Conference Transcript - NOW 2026-06-03

Arjun Bhatia, Analyst — William Blair

All right. Perfect. Why don't we go ahead and get started. Thank you, everyone, for joining us. Amit, thank you so much for being here.

Amit Zavery, COO

Yeah, of course. Thank you for getting here.

Arjun Bhatia, Analyst — William Blair

There we go. For those that don't know, Amit is the president, COO, and chief product officer at ServiceNow. Before we get started, a couple disclosures. My name is Arjun Bhatia. I'm the research analyst here at William Blair, who covers ServiceNow. I am required to inform you that I personally own shares of ServiceNow and a complete list of disclosures and conflicts can be found at williamblair.com.

Arjun Bhatia, Analyst — William Blair

Okay, let's go ahead and get started.

Arjun Bhatia, Analyst — William Blair

So I am very much looking forward to this discussion. Obviously the big debate right now that everyone's going to be focused on is the AI debate in software. And there's sort of two sides to this camp, right? One is sort of AI is going to disrupt income in software vendors, and the other is it's a huge sort of opportunity. You've sort of, at ServiceNow, launched a lot of new AI capabilities, a lot of innovation on the AI front. So for a generalist investor, which I think there's a lot of them in this room, who doesn't maybe live in this enterprise software world, just explain why workflow orchestration, the position that ServiceNow has, is a tailwind to or benefits from AI rather than is being disrupted by it. and maybe you can touch on your sort of moats in that answer.

Arjun Bhatia, Analyst — William Blair

No, again, I think it's definitely an important question. And I think for people who don't follow enterprise software or you understand how enterprises use software, it's a pretty complex environment, whoever has been following it. There are a lot of different disparate systems which need to be connected, which needs to be implemented and orchestrated on a regular basis, as well as there's a lot of versions management, So backward compatibility, forward-looking compatibility, and very intricate in terms of how those things operate. ServiceNow has been in this business for 20-plus years, really managing that various different environments customers have in the enterprises to make sure the business runs efficiently, predictably, as well as give customers the outcome they expect. So that software which we've been building for many years, the platform we provide, is very integral to every enterprise out there, all the Fortune 500, if not all the Fortune 2000. And a good thing we have done over many years with ServiceNow is that we keep on innovating on the platform. It remains ahead. It keeps adopting latest new technologies to make sure customers don't have to worry about doing it themselves. Because what it allows them to do is as they upgrade our software, they're getting the latest IP, latest capabilities, latest innovation in the same platform without having to learn something new themselves and not have to break anything which exists today. So it keeps the business running intact. And with AI, we're doing the same thing. AI is definitely a great technology. It's very, very helpful for automation as well as be able to do things much more efficiently. So we brought AI into our platform today for a couple of years already, and we've been delivering that as an integral part of our platform. So customers, when they upgrade or when they take our latest software as part of their normal day-to-day jobs, they're getting the value of AI with the idea that it works in the existing environment, so you're not breaking anything. while future-proofing you because you're getting updated versions of capabilities to make sure you're getting automation, you're getting efficiency gains, you're getting good revenue growth as well as bottom line improvements as well. And it is something which everybody's used to. So ripping and replacing those things is what I think the narrative out there is that you can go and build anything because software building has become easier. You know, software could be built before as well. People didn't try to build custom software after package software became much more better and made customers' life better long-term. Similar things are happening with AI now, right? Because you can always build something, but building part is very, very small, right? It's 15 to 20% of your cost. It's the maintenance, the governance, the security, the compliance, which is very, very important for all enterprises. You can't operate something in an environment which you can't predict, you can't be secured, you can't be compliant to all the regulations. So what we bring in our software is the value of AI, but also the value of all those different things which people think that it is not needed when you are thinking of consumer software. In the consumer world, you probably never have to worry about it, but enterprise, you do need to care about it a lot. And that's the work we do, heavy lifting, building, as I said, 15, 20% of your cost, but the maintenance and the upgrade as well as the compatibility, a huge amount of very hardening work required, not sexy, not exciting, but you have to do it. And that's where the moat for us comes in because we know how enterprises run. We know how they operate. All of the systems which need to be connected and how do you get the effectiveness out of it, but at a very good value as well. Value creation is happening in our software as well today. So we brought AI to our platform. We've been innovating. If you look at the technology stack we have today and the platform we have is as modern as it gets, better than pretty much any other vendor out there, while we're preserving the value customers expect from these products without having to rip and replace. I was using an example earlier, like just because you can grow vegetables in your backyard doesn't mean you become a farmer and stop doing your day-to-day job. Same thing's happening here. People can say you can build software, but why would you do it if the software which you are using today can do all these things at the same cost, if not better, and give you the value? And that's happening. If you look at our AI business, it's growing very fast, and it continues to accelerate because we're innovating while preserving the investment customers have made. So there are a lot of our discussions we can have around it, Arjun, but I think the reality is that these enterprises do require something which gives them peace of mind, gives them control, gives them visibility, as well as the innovation associated with that. And we bring all of that together for ServiceNow today in our platform. And that's why we continue to please our customers and keep on growing.

Arjun Bhatia, Analyst — William Blair

And so in that, you're the domain expert in all the different fields that you serve in all the departments that you serve inside the enterprise. And that's where you're able to stay a step ahead, essentially, of what a customer might build themselves because they're not an ESM or an ITSM or a customer service.

Arjun Bhatia, Analyst — William Blair

Yeah, good point you make. I think that, no doubt, I think the other part associated with that, and I was going to address some of the, see the context, one of the things in enterprises is not like everything is documented. As many of you in your businesses today, a lot of the content out there, the document about standard operating procedure is pretty partial. It's very small amount. A lot of things happen outside the documents, right? Exceptions, who approved what, why they approved it. So our business processes we are running inside ServiceNow is collecting. We run today 100 billion workflows on ServiceNow platform for our customers. And 7 trillion transactions every year is growing at 20 plus percent. So we're collecting a lot of context about how a business decision was made, why a business decision was made. And that data, all this related content is bring into something called context engine. Which goes into, on top of all these AI systems, to really enrich it. But also make a decision which is a little more guaranteed than any system can do otherwise. So our outcome, usually 90 to 100% accurate, versus all other systems are 50, 60%, because they're just depending on some documents they read and try to run a workflow. We are doing it with the context we brought in, the data we brought in, and the domain expertise we have. So things like employee onboarding, it's a very good example, a very common thing, right? And when an employee joins a company, you have to go and update maybe 20 different systems in one company. Some other company will have 17 different systems. And then it needs to be also done based on what department you're joining, what systems you require, what exceptions you require, what you need temporarily, what you need full time. So all that stuff has to come together when you want to get an employee onboarded. We can get an employee onboarded on our system in less than a day and make them productive next day. If you look at, if you do this from build mindset or something which you don't have the domain or context, it might take you two weeks. It means your employees are unproductive and half of things will not be right. So that means you go redo it again. So by the time employee gets get going, it's a year, a month or two months wasted for them. And that's kind of the example of things we see, it's just not employee onboarding, resetting a VPN access or giving you access to something temporarily if you're going to China, for example, with the right laptop. So HR-related PTO requests, how do you resolve all this stuff? The context we bring in makes a big difference in the domain we bring in. And those systems are now AI-enabled, completely agentic, and lets customers really get the efficiency of AI, but with the guardrails and the harness around it, which makes it much more realistic and valuable

Arjun Bhatia, Analyst — William Blair

to our customers. And this is sort of the system of record advantage that you have. I mean, you've been serving your customers for decades, and you've sort of built this context over that time. One of the questions that I always get from investors on this is, how much of the data inside ServiceNow is ServiceNow versus, you know, the customers? And when you're bringing this context in, is it your own sort of proprietary elements that you're bringing in, in addition to the customer's records, or how does that work?

Arjun Bhatia, Analyst — William Blair

Yeah, I think the customer data is usually not huge, right? I mean, this information, which is in any kind of system out there, which you can easily get access to. It's a lot of the running, the runtime is where the data gets generated, right? So the metadata we create related to a particular process is very unique every time we run a transaction, every time we run a workflow. And that's the context engine. It's taking a lot of the, it of course applies the customer data and the relevance to that particular information. overlays with the metadata, which is very distributed, by the way. It's not like you've got one table. Thousands of parameters constantly being updated and constantly being collected and related to all the different systems you might have. Plus, we have a system technology called Workflow Data Fabric, which is also connected to all the different data warehouses. Today, no company has everything in one place. Everything is very distributed. And Workflow Data Fabric connects and does federated information collection, overlays that with the metadata we have, which is our IP. and makes decisions real time to get the outcome, right? So the data is ours, the metadata which we create, the context is really dependent on that one. And that's not available to anyone. And that's why, as I said, a lot of time people miss this idea that you can do the work, but how effective the work has been is really differentiated, right? As I said, if I finish a task and you never have to reopen the task, I'm 100% effective, where it's something you'd open every other time, that's really a waste of time, and it's not efficient, and it's costing you a lot more money, not just the cost of software,

Arjun Bhatia, Analyst — William Blair

but the business time yeah okay so you have you have the context you have the domain expertise and you're building the agents i want to talk about one announcement that you made at knowledge your your customer user conference um which was basically opening up the platform to third-party agents right um and we see a lot of agents out in the marketplace and you essentially made the decision that um this context um that we have in our in our in our system of record we will allow customers to power third-party agents with it you know something that they want to build with anthropic or open air or anybody else out there what was um maybe just talk about their sort of the rationale behind that decision because you're obviously building your own agents as well in a vertically integrated stack that you're talking that you're trying to provide to customers

Arjun Bhatia, Analyst — William Blair

no we've been always an open ecosystem provider one of the reasons we've been successful for customers is one that we understand that customers have a very disparate and heterogeneous systems We cannot say everything needs to be like us and everything has to be through service now. You have to work in thousands of other environments. So we've been always thoughtful about that. And openness has always been core principle of the way we build software. Specifically in terms of the idea of that, how do you get access to a system? See, in the traditional days, everybody used UX, right? You log in and you try to do things. Nowadays, there are going to be also agents calling into a system. So it's not only humans, interacting through a user interface, but also agents now asking you to do something. Maybe asking for data, but in our case, really asking us to do something, take action. We are really a system of action. So the way we think about this is that you can also ask when a particular system is, when a request comes in from an employee, it can come from Cloud Co-Work, it can come from Co-Pilot, it could come from our own user experience or an AI agent asking for it. We need to really provide the value to our customers that you can now take that action and guarantee the outcome. So that's the experience layer on top of us through agents or UX we provide. That's the headless, or we call it action fabric. And the idea is not data access. It's really action, taking the task, finishing the task. So if you want to now onboard an employee, an agent can tell us, please onboard this employee for this company, and we would take the full work and get that outcome back to the agent. So we're not giving them the context data. We're giving them the full work. And I'm doing that work. That's why my value to every enterprise grows considerably. On top of that, it opens up more aperture for us, not just to our UX. Now, any other system can also get access to us. So now suddenly my, as you said, AI tailwind for us is definitely because now giving me ability to take the AIP I've built for years, understanding of the context and the data and the integration I've built, now open it up to so many more use cases. So I don't give them the context by itself. I'm not giving them access to like, hey, you can ask me. But the context engine which I built, the data is so difficult for anybody to understand because it's really our unique IP and our secret sauce. They can't use it themselves. It is what we overlay on top of an agent. And we do the work for you and that's what they pay us for. That's how we monetize it, right? So we want to open up the opportunity for us broadly with all the work we've done through UX, our UX, third-party UX, agents, whatever it is, we don't really care. End of the day, our job is to really finish the work for our customers.

Arjun Bhatia, Analyst — William Blair

Yeah, and it seems like it's a TAM expansion sort of motion for you that there's generally more agents getting created and used in the enterprise because in either way, you're sort of benefiting. And from a financial perspective, even if you are powering these other agents that are outside of your platform, that is a monetization.

Arjun Bhatia, Analyst — William Blair

100%. So I think you're right. We are opening it up. But also, the reason Anthropic is working with us is because the cloud co-work, for example, when they want to have somebody to do something for them, they need someone to do the fractioning part of it. So Cloud Cowork, integrating with Action Fabric, gives that full end-to-end. Versus they would go and do something in a particular system, the security issues, compliance issues, tracking issues, as well as the employees should not be going and updating things without permissions. So we put a layer, so something we have launched, and I think you saw it at Knowledge as well, AI Control Tower, we launched it last year, giving customers full visibility and control over every AI system they have. Not just ours, but third party, right? So we understand what Claude is doing, what OpenAI is doing, what Gemini could be doing, what SAP, Juul is doing, Salesforce. And we discover all those AI systems in the company and put it into this central control plane. We were doing that for assets inside the company before anyway, for enterprises, right? Any hardware and software. And now you have full governance layer, cost structure management. It's of how much you're spending, which department you're spending, what models you might be using. but also all the security issues you might be running into. And then we bought this company called Vesa which does this access graph. So if non-human identity is becoming a big issue, and what Vesa does is really manages non-human identities and ensures they're doing nothing wrong in real time. And that goes into AI control tower. We have full visibility across everything now. Customers can see that real time. And then we open up our platform for all these different use cases. We have full ability to now manage the security, the compliance, but also finish the work for them. And that is where the monetization becomes much more bigger. And we believe integration with third-party systems makes sense because we have the visibility and control, but also the actioning part of it.

Arjun Bhatia, Analyst — William Blair

Right. Can we talk about just pricing model real quick? Because I think this is another sort of narrative that's out in the market. Historically, you've had multiple pricing models, but a lot of it's been seat-based. and now there's concerns about whether seats go away or the, you know, the seat growth algorithm changes. But you have all these AI capabilities, including, you know, powering third-party agents. What is the pricing model for that and how do you evolve the business? And, you know, I don't know, do you see that as a challenge or is it?

Arjun Bhatia, Analyst — William Blair

Yeah, no, I think evolving our pricing, I think one thing we have to be always aware of is what are the customers, how they want to use our products. What is the best way to kind of show them value and monetize, right? So we have to be balancing on that one. So we have changed our pricing over the last couple of years. We introduced something called ProPlus and now Assist, a higher-end tier providing AI capabilities in a hybrid pricing structure. It's a combination of seat, but with some idea of something we call now Assist entitlements. So you burn down that Assist. It's an entitlement in terms of the number of volume of Assist you get. And customers are predictably in terms of what the ceiling is, but also flexibility in terms of how they use it and when they use it. So we have evolved that pricing structure for our premier higher-end SKU a couple of years ago. And it's been very, very effective business, as we have said, a billion and a half this year planned, ACV, and growing very fast. And that hybrid thing has, hybrid pricing structure has really resonated with our customers. And it allows us to really add more and more capabilities. So what we've done now going forward is now taken that idea and applied to all our SKUs. We have a whole full set of AI SKUs starting from the base SKU to the higher-end SKU, functionally graded, so it's a different level of AI functionality depending on the SKU, and allowing customers to now use AI analysis fungibly across all the different tiers as well. So that is the structure we're going towards. And if you look at our business now, and we shared earlier that net new business, 50% of our revenue is non-seed-based now. It just shows you that our change in terms of how we've been monetizing is more reflective of how the world needs to be, right? So we're not completely dependent on seats. There will be seats always, but there also needs to be another consumptive element, but with predictability. Not this idea that I have no idea how I'm going to pay this month. That doesn't work. I mean, this idea that go away and spend as much as you want and reward you, that's silly and doesn't make sense long-term. So we are being very careful and thoughtful about how enterprises work and how customers think about it. And this idea of now assist, burn down with some predictability is what we're doing now. So our pricing structure is very straightforward now. It's across all our skews. So our go-to-market becomes very simple. Customers get AI across all our products. They have no idea of non-AI and AI because everything needs to have AI as a base building block. But then you surround it with a lot of deterministic and core capabilities around it and give the customer outcome with some prediction.

Arjun Bhatia, Analyst — William Blair

Feels like it makes it a lot easier for CFOs to implement AI in that way as opposed to, I think there's been some reports of individual employees is burning through tens if not hundreds of millions of credits.

Arjun Bhatia, Analyst — William Blair

I think it's amazing that people get away with that. So it's illogical.

Arjun Bhatia, Analyst — William Blair

And maybe just thinking about the agentic capabilities that you're building on ServiceNow on the platform itself, what do you think is sort of the advantage, or how should investors kind of perceive the advantage of you providing a full sort of vertically integrated stack with agents, data, governance, compliance, all in one SKU, Like, do you think customers and enterprises are more likely to go that route or are they more likely to put together, you know, external agents with your sort of infrastructure?

Arjun Bhatia, Analyst — William Blair

Yeah, I think there's always be interoperability required. I don't think there's ever going to be any enterprises used as one product ever. It will be multiple products and everything will have unique needs associated with that. So I do believe even the orchestration layer, there will be multiple orchestrators. You will have to integrate between different systems. We do this through agent to agent, but also from the business process level. There will be some unique build you might do. If you are a manufacturer, you build your own supply chain, which is more your IP. Sure, you'll build it in-house. You're building it before. You might build it with AI. Makes sense. But you will need to connect it to your core operational systems, which is what ServiceNow is very good at. So if you're running your IT department, your HR systems, your finance systems, your customer service, which are more operational, There's some uniqueness, but it's usually a little more homogeneous between companies, which we can provide at a much scale, but it will integrate with your unique IP built in-house or third party. So that's the future going forward. It's agent to agent, no doubt, but in this idea that you will have different layers combined together. So when we build this full vertical stack, it's really to make our product much more, I would say, AI native. So the whole stack is very modern. And it has to have all the elements you require in the AI world. You can't just say that I will build pieces of it and then depend on somebody else to complete the story and not provide a solution. Eventually customers want solution. They don't want piecemeal. They don't want the spare parts. And the spare part world in enterprise software has been done many years, many times, and always has failed. Because nobody can keep up. And you take your best people who should be building a business, building software which is not needed. Where you can have somebody who is much more uniquely qualified to do that for you, right? So that's, I think, going to be the future where people will still buy solutions. And that's why we introduced something we call AI specialists. This idea of autonomous workers. So eventually, you know, what people are trying to do is reduce the amount of human labor, get automation, reduce the time to fix or fulfill some issue. And that's what we're going to provide with autonomous AI agents and take out the human labor cost, but also do something which you should take two days, do it in 20 minutes in a predictable fashion. And that's the solution they want. Then why would you take AI agents and cobble them together yourself if I can give you a higher level solution on top of it at a better price and reduce user labor costs, With a much more prediction because everything doesn't have to be AI. You can have things where like you're updating a database record. with just normal calls through API. Why do I want to burn a token on an LLM? Which pricing might be going up every year? So you need to be smart about how you build your software stack and be understanding of what part you want to do it through a traditional software mechanism. What do you need AI for? What do you use ML for? where do you which model version also use everything doesn't have to be opus 4.7 yeah right you have to look at tiering where you need small models cheaper ones while you also build ip on top of it around it so that's how we're building our top software stack it's not this idea that's fully vertical idea that everything is owned by us but we connect it to everything else

Arjun Bhatia, Analyst — William Blair

yeah and how do you in that sort of how do you view the model layer because you you have you know you mentioned it's not one model for every use case how are you viewing and you have partnerships i think with um pretty much all the frontier labs um but you're using multiple

Arjun Bhatia, Analyst — William Blair

models i presume and just yeah we have model agnostic to be clear i mean we provide customer choice just like across the whole stack we always this idea of you can run with any on top of any system of record you can run on top of any uh any cloud hyperscaler or articolo cocolo or private cloud uh for sure any model any data in data layer as well as any engagement layer now right and any tool. You can build with cloud code on top of us. We have a build agent. So we've been always idea of that very open, but any kind of choices available to customers. On the model is the same thing. You can use any model underneath. Model for us, LLMs, the frontier labs are probably 10% or 8% of the full stack. 80, 90% of IP, 90 plus percent IPs we build. That's where the differentiation comes in. And a lot of these models in some cases are interchangeable. So whenever the pricing changes we look at which is the best pricing for some example. Customers might choose something they say we standardize on this we want to use that that's okay with us. There might be some sovereign requirement some models can't meet. Yeah. And there are also use cases where you can we do do a lot of optimization through AI control tower we know the cost structure of everything who's using what and then we also look at where the cost structures are to understand which model to use for a particular use case. Yeah. So we're switching those things underneath the covers just like I I think if nobody cares what chip you use underneath your cloud most of the time, same thing will happen to the models, right? As long as the results are there. Because as I said, the autonomous AI specialist, if it reduces my ticket volume, fixes something in 20 minutes versus two days, why do I care what I use underneath? I think over time, right now this excitement and the FOMO going on, that everybody's looking at it every day in terms of features and all this stuff, it's kind of even out.

Arjun Bhatia, Analyst — William Blair

Right. And, you know, you're kind of at the forefront of at least getting AI into the enterprise and sort of top-down processes. But I think there's maybe a dispersion of how ready enterprises are actually to adopt AI. So talk about what are you seeing in terms of where are we in the adoption process today for the customers that have adopted it? How are they thinking about ROI? Because they are at least increasing their tech spending. So where does the return come from on AI adoption?

Arjun Bhatia, Analyst — William Blair

Yeah, I would just look at from like last year, for example, early last year when we were talking to a lot, agentic was becoming kind of what people wanted to do and the technology was getting better. We had an agentic solution. But the customers didn't know. Whenever we used to go to talk to them, first, they don't know where to start. What use case makes sense? Second was, how do I do it? Third thing for them was, is it secured? Is it compliant? Can I have visibility? So that was the barriers. last year, early last year. That kind of was the kind of how we thought about AI Control Tower because what we wanted to give them is like let's take out this governance, security, visibility issue off the table first. So when we introduced AI Control Tower, a lot of customers and the CIOs started feeling comfortable. Like hey, I can implement agentic without having agentic go haywire and break systems underneath, right? I mean, you've seen some examples out there like Pocket OS and all where the whole database and the production system got wiped out because you had no control. So that was one part. So we did that early, middle of last year. We launched AI Control Tower, very successful. Second thing we did see from adoption perspective as I mentioned was, where do I start? So what we did was we did 100 different use cases, agentic frameworks with like a point and click kind of mindset, right? You can get going very fast in a few weeks and give them very prescriptive ways of getting going, right? Do you want to start with incident management? Do you want to do resolution planning? Do you want to do triaging? Do you want to do sec ops? So we need to find those use cases, say, this is you currently running this system. This is the genetic version of it, and we can get you live shortly with this control around it. And that took over the next barrier. Where do I start? And the third one was, how do I go doing this? We also brought in some FDE mindset. This is not like armies of people every day available to customers. It's for a short amount of time taking the first use case and getting it live. Specialist engineers. Especially the FD, Forward Deployed Engineers, which are basically black belt, very heavy understanding, deep understanding of AI and our products. And they can get our customers get going in a few weeks so that they can take the barrier out, show them the value, show them the ROI. We do calculations and all kind of stuff. And once we saw these one or two use cases go live, it was just open gates after that. The customer like, oh, yeah, I'm going to do this for this one, this, this one. So we start seeing the volume go up. And that's really the adoption pattern we're seeing. And this year, you see it, the amount of customers now doing agentic with us is pretty high. The volume has gone up considerably. That's why we felt confident to increase our plan for now assist to 1.5 billion, 50% growth, or what our plan was. Because of the volume of agentic going up, with the AI control tower surrounding it, and the security. So we invested aggressively on the security platform to make sure customers feel comfortable adopting this thing. So that's the bigger burden. So that's, I think, the trajectory now we're seeing.

Arjun Bhatia, Analyst — William Blair

And is it, you know, I think you mentioned volume, which I'm trying to figure out, is the breadth of customers increasing? Because there were, last year, I want to say a lot of customers that were in pilots that probably moved into production this year. But then are there more customers and enterprises coming into the pilot phase and looking to expand that?

Arjun Bhatia, Analyst — William Blair

So, yeah, we track kind of the whole pipeline, right? There's a lot of POCs, and then we do pre-prod and prod. I would say 70% percent of our customers now who are doing AI are in pre-prod to prod. So they are going very fast on the production environment and the usage, that's why it keeps on going up because now they're starting to see multiple use cases. Usually the one use case unlocks, then you suddenly get four or five to multiple departments once you get involved. And that's where I think the production systems have gone up a lot more than last year. I think they were a lot more experimental. So this year, I think most of the discussions we're having with our customers are pre-prod and prod, pre-production and production so that they can start seeing the value of the investment they're making in this area.

Arjun Bhatia, Analyst — William Blair

And maybe last one for us to close out on, you have a revenue target out for $30 billion by 2030. Just talk about, you know, now that you are seeing some of this traction on AI, you know, what role does that play in that long-term target? And where are we kind of in sort of, you know, getting to the milestones, the interim milestones to get to 2030?

Arjun Bhatia, Analyst — William Blair

Yeah, I think we are very confident about our 2030 plans. I think this is the base case we laid out based on – we want to be also very prudent to make sure that we know what we can predict and how well we can do in that. So the $30 billion case for 2030, which doubles our business by 2030, is kind of the base case we laid out. We do believe we have all the core products, very differentiated, well-liked by our customers, and with time, which keeps on increasing. There's also, I think, our expectation, and if you ask Bill, 30 is definitely not even to think about, much more opportunity in front of us, and we'll get there. The thing which will drive this, as you said, what kind of gives us the confidence? One, I think we have a lot of unique IP and very modern platform, and it's getting adopted very fast. I think if you look at it and combine the things we have, AI with workflow, with data, and our data business is on fire. and it continues to grow considerably. And then you combine that now with security, which we have invested aggressively, and we have CIOs and CISOs are two big buying centers. CIO number one and CISOs, Chief Information Security Office, number two buyers for ServiceNow. A lot of people don't realize we are one of the largest cybersecurity provider in the market today. So if you look at the growth engines now we have. Security, which is going to be and continues to be one of the leading areas for us. You add data with the workflow data, the Raptor DB, which we launched just last year, 100 million ACB in less than a year. So very fast growth. And we're looking at a billion dollar plus kind of business growing there for sure. And then CRM, which we've been there for a few years, especially customer service, service management, CPQ, doing very, very well. And then you layer our core very solid IT business and HR business. And then you layer the employee works, which we build with MoveWorks, the idea of engagement layer for any employee to really like CVSL, for example, uses it for 160,000 employees every day to get any kind of help they need inside the company. So we have a very solid portfolio. And I think we feel the investments we've made are lining up very well. And the AI is definitely a tailwind to let us get into a lot more conversation when you surround that with AI-controlled tower and the security products. so a lot of growth opportunities and we feel very confident where we are going and it's been reflected in our numbers and it continues to do better and better all right perfect we'll wrap it up there thank you very much yep thank you everyone