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

Welcome, and thank you for joining us for today's event. Before we get started, we have a few housekeeping tips that will help make your experience more enjoyable. First, today's session is being recorded and you are currently in a listen-only mode. [Operator Instructions]

Hello. Welcome to this webinar. We're excited to present this webinar for you today on how ServiceNow's strategic portfolio management can help you refine strategies for GenAI initiatives. In other words, do I have a strategy and a road map for AI? And how do I execute on that road map. The people on this panel today, myself, Jim Tisch, I'm a Director of Product Marketing, supporting strategic portfolio management at ServiceNow, and I have a few other colleagues on this call, so I'll let them introduce themselves.

Hi, Jim. I'm Doug Page. I'm the Director of Product Management for our Strategic Portfolio Management solution.

Hey, everyone. My name is Alice Saiki. I am a Director here for portfolio management supporting our internal employee at ServiceNow.

Thank you both. Excited to have you both discuss this hot topic and the power of AI. We're going to talk today about trends. We're going to talk about plenty in GenAI. As I mentioned earlier, what's your road map, what's your strategy? How do you deliver on that? And then what are the outcomes you can achieve by planning with the strategy? We're going to talk about what a known as Now Assist. And then we're going to look at some use cases would Now Assist for strategic portfolio management. And then at the very end, we'll take Q&A and answer your questions. So feel free to use the Q&A and get those questions queued up, and we'll get to those towards the end of the webinar. We have the safe harbor. We'll be showing you things that are forward thinking, so just letting you know that, and that's what this statement is all about, but I will move forward and talk about why we're here today, which is the fact that AI powered business transformation has arrived. And today, your employees and your customers are already using AI tools for work. From generating code to getting your employees and customers quick personalized responses, GenAI is fundamentally changing the way we work. Our CEO, Bill McDermott, has said every workflow and every enterprise and every industry will be reinvented with GenAI at its core. So we know that GenAI is quickly becoming table stakes in many markets. In fact, there's a great Gartner report that was actually deployed in 2024, and it talks about how 50% of AI adopters have already moved from exploratory to pilot stages into production deployments. So the organizations are moving at lightning speed. And it's top performers are investing in AI, but for some, there's actually barriers and there's roadblocks. And Doug and I are going to look at some of these roadblocks and talk to you about them today. So for example, in this Gartner report that I mentioned, it talks about how 33% of GenAI projects are abandoned before they actually move from proof-of-concept stage actually into a production stage, which can be a problem and often that can be caused by just poor alignment where there's not unified access across product and operations, if you will. Doug, why don't you talk about that second one?

Yes. I think the result of the third -- of the first one is probably due to the next 2, 63% of organizations feel like they're not prepared with an AI strategy.

Yes. It ties into the next one, Doug, where it says 70% of the company's lack a cohesive strategy and roadmap for implementing AI and they see that as a key challenge for success. So other reports and analyst quotes and data show that having a cohesive strategy roadmap is really key. I think when you do that, it basically takes siloed and inefficient processes and helps mitigate those. Some of these challenges actually happened because of just rigid and flexible governance but were just lack of strategic alignment. And Doug, I don't know if that's what you're synergy talk to customers.

For sure. I think overall, the next year, 18 months, in the space of generative AI is going to be very interesting because I think from the onset, there has been so much hype around GenAI. I think that in the short term, it's very much expected that we are going to under-deliver on the promises of GenAI. But I think those firms who kind of give up or see this failure and kind of throw the talent are going to be the ones who are kind of left behind as we really discover what are these applications of GenAI that really are going to change the way that organizations and business work. Those are the ones who persevere and kind of maintain the course, the ones who have a strategy are the ones who are going to reap the benefits. I'm convinced that GenAI is going to change the way that most people work, most knowledge workers work. And I think as people have predicted just like the Internet, those impacts are probably even difficult to understand today and to predict, but they're still going to be there and the organizations that benefit from that are the ones who are going to stick with it.

Yes, I agree with you, Doug. The one of the things that you and I talk about often is that every company should have an AI strategy and be able to deliver on it. And so as we discussed at the beginning of this, that's our topic today, and that's what we're going to dive into. And then we'll look at some use cases. We have our Now on Now team Alice, she'll be talking about how they have a strategy and how they deploy that. And so Doug, let's actually kick this over to you, and let's find out where our audience is on a strategy.

Absolutely. So I as well believe that every organization should have an AI strategy, and that's why we're having this webinar today. So it doesn't mean that you have one today, but we do want to know where our audience is today. So where are you on your road map journey for GenAI? And if you think about this from A to D. A is most mature, most strategic road map going down to just kind of getting started. So I'll kind of read through these, a, is we have an enterprise-wide GenAI road map. So enterprise-wide meaning it's kind of shared understood by everyone, hopefully aligned to strategy. B is we're looking at deploying some successful use cases. So may already have some use cases up and running in production that employees and customers are using. C is we have experiments going on. So they're dabbling in but not necessarily a strategic road map or things that are in production. And then D is we don't know. Things are not clear. They're not being communicated. We don't necessarily know what's going on within the organization. So we're going to give everyone a minute to kind of answer there. Jim, based on what you've seen talking to customers today, where do you think we will see the most results there?

Doug, I'm going to lean between B and C. I think there are -- from the customers I've spoken with, there are some successful use cases that have been deployed through testing and having teams focused on that. And then there's also, though, we have a few experiments with GenAI. I think organizations know that they have some experiments going on, be it at the employee level, people using AI. But not necessarily sure what it is, but that it's going on. So I think maybe between B and C.

I would tend to agree that's where a lot of organizations are today. I would also not be surprised to see a lot of answers around D because I don't necessarily believe that organizations are doing a good job in sharing across the enterprise, what that strategy is. So there may be things going on that aren't clear and maybe there might be a case where 2 different teams are working on the same thing because they're not communicating. So we're going to -- we've got a good amount of respondents, let's see where we are today. Wow. So I think you and I have had some insight there. So almost exact same percentage for a bit of experimentation or not sure we have a plan, also impressive for that 10% that are -- that do have a road map for GenAI. So for those 10%, kudos, you can now join us next time and help share your story. And then there are 5% that are looking at deploying some successful use cases. So I don't think there's anything shocking there that there's a spread of where organizations are today. So let's talk about if -- for those that are in that 10%, they may already be doing this. But for those who aren't there yet, what are the building blocks for having a strategic approach to deploying GenAI in your organization? So there's a few things that should be in place. The first is to have a vision. So like any journey, you have to know where you're trying to get to. Otherwise, you're walking left, walking right, east, west you don't know which way you're going. It is important to say what do we want to get from GenAI? Are we doing this because we're trying to save costs. We're trying to increase market share. We're trying to increase revenue. We're trying to increase customer satisfaction. We're trying to increase citizen delivery if you're in the public sector, knowing what the strategy is, is going to help to determine everything else that we see on this slide because, of course, that will feed into prioritization. Right now, we see this approach where it's kind of like, hey, just experiment this -- it's like the 60s, just experimenting stuff and have fun, which in itself is not horrible, but you could be spending a lot of money, and I do think we are going to see a lot of organizations who kind of get that GenAI hangover are those who are just trying stuff out. They're spending money. They're also using a lot of their employees' time to try and figure these things out. That's going to lead to a lot of [Indiscernible], where we're like, well, we spent a lot of money, what have we gotten? Probably not very much. So once you have that strategy, you can start prioritize and say, "Hey, we're not going to experiment with everything. We're going to experiment with the things that are aligned to the outcomes that we want as an organization." And then execution, I think this is where people are probably spending most of their time today. Those experiments that they're doing. This is an execution. What's important about this is to increase the percentage of those experiments that are successful. You had shown that stat on the previous slide, 33% is not good enough. So how do we improve the execution rate, use learnings and best practices that are out there. And then next, really important is how do we bring in our enterprise architecture teams into this. Right now, when we talk to C-level executives, they can tell you on the tip of their fingers, the GenAI use cases they have in production, which is great. So it doesn't -- people don't see the need today to capture the information of where GenAI exists in their enterprise, what data is being used, who are the users, what applications are being implicated because there aren't enough use cases. But fast forward 18 months, 2 years, you won't be able to answer that question off the top of your head. So being able to have this catalog in a system of record is going to be very important for many reasons, the least of which is not risk management. And then finally, this entire process has to be optimized. You're going to -- especially within GenAI, there's going to be so much learning as I was explaining before, we are all going to learn as we go through this journey. So how do we create a cycle, whether it's a monthly, quarterly, hopefully not an annual cycle of how we go through learnings of we had a strategy. This is the work that we executed, what do we deliver, and how can we continually refine that cycle. So with that, obviously, there's a lot of change management that needs to happen within an organization. There's a lot that our customers, the people on this webinar can do within the organization. Part of that can be technology to help, and what I'm going to talk about for the next few minutes is how 2 specific solutions that ServiceNow offers can help, and there are others that I can refer to quickly. But the two that I'm going to talk about today our strategic portfolio management in the enterprise architecture. Obviously, there are others like we have an integrated risk management solution, which is very important in the domain of GenAI, but the two that I'm going to talk about our strategic portfolio management and enterprise architecture. So how do these relate to what we've already been talking about. The first is that GenAI strategy. So depending on the type of device that you're looking at this on today, you may or may not be able to read the screen shot. But what's obvious about this is that it's a road map. And if it's large enough, you'll be able to see this road map on the left is grouped by the primary objective, the strategy, what we're trying to do. You can see there's cost, there's revenue there. So what are the experiments that we're prioritizing that we're sequencing based on that priority and also looking at dependencies between these things, how does that road map align with strategy. And besides the experiment, there are other key things that you may have to have on your road map. That implementation of those risk guardrails. You may have to do a lot of data foundation work before you get into experiments, all those things should be on your GenAI road map. And then you can also use this to track progress as you go. What you don't see behind the scenes here for those objectives is that you have all the targets by date. So how much money do we expect to save by, let's say, the end of 2024 by mid-2025, what's the revenue growth look like within that period, so you can actually track progress as well. So that's the first piece is the strategy piece within strategic portfolio management. The next piece is really the execution of those experiments, whether you call them experiments, initiatives, projects, all of those are valid, you need to run those somewhere and share the learnings from those. So there's a few places that you can do this. If these are formal projects that happens within our strategic portfolio management solution because these are being positioned as the lightweight experiments that we want to execute quickly, you can also do this within our collaborative work management solution, where teams can just get together, they can leverage and best practice set of steps that they have to go through, and they can execute that very quickly without going through project management training, for instance. So that execution can get better and better. So the percent of use cases that you get into production can get higher and higher. This next piece is really important. This is where I was talking about the enterprise architecture. This is happening within our enterprise architecture solution, which up until September was known as our APM, our application portfolio management solution. You can see here, this is a model of the technology right in the middle to the left of that green circle is a business application that is being connected to a large learning model. We, through our common service data model are making that association of that business application going to that large learning model. So you can catalog all those applications that are referencing large learning models, what data is going between those 2 systems. As a system of record and a single repository to track everywhere that GenAI is being used within your organization. And now let's kind of flip a little bit. And Jim, I'm going to hand it back to you to talk about how we actually deliver GenAI capabilities on the ServiceNow platform.

Yes, 100%. Doug, thanks for going through those examples. As we've discussed, GenAI and as AI more broadly is a faceting technology that will forever transform the way we work. And when Doug and I talk to customers, yes, I want to know this, like what does this mean for me? How is it going to change my industry, my business and the way we work. And then how can you help. And so ServiceNow Now Assist enables a business to transform self-service basically and unlocked productivity and efficiency, just about every corner of their business in every department. So just to give you a few examples, like regarding transforming self-service. So with Now Assist, we have customers today that can basically have quick and who have enabled quick personalized help with a smart conversational AI assistant to unlock productivity and efficiency, you can let AI assistant handle time-consuming, repetitive tasks, freeing up agents and workers to talk more challenging and creative work at your organization. For boosting developer productivity and creativity, you can reduce IT backlog, you can create and configure workflows and build custom apps faster with AI, to accelerate time to value, you could realize value faster with prebuilt AI-powered workflows, running on the world's leading automation platform. And so Doug and I are going to take a deeper dive into this and discuss how this is actually achieved and the framework for how this is achieved as we started this conversation, we talked about how ServiceNow is the AI platform for business transformation and that many of the Fortune 500 companies that we have today trust us because we offer this full stack workflow of automation, all on a single platform. So that makes us very unique and different with our AI solutions in the market today. And so we're putting AI to work by building AI directly into a single data platform optimized for speed and scale. So Doug and I are going to do is look at each one of these room to get data. We're going to look at AI and automation to help you understand more how Now Assist is part of this framework of a single platform. Our domain-specific models are purpose-built for use cases on ServiceNow's platform. So it basically creates fantastic outputs that's very relevant and modernizes workflows in your organization. So Doug, let's take a deeper look at this and let's look at these 3 ingredients, and I'll have you take a deep dive into data, AI and automation.

Yes, absolutely. So let's start with the data piece. And I think where I'll start with is that word that you mentioned already is platform. That term gets thrown around a lot these days. I think it's important for our audience today to understand what the ServiceNow platform is, and how it might differ from other ways that the term platform might be used. And maybe I'll go back to the founder of ServiceNow, Fred Luddy, when he started ServiceNow as a cloud platform, it was good timing because it was before people really were serious about putting data in the cloud, but he did have this vision for having an enterprise-wide data repository for customers that they could leverage. Now I won't give him credit for seeing that GenAI was going to come around, but his vision for bringing the enterprise together in a single database with a single data model was a stroke of brilliance almost hazard when it comes to GenAI, your ServiceNow customers have so much of their enterprise data. They have their technical architecture, their business architecture, on the ServiceNow platform. And as this platform, we've also done a very purposeful job of connecting that other key platforms within the organization. And when you think about the implications of this for GenAI, having that repository, data is key for GenAI because it needs to reference something. In some cases, you might be referencing general purpose data like you want to know how to write business case. But in many cases, customers are going to want to unlock and leverage their own data to within -- with the power of GenAI and ServiceNow becomes an ideal place to do that. So that's the first part is data, super important, and our customers already have so much of their data on the platform. So next, let's talk about the AI itself. So this goes along with what I was talking about with the advent of GenAI, and this is really kind of come to the forefront in the last few quarters. But ServiceNow has been on this journey for a while. I think the executives have done a good job of bringing expertise in-house. A few years ago, we bought Element AI and some other AI companies. We've been working on this for a while. So we have large learning models on the ServiceNow platform. So if customers do want to leverage GenAI with the data that they have, they can do that within the four walls of the ServiceNow platform. Now on the right, you can see they can bring their own model, they can go out to OpenAI or to Microsoft, that works as well. But what's clear here is that customers really have a choice. And I think more and more when we talk to customers and they're talking to their risk teams, their data, which is, in many cases, their competitive advantage. They don't want leaving their four walls as well. So how the AI is delivered, it's super important, not just seeing a cool demo of the use case, how that use case is delivered is more important. And then finally, what's really important about this is automation. What do you do with what you get as a feedback from GenAI as AI is generating content GenAI what do you do with that content? And how is it used? If it's just a text box somewhere what you have to go in as a user grab, copy, do something with, you can see right away, the productivity gain is probably already chopped into because you're involved with this aspect. On the ServiceNow platform, we are known for our enterprise workflow capability. So we have use cases today. I think what we're looking at here is that is our docs component where you might be summarizing feedback from a customer, you can automatically take that data and create a project or create an epic, you could automate these things. And right now, we're doing this with end user who's actually kicking off that request where the workflow becomes really important is with GenAI. And we launched this month, now you can think with Agent doing this work in the background, looking at things that need to be taken care of in the organization. If the Agent finds something, they can't wait for a person to then get involved and say, yes, copy this from here, start something over here, you want the agent to be able to do that around. In fact, many Agents will then go kick off and talk to another Agent who will do something. So the automation or the workflow that's built into generative AI is the key to having a successful GenAI.

Yes, Doug, on that point, I think it's important on the automation aspect of what you said to pin that we're looking at entire journeys or domains. So when you look at AI, you can look at it by use case by use case, which we do. But with GenAI and with our Now Assist, you're basically taking entire domains and entire use cases. And looking at where AI basically build those gaps and improve processes, if you will. So I think with us at ServiceNow, we're constantly delivering new use cases. out to our customers at an incredible speed. But it's to solve problems across the entire domains. And so I think that helps them see the value end-to-end. But back to you, Doug.

Yes. I mean the cool part of the technology when we're talking with customers and analysts, they're asking us, besides the technology, what is ServiceNow's general approach to AI because a lot of people are concerned about GenAI, and how it's going to be used. So I think it's important for customers to know we have a point of view on this. We really want to have responsible GenAI for our customers. That's human center that's inclusive that's transparent and accountable. And that's really, really important because as much as I believe that GenAI has the power to transform the way that we do it, it also has to be beneficial for humans that it adds to us in our lives and doesn't take away from it. So that's a super important point.

Doug, I love that you talked about this because governance and responsible AI is you pick up when a end of the stick, you got to pick up the other. And so if you're picking up GenAI, it's like, how do you balance that strategic plan and that road map with responsible AI and the appropriate governance across organization. And that's something that ServiceNow that we do as we work with customers to help them on both sides of that, like as you plan that out and you plan out what you want to do also with how do I basically map this out for responsible AI. So I think it's very key.

Absolutely. So we've talked so far about how to implement and deliver GenAI. We've also talked about using GenAI in the ServiceNow platform. That's a great time to bring in Alice because she does all of this she's responsible for this. So Alice is going to spend some time explaining how she's actually done this successfully.

Thanks, Doug and Jim, and it's just bring me a smile as you talk through the journey because literally recap like what we have done in the past year, 1.5 years. And well, firstly, I'm going to introduce myself, Alice Saiki, I am responsible for all of the internal employee experiences as a portfolio leader. There are a number of road maps that me and my team manages and a lot of which has to do with employee experiences. And Now on Now team obviously is a biggest fan. I'm the biggest fan for the product, especially SPM, but I'm also the biggest critic as I know, Doug, you hear enough from me in terms of the feedback that we see from deploying them internally within ServiceNow. But what I would say is back to the point -- the very early point that Doug talked about, I mean, in order to have a practical and also usable AI strategy. We really need to start with the vision. So I would apply that to my portfolio from an employee perspective really what we are striving for, obviously, productivity gain and efficiency is one of the key value that we are striving for from doing a lot of this. And that is right. I mean, we actually have metric for every deployment to make sure that the ROI is there. I mean some of it, yes, it's an experiment. We understand it's a learning journey, but a lot of it is we have hard cold value metrics OKRs that we use to measure our success and also measure the learning. And then the second piece from an employee experience obviously is experienced, right? And I think we take it too hard to make sure that whatever we deploy to our employees in intuitive is something that really helped the day-to-day. And of course, last but not least, is we at ServiceNow, love our platform. We are always challenging ourselves to innovate and really how do you leverage the platform to do more. With that, I'm going to give a couple of examples within my portfolio that we are. This is sort of a real road map. I mean this is not made up. This is something that I'm working on right now. And this particular one is how we are leveraging our employee portal, which is our EC Pro product deploying and infusing GenAI into our day-to-day, if you think about portal, we would call it our single front door where all the experiences come to life for employees to engage with. There's a ton of opportunity for us to really learn and understand the employees' behavior and from there, really summarize and starting to suggest action. So we leverage SPM specifically to like for this particular project, like we track all the different outcomes as Doug was talking about in the SPM primary goal, what is the value that we are gleaning a lot of it as I mentioned, is productivity and efficiency, but then also really setting the tone, leveraging our own platform to showcase our customers the art of the possible. So those are the key value we are striving for. And then what I would say is that this really is a savior because there's so much work involved across the board, across different teams, and this is a one-stop shop for me as a portfolio leader to understand the progress and where kind of really the teams need help, well, we have blockers. So we look at this not on a daily basis, but on a monthly basis just to make sure that we are on track for some of these line items. The next piece that -- another piece that I actually -- I will spend some time on here. Within my portfolio, I have finance, legal, HR, risk and then engaging technology, which is my ServiceNow portal. Every one of those are very excited and embracing the GenAI capability and as a portfolio leader like Doug said right, soon enough, like you're not going to be able to understand where all these pockets of GenAI activities are going on. And we leverage, again, our strategic portfolio management product. This is SPW, Strategic Planning Workspace, to really kind of have a pivot of, hey, where are all the GenAI use cases are being deployed within across different portfolio. And we do actually on a biweekly basis because GenAI is a hot topic. We look through those items on a biweekly basis to understand what these use cases are. And what I would say is now that we've been doing for quite some time, one of the key learnings is you start to realize that doing case summary for finance, it's very similar to doing case summary for HRs. So like as the team are talking through some of those use cases, we actually find a lot of possibility and say, "Hey, I didn't know you are doing that already. Why don't we like cross-pollinate and learn." And what -- like the dialogue is so rich in those everybody is passionate about GenAI and the fact that there is some commonality as they talk through the use cases really help us to speed up the learning and also the experimentation because now, we have the code base and people want to kind of share it. So there's just a ton of value that we see from this pivot. And again, we are going to look forward to see some of these outcomes that we see in each of the portfolio, but I would say we really put the power of SPM in display here because that is exactly the information we were looking for is how do I know what is all that is happening across different pockets. So now that I've talked about how we are using SPM to kind of manage all the GenAI items, the use cases, road maps. On the flip side, I think as a deep SPM practitioner, we often think about then how GenAI can help me and my team to do our job better. If you think about in a product development life cycle from strategy to delivering value, role like mine cares about OKRs, care about how we plan day-to-day and the product managers that are delivering day-to-day cares about what are the product feedback tons of epics, tons of stories day-to-day that they have to plow through. And then my team, the program managers need to manage all the different items and always have to be in the tip top shape and understand where things are. I mean, it's just of information that they have plow through. And within ServiceNow, we love this period of experimentation and discovery. And we actually have been able to deploy some GenAI solutions within our space as well. And I will start with like for our product manager, and Doug kind of alluded to that earlier, like where we are headed with SPM. There's a lot of opportunity in terms of leveraging all the data that we have in the platform and generate things for us so that it is more efficient. So a very prime example is something that we rolled out earlier in the year is the ability to generate stories. Because like just last year alone, we have an upward of 100,000 stories being created, tons of works. And what I found is some of these stories are pretty rushed, right? I mean the quality is not fully there and like one liner and then because everybody needs to plow through the backlog. And if you don't have good quality story, you end up delivering less than optimal product. And the way that we solve for that is now within ServiceNow, we have a story generator. So you type in kind of the intent and then it create the stories. Not that it will do everything for you. I mean it still require the human touch, right, we talked about earlier, T.here's always some check and balances. So -- but I would say 70% of the verbiage are there, the acceptance criteria and then our product managers are able to just pass through and find touch and plow through their backlog very easily. And with that, and I will share a little bit and this is like me as a portfolio leader, I love metrics. And within 4 weeks, 1 month of deployment, 30% of the product managers signed up. I mean, this is one of those things I don't even need to like do promotion or marketing. I mean they just, like word of mouth, hey, did you know that this button exist, and they're just all starting to use it. And within that 2 months, 2,000 stories get generated through GenAI. And of course, because the Story Assist allow us to have all the field kind of filled out. I mean there's a lot of quality element that we see an improvement on, and I would say that I'm happy to announce that actually, based on that success, we are now deploying Epic creation similar to story. So I mean, just tons of value we see and tons of time and thoughtfulness that our product managers are able to glean from this capability. So with that, I think this is just a small example of how we leverage SPM to manage our GenAI projects, but also how we are embarking our GenAI journey within our own space as well. With that, Doug, I think I'm handing it back off to you.

Yes, indeed. And thanks, Alice. I think it's always great for the audience to hear from someone who's actually doing this and not just hearing the theoretical way to do this. So thank you for sharing your experience and your lessons learned on your GenAI journey. So what are the takeaways we can have from today. The first one is that GenAI is here. We see Alice's team using this, and I believe it's not just here today, it's here for the long term. As I said before, I truly believe that this is going to be transformative for all of us. If you believe that, then number 2 is super important. It's -- we need a steady hand on the tiller, let's say, on the rudder to make sure that with the ups and the downs of experimenting around GenAI that we have success, and that success is going to come from taking a strategic approach to GenAI and not just kind of doing it haphazardly because I believe that's going to lead to frustration and lack of success and customers actually kind of giving up. So having that considering GenAI as a strategic initiative is going to be very important for it to be successful. And if you're along for the ride with 1 and 2 then 3 becomes natural to say we have to take a strategic portfolio management approach of setting strategy, prioritization of delivery to do this properly. And we believe that ServiceNow with our strategic portfolio management and our enterprise architecture solutions, along with our other solutions around risk, can play an important role from a technology perspective to supporting you with this strategic GenAI initiative. And with that, I'm going to hand it over to Jim, who's going to lead us through some Q&A.

Thank you, Doug, and Alice, this is great. I appreciate the work you put into this and the discussion that you're having on this today. And just a reminder for everyone, if you have a question, please go ahead and add that into the Q&A. We have some questions queued up, but would love to hear from you any questions you have on this. So I'm going to actually start off with Alice on this one. And so Alice, there's a question on when you manage a portfolio of AI initiatives and investments, how do you prioritize different GenAI experiments? How do you go out and create that? How do you prioritize those?

Yes. Actually, I was about to comment when Doug talked about the prioritization. I don't know if the audience have that challenge, but everybody wants to do GenAI and really have that curiosity to try things out. The way that we prioritize GenAI items, to be honest, is no different, than how we would prioritize all the other work items that we need to do our priorities that we have. I mean we -- I mean one of the things I shared with customers always is that with any new technology, the core underlying principle is the same. We always need to understand the business problem you're trying to solve and really understand it down. Is it a worthy problem to solve first. And then from there, everyday is, like be very clear in terms of the goals and outcomes and articulated by key metrics and results, so that we can kind of set the blueprint of hey, for each of the progress. This is what I'm expecting from a trajectory perspective. And so back to your point about prioritization, I mean, every use cases also have its value scoring so that when we do our quarterly planning, they are all stacked GenAI or not, right? I mean in some cases, in my risk portfolio, there's some stuff that we have to take care of first before GenAI can come. I mean, GenAI can be an accelerator, but there are some items that you still need to flow on top. So simple answer is no different than how I prioritize my other work today.

I love that answer. Alice, I just want to lean into that a little more, and then I want to hear from Doug on what he's hearing from customers. But as you look at these different investments that you have in GenAI, the question is, should I hire or train for GenAI skills. So Alice, I'm kind of curious in your organization, how you've tackled that. And then I'm also curious, Doug, as you've talked with a lot of different customers, how they're tackling the whole concept of hiring or training for AI?

I'll share a little bit. I think just like most organizations, we are navigating that organizationally how we all kind of get us in, I would say, upskilling our teams I mean, thankfully, I think we have had like the smart data scientists, the folks that do AI work all along has been alongside us. But now what I see in the organization even within my own is there are specific training programs. In fact, I just did one on the weekend because we have this whole learning path that we need to plow through before the end of the year. And it is important for us to really dive ourselves into understanding the basics and the foundation of what GenAI is. And -- but what I would also say is what I mentioned earlier in my use cases is a lot of my learning quite frankly, is about like get my hands dirty in the use cases and really try things out and a lot of discussion and cross-pollination happening. So that's what I see formal training as well as just putting your hand dirty to learn it.

I would agree. There is research that shows that organizations that are getting value from GenAI are the ones who are upscaling their internal resources. It may make sense to hire certain key roles with people who have deep GenAI experience. But I think when we think about understanding how we're going to apply GenAI within our organization, the people who can see the opportunity are the people who are working on the processes themselves. So taking those people who have that kind of internal understanding of how your organization works and upscaling them to understand GenAI is really the best of all scenarios around getting up speed on GenAI and getting benefits from GenAI.

Doug, let's lean into the benefits. So there's a question on this about value. So it sounds like a building block here how should organizations think about the value they should be given from GenAI? We talked about it throughout the webinar, but just between you and Alice just have kind of a deeper discussion on that.

Yes. So I think it's about for, one, choosing a framework on how you want to categorize the benefits so that when you look at those different possible experience, which is really just a business case, to say how are we going to compare these different business cases against each other, you need some kind of framework. So a lot of what you might see would be, hey, there's productivity savings, people saving time from things that they would have done, Alice's use case around creating stories, for example, how many people are doing that, how many would they have created, how much time are they saving? You're going to be able to get a number that you would compare against other productivity use cases. The other way to look at this is better decision-making. How is GenAI going to get access to broader data that it would be very difficult for a person to internalize and make a decision from GenAI can also help with those kind of decision-making workflows as well. So that's one way to look at it. Alice, I'll pass that question over to you.

Yes. Like when you talked about productivity, absolutely, but I think another bit, which we -- I would say we are still experimenting, but we are very excited about is that decision making the insights that it's -- I mean, the decision, I mean, ultimately, it still needs to be decided by a human, but to talk about the volume of works and even applying to my domain of work, right? I mean we have even just talk about the prioritization question is like we have sees of like demands and opportunities, but which one to pick, which is your best bet, a lot of analytics work that if GenAI can help us to bring those, it's part of our decision making, I think that will be very fantastic. But yes, I mean a lot more to explore and discover and we are excited about it.

Thanks, Alice. Doug, here's a question from Stephanie. How will GenAI interact with scope applications in the system? Do you want to take that one, Doug?

Sure. Sure. This is more of a technical question for those who aren't familiar with the ServiceNow platform. You can have something called the scoped application. From a GenAI perspective, there is no difference with a scope application versus how it would apply to things that we're delivering on our own. So Stephanie, you can use GenAI with your scoped applications. There's no problem there.

And then Doug, just real quick, David is asking about what version of SPM has Now Assist. So we released them in the store and so forth, but why don't you take this question?

Yes. So this originally, our original use cases were released in May store release. So with our Washington release, what the store released after Washington. And then there's been further enhancements to those use cases in the release from this month, our September release, which is our Xanadu release.

Thank you, Doug. I just want to thank everybody who attended the webinar today. Thank you so much, and I want to thank Doug and Alice for also presenting on this webinar there's a QR code that you can scan right there. It gives you more information on Now Assist and the things that we had talked about on that front. So feel free to scan that. There's actually one more question I want to add in here that I wanted to get to before I go on to the next slide. Doug, I'm going to ask you this question. And then at the same time, Alice, if you want to talk about this because this is the front line of what you do. It's like how can GenAI support a prioritization of demands of projects?

Yes. So this goes back to the question we had around the Now Assist or GenAI built into our strategic portfolio management solution. So one of the things that we did release earlier in the year was GenAI leverage demand creation or business case creation where rather than filling out a form, you're actually interacting with the GenAI chatbot with natural language in creating a business case like there. So that is a great way to collect data and make it easier for people to submit what we call demand or a business case. And that's really a foundational building block to leveraging this demand data, as we've talked about -- what I talked about earlier is leveraging data that we already have on the ServiceNow platform. Because if we think about a business case, what happens with that, it gets shuffled around the organization where people have to say like for instance, hey, if we were going to deliver this, Jim, how much time would it take from your team and finance would the benefit be this? And we go all around. There's a lot of effort put into evaluating the business case. You may be only approving 20% of these business cases. So how do we take a lot of that work out? We leverage existing data that we have around those business cases. when we build out those business cases before to say like, "Hey, you've asked for an SAP upgrade." Where you already know the risk around that. We already know the effort around that. We already know the cost and the benefit we can start populating information and then people just need to review. As Alice was saying, there's always going to be human intervention before we go ahead and stamp something has finished, but we can precede that information eventually. So there's a lot of exciting pieces coming around GenAI for sure.

I'll add to that as well, and this is not something we are doing today, but I look forward to, right? I mean, if you think about the history of time, there are a lot of similar things have been done, right? I mean if GenAI, kind of say, hey, in the history of time, this is kind of the trend in the trajectory. It gives you something different to learn from, and I look forward to all that historical data being kind of the greatest teacher for us to make different decisions, right? Because I think sometimes truly when you prioritize something, you're putting on bet on something that I expect that hypothesis to be working out right. But again, if all those things that happen in the past can give us additional insight that we couldn't have thought through before, I think that, that will be another use case that I would look forward to trying out.

Yes. I love how analysis can surface those additional insights, Alice and I look forward to that as well. There's some great -- there's a great [indiscernible] to answer that question as well, too, that we have and the assets that you have available from this webinar. I just want to remind people, too, that there's other on-demand webinars. So be sure to check those out. There's a URL right there. You're going to get this deck. So you have them in front of you once it's sent. So we've got some great webinars coming up. In fact, next month, we're talking about -- or November, we're talking about strategic planning, but with responsible AI. We're going to take a deeper dive into governance and AI. And so we look forward to having you participate in that. Also, we have our World Forums. And so some of you know about our Knowledge event. So basically, we have these World Forms, and you can learn more about through that QR code. They're throughout the world. Here's some coming up in Dallas and New York and Toronto. Highly recommend that you attend these. They're fascinating. Great presentations. They're presented across the platform and the value that customers get from the platform. And then lastly, just a reminder, the world works with ServiceNow and it continues to do so as we have the AI platform for transport business transformation. So we thank everybody for being on this call, and I look forward to seeing you on future webinars. Thank you again to Alice and Doug. I appreciate you both.

Thank you.