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

Hi, everyone. My name is Newsha Sharif. Thank you so much for joining us today. I would like to talk about how ServiceNow SPM can transform your business and help you achieve your goals. But before I start, I want to introduce my colleagues who are joining me today. James Ramsay, you may want to introduce yourself. I think James is on mute. I'm joined with James Ramsay -- go ahead.

Thanks, Newsha. Sorry about that. Hi, everybody. I'm James Ramsay. I'm part of the Outbound Product Management team at ServiceNow and Product Management Director for Strategic Portfolio Management.

Thank you, James. And he has joined us from the U.K., so it's afternoon for him. Thank you for making this time. Laxman, go ahead.

Hello. This is Laxman Rajagopal. I'm Director for Strategy Realization Office at ServiceNow. I'm responsible for SPM product implementation across ServiceNow and, of course, the value realization part of it.

Thank you, Laxman. So my goal -- actually, our goal for today's session is making sure by the end of this webinar, you have a good takeaway of how AI and SPM can help you achieve your goals. And feel free to ask any questions that you have along the way. We have some folks on the line to answer questions as well as we'll take them live. And I actually took a peek at the attendee chart, and I saw -- we have a lot of Canadian folks. Yes. I studied at University of Toronto. So I'm glad to see Canadians. But of course, other people, I saw some folks from EMEA. So Prague, I guess, and U.S. attendees. So again, I have to have the safe harbor. So anything you see here, there are new technologies that we're going to talk about. So make sure that -- keep in mind that as you plan your AI road map with ServiceNow, make sure to connect with your account manager and your sales and do not make any decisions based on what you see here. Thank you. But before we kick things off, I want to take a poll. Again, want to see how you are in your AI journey. So please make sure to select one of these. So what describes your organization's use of AI strategy? So why do we think that -- which one do you think has the most -- Laxman or James? Which one do you think, a, would be use AI in production. They are running a pilot. They are exploring use cases and there are no plans for AI adoption. Which one do you think based on your conversation with customers? I know Laxman every day you talk to customers. And same as James, so which one do you think you would get?

Well, I think from my perspective, Newsha, if I was asking this question last year, I would have definitely seen either C or B being the predominant option. And the more customers I'm talking to this year are actually trying to leverage AI with inside production. So we sort of anticipate seeing potentially a rise in that area from an option A perspective.

Okay. Good. Laxman, do you have any guess?

Yes, very similar to what James just said. I think the last 6 months or so, I've seen a significant rise in use cases getting into production, a lot of customers seeing value already.

Perfect. So I'll let a little bit more of it, our attendees click on any of these answers. I see the numbers are going up. [Voting]

Okay. Going once, going twice. I still see numbers going up. Okay. I'm pushing. Let's see the results. Okay. I think you guys are quite right. So C, we are exploring use cases. And I am very surprised to see, A. We use AI production across multiple areas is more than we are running pilots. So that's really good. And I'm hoping by the end of this webinar, we have more convergence in terms of going up the track and making sure that you use AI in production. All of us -- we use AI day-to-day. So we want to make sure that our lives are easier and more productive. Okay. Thanks, everyone. Well, digital transformation promised all of us that it's going to solve all of our problems. We're going to be very productive employees. We're going to bring really good experiences to our customers and everything will be at speed of very fast innovation so that organizations will be top of the game, making sure that they are competitive in the market. But unfortunately, that wasn't the case, and it was a lot of complexity. Unfortunately, with digital transformation, where people brought more than 300 -- on average, there are 360 applications in an organization. And there were a lot of lack of alignment between what customers would need and what business was in their strategy. Also, a lot of manual processing in terms of planning. So unfortunately, that wasn't the case of giving digital transformation, something that employees and customers were looking forward. It didn't give them that edge in terms of giving them -- become more competitive. It created more chaos. And then AI came along and said, okay, AI would be helping us with this complexity. And that was the North Star everybody was looking at. We're going to solve all of our problems. But 79% of executives, they thought they're going to fix it, but unfortunately, the execution was really bad and didn't help with those goals that they had. The reason behind it is that AI is only as powerful as the platform that is built on. So it is important for the platform to be able to offer those capabilities. And that's why at ServiceNow, for the past 20 years, we've built this platform to make sure that it brings all these -- everywhere, all of these silos and data together to have a platform that is ready for AI. So what is the AI platform at ServiceNow? It connects every single organization from IT, human resource, customer service, finance, all of them are on one platform. All the data, there's no silo. All of them are connected. That's why you can have AI agents helping all of them because the workflows between them, the data is the same. They can access the data and able to make workflow and give the experience that you want from AI agents, autonomous execution. And this makes us very differentiated because we have one platform, one data model, and it's across the business. It's not just one silo in one section of your organization. And I want to dig deeper a little bit. I know it's an eye chart, but bear with me, I want to explain how AI platform helps you with -- our ServiceNow platform helps you have a very good experience with agentic AI that we're going to talk about. Starting from the very bottom, I talk about how digital transformation created so much complexity because on average, organizations have more than 360 apps. I'm sure there is a lot of you on this call that even don't know how many applications are in your organization. You don't even know how many in different places that are being used. So at ServiceNow, we bring all of the data that's coming from all of the applications and infrastructure. It doesn't matter if you're running your data on ServiceNow or other players, for example, like Azure. But we bring all of them together. We have something called Workflow Data Fabric that takes all of the data from different infrastructures and have one unified view of all of them. Then we have something called RaptorDB, which access all of those data very fast. It actually is 27x faster. So if I can do my, I don't know, anything in life, 27x faster, I will do it. But it's [Technical Difficulty] access all of those, and we have zero copy partnership with some partners such as Snowflake and Databricks. So it access all of the data. And then it puts automation because we've been doing this for the past 20 years in terms of platform. So we access all of the data. We put workflow on top of it. And with AI, the most important thing in AI is you have to have good data, you have to have access all of those data. So with agentic AI that we have solution, it has the best data, the workflow and data to work for all of these. And we bring agentic AI, we execute it. It's not just getting data, we really help with business transformation across the enterprise. But once you have a good AI platform, your job is about how to start it, how to have a good strategy for your business transformation. So you have the data that you want, you have the unified view, you can make decisions based on what is the best business transformation projects that you want, you can prioritize them and you can make sure that all of your teams, your resources, your investments are all aligned to make sure that you are marching towards your business goals. And SPM sits at the center. So Strategic Portfolio Management, it helps you to strategize, start with a common goal. It makes sure that the goals that you are setting up are aligned to your strategy and your organization. You want to align all of your teams to that and make sure that your teams that are delivering have the capability in terms of -- no matter what the work is, it could be agile, it could be waterfall, it could be hybrid, they're able to pick what they want and be able to deliver it. So that way, you are delivering much faster to your customer and delivering value to them. And AI, I would say, turbocharges that, make sure that it accelerates that value delivery, which we're going to talk about in details, actually, both Laxman and James are going to talk about in details how SPM brings value much faster on autonomously using agentic AI. And before I go in and ask Laxman about how he is using it in ServiceNow, I want to show you that SPM, using AI, delivers value across the enterprise. It's not just one section, one persona. It goes across business leaders, CIO that I talked about, PMOs, product managers actually use it. I'll talk about it later. It just looks at how you can summarize customer feedback, making sure that you deliver value to what your customers really want. So it makes everything much faster. Program project managers, which we're going to talk about, and of course, teams. And I'm just going to glance at these because James is going to show you a real demo, which brings everything to life. But some notable capabilities that our SPM was powered by AI agents has is one of them is AI agents can monitor project tasks autonomously. So it's using agentic AI. The other one is how you can generate great stories. Actually Laxman is going to show you that and how we can have a more conversational experience in terms of the demand intake process. And of course, summarizing voice of the customer that I talked about, summarizing long documents, which I'm sure all of us, if you're in a project management role, you want to summarize things that are lengthy and gather all of them together to get the decision faster. So all of them are enabled by SPM using AI agents. And before I hand it over, if anyone has any questions, please put it in the chat. We are happy to answer that. So now I want to hand it off Laxman, and he is our person, I would say. He is the Director of Product and Value Management, and he is the one who is helping us using SPM, making sure he brings value to customers. But I do not do justice in terms of what he does day-to-day. So how about he introduces himself and what he does every day, what keeps you up at night, Laxman, tell us about it. Your job is very stressful. Tell us how you bring peace to yourself and helping your team.

Thank you. It's obviously a pleasure that I work here at ServiceNow, and it is very exciting and engaging. But to be honest, it's also sometimes very demanding, right? So my job here in ServiceNow is to implement ServiceNow's all new features, all new capabilities in ServiceNow and also ensure we innovate consistently in how we plan, how we execute and how we deliver our work. I also have the fortunate opportunities to connect with many of our customers and understand how they use the product and share with them best practices for it. So there are 2 parts to my job. One, implementing SPM to SPM experts inside ServiceNow and the ton of questions that they get to ask me. Two, I have the need to ensure everything that we do inside ServiceNow is of high value prioritization, all the process and frameworks that come with it is all my job.

Okay. Great. So before we go dive in on how you are using SPM in ServiceNow and drinking our own champagne, I would say, I want to ask you a question and see how our audience would respond as well. So please help me, our attendees. Let's see which areas do you really want to see. And do you see actually most opportunities for AI to influence your portfolio decisions. Based on the people that registered and attending, this is something I'm sure they're interested about. So is it mostly around goals and OKRs? Would it be portfolio prioritization? Could it be using AI to do your capacity planning or status reporting?

Wow, that's a really nice question. Why not all of the above?

I was going to say, why don't you want all of them so that you can sit and relax, have your coffee and let AI agents do their job. It would be nice really to have everything set up autonomously. Let's do the goals, let's do the prioritization and then let the team do their own things. And I want to get the status report fast, everything needs to be red -- green, sorry. No red, green.

Yes. So let's talk about this for a moment, right? So I know many of our customers and some of -- I mean, the poll answers for the first one, I think many of you are still exploring use cases. So in today's session, we're going to talk about how we think SPM and our approach to not just ServiceNow, not just SPM, but generally, as a practitioner, how do you approach work on a day-to-day basis, right? And how we think the agentic AI and this new advent of technologies is going to have a heavy impact in every single aspect of work, right? So in this, I think there is going to be a layered approach in how AI is going to mature in each of these areas. So Newsha, when you spoke about, you spoke about all the different personas that SPM is influencing and how AI is going to help each of them. So here in this answer, too, you would see goals and OKRs; digitization is going to help business leaders; portfolio prioritization is going to help portfolio managers, portfolio directors; and capacity planning and status reporting are going to help program managers, product managers and every single team that is going to report on status, right? Yes. So for all personas, all areas of work for me.

Yes. Okay. Let's see how our attendees are answering. Again, it goes back to the persona, I would say, who wants to get more productivity and do less manual work for their persona. Let's see. [Voting]

Capacity planning and portfolio prioritization are getting the most. Looks like we'll have AI helping them. That's great. And thank you for the rest of you who selected status reporting, goals and OKRs. We're going to see great things coming to SPM and AI as of now actually and in the future to make sure that we have a better experience for all the personas using SPM. So Laxman, let's hear your story at ServiceNow.

Thank you, Newsha. So we -- at ServiceNow, we still think we are a very small agile start-up. But in reality, we are a budding corporate with so many different functions across our organization and everyone wants to innovate and there is an accelerated need for digital transformation across function. So this is just rapidly changing in dynamics with the advent of AI. There's tons of use cases, the tons of things that people want to do. So who is at the helm for all of this? We have product managers. Our product managers are facing significant challenges, right? So there's a high volume of demand. And because of the nature of AI, there is a consistent change in how things happen in our organization today. While all of this is true, there is also a high bar on the quality of product, the quality of features, the quality of work that the product managers are expected to do, right? It's not new for our business, but it really gets challenging for our people who live this day in and day out. So what did we do? We wanted to see if we can put AI to work for product managers who are using SPM day in and day out first. So we took the challenge on to say everything that we're going to solve for today, let's lead with AI first. But we should also remind ourselves that we are using all of these technologies to help humans, to help people who live through processes, who build features for people, right? So we always keep human in the loop. And definitely, like any other innovative area, we're striving for progress over perfection. So there is a continuous eye on how do we continually improve models, our queries to ensure we get better experiences for our product managers and every persona involved. Let's look at a couple of things that we really did. One, we had looked at product managers and said, where do you spend a lot of time on? None of them were happy writing detailed stories and acceptance criteria just before a sprint or just before a PI planning exercise, right? And it was just way too stressful for them to ensure you cover every single aspect of doing this. And writing acceptance criteria in a standard format and trying to coach product managers to do that, you're never going to see the end of it, right? So that's where we thought AI could play a huge role. So here, we have a product manager who was building a procurement platform across ServiceNow, and we were exploring it to deploy in countries across the world. With GenAI, all of our product managers are likely to write one word sentences, which can now be elaborated to write detailed descriptions. Not just that, you can also use GenAI to now write acceptance criterias in predefined standard format. So any acceptance criteria should talk about what is the persona that we are solving for? What is the context that this story is going to do? What is the action that we're going to get out of the story and what is the outcome that we finally deliver? Imagine me training product managers, every single one of them, to write every single acceptance criteria this way. It's not going to be possible. Now, we also have the GenAI to write summaries for us. And imagine after every release, you go through all of your 20 different stories and writing summary for each one of them. Now we have GenAI, which can write summaries for you and it can summarize by product, by team and by [indiscernible] right? Here is one where we have summarized all of them in a single click of a button. You have the option to go edit, make changes to that summary and ensure you add your personal touch by keeping humans in the loop. You can save all of this, you can say, how well is the summary written and just send an e-mail right from here. So no more taking tons of time preparing what happened in the sprint, how well did we deliver what and all of it, right? So this ensured that we have good summaries written all the time. Now we had product managers who could write quality stories at a rapid pace in time. Two, they could send summary notes at any given point in time in the sprint for all of the completed stories. Why wait every Friday for a status report, right? And the number spoke for itself. We had product managers reporting 70% efficiency in writing stories. Of course, they had to go make it much more nuanced to the specific details that they were asking to do, but 70% of their time was saved in writing stories. Just in the last year, we wrote 15,000 stories using GenAI across the digital technology organization in ServiceNow. And all 100% of those stories that were generated by AI met our standard quality criteria, which meant very clear identification of persona, the context, the action and each of these outcomes. Now we all spoke about all of these different personas and many of you are looking at use cases on which ones to look at, right? Here, we are in ServiceNow. Really excited about how AI can transform the way we look at work are looking at the whole life cycle now, right, from strategy to value, what do each of these personas do? What areas could GenAI or AI or agentic AI impact them, right? So we are going to look at 3 specific areas. One, how do we strategize and how do portfolio leaders look at strategy and the work that they do there. How do product managers ensure they align customer feedback. They understand what needs to be delivered and how do we have program managers who ensure delivery of value to all the investments that we have made. Now let's look at portfolio leaders. Newsha asked me about what keeps me up at night. For portfolio leaders, they're always thinking about, are we on track to meet our objectives, to meet our key results? Are we ensuring we invest in the right priorities? Am I investing to the ones which offer me the most value? Or am I prioritizing the one that's loudest in the room, right? How can AI help them? AI is going to help them by proactively identifying the risks that are associated to each of their objectives and key results. It can summarize actions and insights based on the progress that each of this -- what is being delivered to meet those objectives and key results. A lot of you are thinking about how prioritization conversations can happen. Imagine GenAI generate all of the what-if scenarios that you want to have visibility on before you take a call. And capacity constraints is never going away. For which, capacity should be allocated to which area of work can all be recommended by GenAI. Now for product managers, product managers are in constant pressure by looking at the numerous feedbacks that they get, right? And they get feedbacks across their product features, from numerous customers, many of them could be duplicative, too. How can they identify and resource the right feedback and deploy these features in quick time. So we have already seen how significant is AI's impact in the life of a product manager. We want to extend it further and then think our questions on, I get tons of feedback. Can I do an automated sentiment analysis and say which of those feedbacks are positive on which features, which areas of those interactions do customers like versus not. How about generating not just stories, but epics, writing business cases for you, writing features with detailed descriptions for you, getting insights not just at the end of a sprint or at the beginning of a PI, but all through the PI, all through your release planning on how it can help. Finally, but not least, our program managers, right? So I mean, every single customer that I talk to, they want status reporting to be generated automated, right? So imagine all of these status reports that our program managers write. I write one for my [indiscernible]. I am tying to give a different explanation for my executives. And then there is my team that wants detailed answers on different aspects of the program. And is it all of the work that we do to get outcomes of the program? Are they very clear? Is it really aligned to the strategic priorities of my organization? And the 1,000 other questions that the program managers get, right? We honestly think status report should be any time. It need not wait until Friday evening, right? So anytime status reports and the thousands of questions that a program manager gets to answer, when is the next release? Is this on time? Or is the status on red or green? All of that could be provided by AI and agentic AI to -- and available at an instant for all of the different personas in the organization. We also see a huge need in summarizing collaborative documents, which can be delivered by GenAI in the most insightful way. James is going to show you some of that. Okay. So we have seen the impact of AI across our lives, right? But let's just take a step back and think what do we bring to work every day, right? When we come to work, we are doing 1 of 3 things. One, we create documents. We create work tickets, meaning I create a project plan. I create a product feature list. I create my backlog. I summarize information for my leaders. I am either summarizing status reports, I'm summarizing what went in the sprint, I'm summarizing what happened in the release, so on and so forth. Or three, I'm answering questions for people. Why is this project red? When is the next milestone? How am I going to deliver this? So if you look at all of these 3 things, whether you're creating content, summarizing content or answering questions, all of these 3 things are things where GenAI can make a huge impact on. So we are going to look at how creation, summarization and answering questions can all be done by GenAI and agentic AI can execute those tasks that you have approved to do. So we, at ServiceNow, believe we are at the brink of a huge disruption in how people approach work on a day-to-day basis and how efficient it can be done. So very, very excited for what's going to come in the next 6 to 18 months from our platform. James, here you are. Now I know you get to talk to more customers than I., and you see ton of new features much before many of us do. So I'm really excited to hear you speak about what we have in ServiceNow SPM already.

Thank you, Laxman. Right. So what I'm going to do in my section, I'm going to give you a quick overview demonstration of some of the new things we're working on from an agent's perspective, some of the things we've released and give you a general view of how we're providing AI across some of the personas that Laxman has been talking about. So I will share my screen. And I think from a starting point perspective, if you think about what we're looking at now, generative AI and agentic AI, and how that's being applied to Strategic Portfolio Management. I've sort of been in the world of Strategic Portfolio Management for -- going on 30 years now. And for me, what's on offer now in terms of what agentic AI can do to the traditional strategic portfolio management life cycle is probably the most transformative thing I've seen in those 30 years. So really excited about what's been able to be done. So I think as a start off to the demonstration, I'm just going to give you an insight into what we rolled out in the Yokohama release in terms of AI agents and the AI Agent Studio because this is sort of the foundation in terms of where we're going to drive all of the benefit that Laxman was talking about in his section. So first of all, what we can see here, this is the AI Agent Studio. And I think one important thing to recognize from a ServiceNow perspective, whether we're talking about Strategic Portfolio Management or we're talking about enterprise architecture or IT service management, et cetera, we're going to be rolling out and shipping out of the box AI agent use cases or agentic workflows. And these are going to be able for -- customers to be able to take those and use them as they come out of the box. Customers will be able to tailor them with inside the AI Agent Studio may be to air specific to their particular organizations, but they're also going to be able to create their own army of AI agents to actually align across that process. Now sort of introduce the concept. I'm going to just walk you through one of the first use cases I created. Now this is something around technology rationalization. It's got a lot of interest from organizations who are looking at driving down their operating costs. So effectively, I created a use case that would help transformation leaders, architects of various different formats in order to help them drive that rationalization. So essentially, in order to start the process, what we would do inside the studio would create what we call a use case. So in this case, we describe what the use case is, give it the name. We're going to help enterprise architects in terms of rationalizing their technology. So basic description. And then at a high level, we give a structure to what this use case is going to be about. And essentially, what we're doing here is we're going to start off by analyzing the cost of our technology portfolio. We're then going to go away and have agents identify where we've got some rationalization candidates. And then on approval, we'll then get the agents to go away and then create the demand records that are going to implement the decisions being made and they also feed into Strategic Portfolio Management. So at a very high level, we provide that description all using natural language. Allied to that, what we then do for the use case is define what we call a trigger. Now a trigger is something that the AI agent orchestrator in the background will utilize to identify whether or not this use case needs to be instigated. Now triggers can be anything. This one is a very simple trigger. It's based on an OKR where the target value of the OKR changes. But it could be something relating to a change of information in the database, the creation or update of a record. It could be related to an API call from a third-party application. It could be only instance of a user wanting to start off the use case. So lots of ways those triggers can actually work. So how we define the overall structure of the use case, the trigger from which it will kick off, we then associate to the use case a series of agents. And you can have multiple agents supporting a use case. Now one of the things you should think about an AI agent is it's something that's got a task to do, and it can go away and autonomously do that task. So in this particular use case, just to make it simple, I've got 3 agents. One agent will go away and do my cost analysis. One agent will go away and identify my candidate applications. And the third agent will implement the decisions on the approval of the human that we keep in the loop to implement that. So we associate those agents to the use case. Now if I just quickly drill into one of those agents. Again, following the similar process to defining the use case, what we do when we create the agent is we will provide it with a description. We tell the agent what its role and purpose is. And then we give it a prompt of what we wanted to do. And again, all using natural language. Now when I write my prompts, I tend to be a little bit verbose. But you can tailor those prompts in terms of how you want to do it. So that's basically telling me that this particular agent is going to go away, get me my scores for my technologies and feed those back and have a dialogue with a human in order to refine what that rationalization list looks like. Now in order to help the agent perform its role and perform that task, we provide access to what we call tools. Now these tools are enabling an agent to go and maybe find information to analyze information and maybe to generate information. And tools can come in, in a variety of different formats. So here, we've got a very simple tool that's based on a script, but I might want to use something like analysis skill. I might be wanting to do things related to records in the database, creating them, updating them, et cetera. I might want to use search retrieval where we're using racks to actually go and look at content or going to see an external document. And for things where we're going into automation, maybe into integrations and things like that, I can use workflow as well. So lots of different capabilities we can provide our agents in order to do their job successfully. So how you build the use case, associate the agents to the use case, associate the tools the agents need to do. You can then in a position to then look at how you're then going to roll out that particular use case. Now in order to test it out before we go into the live environment, we can use testing with inside the agent studio. So I can elect to either test a specific AI agent in isolation or I can test the entire use case. So here, I've got my technology rationalization use case. As part of the test, I give it a quick task that I want it to do. So in this case, we're going to reduce our legacy application cost by 50%. And then I will click on just start test. Now the output of the test then forms in 3 windows. The left-hand side gives me a view of what this use case would look like utilizing then the assist panel. The middle window will give me visibility of how once this use case has started, how the agent orchestrator will then work with the various agents in order to complete the task. And on the right-hand side, I have an important view of all the decision-making processes that happen throughout the running of this use case so I can analyze how my agents are performing and maybe I want to refine those before we put it live. So in this particular case, as I've run this, my first agent has run and has given me that analysis of my portfolio costs. So it's gone away, use the tools to go and find out what the current customer application portfolio is. It's worked out what 50% of that is going to be. And then it's also then going to look at my demand pipeline and identified I've got $8 million worth of savings there already. So I now need to find an additional 5. So that's that first agent. The second agent will then kick in because that's going to come and give me those rationalization candidates. And for this particular use case, I've asked it to provide me with 5. So it's going to go away and provide me with 5 applications in this particular case that we could be looking to rationalize and it's done that based on the application score. So it's giving me information around the business value, the technical fit, whether or not that application is a critical business system or not and importantly, giving me information relating to the potential savings. And then I'll scroll to the bottom, it gives me an overall view of -- if I was to rationalize all of those, what would be the savings I need to correct. So essentially, that's showing me how that agent would run. And with inside testing, I can then engage with the agent as if I was running in real time and maybe I want to refine that list before actually going ahead and implementing it. So I hope that gives you a feel for the real powerful structure we have now in place using the AI Agent Studio and the power you can get from leveraging those AI agents to perform specific tasks. And I know in the chat, one of the questions that was asked was around can we use AI agents, for example, to identify where we've got duplication. The question was around duplication of projects. I've worked on use cases with customers already where we've identified AI agents that helps them identify duplications on demands being submitted and asking whether or not human wants to merge those demands together because we've got multiples of the same submission. So lots of use cases like that possible utilizing the AI agents. So not only, as you will see in a moment, you can utilize the AI agents we provide out of the box, you'll also be able to create your own agents utilizing the studio going through the process as just defined there. So let's go back into Strategic Portfolio Management. Just as a recap so that all of you be aware, strategic planning workspace is at the hub of Strategic Portfolio Management. It's where we can road map and plan out what our transformation is going to be and it's where we can track the progress that we're being made against that, against all the different types of work right the way across an organization. Not only that, as I'm delivering all of that work, I can monitor all times the capacity of my resources and identify proactively what those bottlenecks will be and at the same time, having a full visibility of the financial situation. So really powerful because we brought everything together in one place. And not only that, we're also aligning all of that activity to strategy. Now one of the areas we've seen that is a useful area where agents can participate in the process is remember OKRs and organizations coming up with the correct structure for their objective and key results or their goals and targets. Now before the start of the presentation, I went ahead and created a new objective, improving AI performance in our products and services. Now as I created that in the background, my agentic AI use case kicked off and my agents got to work. And I can see my Now Assist pal that as I've created my new objective, I've now got 3 suggestions for key results to associate with that particular new objective I've created. So it's created me 3. So I've asked for 3 as part of the use case. And as I'm working in here, I can actually say something like remove item 2 from the list. I could do comments on updating percentages or time lines with inside the OKRs. And what -- like I can then have a dialogue with the agents as I'm looking to refine the recommendations that they've made. And once I've refined the recommendations, I can then approve them and add them directly to my objective. So we're very much now working in a model where agents are identifying when something has happened, coming back to me with suggestions and then I can work with the agent to refine those suggestions and then implement them. So the humans at the center, but it means that the humans are not starting from a blank sheet of paper each time or not having to run reports to find out when they need to do things. So really, really exciting in terms of how we can improve productivity for various personas. Now let's go into the product manager persona that Laxman was talking about. So as part of strategic planning workspace, we have a capability around product feedback and say I'm a product manager for say, facilities insight solution. So this is something that we're utilizing across our operational technology across an organization, maybe as part of our various production sites. And I've been tasked with improving the AI with inside my product. So I can go and look at all the AI-related feedback that's coming. Now this could be feedback we're pulling in dynamically from emails. It could be things we're pulling dynamically from incidents or vulnerabilities and things like that, bringing that all together. And I can then utilize AI through Now Assist to actually instead of going into each one of these line by line, I can select all of the feedback relating to AI that we received. I can click on summarize. And Now Assist is now going to go away and give me a summary of what needs to happen relating to my feedback. And we can see top of the list, I need to do something about introducing a new AI chatbot for my facilities insight solution. And not only do I get that fast view of all of the feedback, I can then quickly action it. So whether that's automatically creating the demand based on that feedback or maybe create an epic that I can directly associate with one of the development teams so I can quickly action all of that feedback. So saving, again, product managers lots of time and enabling them to action that precious feedback as quickly as possible. Now on that, let's say we've gone ahead and we've created our epic. So you can see here, we've got our AI chatbot for our facilities insight solution. I'm now looking in what we call enterprise agile planning. So as part of the operational technology team, I can see all the work that we're currently looking to plan out. And I can then drill into that epic. So let's go and look at the full details. Now a lot of the information, as Laxman was talking about in terms of, well, what am I going to do with the epic? We need to actually break it down into stories and then we need to -- now again, what I'm able to do here is instead of working on a blank sheet of paper, I can use Now Assist. I can use Now Assist to create stories. So let's open that up -- just refreshing my screen. So those stories from Now Assist. So as I click my stories. The solution is [Technical Difficulty] content it's looking at, i.e., the epic, and it's then gone away and created me 3 suggestions for stories. And again, as part of the setup for this out-of-the-box use case, you can define how many stories you would like Now Assist to come back with. So again, we've got my 3, I can review those. I then got a series of options. I might want to start -- actually [indiscernible] go away and create me some others. I might want to combine them. I might want to take a story and split down to others. But for the time today, I'm going to click on confirm and say I'm happy with those 3 provided. And automatically from those suggestions, I can now see that we've got our stories associated to our epic really quickly. Now we're going to build out on this capability. Now this was launched as part of the Yokohama release. We're going to extend it so we break down features into stories and also the ability to create other different types of planning items throughout the year. What we're also doing as part of the teams working together is help them from a collaboration standpoint. So right the way through Strategic Portfolio Management, we have our docs capability, our collaborative documents. So here we can see for our AI chatbot, I've got my product requirement document. As part of the collaborative document, I can see we've got multiple people already working on it together, and I've been at message to shorten the purpose section as we can see it's quite lengthy. So let's go and select the text in the purpose section. As I do that, I've got access to Now Assist. I can elect to shorten and Now Assist will go away and give me that shortened text that I can then put back into the document. So we're embedding that AI capability into all aspects of SPM here with insight collaborative documents as we see throughout the year, we'll be embedding it with inside the forms themselves. So as we're working on various different aspects, we'll be able to utilize that capability to improve the writing of information throughout the system. Now for the final part of my demonstration, I'm going to change hats from being a product manager, I'm going to become a project manager. So as a project manager, I've got access to the project workspace. And this gives me the ability to manage all aspects of my project together in one place. So I got my deployment of predictive maintenance, and I can see the project schedule. I can see information related to my resources. I've got access to all of my financials, my project controls and all of my project documents, including status report, all together in a single workspace, really, really good. Now what we introduced in November was the ability then for a project manager to utilize AI to get a proactive view of what's happening inside their project, utilizing the capability, we call it Email Insights Project Summary. All the project manager needs to do is come into the project, set up the project summary, identify the cadence and when they want to receive that insight. Now what would happen then is Now Assist working on that schedule, would then send directly to a project manager's inbox what that insight would look like. So here is an example. So this is sort of a preview email rather than trying to demonstrate my email inbox. But here we can see what that would look like. So I've got a summary of this particular project, my deployment of predictive maintenance. I can see in the structure of the email, it's giving me an overall insight to what's happening in the project. It's telling me what needs attention and then breaks that further down into insights and milestones and resources and the project [indiscernible] themselves. And at the bottom, I've got a link directly back into the project with inside the project workspace. So what I found when talking with customers was project manager would like to have an update rather than having to come into the system and find out what's going on, let's be proactive about it. Let's have that before they actually even get into the system. And then from that, they can determine what they want to do. Now within Yokohama, we went a stage further, and we launched our first AI agent for Strategic Portfolio Management, we call it Monitor Project Tasks. And this takes that insight email summary to the next level. Because rather than waiting on a schedule, this is going to look at it dynamically. So it's going to go down, it's going to look at the project tasks and it's going to look in terms of how we're going to find out what those critical updates are. Now associated to that use case, we've got 2 agents. One will identify whether or not the change to the project is critical and the project manager should be notified about it. The second agent will then create a summary for the project manager based on that critical update because obviously, we don't want agents just sending updates to project managers on anything that they're not really [indiscernible]. So in this particular case, that will look something like this. So as we can see, the email directed to the project manager, we can showcase that has come directly from the agents. And as we go and look at the body, we can see what's triggered the update. In this case, it's the relationship of the business case deliverable that's going from green to red. So obviously, something is wrong there, I need to know about it. And then I get a further update associated to the project. So as you can see, very dynamically, AI agents are adding additional value to project managers, product managers, portfolio managers by being dynamic and enabling us to identify very quickly what needs to be done and enabling those personas with it directly. So with that, I'm going to hand back to Newsha.

Thank you, James. This was very engaging. It was a really good demo. It brought to life what we were talking about from the beginning in terms of slides and you show actually how it works. And the things that James showed are out of the box, so you're able to leverage them in -- and I saw so many questions. If you are existing customers, you can be using SPM Pro Plus. But I know we are running out of time, and a lot of questions were asked both in the Q&A as well as the chat. So we were able to answer them. If we haven't got back to you, we will. Don't worry about it. We have your email addresses. But before I wrap things up, we have acknowledged the largest customer event of the year for ServiceNow is happening May 6 to 8. Make sure to register and attend if you can. You can learn a lot of new things about AI, a lot of new things about -- interested about SPM. There are demo, live demo, customers like you who were dealing with challenges day-to-day and how they overcome those. And so it's filled with a lot of useful information and sessions. And there is a link in the chat. There are so many assets that are in the resources section, make sure to look at those, and it's all related to how SPM and AI that you all looked at. And we have a link to all the on-demand webinars. This one will got recorded. It will be there. And I saw so many questions, and I'm so sorry, we are not taking them live right now. We are at 1 hour past. And this was a very informative for us actually. Thank you for asking questions. Thank you for taking the polls. And again, make sure to tune into other webinars we have. Thank you so much. I want to thank James and Laxman as well, and [ appreciate it ].