ServiceNow, Inc. (NOW) Earnings Call Transcript & Summary

Okay. Thank you, everybody, for joining us today for our session on adopting AI strategies for low-code enterprise app development. So we're appreciative of you taking your time to join us today. And so definitely appreciate you being here. Before I get started, we just wanted to mention our safe harbor notice. In general, everything we're looking at today is currently in the platform. But if we do reference anything forward-looking, particularly in the Q&A portion of it, we'll definitely mention that is not currently in the product and on our road map. So we will be very clear about that. Just to introduce myself. My name is Dan Munk. I'm located in Sacramento, California, and I'm an outbound product manager for App Engine. So my particular specialty in the platform is -- professional in development on the platform. And so I focus a lot on custom application development. And so that's why we're here today is to talk about, one, how to build products; but two, how to include all of the different AI capabilities of the platform and how to think about those and realize value from them. So we're currently at an inflection point, and it's a really interesting time because when we're looking at AI and we're trying to determine the best ways to realize it's a full advantage for our particular use cases or for our enterprise, there's so many different facets that we need to consider. And so that's one of the things that we're going to address today. And so one of the nice things about being on the ServiceNow Platform as always, it's one data [ model ]. And so the way that we approach AI is a little bit different than a lot of other tools and the fact that we want to take what we already have, this ability to develop applications, utilizing no code to sequence business processes but then also augment those with AI. And so there's a number of different ways that we can do that, and we're going to talk about that today. So one of the ways we can do that is the creation of capabilities, right, the building of applications. And so we can use generative AI to describe what we're looking to build from an outcome standpoint, and it could help us build it. And so that's one of the ways that we consider using generative AI and AI on the ServiceNow Platform. But there are some other really important aspects, too. And so that's related to how people can use AI within their existing business processes. And that's one of the important things about ServiceNow's approach is what we want to do is take the structured workflows that we already have, the ways that you are already realizing value on the platform and look at ways to make it better and faster and more efficient. And so we're going to talk about those, but there's a number of different options where we can have generative AI capabilities that our end users interact with, but then also generative AI capabilities utilizing agents that can do things in the background on their behalf. And so there's a number of different ways that you can think about using AI on the ServiceNow Platform, again, from a development and creation building capability, from a direct user interaction standpoint and then also doing things on the behalf of users. So we're going to talk about all of those facets today. Okay. Let's get to the next slide. So what we know is companies want AI-powered apps. And so the reason why this is so important is that, it's so easy to realize value from these AI capabilities. But one of the important things, and this is a significant advantage from the ServiceNow approach is how we incorporate AI into your existing workflows. And so what we see with a lot of generative AI capabilities, it's almost like a search where you can type in text and then you get a response, and that's great. It's very useful. But one of the ways that companies are building AI applications in realizing value from it is taking a look at some of the existing business processes and then looking at different points in those workflows and how can we now make things easier, how can we explain them, how can we possibly create something on the behalf of our end users. And significantly, how can we take the knowledge that we've accumulated in these business processes and then apply that to improve business outcomes. And so -- it's -- so what this really gets us, right, is that we're experiencing this really accelerated adoption curve, where more and more of these enterprise software applications will include agentic AI. And like I said, some of that is going to be the creation of that applications and some of that is going to be how our users interact with AI and then also will utilize AI to do things on behalf of users in an autonomous manner where we could expand the capacity of our existing user base. And so that's an important part of this conversation is there's a number of different ways that AI will be utilized in all of these different applications. Okay. And so I've mentioned before, one of the advantages of ServiceNow's approach is what we're doing is we're dovetailing that into our existing platform capabilities. And so there's all of these out-of-the-box products that already have prebuilt workflows. But what we also see is the low-code capabilities utilized to build applications for net new business processes that don't align to those primary value chains of those product workflows. And that's really where you can unlock an advantage with your custom applications. And so one of the things that ServiceNow does is it provides tools that allows you to inject AI and AI agents into those business processes without going through the overhead of governance and data sovereignty and the liability of utilizing end-to-end custom generative AI workflow, right? So it makes it much easier to incorporate these -- incorporate all of these AI capabilities into your business process. And so what we're looking at here is our AI Agent Studio. And so what this allows us to do is take your familiarity with what ServiceNow can do already and increase the capabilities of the application using generative AI. And so part of that is thinking about things less in a deterministic way like where if you were to structure a business process using Flow Designer. And I think more about flexibility and the outcomes. And so we'll talk about this a little bit more. But what you can do is think about platform capabilities and then how you can use a collection of AI agents to make decisions and do things on behalf of your end user base. And so really powerful opportunity to quickly utilize AI and AI agents in your application. And so what we can do then is take the introduction of those capabilities and really expand end user experiences. And we'll look at that in a demo where we are -- in this demo, it is a traditional ServiceNow custom application with workflow and with all of the things that you'd expect. But what we can do now as a user is have a conversation with the agent and really understand the data and utilize the agent to help us make decisions around that data. So really good opportunity to empower people accelerate business outcomes. And also one of the things that people don't think about a lot is by moving some of that decision-making into agents is that what we can also do is increase the spectrum of people that can be productive in these applications. So in the demo, we'll look at commercial real estate management. And so there is a fair amount of industry and business knowledge required to process the data and the number of requests that are received in that application. But by utilizing AI agents, what we can do is lessen the learning curve and the amount of business knowledge required to be productive in that environment but then also if someone's more experienced, it can increase their efficiency and capacity. So a lot of benefits can be realized from adding all of these AI capabilities to your existing application. Okay. And so I touched on this before. But what we really -- what we can really think about is these different capabilities on the ServiceNow Platform. And so you can think about this in sort of in building block terms. And so what you can do is you can access built-in skills, different ways to prompt an LLM to provide it with information and receive generated content back. And so what you can do is you can think about these different AI skills and the applications in a way where we can enable our end users or our other processes to access these LLMs in predetermined ways, right? So we can think about them almost as functions that you can utilize as building blocks either in our application or that we can utilize in AI agents, right? And so what AI agents are a more -- a process that's basically aligned to a specific use case in the ServiceNow Platform. But rather than a structured business -- structured process using Flow Designer. What we can utilize is the AI agent to make decisions and then access all of these different skills available to it to best serve the business outcome. And so what we can do is take these smaller building blocks, make them available in applications, and these are the AI skills, but then also create bigger, more capable processes that are the AI agents that we can also incorporate into our apps. And so we have -- and that all adds up to a better way to work, right? So what that really gets us to is better business outcomes and faster resolution. And it can really help -- in general, we talk about our agents and fulfillers and how we can improve the people that are -- the work for the people that are involved in the business process. But what we can also do is improve our end users experience like our requesters, right? The people that are realizing value from this business processes, we can help them get answers faster. And a lot of times, we can introduce self-service where they can completely resolve their issue or get their questions answered without engaging any of our fulfillers or users on backend. Okay. So really, what we want to think about is these AI skills and different capabilities of the platform and how we can inject them into our custom applications. And so the way that it works is we already frequently have all of these business processes that we configure, right? So we have -- if you imagine if you have an existing application and you've already built it out to align to business demand and the needs of different specific use cases. And so what we really want to look at in this business process is how can we introduce the efficiency and the intelligence of these different experiences, right? So how can we make it so our end users can have a conversation with their data and understand how they can get to the outcomes that they need to resolve their issue, right? Another thing that we can do now is use these built-in capabilities to simplify something very -- to require very specific knowledge, right? So we talked about and we're going to look at this loan real estate application. And so that requires a fair amount of business knowledge to understand and process these different requests. And so we can use generative AI now to take that data and then simplify it and explain it in terms that aren't -- that are in plain language, right, that aren't specific to that industry. And so that's really the theme here where what we can do is take information that we've accumulated in our business process and make it easier and faster to deal with, right? So another example here is, generate. And so one of the things that we could do is maybe take a lease request, right, that we receive in our application and generate a response to it based on whether we want to approve or deny their request. And so that's a real time saver where we can look at in the past, what might have taken someone who would need to evaluate the lease request, find all of this information related to the potential client and then generate a response to them. Now maybe that's something that we can do in a fully automated fashion that will require basically only a final review of the content. So that's another example of where we can take this capability from AI that can interpret and accumulate the information and then generate new content based on that, right? And so again, let's just take a look at one final one where we can recommend, right? One option, and we'll look at this, too, is rather than this loan request agent, right? That is -- I mean, sorry, the lease request agent rather than them having to look at the credit score, find information related to their prospective tenants, what we can now do is use an AI agent to really consider a lot of the inputs that have been provided and then make a recommendation on that decision. So just another way to again, increase efficiency and really make it easier for everybody to handle these very sort of industry-specific topics. Okay. So let's take a look at how this might be implemented. And so here, we have a journal entry app, and this is built according to ServiceNow best practices, right? It's what we want, it's -- and we're receiving and take for journal entry. There's a number of steps here that need to be performed. And then what we want to do is connect the workflow, the different approvals and tasks that people need to perform with -- and also to integrate with other systems, right, to update or pull information from -- that we -- that is required for this process. So what we really would like to do now is when we think about this process and how we might build it and instrument it in the traditional manner, what we can also do now is think about how we can dovetail all of these different capabilities that are available through Now Assist. And so this really ServiceNow's particular area of advantage is that there's the ability to take all of this information, engage all of the different users through this -- through an experience that's specific to their needs, build out and structure the work and the workflow and then also add all of the different AI capabilities. And so that whole story from cradle to grave is really what is specific to ServiceNow, right? So we have the best-in-class workflow and business process engine. We have integration hub and workflow data fabric, the ways to extend the reach of the ServiceNow Platform throughout the enterprise and expand the value. But now we can look at where can we utilize generative AI in Now Assist to add additional value to this business process. So if we think -- if we start on this converse, where our end users are going to interact with this business process. Now we have an AI virtual agent, it can make this a much more personalized experience, where the end user can ask questions, make modifications to previous responses and really have a much more engaging process throughout this conversation. But then what we can do is as we transition into engaging our end users on the back end as well as these different platform capabilities. We can prepare the data, right? We can generate new content that we need it. And what we can also do is summarize this whole process for user consumption, right? So a lot of times, we might have multiple agents or fulfillers involved with this process that are pulled in at different stages of the workflow. And what we can do is make it very easy for them to see all of the different steps that have happened previously and all of the different actions, interactions with our end user. And so it enables them to onboard and offboard much quickly. But most importantly, it increases the user experience for both of them because they can fully understand everything that's occurred there, usually, but it can also increase the value of our interaction with the end user. Because how many times have you been on a call or an interaction when you're trying to resolve maybe an issue with the bill, right? And you get transferred to a number of different people and they have to ask you again and again, what is the issue, what have people told you previously, what are you expecting? And so that's a great opportunity for -- to utilize AI within this business process to help people understand it. But then also if you think this next step recommend, right? So based on all of the different things that have occurred, now what is the next best thing we can do to get to the business outcome that we're looking for. And so this is extremely powerful, this relationship between recommend and action, right? So we can recommend what maybe the next step, but then also allow the end user or allow our fulfiller or our agent to take the next action or maybe do it in an automated manner on behalf. And so that's really sort of the difference in our unique value proposition is not only to have AI, not only have local capabilities to build this application, right? And we'll talk about that as well. But then also the way to engage all of these different users, structure the work in a consistent business process and then add value at each step, utilizing our built-in skills and AI agents. And so a really good opportunity to really kind of get control of what's happening with so much kind of breadth and velocity is there's so many things happening so quickly, it's really hard to, one, kind of understand all of these different innovations. But then also how can we include those in a safe way that we could govern. And so that's really the meat of this is that it provides you the opportunity to pull all of these cutting-edge capabilities in, in a manner that has been basically vetted and placed in a walled garden right in the ServiceNow ecosystem where you can incorporate all of these brand-new capabilities safely within your workloads and within your enterprise. Okay. So that's a little bit of an overview of how you might think about incorporating AI into your applications. But let's now transition to App Engine Enterprise Plus because this is a new product that enables you to add all of these different things we talked about to your custom agents, right, is -- or I mean your custom applications, right? This enables you to build AI agents using our AI studio. And we talked a little bit about how these agents can do things for our end users. And again, it can do it in a number of different ways. The important thing is we don't view AI agents is replacing people, what we do, but we view them as augmenting what they do, increasing capacity and increasing efficiency. So these AI agents can either run autonomously in the background to look at -- capture different events that happen in the platform and then utilize their sort of ability to adapt to a variety of data and a variety of needs to utilize different platform capabilities or -- and we'll look at it shortly, it can also be utilized to really make the end user experience much richer and much more efficient and easier for them to complete their work. What this -- what Enterprise Plus also allows you to do is to create brand-new GenAI skills, right? And so skills can either be directly incorporated into your custom applications, where you can add these Now Assist capabilities directly to the applications. And they can also be utilized as building blocks for these AI agents. So a very powerful relationship and hierarchy between these custom skills that you can build specific to your business requirements and needs, and utilizing those collectively in an agent. And then what we also have now is there's a large number of platform skills that are available with the platform. And this can really give you a head start in incorporating AI into your applications because those are prebuilt, their capabilities that you can easily pull into your applications very quickly. And take those existing business processes and again, use those different AI inference capabilities to enhance those processes and make them more efficient. And so what the benefit of having all of these different capabilities together, right, on this platform is it's just so much easier to implement those. And so what we're seeing is what a lot of times people understand the value of LLMs, of traditional machine learning, right, of AI. But it's such a heavy lift to understand how can we take all of these different possibilities and now make them real and incorporate them in our business processes and create a situation where our enterprise can realize value from them quickly and also safely, right? And so that's where ServiceNow has this unique opportunity that we can offer you is that we can take it, like I said, this best in class way of structuring this work in integration in building out these business processes all on one platform with your data, but then also now enhancing those capabilities with all of these AI agents and generative AI skills and have all of the scaffolding built, right, where the tooling is already there, a number of the skills are already there, a number of the different agents already there. And so what you can do is take all of these different capabilities, look at the opportunity provided by what the benefits that AI gives you and then now you have a clear path to implement those capabilities and do it in -- where you can realize the value very quickly. So that is an overview of App Engine Enterprise Plus and it's a way to add all of these capabilities we're talking about to your custom applications. Okay. So now let's talk about or think about AI agents because this is really where everybody said it, we don't want to minimize the importance around AI for building applications, right, where we can -- and that's so powerful because when we think about AI for building applications, we can think about describing an intent for a business outcome and then the LLM building that on our behalf. And so that's a little bit similar with AI agents as well, right, where what we can do is in our AI Agent Studio, we can describe what we're looking for, right? What is our business outcomes that we're trying to achieve. And so in the demo, we'll look at this property management application. And so one of the AI agents can look at a lease request and then perform a number of different steps that -- and it can make decisions as to whether those steps are going to be required based on the information that we have around the possible tenant, right? So this ability to describe what we're looking for is very powerful. And then what we can do now and when we think about these agents is we can give it tools, right? So we can provide these different platform capabilities, data and the building block generative AI skills to accomplish the intent that we've defined for this agent. And so what we can do now is connect all of these different agents into specific use cases. And so what these use cases now are aligned to a business need, right, to business demand or something that we want to achieve. And so what's great about this is that once we've built out these different use cases, we can also define how our end users are going to interact with them. And this is incredibly powerful and the difference here between building out these capabilities using an agent versus utilizing workflow or a business process in Flow Designer is, when we build something out using those tools, those traditional tools, they're great, right? They're a way where we can very quickly structure this business process. But what it doesn't provide is the adaptability that the agent can. Because what we can do here is provide all of these different -- or describe these in different intents we're looking for, provide all of these different tools. And then the agent itself, right, within the use case can start making decisions on our behalf. And so when we look at the property management demo, we'll see where if someone has good credit or bad credit or it's based on our previous interactions with this potential tenant, how do we feel about them being a good candidate to lease this space. And so it can really introduce adaptability and be honestly an advantage where what we can do is take that decision making capability that previously had to be done by maybe our most experienced agents in this property management work space. They would have to make decisions. What we can do now is make it where really any of our agents now can interact with this -- I'm sorry, any of our users can interact with this agent and it could help them make that decision. So it can introduce efficiency for our more experienced users, but it can also now unlock the capability of us feeling comfortable maybe with a newer user utilizing this agent to help them make a decision as to whether they want to lease this unit to the potential tenant. Okay. So we talked about agents and skills. And so skills are really the building block of these different tools that are going to be utilized by agents. And again, AI agents are built using our AI Agent Studio, skills are built with our Now Assist Skill Kit. And these are the different building blocks that can utilize different LLMs at different stages of process where we utilize as tools in the context of an agent. And so what we can do here is describe the input, right? What this skill receives, the prompt, right, that is going to guide the LLM to understand what we're looking to accomplish. And then what we can also do here is provide a hierarchy here of additional tools that can be utilized by the skill and then this is something that we want to test and validate and then publish. And so like I said, there's a little bit of relationship here where the skills are essentially the building blocks. This is a little bit more of a technical process where what we're doing is building these different reusable capabilities now that can be utilized directly in your application or assembled to -- in aggregate to create an AI agent. And so this is a good opportunity to introduce these kind of smaller brand capabilities to our end users to other capabilities of the platform. And what we're seeing is part of the value of this is building up the skills and the prompt library into these reusable building blocks that can be included in various aspects of the platform, whether it's in a flow, whether our end users directly interacting with it or if it's also composed into a larger AI agent. Okay. And so what's really great about App Engine Enterprise Plus is it has all tooling, in this possibility and opportunity to build all of this different value that's specific to your needs and your business processes very quickly. And -- but what you also have is a number of different platform skills that are available to you that align to very common business processes and business needs. And so Now Assist in DocIntel, right, what we can do is we can take -- we can utilize Document Intelligence to take unstructured similar data like in the driver's license, use AI and machine learning to understand how to find the person's name, their address, the expiration date, even though there's a variety of different typography and layouts so we can use generative AI there. We can also use it in virtual agent. And a number of these different common scenarios where these capabilities are used in existing product workloads but what they can also be utilized -- but they can also do is be utilized to enhance your business process as well. And so this is another accelerator of where you can take these common themes and generative AI and apply them very quickly to your customer applications. Okay. So we've done a lot of talking about sort of ServiceNow's unique approach to AI and App Engine Enterprise Plus, right, the product that allows you to add all of these capabilities to your custom applications. And so now let's take a look at an AI agent and use. And so I'm just going to switch to a platform view here, and we'll take a look at this [indiscernible]. [Presentation]

Okay. Let's just blaze through at the next couple of slides, and then we'll have a little bit of time for Q&A. All right. So again, there's -- what we looked at is this property management demo very quickly. But that's just one of so many different use cases, right? But we are seeing use cases for things like this clinical trial management, where we can improve the validity of these studies or this promotional campaign plan where what we can do is we can automate campaign management from beginning to end, because, again, we have this ability now to utilize generative AI to suggest all of these different -- all of these different steps, utilize all of these different tools and it's just a really powerful way to amplify your existing platform capabilities. Okay. So you know what -- while that is all very powerful. And hopefully, what we've done is a good job in conveying the ability to take your -- these applications that you've already built on ServiceNow? Or you are thinking about building on ServiceNow, utilizing all of these great low-code tools, right, to get the value much faster. But you also have the opportunity now is to make them more powerful, more beneficial and more useful. But what we also want to do is do this in a very safe manner because with sort of this brand-new technology that has all of these different capabilities, we really want to make sure that we have a lens on all of the different AI skills and models and data sets and agents that we're utilizing, both from a risk and regulatory standpoint, but then also from a fit-for-purpose standpoint, right? What we want to build and maintain are things that are useful to people in the context of these applications. And so what we have is our AI Governance dashboard, which allows us now, from one single workspace have a lens on all of the different AI activity on the platform. So here, we have -- we can manage it from a risk and regulatory standpoint. But then what we can also do is monitor these different use cases, right, that we are -- that we're building out. And so what we can do is from the very start to finish, look at the utility of them, how useful they are, but then also get a lens on how risky and they are and maybe how compliant they are with our standards. So we just have another couple of slides here and then maybe what we'll do is we did have a demo for this AI Governance workspace. But maybe what we'll do is just look at it here in this last slide, and then we'll have a few minutes for Q&A as well. And so the previous view of this AI Governance workspace allowed us to view these different capabilities more holistically, right? Do they align with our regulatory needs and our internal compliance postures but then what we can also do now is view all of the different building blocks that we also utilize on the platform. And so the different skills, these different prebuilt connections to these different LLMs, the prompts, the way that we instruct the LLM to infer and take information we provide and generate something new and then also the different data sets that we might utilize to inform those LLMs. So let's just in lieu of the demo of this AI Governance workshop switch to Q&A.

And so we see here -- let's see, the first question is, how will the platform help us identify and manage AI agent failures or errors? For instance, if there are errors with the tools an AI agent is using or underlying integration script fails? So there's a couple of different answers to that. The most powerful one to me is an AI Agent Studio just like a Flow Designer. Once you assemble and orchestrate all of the different agents and skills, what you can do now is test that process. So you can provide input to trigger that AI agent. And for every step and for every skill, you can see the prompt that was built out, you can see what was provided to the LLM, what you received back if there were errors. And so it's a really powerful way to -- when you're assembling these agents to understand exactly how all of those different pieces that in aggregate make up the agent will work in concert to fulfill that business requirement. And so that's a really good way. Certainly, there are also runtime -- there's a runtime ability to look at the log and see all of the different steps and outcomes associated with that. Okay. Another question we have is how can I know whether or not AI can be used in Flow Designer into my client environment? And so there's a variety of different options that you can utilize from a licensing standpoint, right? In general, when you hear a Plus SKU, what that is going to do is enable that these AI capabilities at runtime for your client, right? So there's a -- there's Now Assist SKU for all of the different product workflows as well as for App Engine, and that's App Engine Enterprise Plus. And so you will need that for the runtime capability. For the build time capability, there's the Creator Pro Plus SKU that provides all of the like text-to-code, text-to-app, text-to-playbook where you can describe what you're looking to accomplish on the platform and will generate it for you. So there's just 2 options, the Plus SKU at the one time and then the Creator Pro Plus SKU all of the different build tools. Okay. So related to demo, to pull in the credit score, the AI is getting information from which credit bureau and is that an integration that we would have to set up? Yes. So that is -- we talked about the AI agent, and then we also talked about the different tools that are available to it. And the tools are essentially reasonable components that are -- that is either a data set or it's going to be some -- a platform capability or an integration. And so yes, you would need to build out that integration or if there are certainly integrations that are [ preplumbed ] available Workflow Data Fabric as well. And so it might be as easy as utilizing one of those spokes as a tool, just kind of depending on what system you're integrating. Okay. So question 4. Has Moveworks been incorporated into existing capabilities? If not, is there a safe harbor road map what and when that acquired technology will be part of ServiceNow? I'm sure that obviously, there are plans. I don't have anything that I can share at this point, but that will be -- that will be coming shortly. So stay tuned for that. And that's a very exciting acquisition because there's certainly a lot of things that we've done for building out our generative AI capabilities but they have as well. And so that would just make both of those products a lot better. So definitely stay tuned for that. What are some good examples that agentic AI has been used with customer service and/or IT system administration? So -- the way to think about AI agents is the flexibility and the adaptability of what's currently happened in the business process and then how the AI agent can be utilized to creatively assemble the different tools to get to an outcome, right? And so I think that this -- the property management example is a good way to think about it, where we know that when we receive a lease request, there are a number of things that need to happen, right? We know that we have a credit score. We know that we need to verify income, we need to verify company and personal information. But what the agent can do now is, it has the full history of that potential client and tenant as well as all the information that has been provided so far in that request. And so what it can do now is -- receive -- take all of the data both in the current request and what's happened historically now and make different decisions based on this. And so maybe one of the things that you want to consider is that if we had a really good credit score, and this is a known tenant, maybe you don't need to go through the cost and time of verifying income or looking at additional data, maybe in that specific content, you already have a high degree of confidence that this is going to be a good tenant. And so the agent can make an adaptation there and decrease the amount of verification that happens. And so that's just really how you -- when you're kind of getting your head wrapped around this is -- when we're thinking about Flow Designer and how we build things historically in step 1, step 2, step 3 with some basic logic even with something like a decision table, right, where you can introduce a little bit more sophistication, you're still fully defining how we get from A to B. It's just really the different paths. What you can do now is use AI agent to help in that decision-making and really pull in so much knowledge that these different LLM have in ways that -- how maybe this service scenario has been handled in the past. Okay. But after -- and so you've said customer service or IT. I'll just give you another one for IT. Another opportunity is -- if we're talking about event management and we're seeing signals or issues with maybe a specific external product that requires very specific product knowledge. A lot of times, that might require a specific type of IT worker to understand and remediate that issue, right? Because when we get that event, it's something specific to like some type of hardware. What we can utilize now is generative AI and agents to explain what is happening in nontechnical, nonspecific terms to that appliance. And then maybe suggest ways that, that issue could be remediated, right? So that's another way where we can receive this information, simplify it, make it more accessible and then also actionable to a larger spectrum of people. Okay. Well, it looks like we are just about at time. So thank you, everybody, for joining. Hopefully, this was useful and help you understand sort of ServiceNow's approach and how you can realize benefit from it because the thing that is most exciting to me is that it's actionable, right? These things are here today. And there's all of these different guardrails and tools that are available to you to take these concepts and incorporate them into your custom applications now. And that's, I think, really the power of it, right, is that you can take the data that you already have, the workflow and business processes you have and now all of these different capabilities. And now you can start incorporating them today into your business processes. So thank you very much, and have a nice day.