All right. Welcome back, everybody. I'm Brad Zelnick with the Deutsche Bank Software team. Delighted to have you all here, both in person and online, for our conference here in sunny Dana Point, California. For this session, extremely delighted to have Pablo Stern from ServiceNow. Pablo is the Senior Vice President and GM of the company's Technology Workflows, biz products. Great to see you, Pablo. Thanks, Brad. It's great to be here. Awesome. Format of the session, I've got a bunch of questions I'm gonna go through with Pablo. I think we're gonna start off with a topic, we'll just get it out of the way, that nobody really cares much about: Generative AI. Pablo, as the leader of the company's workflow business, you've got a unique view into how CIOs and companies think about IT, which has evolved over many years- Mm-hmm. Increasing degrees of automation, even AI. But, but what's your vision for how generative AI really changes the game for IT? Yeah, it's a great question. You know, I've been in the company for six years, and if you go back six years ago, we were really starting down the journey of AI. And over that period of time, we've done a fair number of acquisitions, we've tucked technology in, we've actually seen growing and growing demand from our customers around how AI can help them. Because if you think about our goal, it really is to help our customers drive workflows, automation, and do so intelligently across the enterprise. And so, you know, machine learning AI is a huge component of that. As I look at generative AI, you know, we're in the early stages of this, but one of the things that really comes to mind for me is when we're internal, we're talking about, like, the vision of where our customers are going and what the potential is for some of these outcomes. Generative AI ends up being a core building block that really builds on top of a lot of stuff that we already do at ServiceNow. I'll give you a couple of examples of use cases that I think, like, really help our customers, both from a productivity, effectiveness, and ultimately experience perspective, where generative AI, coupled with workflows, coupled with the data that ServiceNow has, can really deliver end-to-end value. The clear one is, think of an employee. You know, we've talked often about employees who have problems that need to be resolved. They reach out to IT. Oftentimes, that takes a couple of calls. It may require somebody coming in and doing a screencast on their laptop, trying to resolve those issues. And between people interacting, between the machine and the person back and forth, like, there's a lot of time spent, a lot of time spent on the agent side, a lot of time spent on the employee side. What we see really in that vision future for where generative AI goes is, you can start breaking those silos and interconnecting that workflow with intelligence, disintermediating the multiple steps, disintermediating how many people need to be involved. And so a person having an issue with a laptop can either reactively or proactively be diagnosed by the machine, be proposed as an insight, potential solution, and then actually an action or a workflow could then go and help that user. And those things can all happen without a scripted interface, a scripted chat. All that stuff could actually happen with the power of that insight, Generative AI, and the workflows beneath it. So we see that being a huge productivity gain. And the really cool thing for me is, as I think about that outcome, like, it's a completely tractable problem now. Makes a lot of sense. And from your conversations with customers, Pablo, how would you characterize their readiness or appetite to actually adopt these solutions? And what are the greatest obstacles, perhaps, in actually getting their adoption of generative AI? So, so I'll tell you, you know, in the last couple of months, and I've probably met with 50, 70 different customers, there hasn't been a single conversation in which Gen AI is not a topic, and that... Those are, you know, all, all these conversations we have with customers always starts with the voice of the customer telling us about where they are, what are their top priorities, and what they need to solve for. And then we will have a conversation based on what they want to talk about, and generative AI is in every one of those discussions. And so the demand is there, the interest, the potential, and we not only see it from the executive. So when I talk to, like, CIO or CTO, we hear it, but then also from a practitioner perspective, and we have over 100 customers who've already signed up with us on Design Partner Programs for our Generative AI solutions, customer-facing use cases, employee-facing use cases. That type of demand is really unseen in terms of, like, how quickly we've seen for what is a new product, demand coming in from the market. Now, you talked, you asked about some of the risks. Well, one of the things that we really pride ourselves on is, if you think about a lot of the acquisitions we've done in the past, so companies in the AI space, like Element AI, we've not only focused on getting IP on the technology and being able to move machine learning forward, we're also really focused on trustworthiness and what can happen with that AI, and how do you, how can you trust what the AI is doing to not only deliver insights but make sure that your data is your data and things are protected? And so I think, like, one of the things that does come up in a lot of the conversations with our customers is, you know, they trust ServiceNow. We have an incredibly strong overall security perimeter. We've been, you know, held to the highest standards by regulated customers and others, and that is very important to our customers. And so, you know, it's a bar that we're setting for ourselves, and we continue to set for ourselves across all the solutions that we provide, including what we're doing in the AI, AI space. Great. And Pablo, I'm hoping maybe you can elaborate on that a little bit. Trustworthiness makes a lot of sense as a generative AI core principle. But maybe if you can elaborate on ServiceNow's generative AI design philosophy. I know you offer customers freedom to bring their own models. You also offer domain-specific models- Mm-hmm. -for them. Can you explain just the thinking behind that and how it's being received by customers? You know, even since a lot of this was just launched, I know, at Knowledge only a few months ago. Yeah. You know, I'll, I'll start by saying that at ServiceNow, we fundamentally believe in the ecosystem. We want to be supportive of our customers, the technologies that they have, and being able to bring them on board. And so in that sense, we're going to support a customer bringing their own generative AI, working with some of our partners, you know, third parties, or stuff that they have internally. And that has been a core design principle for us in the ecosystem, because as a connector across multiple systems of record, we know that to deliver value and to drive those workflows, it's key that we connect to the rest of the technology ecosystem. That said, what we've seen, and we've done a fair amount of testing on this, is if you think of, like, domain-specific use cases, we really have an ability to not only deliver better value in terms of hitting true positives and actually getting the right answers for questions, we also can reduce hallucinations and give all those answers in a much more compressed, smaller parameter model than what exists in some of these large, generalized models. And so our belief is, you know, we will support the ecosystem, but for our customers, the value will really come out of these domain-specific LMs, now LLMs, we're referring to it. So we're focusing a lot of our energy in making sure that we're driving those because we think they'll give you better responses, they'll have fewer false positives, and we can do that much more efficiently based on the data that we have, which is really around the domain-specific use case that we may be serving for our products. Got it. And I know it's still early days, Pablo, but what is it that you see in the business, in all of these customer interactions, that supports your confidence in ultimately being able to monetize? Yeah. these Gen AI features- No, the- - capabilities? ... It's a good question, and, you know, as I said, like, the early signs that we've seen from our customers, like, the interest, the demand, both from C-level all the way to practitioners, gives me a lot of confidence that this is, this is a technology that can ultimately really deliver value. And that's on the outside in, right? That's from a customer perspective. But on the flip side, if you think about it from the inside out, I mean, we've spent a lot of time doing our own internal research, doing productivity analysis, trying to understand what are the potentials in terms of productivity gain for an agent, an employee, a service desk agent, or an operator in IT. What we found is, you know, firstly, you don't have to have a tremendous amount of productivity gain to be able to really drive or return on an investment because of the cost of labor and the shortage of labor that exists in the market. So the productivity gains ultimately can actually return a fair amount without requiring a tremendous amount of investment from a customer on a basis of what they're currently spending to deliver the same type of outcomes. And so that, those numbers ultimately give us good confidence that we can help our customers, one, deliver more productivity, drive not only that, but also a better experience, and do so where even if we just, like, say, we're able to, to monetize 10% of that productivity gain, it's still a pretty large number in terms of the ability for us to, to drive those outcomes. And what I'll also say is, we already have a history of doing some of this stuff. So you think of, like, ITSM Pro and our Virtual Agent and the, you know, 40% penetration that we've driven over the last five years on this, on this technology. Like, that was, like, in the earlier days of AI, and we've already seen a lot of boom on, you know, being able to uplift over our ITSM Standard product line because of, you know, embedding some of those capabilities, inclusive of AI. So all that gives me good confidence that if you look at over the long run, this technology not only will help our customers, but it is monetizable. Cool. I want to come back actually to the Pro Plus view that you guys talked about more recently. But if I reflect, and by the way, we're talking about Gen AI, Gen AI, Gen AI. You're responsible for the largest workflow- Mm-hmm ... of the leading company in IT. There's so much that we can talk about, which we're going to delve right into. So at Financial Analyst Day, it was disclosed that emerging technology products like Ops, Risk, ITAM were all less than 20% penetrated within the base, growing 50%+. I mean, still a massive, massive- Mm-hmm ... opportunity. Can you talk about which are most promising and how to drive that penetration even further? Yeah. I, you know, I mean, I'll start off by saying I love all my children the same. We've got some amazing products, and, you know, they serve different personas and different use cases. And as many of you know, the, I think one of the big benefits that we have at ServiceNow is that our better together really is unmatched because we can really, through one platform, integrate outcomes where an asset management team can connect to a risk exception or risk outcome, or it's something that you're trying to drive from an ESG perspective as you're trying to drive your e-waste policies. And so we have example and example of that. But I'll hit on some of these products, and I'll just talk through them. So, you know, asset management has been one of our products that's really, really been excelling, especially in, in the market that we're in, where you have a bunch of headwinds from, from macro climate. You know, there are hard dollar cost savings you can drive as you think about managing your assets, you know, software costs, driving audit protection. And because we have the system of record, we basically already have a lot of metadata of all the, our customers' environments. And so by providing contract license information, we can drive workflows that make our customers much more effective and efficient, and we can automate a lot of that stuff so that it doesn't require manual intervention. It can happen in an automated and a time-based fashion, and so that product's been growing very fast. The other thing that, if you think about that product line, the asset management product line, which really started with the software space, you know, a couple of years into it, we went into the hardware space. There was a lot of synergy there. Both those products have grown incredibly fast. And just last year, we announced we're going to extend into enterprise assets, to start looking not only at assets that IT has, but the rest of the, the rest of the company. And, and that coupled with use cases in, you know, field service, helping technicians, like, there's just this tremendous amount of value that we can go after. So that's all really been helping that product line. On the risk side, and I say this a lot, which is my realization and, and when, when I arrived, risk was a pretty small product. We weren't investing a whole lot into it, and it was really focused on the IT side of risk because that's where our, that's where our DNA was. We made the decision that we were going to extend our capabilities to really drive enterprise-wide risk. Over the past few years, we've done some tuck-in acquisitions. We've grown our capabilities into business continuity and audit and a bunch of different areas, and we've really taken that market by storm. And the reason is, I really do see the risk product line as something that is really a platform capability that we can deliver. Because if you think about the work you're doing, if you think about all the digital assets that you have, those end up being the things that you need to govern and the things that have compliance requirements. And so not only can we do what the rest of the industry can do, which is drive assessments, push out surveys, help you, you know, pass your annual audits and whatnot, but we can also do that from a continuous monitoring perspective, from a truly automated perspective, and that has been incredibly powerful for our customers. And so we see this, like, you know, the financial services market, enormous market for risk products, has been, like, taking our products and our capabilities and just extending across their firms with them. So that's really been what's been driving that product line. And then, you know, security, I was. You know, you know this, prior, prior to being here, I was at a security company, and, you know, we'd always joke that security is like recession-proof as a, as a, as a business. I think that there definitely is some truth to that, given that, you know, what's happening in the cyber world, what's happening as these digital estates continue to grow, the need to be both proactive, understanding, you know, which vulnerabilities you have, how you address those, how you get your cloud security posture really understood as you're putting more and more stuff into the cloud so that you can protect your space. And then the reactive side of, hey, you know, running your SOC, your security operations center, and being able to protect anything that's coming in and react to it quickly, which has been very analogous to what we did on the service management side. Like, those two use cases continue to be growing and growing in our customers because their digital estates just continue to grow. So we have a lot of tailwinds in those businesses. You can see that you do love all your children equally. It, it really shows. So Pablo, a lot of investors are trying to, you know, keep coming back to the P times Q math- Mm-hmm. -when they're thinking about ServiceNow. So you recently announced the Pro Plus SKU- Mm-hmm. at a 60% price lift- Yep ... to the Pro SKU. What's the compelling benefit that gets customers to adopt? And I know it's not even released yet, but are there any proof points of productivity gains? I think you touched on this a little bit before- Yeah ... generally speaking, but if you can maybe expand on, you know, anything that supports the, the- Yeah ... the 60%- Yeah ... price point. Sure. And, you know, I, I gave that example around how an employee can drive self-service, which is a better experience, means fewer touch points, means that your IT teams can go and focus on other work. And, and that's one that really we can drive through generative AI. But if you think about that IT organization, I was having this conversation with, you know, one of the largest technology companies in the world yesterday, and this was basically their head of their cloud services, basically running their cloud operations for all, all their businesses. And his question to me was: "Hey, like, what are, what are you guys doing in this space? Like, what? How can you help my operators, my level one, my level two agents? And if you think about, you know, where they're spending their time and the tasks that they're doing, and basic things like, as you're going through an issue, it could have been going on for a while. There's a lot of data around that issue. You may spend 5, 10, 20 minutes just trying to read up, get smart on what happened, because you're either picking it up, it's happening as a handoff, it's been a few days, you're coming back to it because a customer had just responded. And all that stuff, we can really drive a lot of time savings for those people by summarizing what happened, helping them as they, you know, author resolution notes, as they're doing a handoff. There's a lot of those different use cases, and those are, you know, that's what we've been testing internally. We have a lot of customers helping us with that validation, and we are seeing those productivity gains. And I come back to that notion that it, you know, it. On a task basis, I mean, you can think of, you know, saving 30, 40, 50% productivity. Now, that doesn't necessarily mean that that person's saving, you know, 30, 40% productivity over the course of their entire week. But the super interesting thing is, we don't even have—like, it doesn't have to be anywhere close to that amount of productivity gain because, you know, the amount of time, energy, and spend that's happening for those people and the tasks that they're doing, if you can help them focus, you know, say, 10% of their time on other tasks, doing other things, then you're driving a much more productive organization... And And that actually shows you some real dividends and a willingness to spend for customers to claim that productivity gain. And in my early conversations with customers, like, I have that discussion, and they nod heads. They're like: "Yeah, like, those, those are real dollars. Like, that's a real benefit for me. And given what I said earlier, where there's this dearth of IT specialists in the industry, like, people or most customers I talk to will tell me it's really, really hard to hire. Like, the ability to say, "Hey, you can extend the workforce that you have because your estate is growing," is a real problem that they want to try to solve and something that they would pay for. Makes a lot of sense. Can we maybe pivot the discussion to observability? Sure. So a couple of years ago, you acquired a really great company, Lightstep. Mm-hmm. Recently, I think you've won some nice observability deals in cloud-native companies. But how do you more broadly flank the com- Yeah ... competition out there in what's a pretty noisy space? And what portion of the market should we think of you as really going after with Lightstep? Yeah, it's a good question, and I think, you know, folks who've been following the space know it's a large market. There's a lot of players in this market, and to your point, there are many different use cases that get served, you know, from the infrastructure level all the way to the application, from like, you know, legacy environments all the way to, like, the cloud natives. And so what I'd say is, we think about this really in a couple lenses. One is, you know, the benefit of ServiceNow has really been: As a platform, how do we help our customers deliver outcomes that they can do natively across our workflows? So when I talk to our customers, one of the large healthcare that is using both our Service Operations product line, so ITSM and ITOM together, as well as our cloud observability solutions, they just want a native, end-to-end integrated solution that takes them all the way from the insight of something that they're observing in that environment, then actually being able to drive the action or the remediation to solve that problem. And, you know, their goal is, over time, to get more and more predictive and proactive on solving those. But, that connective tissue is incredibly important to them because the more places that you have to go and pass data from one system to another, the more fidelity that you end up losing. And if you can do that all in one place with all the right meta-information around what the issue was, you will get the right people on, you'll get the right information, and you'll be able to resolve those issues faster. So that's really our broad piece and extends that foundation that you and I have talked about in the past around this core notion of Service Operations and bringing the servicing and the operating estate together to help disintermediate how many steps along the way it takes to solve a problem for an employee or a customer or, you know, a business service maybe going down. So that, that's part one. The second part is, as you said, it's a big market. We really focus on where our customers are going, and we're looking into like... you know, all our customers are pushing to cloud. They all have huge initiatives, and if you look, most are in a cloud-first priority and will have, among their top 3 to 4 initiatives, something that is a cloud journey or cloud, cloud approach. And so that's really where we're focused. We're, you know, to use the, the Wayne Gretzky analogy, we're going to where the puck is going, and that it's going there quickly. And we really do see, as you have these really distributed microservice environments, like cloud natives really started 3, 5 years ago, those are all now moving really fast into the large enterprise. And so we believe we can really help our customers as they build those out, drive that truly integrated solution from the observability side into AI ops, and then out through a service operations notion to be able to identify the issue, get the right people on it, resolve it as quickly as possible, and really reduce or mitigate any customer impact. Got it. Pablo, not to beat you up, but if I just reflect on things that I was hearing in the field- Mm-hmm ... going back, it could have been 6-9 months after the acquisition, it sounded like it was a little bit maybe slower, even in the field, for folks to be enabled, selling on a different, separate contract. Like- Yeah ... in terms of innings or how you would characterize it, and I think the more important part is what you just spoke to in terms of being able- Mm-hmm ... to integrate it into the product family. But, like, where are you- Yeah ... versus the opportunity? It's a fair call out. And if you think about product line, it probably took us longer than it should have to go and take, you know, as we announced at Knowledge, a rebrand right into the ServiceNow portfolio, driving from Lightstep as a brand to Cloud Observability. And the other thing we've done is, you can think of two things going on with that product line. One is we have this roadmap on: How do we bring this in to deliver those native outcomes? Which means you'll see a lot of this coming out. Like, how do we get the meta information that's happening in that cloud environment into a workflow to go and drive outcomes? And there's a lot of stuff coming out over there to deliver a true ex-- like, integrated experience for our customers. The other thing also is, you know, when we got that product, we really had a tracing company. Like, they, they were the, really the vanguards in the tracing space. You know, Ben Sigelman was one of the, founders, not only of OpenTracing, but also the OpenTelemetry initiative, really democratizing, tracing and telemetry across all agents. And over the past couple of years, we've been building out this capability, adding a super scalable, robust metric back-end. We acquired a logging company, Era, last year, that really does logging, and does it at scale. And so the other thing that we've also been doing in parallel is, making sure that we build a truly robust, you know, across the different trace metrics and logs-... systems, a way to, on the back end, provide one data platform that brings all that stuff together, that gives you the insights that span these really complex environments. So a lot of stuff going on there. You know, we'll have our logging product out this fall, so, we're very, very excited about that. We already have a bunch of early customers in testing on it, and I think that really brings all those pieces together. Obviously, we have a rich roadmap. There's a lot more stuff we wanna do, but being able to deliver that data back end and then that integrated experience into our service operations, to me, are sort of the, those two key things that we've been focused on this year to make sure that our customers get that integrated native value. Cool. We look forward to seeing it continue to evolve. I wanna go back to something you've touched on, but I wanna, for explicitness. You know, you've highlighted several convergences- Mm-hmm happening in the industry, you know, across DevOps, ITSM, workflow management, and service and operations. Mm-hmm ... IT and business. Can you just elaborate on what this all means? Yeah and the implications for the future? Oh, man, this could be, this could be a 45-minute conversation. I've spent a lot of time thinking and talking about this stuff. You know, as a product person, we spend a lot of time with our customers, and you will often hear their unmet, underserved needs. And sometimes they'll tell you explicitly, and sometimes you have to implicitly understand, like, where is it that they're going, and where is the future of the world, and how do we build to that end? And so I spend a lot of my time thinking about some of that future state. And in... I'll give a couple of examples, 'cause I think each one of these could be a topic in and of itself. But, like, the world of DevOps, you know, the—if you think about the service management and how you do and drive, you know, your change processes and, you know... In the historical world, you were doing that one, two a month or one a month, and things would come in, and you'd have, like, reams of paper, and you were trying to really understand, you know, what changed and what was the risk of the change. And that just doesn't work in a digital world. And so the thing that we saw, you know, five years ago is, our customers need to understand what changed. They either need it for regulatory reasons; they need it because what changed could be really the source of what broke. And so driving that connection was super important to them. A lot of our customers were either in a world where it was manual and things were just weren't moving fast enough, or it wasn't, and they were moving really fast, but things were just breaking all over the place. So we saw that opportunity of convergence of technology to be able to solve that problem and bring some of the practices that ITIL instituted, you know, dating back now 30 years, in a much more agile world, and that's been really where our focus on the DevOps side has been. Then on the service and operation side, this one to me is super simple. It's how you bring the people and the machine together. You have a lot of people that have been serving, you know, the filler world, serving people, your support agents, your L1, L2, L3, and that world is not scaling at the speed of the digital estate that you have. These, you know, use cases that we talked about earlier, where you're trying to disintermediate how many steps it takes between people and, you know, phone and telephone broken telephone that you're doing, that only happens if you connect the people in the machine more closely together. To us, that is what the world of service operations is. The last thing I'll say on this IT and business side, you know, I hear this more and more from customers, which is that, that world of, like, you know, IT, employee-facing IT, and, you know, customer-facing technology in the large enterprise is definitely blurring. It's coming closer together. We see the use cases are much more integrated, the back ends are integrated, the data centers, clouds, and others. In that convergence, we, as sort of the system of record for the enterprise, have the ability to deliver the same outcomes that you want from employees: great experiences, no downtime, if there's an issue, it gets resolved really quickly, to the customers as well, who want the same things for the digital products and services that more and more businesses are creating. That was a lot of stuff, and that was my quick synopsis of it. Oh, I appreciate you're very good at taking 45 minutes and distilling it down to something that we can all understand and digest pretty easily. I wanna come back to generative AI for a moment, but in a different sense. We talked a lot about all the optimism and opportunity and value creation. How should we think about the risks of this technology from a competitive standpoint? Mm-hmm. Does it, in any way, maybe lower the bar for new entrants in some of the markets in which you're competing or maybe even reduce the friction and switching costs for a customer to move off your platform? Yeah. Yeah, it's definitely a, a question that's come up for us. I'll start off by saying, like, I do think that Generative AI is, is gonna... as a technology, is gonna be a, a game changer in the industry. I don't believe that just Generative AI, in of itself, is a moat. I think where moats really exist are in places like where you drive workflows that interconnect very complex systems across all your systems of record, because that's a, that's a complex exercise that takes a lot of energy to, like, drive that. Then you can definitely supercharge that with more intelligence, with more AI. The ability to do that, connect your, you know, all your IT systems to your HCM, to your ERP, as you're driving a workflow that goes across the enterprise, is a hard problem. It's—it's not only a hard problem, it's also one that has a lot of, has a lot of stickiness, a lot of inertia around it. So, to me, like, that ends up creating our workflows ultimately have created a moat because we really have a truly cross-enterprise workflow platform. And if you go to the enterprises that have chosen ServiceNow as their platform, we've really integrated well across that ecosystem. The other part of it is, Generative AI is only as good as the data that you have, and that's really where I think like a lot of the moat in this world is going to exist. And, and that's where, you know, as a system of record for the enterprise, we have a very strong moat in the data, you know, the assets, services that you're ultimately delivering. And that is not only about having that data, but it's also about being able to help our customers use that data, to deliver more effective outcomes. And so, because of that, I think this is a, this is going to be a really good end, and it's going to be something that we can supercharge and deliver outcomes to our customers very quickly. But I don't, I don't see it as a disruptor that in of itself is really driving, a wedge into the other moats that I just mentioned. Makes sense. Pablo, if I think about the enormous TAM that ServiceNow has, technology workflows, it has the largest opportunity, I think, over $80 billion by numbers that you guys have all shared, but it's also the most mature. Is it therefore fair to assume that it's also the highest margin workflow category? And, you know, how does that conversation go internally when you're asking Gina for investment versus perhaps some, you know, some of your peers? And what's the philosophy just around capital allocation? I don't want you to speak for Gina, but- Yeah. your perspective, and you know, could you drive faster if you had more funding? Yeah. -behind IT? Yeah. You know, I'll start off by saying that first and foremost, I wear my ServiceNow hat. I'm a company shareholder. I care a lot about where we're going, and I ultimately want all our workflows to drive outcomes, 'cause I know that if John Ball and customer workflows can drive better outcomes for his customers, I can deliver more value on the back end of that by connecting operations and service to the teams that are supporting a lot of that infrastructure. And so I just... At a philosophical level, like, yes, you know, we all want investments. We all want to make sure that our products continue to grow, but that to me is the starting point. Now, on your question on the margin side, you know, given that we're a platform, that's not a way we necessarily think about our products, because all our products sit on top of one platform, and so you kind of they're generally, from margin perspective, pretty similar in terms of how we build the technology. Now, it is the case that, you know, it's a newer product, you're going to have to invest more, you're going to you're investing ahead of revenue, and so those are some of the things that you have to bear in mind. And I do as I look at my allocation on an annual basis, one of the things I spend a lot of time thinking about is: how do we invest in our incumbent products to help them grow and continue to be, you know, the top products in their market? Conversely, like, what are the bets that we're making that could really drive outsized growth for us in the next 3-5 years? And I think that's generally a pretty good way to think about how we invest across the company, which is... It's really about what the potential is. You know, you look at the market, you look at how we can serve that market, you look at places where we have a distinct advantage, where either the system of record that we have, the workflows, access to the buyer and the user, really gives us, you know, a great starting point. You know, as I said, I gave the example around enterprise assets. Like, we have, I'd say, 80% of the foundation to deliver an asset management capability, but based on what we've done for IT. In enterprise assets, it's really about getting the content, those enterprise assets, as opposed to IT assets, and then, you know, tailoring the workflows for those use cases, which won't be completely different, but may have a few differences, and making sure that we integrate into, you know, field service management or some of the other technologies that are going to really bring it better together for our customers. And so as you look at that and you evaluate it, and you look at the size of the market, you look at where other players are on the market, you look at where the demand is for customers, like, that really helps inform the strategy. And it's generally like we've been pretty good at making some of those bets, because we spend a lot of time with our customers. We understand the markets that we're in and the markets that we can go into. You know, not every bet is going to be successful, but in general, I'd say our hit rate's been very good based on that approach. And Pablo, to date, outside of, you know, some really great technology and talent tuck-ins that you guys have done, all of that investment is really, by and large, been organic. Mm-hmm. As Bill says, and I love when he says it, you know, "Organic is delicious." But what do you think needs to happen for us to wake up one day and see ServiceNow do a significant deal, let's say, I don't know, $10 billion+ in scale? Yeah, Bill, Bill's right. You know, we really built a motion around tucking technology in, driving that into the platform, and driving value for our customers, and we've seen this time and again. When we do that, we can deliver at a platform level great technology, like the stuff we're doing in the AI space, not only in generative AI, but across, you know, a lot of the acquisitions we've done in the workflow space, like within our PA or other technologies. And that is a leverage point for all our applications, and then, you know, supercharging some of our capabilities like AIOps, with the Loom acquisition that we did a few years ago. So you're going to continue to see us do that. I think that, you know, that approach, which does require some time, it does require some concentration on replatforming, is where we think we can with, you know, people and IP, we can accelerate our roadmap, and that is our focus strategy. Now, I would tell you that, you know, we always want to serve customers, and we're always looking at, like, what are the better ways to serve customers? Like, that, it always starts with the customer. The bar is incredibly high for anything that would be a large acquisition. Anything that, like, would not be a replatform is. I mean, the hurdle is high. You know, you think of like, hey, we're in a market, like, a lot of valuations have come down. Like, does that make things, you know, more palatable for us? I'd say, like, it's not just about, it's not just about cost. Like, we have to think about our customers. We have to think about the outcomes we can deliver. We have to think about how we could do that based on where we are and some of the value proposition, and so it is a very, very high bar for us. We are obviously never stopping. We look internally, we look externally, we spend a lot of time just trying to make sure that, as I said, we serve our customers, but bar is very high. Cool. With that, Pablo, we are out of time. It's always great to see you. Even better to see you here at the Deutsche Bank Technology Conference. Thanks so much for coming. Thank you. Thanks for having me.