Now Sam the AI specialist quickly realizes there's no licenses available. Zero capacity in licenses and 52 employees are blocked. And at CVS, every minute blocked is a minute they're not serving their employees, partners, and customers nationwide. So Sam the AI specialist acts by first reclaiming the idle licenses and then procuring the delta, and in parallel sending approvals to finance and procurement and proactively updating all 52 employees. Now the moment approvals are cleared, access is provisioned. Now what would have taken weeks is resolved in a matter of minutes. This is what a coordinated autonomous workforce looks like in production. Work doesn't wait in queues anymore. It gets done even before anyone notices it's there. End to end across your workflows within your guardrails. Now, next up, let's hear from one of our amazing customers who's helping people on a path to better health. Take it away.

Thank you. My wife picked them out.

Well, look, every story starts with a bit of gratitude, and you go along with people. I'm fortunate to work with the best colleagues, best engineers in the world, and that's our CVS Health engineers and colleagues that make this possible. So my gratitude goes to them.

And to extend upon that, you don't do it alone. Our partners, our account team from ServiceNow who are with us every step of the way, our partners from Deloitte, KPMG, RSC Consulting, and Tech Systems are in the audience today supporting us, cheering us on. So our gratitude goes to you as well.

But let's get down to the tactics. The denominator here is discipline and focus. You got to stay on task. The numerator is defining your problem. Making sure you get rid of the tech debt. You're going clean, not fast. And finally, your architecture matters. Let's break that down a little bit, right? We define the problem. Frictionless and disintermediation. We want to make sure that our colleagues know that problems are going to get solved. They're not going to use the tool if the tool doesn't address the issues that they're having. So we settled on what the problem definition was and then we aligned internally. This was not a product selection. It was a change in operating model, the way people worked. And then we aligned ourselves internally. Technology, HR, finance, procurement, store operations. There's no sense in building a front door if people aren't going to walk through it.

And then secondly is let's not go fast, let's go right. Let's remove the tech debt. So we initiated a seven-month project called Greenfield and we essentially rebooted the platform, all of it, and we were able to do that in such a way without disrupting the business but re-enabling it so that when we launch these new capabilities, ServiceNow is ready for it, and we did that together. Then lastly is the architecture enabling the conversation. MoveWorks handles the intent of the conversation, but ServiceNow does all the heavy lifting, the approvals, the workflow, the system of record. Now we're moving from a conversation to an outcome. So what do the results tell us? 220,000 people using the platform, 4.65 million plugins that are being leveraged. We're talking about removing 255,000 calls to our service center operations. Those are real outcomes, as we say in CVS Health. Boom.

So, this is a slightly different conversation. Trust is the only capital that CVS Health really has. We lose that, we stop functioning as a business. You trust us for a reason and we take that very seriously. So, we engage in an AI conversation, we're already going to do things that make sense that we can deliver that value. Everything we look at from an AI perspective is about privacy, legal, security, and governance, or we're not doing it. Full stop.

So we undertake this, we think about, okay, can we put the guardrails in place to ensure that we can protect you, our members, our patients, and our customers. Now as a CISO, the other thing I think about is that AI is breaking every single mental model we have when it comes to security. You're thinking about prompt injections, you're talking about data leakage, you talk about data validation, model validation, and it's a perpetual evolution. One of the reasons that we're partnering with ServiceNow, and we're excited about Control Tower because we're building a warrant system that's not as human dependent. That's too slow. We need to innovate at horizontal scale, especially for a company of our size. This is the optionality you're now giving us, and we can build this together. And lastly, zero trust.

Trust nothing, verify everything. We have to make sure that we're taking the right steps. The last thing, and it's the most important part, engagement of your board, your ELT, and your stakeholders. Make sure you're bringing them along for the ride. The frameworks are there. We all know NIST. We all want to get out the ISO frameworks. Listen, we all want to get up the MITRE ATT&CK stack, right? You're doing the right things, right? Tabletop exercises, penetration tests, that gives you the remediation roadmap. Follow it, right? But understand where your duty of care comes from, right? The MON standard, in re-mark, that's the case. You don't know it? Google it, it's out there, right? Manage your risk, put it together. The tools are there, get them out there.

Thanks so much, Amit. It's such an honor to be here in front of you with two absolute legends in the cybersecurity industry. Please help me in welcoming Yavghani Drav, the co-founder and CEO of Armis,

and Turun Fakur, the co-founder and CEO of Vasa.

John, thank you so much. It's incredible to join forces. Let's go.

Thank you so much, John. It's so great to be here. We are super excited to join the ServiceNow team. I can tell you, having spent most of my life in cybersecurity, there is no better time than ever to be in this market, and AI is the best tailwind for this market. Let's go.