NICE Ltd. (NICE) Earnings Call Transcript & Summary

So hello, everyone, and welcome to our Investor Day at Interactions 2025. My name is Marty Cohen. I'm Head of Investor Relations at NICE, and I know most of you. So I hope you enjoyed the general session. And so what I'm going to do is just take you briefly through the agenda for the rest of the day, and then we can begin. So for this session, we're going to hear presentations from different members of our senior management team as well as one of our customers. And then we'll end with a senior management panel where you have the chance to ask questions. We'll hear from Scott Russell, our CEO; Barry Cooper, who's President of CX; Elisha Wright, he's Global Director, Learning Design and Delivery for Hyatt Hotels and Beth Gaspich our CFO. So Scott is going to provide a general overview on our strategy. Barry is going to discuss in more detail our CX and AI strategy. Elisha will talk about how he's using NICE AI to improve the customer experience and Beth will discuss our financials. So please hold all your questions until the Q&A session. And for those of you that are listening on the web, there is a space to ask questions. And again, we'll take those questions during the Q&A session. So after the presentation, we'll take a quick break. We're going to grab lunch. The lunch will be in the back of the room. We'll come back, we'll sit down, and then we'll begin our Q&A session with Scott, Beth & Barry. And then after lunch, we've arranged for you a private tour of the innovation hall and you'll see several demos of our new AI solutions, and I think you'll enjoy that. So that will basically end our day. But for those of you who are staying tonight, we do invite you to our customer party or what we call our customer appreciation party and that begins at 6:30 p.m. It's at the Virgin Hotel and the band of OneRepublic will be playing and buses leave this hotel at 6:15. So let me put up the famous safe harbor slide here. And finally, all numbers in the presentation are non-GAAP. And I'll now invite Scott to the front of the room.

I thought you were going to talk through the second part of the slide. Good morning, everybody, and I appreciate your all joined us here today. I know many of you joined for the full two days, but the opportunity to share directly with each of you. A little bit more detail, obviously, about where we're at, where we're going as a company and also an open dialogue in QA session to answer your questions and make sure that we provide as much information as we can to help you not only in your analysis, but also hopefully a positive view about our company. I want to just highlight 2 things before I go in a little bit of detail. The first is, and you will notice, today, we're really growing into CX. And it's in line with the event. It's obviously the major part of our business. Okay. Can't hear me? You didn't mic me up. Yes, I rushed here so I am non-mic'd. I can do this, I can juggle in, chew gum at the same time. So we obviously focused on -- we're focusing on CX. So the presentations today that from Barry, myself, Beth's, obviously, more at a company level, but even then it zeroed in on the CX side. So I wanted to highlight that. And the second is probably even more important is we realize and one of the things that I've spoken about in earnings in other forums, is the need to provide more disclosure, more transparency so to help you. And so whilst we're really excited about what we're going to present today, as you can probably appreciate being 6 months in where we've got a lot that we've done, but with a lot that we want to do in sort of building out those midterm and those longer-range targets, we're going to come back to you with a Capital Market Day, another Investor Day in October. I can hear a bit of -- can I hear myself? Well, that was awesome. So we'll come back to you with more details. Beth and Marty and the team will come back to you on Capital Markets Day in October. Early mid-October, we'll come back to you with the exact dates. But the intention is that point is to give you even more detail than what we're sharing today, and we're going to give you a fair bit, but the opportunity to be able to combine the innovation, the road map the expectations of not only of what you'll see this year, but for the midterm as well. So today, I'm going to focus on 3 things. I'll be covering 3 pillars of our strategy. You obviously heard me on stage this morning talking more at a high level about what we're doing as a company in creating a nice world. And by the way, I have said this to my own team, but I will say it here as well. That is not a marketing slogan only. Now clearly, we're using our brand and our name but the opportunity to create incredible human experiences in the world of CX, in the world of self-service, in the world of customer experiences, it is a differentiator. And I believe that if we deliver seamlessly and consistently and purposely against that vision that it will be another reason why NICE is the winner. But I'll be covering our market, what we're seeing and why we have confidence because of the market dynamics. I'll then share at a high level our innovation. But clearly, I'm going to wait for Barry to come on and he'll talk in much more depth about the innovation, the capabilities that we have. And then I'll also spend a bit of time on our go-to-market and I'll conclude with a wrap-up of that as well. So let me go to the -- what's happening in the market and what do we see. And this is really important because the changes in the market are trends that are -- give us confidence on our -- not just our short term, but our mid- and long-term growth potential. The first -- and we've been talking about this as a company, and the industry has talked about this for some time, the move from CCaaS and we've been talking about on-prem to cloud. Well, what I'm here to tell you is companies, and you heard a few of them today are moving from CCaaS and they're moving to AI-powered platforms to drive customer experience. They're moving from human interactions to AI-powered self-service and I'll talk in some more detail. They're going from scripted bots that are largely deterministic so very tight boundaries of what they can do in retrieval of information to true AI agents. Moving from orchestrating interactions, and we've been graded this over generations. So orchestrating an interaction between a brand and the consumer, but moving that to automating workflows. And by virtue of that, automating workflows that go beyond the interaction, go into the mid office, into the back office, and you heard me say that a few times this morning. We are definitely moving from an agent-centered world to an interaction and engagement centered world. So moving from the 15 million agents that everybody talks about in this industry and going to billions and billions of interactions, and you will see it is growing dramatically. And then last but not least is moving from a world where there's been a high emphasis around labor spend and about how to manage the contact center into technology spend and why companies like ours are going to be a key pivot point of the way that customer experience is being delivered. So let me drill in a little bit more detail to give you some context of why we see these market changes and what's happening. As I mentioned before, we are moving from a CCaaS, on-prem to cloud. But remember, I do say this knowing, of course, that 35% to 40% of on-prem has moved cloud. So there is still the move. We are still seeing an abundance of RFPs and abundance of customers that are sitting on those old legacy systems. You heard Carnival U.K. today, Walmart was the same. Disney was the same. They moved from the on-prem and they moved to the cloud. But what we're now seeing is they're making the jump. They're not going on-prem cloud, cloud AI. They're going from wherever they're starting. They could be at an on-prem. They could be a cloud-enabled platform that we have, but they're going to an AI platform that drives their interactions and that drives their experiences. Why this is important is we have an opportunity as a company, for example, to monetize the agent in the work that they do. But from an interaction standpoint, as you're growing every time they use a copilot or an autopilot or for a supervisor for agent to be able to do an AI agent, we're able to do it in a cost-effective way, and I'll come to this later. Not only is the opportunity for us to be able to grow and earn, but for our customers, they're able to leverage the AI-powered platform to be able to serve their customers more effectively, not limited by a siloed tech infrastructure. And if you look at this, this is a depiction of our AI traffic. And you can see it in the last 12 months, in fact, I would argue in the last 3 or 4 months, the growth has -- it's going way more than what it was a year ago. Now that will continue. That trend line continues to explode because companies start with a small interaction. And you heard the experiences today. They start with one and then they move to another and then they move to another and they're able to then get the benefit of those -- that AI-powered platform. And the beauty is -- it's all on the same platform, the same data, the same models. So whether they do it for a copilot or an autopilot or they're able to do it for an AI agent, it's the same platform that they're leveraging from, which means it's consistent. The second is that we are definitely moving from human interactions that are agent-based to rapidly expanding AI-powered self-service. Self-service is clearly here. And I guess there's a couple of points that I would like to make to this. While at the moment, there is a lot of self-service and a lot of companies, and you will hear about those companies. There's a lot of start-ups out there, good talk about their self-service capability, their AI bots and they're really good. But the reality is they're only delivering 14%, 14% of the service issues can be delivered by self-service. So even though there is a self-service capability, the potential, so nearly all customers are using some sort of self-service, but full fulfillment, full resolution, full task is only done at 14%. And we call it the self-service resolution gap. And so what we see is the opportunity with AI to be able to bridge that gap, more and more complex use cases. I really loved Anderson's presentation today. But you think about it for a second. They've got an AI agent that used to handle a delivery issue for pharmaceutical and then they had another agent that handled delivery of groceries and they had another agent around calling for support for tech issues. The role of a human agent now can handle it all, but it's done with the power of AI, delivering self-serving a lot of that which means the human agent then does only the complex tasks. So the opportunity for us, the opportunity in the market is to be able to help companies increase the amount of self-service through our platform, but still coexist in a seamless conversation with our customers. The third is we are definitely seeing the move. We launched Mpower agents today. Barry will go into it in a level of detail but moving from scripted bots that are very deterministic, they've got tight guardrails to true AI agents, and I'll put it in very simple terms. A box will have a certain things that it can perform. It will be preprogrammed, determined. So yes, you have that interaction. And by the way, my experience with -- that's real. I literally logged in and I was visualizing the experience, and it was a nice customer. It was a great experience with the bot, but there were certain tasks that it was not scripted to do. The reality now is with AI agents, not only can you handle the contact and the interaction, it performs tasks. That is the most important part. And those tasks are not limited to the interaction. It is fulfilling tasks that a human agent might have done, but it also is fulfilling tasks that the mid and the back office could be done. So you might log in and say you want a new credit card that needs to be available within 24 hours. Well, historically, that would have been a bot that flipped to a human agent that would have had to check with credit, whether they can issue that card and whether they're allowed to go to the limit, completely automated with an AI agent, and we can give you countless examples of that. The fourth is orchestrating interactions, and this is obviously a really a point when it's tied to what I just said, orchestrating interactions into automating workflows. It is no surprise that you will see when I announced the strategic partnerships, they have a clear purpose. This is not just partnerships about go-to-market. These are partnerships that help us achieve end-to-end fulfillment via our platform. So whether it's ServiceNow because they're building AI agents themselves that very much cover the mid and the back office, what humans do in different tasks, automating workflows or whether it's AWS and leveraging their underlying platform and their data and their Bedrock and Q business or whether it's Snowflake, and they're able to federate data real time and data sharing, the ability for us to be able to perform tasks and automate workflows that goes beyond the interaction. This is really important because I believe we, as an industry, have been limited to what happens between consumer and agent. The interaction point. But once it goes beyond, we didn't really participate. AI gives us the ability to participate and engage and deliver value across the organization. Not exclusively, it will be through partnerships as well as our own, but our workflow orchestration definitely has the ability to do that. So you can see that we're going to blur the lines and so the tasks, and I guess this is even more important is not only are we doing what the agent does, but we then you'd be shocked at how much, and you'll hear the example later on with our customer presentation with Hyatt is one interaction with a human agent, often has 1, 2, 5 -- there's a lot of people that are working on behalf of that intent, that interaction. And so our platform, the intent is to be able to blur those boundaries and be able to go from intent to fulfillment using CXone Mpower. Second last one is around the agents versus the interactions. So as you can see here in simple terms that our growth of digital interactions is exploding. And I need to highlight voice calls are not reducing. There's no material change in voice. In fact, I'm amazed. My kids tell me this, but Gen-Zers are just as likely to make a voice call is what people of my generation would be. So voice is still there. But the digital interactions to be able to interact with their brand of choice is growing exponentially, and it's not just the chat. The chat is the first point, but they then -- but it's the chat on any platform and then the interoperability between those. Think of the proactive side versus the reactive side. And you can't do this on separate platforms. Siloed solutions and being able to handle different interactions. Again, you heard the examples. You'll hear more. If you've got one experience that handles it does a bot or interacts digitally one way, and they're in a different experience on another and then a different on another and then you got voice on NICE and then honestly there is no way you can interoperate seamlessly and have a great consumer experience or make it easy for a human agent when that occurs. So a unified platform truly matters. And then last but not least, and I know this is probably important to you when you think about our addressable market and our modeling. The way we view this is very clear. The market will definitely shift in the contact center and customer experience, where labor spend ultimately will come down. We haven't seen any dramatic reduction or any material reduction on the human agents at this point. A lot of businesses are still using human agents. They become more productive, but they're doing more and more, and they're doing other revenue-generating things. But whether they decide to reduce it or get more efficiency, the reality is the technology spend is definitely increasing. So it gets bigger, it gets wider, which means beyond '25 our addressable market as businesses move from where they are to where they're going to go through AI, the increase of technology spend will absolutely increase and it's a massive TAM opportunity for us. So the market is a dynamic one. Yes, there are competitors there, but it is a market where we are well positioned to win. And I just want to touch on in a little bit more detail in context, the 3 pillars that I presented this morning when I talked about reimagining customer experience being the platform. And it's so critical, and it was work that was done before I got here, the rearchitecting of CXone into CXone Mpower is a critical foundation because you're able to handle automation of workflows. You're able to handle human and AI agents and any sort of bot. And you're able to consolidate and then leverage and learn from the knowledge all in one place, no matter what deployment that you choose. So first of all on workflows. Workflows let's just remind ourselves what they are. They are a series of tasks, underlying process, a series of tasks in order to be to complete from -- so from an intent to customer calls or texts or chats or whatever, and they have an intent. And often, it's not just one, it's many, many things per the example that Walmart gave, there might be 4 or 5 different intents in the same interaction. The workflows is the ability to be able to orchestrate that of one interaction with the billions of interactions and they're able to do it seamlessly that cuts across the interaction into the mid and back office. So for example, in the -- sorry, let me go back one. Where is the workflow slide? Okay. I'll just talk to it. There it is. Apologies for that. So this example is a good one. So it's a financial services provider. They had 3 million appointments that they received each year, 3 million appointments. But 84% of those appointments were scheduled via a call. So a high number of the tasks, scheduling appointment with their financial services analyst or a provider handled -- were handled by their contact center. Now what they did with us is they replaced their workflow with our self-service, they were able to connect not only from our self-service that handled the interaction but we connected automatically to the appointment system. So we took the human out of the loop and we're able to then fulfill that with a containment rate of nearly 70%. So you think about the volume of those voice calls we're able to get the fulfillment of a very simple but very important task for that organization. It's a simple example, but you multiply that across every industry, it's an enormous opportunity for NICE to take a broader step in what actions get taken in the back office that used to be the domain or the world of either the CRMs or the ERPs or the hyperscalers or other platforms. And let me be clear, we're an unavoidable contact point. They will come to us first. Please, we're not the human, we will get -- the contact will come to us first. And by coming to us first, if we've got the technology platform to fulfill, we have the right to solve it without ever needing to go to any other technology platform out there. When the customer comes to their brand and they leverage CXone Mpower, we're an unavoidable first point of contact, a single pane of glass, and we get the ability to expand our TAM seamlessly and it's easier for companies because they can do it all at that point of interaction resolving it real time, why go handing off to other enterprise systems. The second is agents. And clearly, you heard today the launch of Mpower agent, it's exciting. I hope when you go to the innovation hall, you'll see this and you'll see it live. It's really important because it does move from the scripted bots to AI agents, and it truly is as simple as I described it. Barry will show it tomorrow on stage as well, but we can show it. But literally, you'll do an English prompt or command prompt, I guess, in any language in the future, Barry, but you're able -- and it will create the code, creates the AI agent automatically, ready to be deployed. And the thing is, if you think about it, AI agents are not going to be only in the domain of when it's going to be in auto is when it's going to be a self-service option. Think about a human agent that's in contact and they want to get a task done. They want to update that appointment. They trigger the AI agent, it updates to the schedule, updates the appointment and tells the customer real time. So yes, it can be in a full self-service scenario, but quite often, it will interoperate with a human agent and an AI agent together. Why is that important? Because if you're not on the same platform, you can't do it. Everybody asks me this question. Oh, other platforms have also got AI agents? Yes, they do. But they don't have the context of the customer engagement, and they can't do it without knowing that. So you have to have that interoperation, the guardrails, the experience, the AI models, they're explicit for CX. And Barry will share that in more detail. And then last but not least, and I'll keep moving is knowledge. And I was interested in what Jon Wells said this morning at Carnival U.K. when he said that knowledge was the -- I think it was the last bullet point that he presented. But it is the foundation. It connects the dots. The single platform and calling all data, which right now for most businesses is spread across a series of different bases, but we unify it on to CXone, onto the knowledge base, including our AI models, our CX specific ones as well as the foundational models, you can choose your LLM and you're able to transform that knowledge into actions, into outcomes. You can transform it and the opportunity, obviously, is to be able to go with the data and knowledge and the workforce is to be able to give more contextual insights for our agents, to be able to sell service models, but the guardrails do matter. And you'll hear that all with customers. The guardrails that they put in place about what can be delivered is really important. They're not going to give a premium account upgrade to every customer. So there are barriers and their limits, and we've already got that built in. Okay. Let me move forward to our go-to-market. So there's a number of levers, and I won't go through all of them, but needless to say, we do have the industry's largest customer base. We do have a vast ecosystem. We have strategic partnerships that we've started, and we're not done. There's more to come. And we have an enormous opportunity of growth through international expansion. Honestly, we've grown really well here in North America, but the growth opportunity internationally in Europe and in Asia is a tremendous opportunity. And obviously, I've got a lot of experience in that, and we've got a well-diversified business across all verticals. I do want to highlight that our partnerships are not only the ones that I presented on stage. We have an enormous -- we added 110 new partners last year. This year, we're on track to do something similar. And 75% of the CXone Mpower new logos are delivered through partners. So yes, we've got an amazing ecosystem. And yes, we've got a direct interaction with our customers, but we are very focused on leveraging the power that the ecosystem is to be able to extend our breadth and our depth with our reach especially in international markets, but not exclusively. And the other part is that all of our large enterprise deals or 2/3 of our large enterprise deals are also partner-led. And we have a great customer market. Our customers are an enormous opportunity for us in financial terms to upsell and cross-sell. Many of those customers they're at varying stages of their journey from their on-premise siloed systems to an orchestrated single platform that they're able to deliver and leverage the AI benefits that we talk about. So no matter where they are, but the point is, every one of them are on an AI journey. And the opportunity for NICE is to drive that AI journey using our platform and using it consistently not only to automate what they currently do, but to transform the experiences with their consumers. And I guess this is the best way I can describe how it manifests itself. If you want to have a depiction of how a journey of an existing customer goes on the journey with NICE, this is a good one. This is a global entertainment company in 2022, when they started with base platform of ours, non-AI, but they started with CXone Mpower, agent experience, OCR, recording and they implemented the CXone platform. And in the last 3 years, they've gone from an ARR of $3 million to $10 million, 40% of the ARR is now AI and self-service. And you know that we represented and we're sharing that stat. Why is it important? Because not only new customers that go straight to AI but existing customers who are leveraging our platform are increasing disproportionately the amount of services of AI services rather than historical platform services. And that gives us growth. The thing I love about this is economically for them it was a no-brainer. It was a no-brainer because they were able to automate what was human tasks that were able to get more efficient and reduce redundancy in the workflows of their business and obviously, they were able to deliver delightful experiences for their customers while containing cost. So our view is very simple. We have an amazing market of which we're operating in. We've got a great innovation platform that gives us the potential and the opportunity to grow and succeed. And we lastly have got a go-to-market and the customer opportunity that gives us the right to win. We have got a proven track record, but we are impatient. We are impatient. If we sit and wait this market will be taken up by someone. There is no doubt about it. I actually thrive on the thought of it is a competitive environment. Yes, there are others coming in here because they see what we see. A huge market opportunity, an increasing total addressable market, but what they don't have is the domain expertise and knowledge in customer experience. And to share with you why that is different and why that is important I'm going to hand over to Barry, who's going to talk about our innovation.

Okay. guys. So I asked for 3 hours to share our innovation with you. I've got 20 minutes, probably now 10. That's all right. For those of you, seriously, those of you around tomorrow, we're doing a main stage as always, it's like 50 minutes going really deep and not the new innovation. If you can stay for tomorrow guys, we're going to show some incredible, incredible stuff. Anyway, a couple of foundational slides, and I'll just go a little bit deeper into some of the things that Scott mentioned. Look, I'm going to -- not to repeat everything here, it's clear that AI is real. It's not hype. I think everyone agrees that. It's not only allowing us to automate customer service and augment customer service, we're actually redefining it. We literally are tearing up the rule book and starting all over again. And that point that Scott just made about the compression of the middle and front office together and back office. This is real, and I'll talk through some examples of that, that we have. And those words we show here, they are chosen deliberately. So hopefully, the orchestrating workflows, agents and knowledge by workflows, not just interactions, not just calls and chats, end-to-end intent fulfillment by agents, not just human agents in the front office, human agents and AI agent and increasingly middle and back office humans and AI agents as well. And then finally, knowledge Jon said it perfectly, is actually our terminology, but it's right. Knowledge management was something that was the depths not really managed by organizations, it wasn't a priority, then along came GenAI and basically puts a magnifying glass on that knowledge. And then suddenly, it's very important to make sure your knowledge is in order and structured and correct because suddenly it's available to everyone. So knowledge management is AI management. And one last thing that last line there as well, please don't underestimate that. There may be many competitors at the low end of the market. There's very, very few at the high end of the market because it's so complex. And one thing, again, I think it's really important. All 3 customers that spoke on stage, including Hyatt, who will speak to us here as well, they're all multi-brand organizations. And there, like if it's Disney, and they're talking about Disney, ESPN and Hulu. If it's Walmart, it's Walmart, the online Walmart, the stores, it's Spark, you name it. If it's Carnival Cruise, it's P&O, it's Cunard, it's all of those different brands. All of those customers, our largest customers that actually put all of their brands on to one instance of CXone. That's huge. And only very few providers can do that. I've included this slide here. This is from our sales deck. And I think it's really important because it really communicates our 2 value propositions. This is the traditional one, everyone's familiar with here, and Scott mentioned it already, but we are the single pane of glass between consumers and organizations. What does that do? It solves problems for 3 stakeholders, the consumers. We've all been consumers that had an experience on an IVR or whatever, and then that experience is not carried over to a bot and vice versa. The consumers, the silos are broken down. Everything is in one place. For an employee of an organization. They're not all tapping between 5 different systems trying to find the history of what you did, it's all in one place. And really important for the organizations, they're no longer SIs. We've pre-integrated everything for them, so they don't have to integrate it themselves as well. This is our traditional value proposition. It's a nice little picture. But really, our value proposition now goes further than that in that we are hiding the complexity of what it takes to deliver fulfillment through workflows that reach into the middle and back office. And there's 2 really important things about that. In almost any organization that goes beyond a basic level of sophistication, there are multiple systems of record and multiple systems of workflow. So when we hear about Salesforce or ServiceNow with their bot that goes to the front end, they're only seeing their part of the business. We see everything because we're the single pane of glass. So that orchestration is managing multiple steps of fulfillment into multiple systems and record systems of workflow. And the other thing -- and this has been something we've done for 3 or 4 years now, and I hope it's -- you guys are aware of this, this hub strategy is, in my opinion, genius. And what Hub does basically is we allow our customers to use their own technology or use ours, it's up to them. Because it may be they have a strategic need to keep knowledge in a certain place and not migrate it to CXone or it may be a large customer can't migrate all in one go and needs to go over 3 or 4 years. The hubs allow our customers to keep those technologies while adopting the value of the platform. So knowledge can be in CXone or it can be in Salesforce SharePoint. Agent assist, you can use our copilot or use Agentforce or 10 other kind of agent assist services. AI services, you can use our LLMs, our ASR or bring your own LLMs or own ASRs. Our virtual agent, same thing applies. So this hub strategy is absolutely key to making us very viable for the high end. What really changed over the last 6 months, and this is really important. Since I last spoke to you guys and some of you I met at Enterprise Connect, these are the 3 things that really changed. When we first came to market with our AI solutions, our Copilot, our Autopilot, our Actions, AutoSummary, [ XO ]. Each of those -- our focus was getting them to market as fast as possible. So they came with their own AI services. They came with almost duplication of functionality amongst those things. What we did in release [ 24.4 ] -- at the end of last year is we removed all of those AI services from those individual applications, and we put them inside the platform. So the platform now manages LLM selection. It manages the prompt editor. It manages our LLM selection. It manages our ASR selection. It manages our knowledge management. It manages our RACH indexing all in a single place. And that was really powerful because it means all of our applications now are getting their AI services from the same place in a consistent way. And that, by the way, was the prompt, pardon the pun, to change CXone to CXone Mpower. CXone is the CCaaS brand, Mpower is the AI brand. And together, that's CXone Mpower. First kind of major change that happened over the last 6 months. In the middle, the second major change, and this was driven by a lot of our customers, including the likes of Hyatt, who will speak here later. Increasingly, our customers are asking us to move and work with a different kind of user, a different kind of function as we do what Scott is talking about around fulfillment. So at H&R Block, for example, we're working with tax pros. At Walmart, we're dealing with fraud specialists. At CSAA, we're dealing with adjusters as part of the insurance process. At UHG, we're dealing with pharmacists, at some of our financial customers we're dealing with mortgage specialists. These are functions and people that aren't traditional front office people. But our customers are saying, we want you to support this function and automate this function because it's part of our fulfillment of medium to complex intense. This is huge. And so you'll see new capabilities like CXone desk, and other things we're doing around fulfillment, and it's obviously a big part of what we're doing with the Mpower agents supporting that new kind of user, that new kind of function. Because when you align those middle and back office functions to customer service, you achieve the Holy Grail of amazing customer service and reduce costs. And the third thing along there related to that is you talked to us about 2 or 3 years ago, a lot of our automation we're automating the dissemination of knowledge. We're automating basic routing getting to an agent. Increasingly, our customers are asking us to automate really complex intents and multiple intents that reach into those systems of workflow and systems of record. So you're familiar, hopefully, with this. This is how we picture the CXone platform. Really 3 things to take away from this. It's a real platform. It's 8 years, millions of lines of code, incorporating data visualization, data store, UX, as I mentioned before, kind of the AI services, cloud security, user security, all of those things in a single place. Every time we build a new application, it inherits all the capabilities down there in that platform and we're basically done with about 40% of the application before we write the first line of code. Here is our core value propositions. We augment the workforce, making them incredible at their jobs. We orchestrate workflows, not just interactions, and we automate service, not just knowledge dissemination. They're our core value propositions. And I'll talk about this in a second, CXone Mpower is a platform that allows organizations, not just to operate, and that's really important. Operations is really hard. And it's why the likes of Salesforce and ServiceNow I've got a surprise when they come into this market because operations -- operating a mission-critical system is very different to operating a database with a front end, but we allow organizations to design, build and operate through the capabilities that we offer. As I mentioned, core part of the platform is our AI. And I think you're constantly asking people at NICE. So what's different about NICE's approach to AI compared to everyone else. And it's extremely different, I can't tell you how different we are to the likes of Genesys, Five9 and even Salesforce and others. Because what we're doing, we're combining 2 technologies together in that platform. We are using large language models and large action models. And by the way, we're swapping and changing models all the time based on the usual criteria, the accuracy, the cost and the speed, depending on the use case. But we're combining that with our enlightened models, our CX specific models that we developed starting back in 2019. So every use case, almost every use case we do around AI, those 2 things come together. I'll give you a very, very simple example, okay? AutoSummary. Everyone has got an AutoSummary. Most organizations, they just take a transcript of a call or a chat. They send it to an LLM and they write a prompt to say, summarize what went on in this. That's great. You can do that. You all know by now the way LLMs work. They're not consistent. Everyone is going to be a little bit different. It's just information theory, condensing down a whole bunch of words into a summary. What we do, our approach is not that. Our approach is we first run our AI models, our CX specific models on that transcript. And we say, what were the intents, what were the actions that took place and what were the outcomes that happened during this interaction? Those are then the metadata we send to the LLM. So again, as an example of creating the guardrails, what we get at the end of the day is an AutoSummary that's referencing those intense actions and outcomes in a consistent way using the industry terms. And that's one example, a very simple example, and I can give you 20 other examples of how we combine those 2 things together. The results, far greater accuracy first of all, it's CX specific outcomes, not generic outcomes. And something you guys may find quite interesting is a lot less cost for us because we're actually not sending a full transcript to the LLM, we're just sending the metadata, which is a lot less. I guess the key point of all of this is when you have AI embedded in the architecture, embedded in the platform, unlike point solution, you're using AI consistently. It appears in the same way in all of the applications. It means the customer goes to one place for their AI governance, not 10 different places. It means that all of the information captured by these capabilities is feeding the AI data so it gets better. It's all fed together, it's all connected. So bringing me to the announcement we made this morning, our Mpower agents and guys, you're going to go down to the showcase and you're going to go see these in action. It's incredible technology. It's release one of the Mpower agents. So I'll tell you that now, but it's a release. It's out there today. Mpower agents, the way to think about these is if what we have with Autopilot and Copilot, that's conversational AI. It's focused on the conversations with humans, employees or customers. Mpower agents are process AI. They're actually doing the fulfillment that all of our customers are asking to do -- asking us to do, working with adjusters, working with mortgage specialists, working with tax pros, those kinds of things. So they're actually -- they sit behind our Autopilot and Copilot and actually execute those tasks. Now you'll see the way these are created and they are truly created in agentic way. I'm choosing my words very carefully. I'm saying that they are created in an agentic way. You don't build a flow like you traditionally build an AI bot. What you do is you give it a job description. You give it a prompt. You say, your job is to go solve, go and find, as I said today and as Scott said today, go find customers in the database who are eligible for an upgrade based on this criteria if they're available, go and then reach out to them in their preferred method of outbound communication and then offer them an upgrade, if approved by them, then do the upgrade. You literally write that as text then the Mpower agent builder goes away and builds that flow. It's going to use all of the capabilities available on the platform to do that. So it's going to use the APIs that are set up on the platform into the system to record system of workflow. It's going to use all of the channels that are available on the platform. It's going to use the preference data about the consumers that's on the platform. It's going to use the integration to use the knowledge and the knowledge basis on the platform. Those resources they form the guardrails to make sure that it stays on track even though you're building it in an agentic way. So these Mpower agents become a big part of the design, build and operate. So with Mpower actions and some of the capabilities on there, I'll show you just in a second, we allow customers to uncover opportunities to design a new highly efficient CX. With the Mpower Agent builder, we allow customers to build capabilities on the platform in an agentic descriptive way. And with our Copilot and Autopilot, we allow those agentic bots to operate the business and go deliver those results and take out huge costs and improve customer service. A few just a screenshot on each of these things, I'll be very quick I'm limited to my 20 minutes. But this is going to be our main stage tomorrow in a lot more detail. So within actions, remember, actions is our AI capability for the CX leader of an organization because all of the customers' data is on CXone. We can see everything that goes on. And again, we're applying the CX specific enlighten models, first of all, to undercover -- uncover opportunities, things that aren't working well, sometimes benchmarking against other industries or other companies in the same industry to find what works well, what doesn't work well. But then again, we're using LLMs and foundational models to actually visualize and verbalize what that is. And so you can uncover opportunities to improve cost, reduce cost to improve the customer experience. I'm not showing it here, but again, then once we've used our CX-specific AI to uncover those issues, we're using generative AI to create the prompts. I'll say it again, we're using generative AI to create the prompts that describe what the Mpower Agent needs to do to solve that problem. The kind of problems we're able to find with this, and this is absolutely transformative for our customers. We're doing this right now for about 50 of our customers and yet to come across a customer who hasn't been blown away by the opportunities that we're finding. We're finding generally opportunities to reduce cost, improve customer experience, increase revenue or make sure they stay compliant in a highly compliant industry. The way we do that is either to do automation, take people out and that could be people in the front office or middle office, augment people, to give people tools to do more in a more consistent way or increasingly, and that's incredible proactive outreach. It's very interesting that frequently the best resolution is actually to go and not wait for an incoming interaction, but reach out proactively to an organization -- or sorry, to an individual to do that. So that then results in very, very, very practical things that go out. a new IVA, an Mpower Agent that gets built, changes to our website, for example, updates of knowledge, for example, changing the workforce scheduling of people updating the quality plan, these are all things that are very easy and relatively incremental changes to make on top of the CXone platform that then delivers that ROI to our customer. Here's the builder for the CXone Mpower Agent. Again, this doesn't do it justice. If you go down stairs, you'll get to play with this. You can play with it yourself. This is where you write your prompts. This is where you can copy and paste a prompt that came out of that first design thing to build a bot. It then goes away and creates this. You can then click on these nodes and you can make change behind each of these code, node is code. That code can then be changed to customize those things. We expect our customers to create hundreds of these Mpower agents to do all the different kind of business tasks that they need to do, and they'll sit behind our customers' Copilots and Autopilots because, as I mentioned before, as they operate, here's an example of Copilot. An employee because our Copilot, again is listening in, in a conversation. It will trigger an Mpower Agent. I'll give the option for Mpower Agent to run and do a bit of fulfillment for that agent making it extremely efficient or, of course, on the front end, when we have Autopilot running on our website, make the Mpower Agent available to the end consumer as well, taking out the human being altogether because you can actually automate that process from the front end. So it was rushed, but Marty be happy with me. I've got one -- I've got 75 seconds to go, so I'm going to take my time on this last slide. If I want to summarize what differentiates the CXone Mpower, it's a unique AI platform. We combine foundational LLM and CX specific models. No one else does that. We do it this way because we have those CX specific models that we've been doing since 2019 with Enlighten. The fact that we're using AI consistently as part of our platform, we're not tacking it on or at a point solution, it means that AI is used consistently across all the different applications and those applications feed data back into AI, so the AI gets better, irrespective of the application or the touch point. Our automated insights, again, leveraging those CX-specific models that appear within actions like they -- it highlights those proactive decisions, the actions, the outcomes at scale. It's how our customers can really transform their organizations and deliver the ROI, save their money, build them revenue basically funding what they then pay us in license fees. And then finally, our new Mpower agents truly agentic in terms of how you build them, but actually focusing on the resolution of complex intents around fulfillment and delivering on those workflows. Marty, I was on second over, I apologize. Thank you.

Now Elisha from Hyatt. And this is an amazing customer. I'm going to say a few words now. I'm going to take a bit much for time. Elisha great to have you here. This customer, Hyatt's, and I'm sure you're going to speak to, they've been a partner of NICE for a long time. What your team has just done in record time is so impressive. I get like daily updates from Andy and others about how the project is going. And I'm just -- I'm so proud of what you guys have done. It's amazing. Congrats.

The pressure is on, I guess. So today, I want to just talk to you guys a little bit about our journey with AI adoption with Hyatt, particularly in the knowledge management space. So I'll just go through why did we approach this in the first place? Why do we even look at knowledge management, why are we even partnering with CXone for this? I also want to touch on something that we call the digital rocketship as well. We also -- on top of knowledge base, we also looked at Copilot as well, a tool that we use to make knowledge access even easier and faster for our colleagues. So I'll walk through how we evaluated Copilot and how we partner with our teams that we have across our operations to make that come to life. And then I want to talk to you guys a little bit about some feedback that we're getting from our colleagues and from a performance perspective, what exactly are we seeing now that we're more than 90 days past our rollout for Copilot. So first, Hyatt Hotels. If anybody is not familiar with Hyatt, we were founded in 1957. There are 28 unique hotel brands that we have. And if you guys can think about that 28 brands that mean there's a lot of information that our colleagues have to memorize in order to better serve our guests. For the contact centers, when the guests are calling us, they think that they're talking to the hotel. So we want our colleagues to be as knowledgeable as possible. So that way they can help them fast and make it as easy as possible for them. But you guys can probably tell that's a little bit difficult when you have to memorize 28 brands and then all the booking rules that go within those 28 brands and then you have all the different segments like luxury, you have all your select service, everything there, a lot for our colleagues to remember. What we're going to focus on, we see 160,000 global colleagues. That's Hyatt overall. We are going to focus in on the contact center space. So if you hear me say GCC, that stands for Global Care Centers. That is the name of our contact centers. For the contact center, we have a mixture of property services and guest services. So on one hand, you have the colleagues that take care of the guest experience. I mean on the other hand, you have the colleagues that take care of everything behind the scenes to make sure that our hotels are operating. And so it really is like a perfect blend for us to really try AI in this space. So we have 2 different tracks of works. You have 2 different levels of complexity that they have to go through in order to get their job done. For our guests, they have to move fast. They have to make sure they're getting them answered as fast as possible, making their reservations as fast as possible because I think what you said, people are still calling us, but they don't want to be on the phones forever either, right? So they want to talk to somebody, they don't want to be on the phones forever, and that's exactly where our colleagues come in. On the operational side, they have to support our colleagues. So they need somewhere to go that we can get information fast to resolve things as best as possible. So that way our colleagues have the space to take care of our customer. Just a little bit about me. My name is Elisha Wright. I have been with hospitality for about 14 years now, 2.5 years of that has been at Hyatt. Fun fact, when I came to Hyatt, I did not expect to have knowledge management under me at all. I was strictly learning and development. So think about being in a classroom, training people, falling to the floor and then you do it over again. So knowledge management was not in my purview. So I really had to think about how do I associate this now with my role. And that's when I started thinking, well, knowledge management, to me, is way more important because that's what happens after training. That's how we can bet knowledge in the flow of work, which is what they really need. We just went through 4 weeks of training, do we really expect them to remember all of this. We need to give them a tool where they can find that information fast. Now it is my favorite part of my job, to be honest with you. I was scared a bit at first. There's a lot of content that has to go into it. Once you start realizing, well, you have to write it like this for the colleagues to be able to digest it easy and they're helping their customer when you see the excitement on their face saying, wow, I learned all of this, and I don't have to memorize it because Elisha or Tia Becker who is on my team, by the way, showed us where we can find that information. Something I try to tell people is true market intelligence, it's not everything you know is how you find that answer. So why do anything in the first place? When I came to Hyatt, we had a knowledge-based system, and it worked, it worked, but it did not work well. You had to be very, very specific on what you are wanting to find and Hyatt, we are very known for our acronyms internally. So if you can't think about, okay, what is that acronym and you get one letter wrong, you aren't getting results. Sometimes you would put the article number that goes specifically to the article that you're looking for because you use that one every single day, and you still can't find that information. Also, the system was a little bit slower. So obviously, if it's slow, that means you're slow responding to your customer, and that means they're getting more upset because it's taking a long time for you to resolve either issue, make their reservation, that way they can get on their way. It also was outdated UI. So I think a lot of feedback that we got -- well, a folk a colleague particularly. This is not really pretty, this is ugly. I wish we had something a little bit more modern. So we knew that we needed something more modern. We knew that we needed something with natural language search and not just a key word search because there's going to be time when you say, what is something or you're thinking like something that's close to it, but I don't know the exact word. We need something that our colleagues do not have to think much. They can just type in and find exactly what they need. And that is what Expert gave us. The rollout for us was absolutely amazing. Honestly, it was like night and day for the colleagues, something I was super nervous about. It was my first big rollout that I have ever done even at my previous job. So you're expecting to get that feedback where we're saying, I don't like it. We're so used to the other way. It was ugly before, but we were so used to that ugly that we want to keep it and don't call my baby ugly. But when they got this new baby, it was almost like immediate positivity, especially when they started realizing that this article that I searched for before, it was at the bottom of the list. But now it's at the top because it's realizing that I'm looking at this every single day. So although we have fears of AI because our colleagues fear AI, they start freaking out because they think it's going to take over their job. We were able to settle AI for them. They don't even realize that they're using AI. So CXone Expert. I'm going to touch on Copilot a little bit more as we talk here because Expert for us was just a launching pad. And with Andy's help over here, we came up with something that was called a digital rocketship. And what that means is we have Expert as that launching pad is our foundation. But then we start to layer tools on top of that. So that way, we can continue having knowledge access, not even just to our colleagues, but also to our customers. That's when you start to hear things called like Autopilot for instance. And although it wasn't part of Autopilot, it was part of that digital rocketship that helped bring knowledge to our customers as well and also still support our colleagues. The main next step for us, though, with the digital rocketship was going into Copilot. And what that was going to do was instead of now me having to go into knowledge base and find this answer, Copilot is analyzing my interaction and it's sending that knowledge to me based on the intent of what the customer is looking for. And that has been a game changer for us as well. So I'm going to go a little bit into Copilot. You probably could have taken a few minutes too by, by the way, because I'm looking like, I still got some time here, but probably could give you a few more minutes. So evaluating Copilot. Again, we started with CXone Expert as our single source of truth. But we had to first partner with our digital team and our AI council. The cool thing that I love about Hyatt is that once they realized that we were looking to adopt AI, they did not want to slow us down. They did not want to stop us. What they did was they formed a council so that way they were able to put governance in place and also help us make sure that we're assessing the risk, and we're doing all the right things as we start to bring AI into our environment and making sure that we had a soft landing for our colleagues. So we partnered with the digital team and the AI council to go through what were all the pitfalls that we may expect, what do we anticipate? What do we need to partner with NICE to make sure that we have done before we even bring this in front of any of our managers even. So that was a big help for us. We also partnered with NICE team because they helped us with making sure that we made all those changes. They made sure that they got all the answers as fast as possible for us as well. So it really helped us move fast so that way we were able to adopt Copilot as fast as possible. Just like you said, it was record time, and that was a partnership that we had internally and with our partners at NICE, which I already talked about this year for the partnership with NICE. We also partnered with our operation leaders as well. Operation leader was somebody that I noticed that they were being ignored in all rollouts that we had. Even I have to say if we're CXone Expert, I could have done a better job of bringing our operations leader in. So that was the lessons learned. If we wanted to move fast, we had to make sure that we had their buy-ins because ultimately, they are the ones that are going to be leading the colleagues. They are going to be the ones listening to all the complaints if there were any, but they're also going to be the ones to be the cheerleader for us as well. So we made sure that we partner with the operations team. So that way, they can not only support the colleagues, but also support us as we were building the training around Copilots. So we made sure that we are talking about all the right things and ensure that we understood what are the fears of AI that they are hearing out there. Let's make sure that we're addressing that. So a strong partnership with the operations team and then we also have our partners at ALG is one of our brands within Hyatt. We're going to be hearing Nick on the main stage tomorrow speaking a little bit more about AI, but they were also going through Copilot. So we ended up partnering. So as we were testing, we were sharing our lessons learned. As they were testing, they were sharing their lessons learned. And because of them, we were able to really find that ticket to say, oh, we know exactly what we need to do in order to get Copilot working. So again, I want to stress the partnerships that we had to really make this happen because it really helped us with that real-world impact that we want to see. So Copilot, adopting a Copilot for our colleagues. We focus more on chat and not voice first. It was a little bit low risk for us because we know that if they have to read it twice, 3 times, they have a little bit more grace from our customers, where if you have a voice and you don't understand something, they might say something a little bit out loud and the customer is like what are you talking about? So we decided to test it within chat specifically with North America, but we had our voice colleagues still using Expert, so they were still doing the manual search. The one thing that our team had to do, and I have to give us big shut out, again Tia, she's our Senior Manager of KnowledgeBase. One thing that she did as she was doing all the assessing with our ALG team is we recognize that the way that we had our articles written. They were great for our colleagues. And it was amazing they were able to find information faster. But our AI was not able to find that information. Our colleagues get 4 weeks of training. Copilot gets 0 weeks of training. So we had to go and reformat almost all of our articles so that we can make them a little bit more concise, we can make them shorter, we can make them more action oriented. So that way, Copilot can understand, okay, this is exactly what the colleague is trying to do and be able to give them that correct answer. When we first started testing it, we started noticing that we were getting some responses that like, man, why are we getting this response. And as soon as you're ready to give up, you're like, wait a minute, we're realizing that these 2 articles look the same. So we also saw opportunities to even combine some articles and realize that we're really duplicating information where we don't need to. So I'll talk about our voice colleagues still using Expert manually. It also helped them out even though they weren't using Copilot, now they have a knowledge base that they can go back to and they're not finding redundant information anymore. We also had to make sure that we address the emotional side. Again, there was fear of AI. People thought they were going to be losing their jobs if we incorporated AI. So it was important for me to people understand that this tool is here to support them, not to replace them by any means. And how we did that was tuning a few colleagues to sit with us letting them use it, give us their feedback. Let's make sure we're getting back to NICE. So again, we go back to that partnership that we had. Going back to NICE to make sure that we address any of those concerns and getting back in front of them and getting them more comfortable. So then we created cheerleaders within our colleagues from our pilot. So that way, they went to the masses able to see the praises. And I have to say that we had an amazing rollout. It's probably the best rollout that I've ever had even with Expert. We have a heavy population of outsourcing colleagues now. And if you think about it, those outsourcing colleague there from different countries, they're trying their best to understand the terminology, they're trying to best understand hospitality overall. And now they have this knowledge base, they have Copilots to help give them the information as they're trying to get comfortable with, hey, there's this new company I was with. If you guys don't know outsourcing colleagues, they typically are with another company first before they come to us. So now they're thinking about all the different companies they were with before and having to get out of those habits and Copilot and knowledge bases would help them do that. And now I'll show you guys just a little bit snippet of how Copilot works in action. Let me go back. So they had their CXone open. They see a new app for Copilot. You guys see they're talking here with the guests through chat. We can see if the customer sentiment is positive, we can see that if they're getting angry with us, the confusion, yes, a summary as well. So it summarizes the interaction. So if they are looking for something, they can always go back to see, wait, why am I here in the first place? This is what we call the KB answer. So this is where the answer from the knowledge base comes to our colleagues, and they can read it quickly and then be able to respond to the customer. If they wanted a little bit more information, this web links, it takes them straight to the article. So again, it is still cutting down on that search time because they're not having to go look for it manually. The article link is coming to them directly as well. And this is an example of the web link. Again, took them directly to the article. Another thing I love is that colleagues can give us feedback as well. So we take that feedback to continue making Copilot better, continue making knowledge base better. I can say as the conversation progresses, Copilot provides updated responses, additional resources and then continues to update that sentiment as the conversation goes on. So they might start happy. They might get mad in the middle based on the answer that we gave them, but our goal is to make sure it's positive by the end. So that is Copilot. Again, I want to talk to you a little bit about some performance highlights that we've been seeing. Almost immediately, we saw a decrease in our average handling time. So that means that the colleagues are getting the answers fast and they're responding to the guests faster as well. So now we are saving the customer time, we are saving the colleague time and saving the organization money as well. Another thing that did not include in here though is customer satisfaction. So one thing that you can see is that they didn't decrease, but what if they were giving wrong answers. That could send you something and say, hey, you're good, but do we know if it's right. We did not see any decreases in customer satisfaction. So that means that they are getting the right answers. They're not having to call back and say, hey, I just complained, so this person told me this, and I was given this instead. So customer satisfaction is still exactly where it needs to be. And this is just some quotes that we have from our colleagues, just talking about Copilot. They see the value in it. They look forward to seeing all that it has to offer because we continue working with our friends at NICE to make enhancements to Copilot. We have a few more features that we want to release. They're happy with what they have now, and they're excited more that we're going to give them more features to make this easier for them. And my favorite thing is always when people say that they would recommend Copilot to others because you come back to me and you can say, hey, I like it. but it's something about recommending it to somebody else. That to me is that's the true result, but the true satisfaction I'm looking for is that people are telling other people you need to use this especially because we have colleagues who still had that mindset of I don't want to use it because if I do, that means I'm contributing to it, taking my job. But when they start talking to their friends, they're talking to other colleagues, they're saying, no, I use it for XYZ and now they're getting best practice as well. Looking to the future, we are looking to expand Copilots to different languages. Specifically, we're looking at German right now. We've actually done demos for our contact centers globally. That's within China, Japan, Korea, Germany and India. Every single one of them saw it and say they want this and they want it now, so much so that I now have weekly meetings with them. Even though we're not ready to go there yet, they just want to know more about Copilot and what we can do to get it to them. But Germany is where we're going to start. I'm excited because we are a global company. So we want to make sure that we're rolling out a tool that everybody would be able to use and all the products that we've had with NICE so far, they have that global reach. If we go back to Expert in China, they have a different level of expectation when it comes to service. People want you to move fast. So they needed a knowledge base that was going to provide that for them. Expert did that. And I believe that Copilot is going to do that as well. I think that this is just the beginning. Again, this is something that I was not expecting to come into whatsoever, like you said with knowledge base. And now it is the favorite -- my -- most favorite part of my job, and I look forward to just continue building with the NICE team as we implement more features and probably hopefully more products from NICE. So that way we have an ecosystem of NICE products that take care of our customer.

Okay. Thank you, Elisha. There's nothing better than hearing directly from our customers how CXone Mpower is really driving tangible results and really enhancing customer experiences, your consumers and your employees as well. So thank you for presenting today. So I want to start off today by really talking about a high-level overview of the last 5 years of financial results at NICE and demonstrate the strength we've consistently delivered. So we're really well positioned for a strong financial foundation to launch our next era of profitable growth. At NICE, we have consistently delivered expanding revenue in growing at a compounded growth rate of 13% over the last 5 years. This total revenue growth not surprising to anyone is really being driven by our cloud and AI platforms and in particular, of course, CX Mpower solution. At NICE, we do have the largest cloud revenue base in the industry. Our cloud and AI platforms are made to scale to the organization you've heard from today, Hyatt, Walmart, Disney. So Barry highlighted earlier that really, there's only a few companies that are able to scale at this level. And you can see that our growth is demonstrating our success. Our cloud platforms with that underlying ability to scale means that we consistently deliver high margin cloud platforms. And you'll see that that's playing out in terms of how we actually manage our business at NICE. We have always maintained a disciplined approach in cost management. And you can see this ongoing continuous margin expansion where last year, we exceeded more than 30%, delivering a 31.1% operating margin in the business. We have a proven ability to optimize our resources, scale effectively and continuously enhance productivity. This is something we've always maintained as a balanced approach at NICE, which is driving the top line growth while always having a focus on also our profitability, and this produces our long-term financial results and this profitable growth model. All of this profitability is conveyed and is translated into our best-in-class free cash flow generation. Our free cash flow generation was more than $700 million last year with a free cash flow margin that exceeded -- almost reached 27% last year. So our free cash flow is really unparalleled when you look at our industry. It provides enormous flexibility and optionality for us at NICE to make ongoing strategic investments, execute on value-added M&A and also continue to return capital to shareholders. So this is extremely well positioning us as we look at the next phase of our profitable growth with this strong foundation that we have. Next, I want to spend a little time on our 5 key growth catalysts at NICE. These key growth catalysts quickly are both catalysts that have been recognized and represented in our financial statements over the last 5 years that are also key to driving this ongoing growth looking ahead. The 5 key growth catalysts are: first, the secular tailwind of organizations that continue to migrate from legacy on-premise providers into the cloud. The second is our global profile that we have at NICE and the international momentum that we have been delivering. The third is our proven ability to continue to cross-sell and upsell into our existing installed base. The fourth -- Scott talked quite a bit earlier today, which is that we already have a strong ecosystem of partners that is continuing to be expanding. And finally, the rapid adoption of our AI. So these 5 key growth catalysts are really a common thread across all parts of our business at NICE. But given that our customer engagement business is such a large portion of our overall revenue, it represented 85% of our total revenue last quarter. When I go from here and drive -- dive into each one a little bit, I'm actually going to focus mostly on the customer engagement business and provide some more specific CX data. So the first growth catalyst is what we talked about this ongoing secular tailwind of organizations looking to shift to the cloud. And you can see that we have benefited from this strong tailwind over the number of years with ongoing increases in our cloud revenue. The secular tailwind and for NICE over the earlier years was really in the small and midsized markets. And we've increasingly taken a larger profile in the large enterprise, which I'll talk about a little bit momentarily. And importantly, as I highlighted earlier, we are the largest cloud revenue base in our industry. In fact, in the most recent quarter of Q1, we exceeded $2.1 billion of ARR in the cloud. And it's really important to highlight. And here, you can see this migration and the tailwind that's happened that over a 5-year period, we've gone from less than half of our revenue at NICE to being now at 73% of our total revenue, and this was at the end of 2024. So actually, in the first quarter of this year, that has further increased and now we're at a 75% growth rate. So when you think about our overall total revenue growth and the expectation there, more and more of our total revenue is being driven by this higher cloud revenue growth and that is what we expect to continue to see in the future as a cloud-centric and AI-centric company. And before I leave this slide, the last thing that I would highlight, it's really critical is that every CCaaS deal that we signed at NICE is incremental to our revenue. So our business was really built on the CCaaS offering, which was something that we introduced several years ago and continues to provide that incremental revenue to our top line. I'm staying here with the key growth catalyst of the secular tailwind of cloud migration, and I talked about how at NICE, our customers have really shifted over the years to starting more in the SMB. And today, we continue to work with the SMB, but as well, we have expanded into having more and more large enterprise customers. And in fact, we now have more than 401 million-plus cloud customers. And you can see as well the great impact that has had on our revenue. So now if you look at our cloud revenue, more than 1/2 of our cloud revenue is coming from those 1 million-plus large ARR customers. So our entry way into the large enterprise is really just beginning. Scott mentioned earlier that the market is really estimated to be about 35% to 45% penetrated at this point. And that means there is still a significant opportunity looking ahead for us in the large enterprise, where we have always played extremely well and have years of experience at the high end of the market. Further, the penetration of AI in these organizations is even less, which means that we just have an enormous opportunity as we expect to see these large enterprise continue to accelerate their expansion and shift to the cloud into our CCaaS platform over the next several years. The second key growth catalyst is the international momentum that I talked about. Our international business now represents approximately 10% of our overall cloud revenue, and that's grown at an impressive 52% compounded growth rate over this 5-year period. We've recently announced 2 significant wins in our international business. One was in the APAC region about a year ago and more recently won in our EMEA region. Both of these impressive international deals were 8-digit ACV deals and each had a TCV exceeding $100 million each. So really great momentum that we're seeing. This is an area we've been focusing investment in the last couple of years. And given the strong growth rate that we're seeing, it's continuing to be expected to deliver more and more of our revenue from our international business. So the expectation is that as this concentration grows, you would see a positive impact -- an accretive impact on the overall cloud and total revenue growth rate. The third growth catalyst is our proven ability to cross-sell and upsell into our existing installed base. And here, you'll see the validation of that. Starting with the first quarter of 2022, you can see that how our customers are repeat customers, we have very sticky and loyal base of customers that are continuing to come back and buy more and more with us. And this is increasingly important if you think about what you heard from Barry and others talking about today, the complexity at the high end of the market, the needs that they have, and we see these customers continuing to come back over and over. These customers, really, as I said, are very loyal customers. And once they're with NICE, we see that they're customers for life. The fourth growth catalyst, I won't spend much time on. Scott talked about it quite a bit earlier. It's the strategic partners that we have. I think Scott mentioned already that we had now 70% of our business was partner-led during the course of 2024. And this is a very big transition from where we started at NICE. Typically, many years ago, we were really a direct go-to-market. And now more and more of our business is being led by the partner ecosystem. And in fact, 70% of our new large enterprise CXone ACV deals were driven by our channel partners. So in terms of some of the newer partnerships that Scott highlighted earlier today, we would expect them to start coming into our financial results really at the very tail end of 2025, but much more gradually to see those results during the course of 2026. The fourth -- or I'm sorry, the fifth and final actual growth catalyst that I'm sharing is our AI adoption. And we've heard -- you've heard a lot this morning on the main stage. You heard from both Scott, Barry and Elisha from Hyatt about the strength of this business. We wanted to share that in the first quarter of this year, 25% of our new cloud bookings were our CX AI bookings. And when you look at the success we're seeing, you can see it's going to continue to have a great impact looking ahead for our growth at NICE. One year ago, we had growth of about 29% year-over-year, and that has already accelerated to a 39% year-over-year growth. And what's equally important that I want to mention is, if you were here last year, I talked a lot about our consumption-based pricing model. And when you look at this AI and self-service revenue, more than half of this revenue, the majority is coming from a consumption-based pricing model. So our customers are really in the very early stages of AI adoption. And you've seen some of the data that shows the great increases we're seeing in transactions. And so those transactions will continue to come into and drive this growth ongoing. So that's an important facet. And similar to what I shared for international expansion, the growth here is expected to be accretive and based on the amount of AI that we're selling, we'll continue to have a positive impact looking forward in our growth rates. Another share or a slide that I'd like to share with you here is how much of our base has actually been penetrated by AI so far. So 2 years ago, about 1/4 of our customers had adopted at least one of our AI and self-service solutions. Today, that stands at about 1/3 of our customers. So we are seeing this greater adoption. And what's extremely important to highlight is that on average, our AI and self-service customers have an ARR in the cloud, which is typically about 7x higher. So this is really important to also when I showed the opportunity ahead in the large enterprise, the large enterprise typically has a significant amount of complexity. They have greater needs for the AI. And I'm going to share with you now a few success stories of customers that demonstrate a few customers that have already deployed our AI and how it's playing out in terms of the monetization opportunity for us at NICE. So I'm going to go through 3 customer success stories. The first is a long-standing customer of NICE. They are a global hotel chain. And this customer several years ago adopted our CXone platform back in these early years where you see the OCR and workforce engagement. And this customer decided that they wanted to go through a self-service transformation. So they went through a competitive bidding process. They included us in that bid as well as some of our competitors, but ultimately selected NICE. First, due to the strength of our self-service application of Autopilot, which they adopted, but also because they had a really great deployment with our CXone Mpower platform earlier on. So that experience they had and the stickiness that I talked about as a customer or what keeps those customers coming back along with our best-in-class technology around AI. And now our AI as a percentage of this customer's ARR is about 1/3 of their recurring revenue. So this is the existing customer example. And now I want to go into the next customer success story, which is a new logo win. So this is a large, well-known financial services company. And this company decided to go through a DIY or do-it-yourself initiative in their organization. They decided that they would go on their own and try to deploy self-service capabilities. But ultimately, they found out that it was much harder than they expected, and they failed. So ultimately, they also went through a competitive bidding process. And they selected NICE due to the strength in best-in-class technology we have an AI and they have now adopted 3 of our different AI applications. They're using Autopilot. They're using Copilot and they're also using some of our enlightened models, which were specific to their vertical as well. So we have a significant amount of data that we've accumulated over the years that's really focused on intent and so that also was a significant factor in this customer ultimately selecting Mpower in our platform with the AI capabilities. And you can see that for this customer in less than 1 year, we've increased the recurring revenue to $8 million, which is a multiple of 4x increase year-over-year. The third customer success story and the final customer success story is really interesting. So we've gotten a lot of questions over the years around customers and what will happen to our business as agents decline. So this is a terrific example of a customer that doesn't have human agents with NICE. This customer is using only our proactive AI agents, so they are 100% self-service customer. This customer has had a great increase in their ARR as well with almost a 4x multiple. And we have a very satisfied customer. Ultimately, they were looking for containment in their organization. And because they've had such strong results, they've also been able to now shift more of their focus and are sharing with us that they're driving a lot of top line growth as a result. So these are 3 examples that share the success we've had with some of our customers. These are real examples of well-known marquee brands. And now I want to just transition to going from how these customer success stories are playing out in terms of our financial results, these customers as well as many others. So this is a Sankey diagram, and this is demonstrating our 2024 results. And I wanted to share this with you because it really demonstrates the strength that we have at NICE and the resilience and continuous focus on how we drive our business. You can see our impressive 71% gross margin, the operating income that we delivered last year of more than a 30% operating margin. And of course, all coming with the robust and durable free cash flow generation due to the strength of the top line growth as well as keeping that focus on profitable growth. It underscores the strength and durability we have of our strong operating model. And it also demonstrates that at NICE, it is one of the muscles we have always maintained, which is a focus on delivering operating leverage. And we continue to do that through our best-in-class cloud and AI platforms. When we drill in a little bit further, you can see just the strength that we've delivered from this profitable growth model and how that played out during the course of 2024. So we delivered more than $700 million of free cash flow last year. And this really makes us unmatched in our industry. It provides us with optionality to drive business quickly, to make strategic investments and have a very clear focus and much flexibility in terms of what we do around capital allocation. So this is a view of the 3 prongs we have in our capital allocation strategy. We continue to always have a robust and we have an ironclad balance sheet at NICE. So we want to maintain that strong balance sheet. We want to maintain that capability to be able to put our liquidity at work. We had about $1.6 billion of cash available to us, which we continue to put to work through the next 2 prongs. The first being the strategic and disciplined investment. So regardless of whether we are investing internally or whether we're looking to execute on acquisitions, we are always doing this in a very targeted and focused way. Earlier this year, and today as well, Scott talked a little bit about where our focus is. And so first and foremost, we are continuing to drive innovation around our R&D spend and specifically focusing and continuing to hire in those areas. In addition, one of the things you'll see shortly is where we've invested in our cloud business, meaning specifically in our international regions. So over the last 12-plus months, we have been very focused on making investments there, and it's really paying off from what you've seen. The third and final program and the final prong of our capital allocation strategy is around our share buyback. So I talked about the strength of our free cash flow that we generated last year and as well in the first quarter of this year, we delivered a record cash flow generated from our operations that allowed us to complete a record share buyback of more than $250 million. And in parallel, we announced a new buyback program of $500 million at the same time. So all of our capital allocation strategy really comes together, and we're always looking to balance investment with value creation. And of course, it's all underscored by our ongoing commitment to return capital to our shareholders. So I've talked a lot about the discipline that we have at NICE and the way we intentionally focus where we spend. Here, you can see the strength of the cloud margins and the overall gross margins we have at NICE. And one of the things that I would highlight that I mentioned as we came into the year of 2025, is that we do expect to see a flattish gross margin in the cloud this year in the overall gross margin. And this is as a result of those intentional investments that I talked about, and they are already paying off. So we have created foundational infrastructure outside of the Americas to continue to grow that 10% overall concentration from our international business even faster. And so that is one of the key areas that we have focused this year. So these investments are continuing to pay off, and you can see that we continue to maintain that focus on this really constant delivery and a great operating model driving this profitable growth. Next, I want to flip back and actually reiterate all of the guidance that we shared last quarter. So last quarter, we reiterated the top line expectation of total revenue at 7% and the cloud revenue expectation of this year of 12% growth. Our operating margin, we expect to see about a 50 basis point expansion this year in the operating margin. So despite accelerating investments in key areas, we have other efficiencies we will utilize to continue to deliver on this expansion. And finally, the EPS growth of 11%. So I can say that with Scott at our helm, we are super excited about our next phase of growth at NICE. And we started the day by mentioning that we do plan to have a Capital Markets Day during the month of October. Currently, we expect it will likely be in New York City, just so you can mark your calendars. And the expectation is that we would unveil some of our financial expectations beyond 2025 at the Capital Markets Day. So please stay tuned for that. So finally, in summary, on my end, I just want to remind everyone about the fantastic market that we operate in and the competitive advantages we have. So we operate in this fast-growing TAM with these continued tailwinds that we talked about of organizations and the market, which is still highly underpenetrated, especially at the high end and even more so with the AI opportunity. We have an increasing global presence with the international momentum we're delivering. We're very excited about that business, which is really thriving. We have an expanding customer base with a proven validation that they come back and are continuously looking to purchase more from NICE. And we have a very broad and deep rich base of our platform, CXone Mpower that allows them to continue to come back and meet more of their needs and their organizations. Finally, we have decades of CX domain experience. And of course, all of this is underscored by the financial strength that I talked about, which really is unparalleled in our industry. The amount of liquidity available for us to invest, the amount of liquidity available to return capital to shareholders, all of that is very unique. The runway ahead for us is significant at NICE, and we are extremely well positioned to continue to capitalize on that opportunity. And as I mentioned, very excited about the growth ahead for NICE. And with that, I think I'm going to hand it back to Marty before we take a short break. Thank you.

So let me take a 15-minute break. They're going to roll lunch into the room. We can grab lunch, and we'll come back in 15 minutes. For those on the web, please stand by. You'll hear music for the next 15, 20 minutes. And then again, we'll be back with Q&A. Thank you. [Break]

So why don't we begin with Q&A session. We have Barry Scott and Beth, and they're here to -- they're happy to answer any questions you guys have. [Operator Instructions]

Siti Panigrahi, from Mizuho. Great presentation. Keynote was a lot of energy. So Scott, I think your message, if I understand correctly, like you're saying 15 million agents that we used to track the market opportunity. Now look at the billions of interaction, massive interaction, that's where the opportunity is. So help us understand how should we think about the market opportunity there and your monetization strategy? How are you going to monetize that whole billions of interactions?

Sure. So I'll start, and Barry, I might hand to you if I miss anything. So you're right. So if you think about the history of contact center in the world that we've been in, we've been centered around the agent and the platform to interact so that interaction, so whether it be the ACD and being able to handle voice or any type of channel in the interaction, all with the agent and the experience, the performance, the accuracy of the agents all around that initial contact. But if you think about that world, it started and stopped at the interaction. Now we were -- we are the best in the market, and we're really good at that. But if you think about our addressable market going forward, the interaction will only increase. And whether it's via voice or whether it's via chat or whether it's e-mail or any other mechanism that happens going forward, that interaction volume continues to increase. But what you do with the interaction how you solve the intent, that is a huge opportunity for us. And that's why when we talk from intent to fulfillment, we used to talk about our role on intent and we would be the intent to interaction. And that's only a very small step in the journey, but intent to fulfillment means that you're handling workflows, you're performing tasks, you're delivering services, you're automating outcomes, you're doing it by humans and by -- through obviously AI agents and on bots. And so that gives us two means. One is, it means that we can increase value for our customers to serve the end customer, which means we've got a premium that we can drive there, which right now, if you just talk about bot conversation, it's a pretty commoditized market because it was based on such limited information and knowledge that it could use. But the more value we drive, the more savings with the more -- the more we can drive that, that sets the first part. And then secondly, if we're performing tasks that were otherwise done by back-end systems, people, processes that were fractured and redundant and we're able to optimize that, then clearly, there's an opportunity for us to be able to monetize that. And ultimately, the way I view it is, right now, if an interaction occurs, we have one shot of monetization, and that's usually through the agent. But one interaction, if you take the Walmart example this morning, that one interaction might have 5 or 10 different intents within that one interaction. Every one of those intents will trigger an Mpower Agent. Every Mpower Agent might use Copilot. And then you might then use AutoSummary. So the AI monetization for one interaction could explode. And that's part of the reason why we've presented. It's not a one for one, and that's the opportunity in front of us. Barry, do you want to add anything?

Yes. I mean, you said it. I mean, we've talked about it a bit earlier as well, but ignoring agent-based pricing for the time being, looking at the interaction-based pricing, the market's, as Scott said, very commoditized. Conversational AI bot is going to cost you about $0.20 to $0.25 if you're a customer to use that. And Salesforce came along saying, "Oh, it's going to cost $5." Everyone laughed because it's not going to be the case. You can't go there in a massive market and change that overnight. So we realized, it's very hard to differentiate what you charge for that conversational AI experience. It's going to stay $0.20, $0.25, I believe. But what we can do, as Scott just said, is monetize the back end and the fulfillment. So our Mpower agents are charged per database that's updated, information that's retrieved. And that is tied directly to value because that work is replacing middle and back office work by expensive people and it's differentiated from the Copilot's cost or Autopilot cost as well. So that's the plan. And look, initially, we'll bundle Mpower agents with Copilot and Autopilot. So it's easy to get adoption, but then it will be turning on and we'll start monetizing fulfillment.

One more question on CCaaS migration. I think you talked about maybe 35% to 45% right now seems massive, 55% to 65% still on legacy on-prem. So first is, what do you think going to catalyze that kind of migration to that massive legacy base? And second is, why are you not going with your AI solutions like going to this legacy because we keep hearing from customers, they're using some other stand-alone AI. Why are you not going there like a beachhead strategy, trying to capture them and when they are ready, they'll move to your platform.

So the answer is we are and we will, and that is a really critical point. We have competitors who are very clear about trying to monetize either their own installed base and moving from on-prem to cloud, that has a limited runway. And we will win more than our fair share of that. But if you only get them to a cloud, but you really get them to an AI-powered platform and then you find a way to lead with that rather than an add-on, then you're truly changing the game because you're giving them a leapfrog capability, and it gives us a differentiated way of being able -- though it's not just about our CCaaS platform versus somebody else's. We believe very strongly that our AI platform is leagues ahead. We're obviously making it even more powerful through partnerships. But you're absolutely right, we are -- and there's no surprise that the volume and the usage rates and the expansion that you've seen in the last 6 -- even in the last 6 months is a direct correlation to that.

Just anybody on the web, remember, if you have a question, just please type it into the web interface, and we can take it here. Tyler?

Tyler Radke from Citi. Thanks for doing this. Great to see you and see the energy from the keynote. So first question, just kind of bigger picture. NICE as a company had grown its cloud revenue north of 20% for many years. I think as you look at some of your bigger competitors in the space, they're talking about well north of a 20% cloud growth rate. So Scott, just as you've come in, I'm sure your aspirations are much higher than 11% or 12% that you guided to this year. But how do we get back to that 20%? Is it going to kind of require these new use cases around knowledge management and orchestration or is it simply just kind of a timing thing and macro-related?

Yes. I think there's a few things. So first of all, I think I might have mentioned once before. I've got a pretty good track record of taking businesses that have got moderate and good growth in the cloud and making them exponentially. And it does cover a number of pillars, but I'll just reiterate. One, is using the market forces to our advantage. In the market force of AI, we have not seen the potential in our -- you're seeing the glimpses of it. But it's not yet at a mainstream. We've just launched Mpower agent. You fast forward 12 months, 18 months and our ability to be able to monetize that addressable market, that's a growth catalyst. But more broadly back to the earlier question, starting, finishing and all being in the AI platform is a key element of our growth. Now that will then lead to secondly, using the CCaaS migration, which is still ongoing, and Beth mentioned the big international wins recently. There is more and more and more of those, but not using that as the endpoint by getting them straight to the AI-powered platform, which gives us that exponential growth that we've got. The third is international expansion. I guess what you've seen with NICE is we've been really good recently of being had some international wins. You can rightly expect that, that will continue. And Beth talked about it because the same platform that we built for the North American customers, but these are not North America-only customers. You listened to Elisha. He's got -- in Japan, in Mexico, in Venezuela, they're all around the world and our ability to be able to help international companies expand and then local companies utilize the same platform. And then last but not least, I am excited about this is, the partnerships as a growth driver. So what I can tell you is, every one of those partnerships that we've signed, I've got direct target on it. Yes, there is innovation that Barry and the team are driving. And I care passionately about building 1 plus 1 equal 3 or 5. But ultimately, it's got to drive exponential growth for us. And it's probably something that is a newer muscle for us. We've done some good partnerships in the past, but it's something that I think that will give us a huge lift. I have obviously not talked about inorganic, but there is clearly the flexibility. And I've talked about this before, we do have the flexibility, but it will be strategic growth. It's all about what will drive long-term shareholder value. And so when we look at those investments, it's very much about -- and potential inorganic moves, it is very much around the long-term growth. But I think we're ready to seize that opportunity. And I -- look, obviously, we haven't talked about it much today, but I think we can talk in much more detail in October, as Beth mentioned, around those midterm and that long-term outlook and what that growth prospects. It's fair to say I have high expectations.

It's John DiFucci from Guggenheim. Bear with me for a little bit because there is a question here, but I saw a lot out there, and Tyler's used to my long-winded questions. And really, my thoughts around AI. Like I'm not as close right now to NICE as maybe some of the people in the audience, but I have been close, and I do pay attention. And I was talking to Beth about this in the break. I mean, it was great to see you owning AI here. Because I remember in 2015 going to Israel on a bus tour and seeing a demonstration at NICE, and Barry probably you are part of this. And actually, it was AI. It blew away the room, but it wasn't called AI back then. So I guess my question, and it's a bigger question, even bigger than NICE, but it's sort of twofold. Like we hear like some people cover Salesforce and ServiceNow, and ServiceNow is in the back of my badge here, too, so they're a partner. But in about a year ago or 1.5 years ago, they woke up and said, hey, we're going to be an AI player. But NICE has been doing this because you had to do it because if AI became anything, it was going to kill you and that's what you had to really work on it a long time ago. And it's not even just you, it's everybody in your sector; well, maybe not everybody, but I think a lot of others too. So I guess my question is, is that advantage, the fact that you've been working, and this is for Beth, is that advantage? Is that something that matters today? Or is AI just taken off in such a way that, like, you know what, I can become an AI guy if I want to, I guess it's all out there, I just go out there and get it. And then on the go-to-market part, and Scott, I think this one's for you, is it really against your peer CCaaS vendors? Or is it against everybody? Because I don't know, maybe NICE isn't just a CCaaS vendor anymore. I'm sorry, for the long-winded question.

And by the way, we're not a CCaaS vendor anymore, we're not. So that's clear. Look, when it comes to competitive AI solutions, you're asking kind of two questions, I think. One is, can generic AI just win? Do we need the sophistication that we've got? Is that the question you're asking?

I mean, the history that we have.

Okay, the history, yes. I just want to mean by the sophistication of the history kind of thing. I think there's one question. There's another point, I think, which I'll tie it to, which is kind of the benefit of the platform. I talked about it a little bit there. Don't underestimate that. So when we compete, we're either competing against other platforms and maybe not CCaaS platforms or organizations that came from there. And they don't have that heritage of -- we call it ENLIGHTEN, the CX specific models. Now for certain use cases, you're right, you can get away with it. Like the AutoSummary example I gave, the benefits of summarizing a contact or a call, a chat or an e-mail, if it's 90% accurate or 70% accurate, doesn't really matter if you're still reducing 2 minutes of call time. It doesn't matter. Other use cases, like the other one I showed here, the auto discovery of what you can do, that really matters. That really delivers value. So there are 30, 40, 50 AI use cases in CX. I would say that the 10% of them, Gen AI versus our heritage in what we do, it doesn't really matter at the end of the day, but the rest of them, it makes a massive, massive difference. And the other thing I would say -- and that's where we're competing against other platforms. The alternatives that we keep competing against like AI specialist point solutions. And this is where the power of the platform really comes in. There's -- well, I'll incorporate in there as well as the build option where customers say, I'm going to build my own AI solution that's specific to me. The benefit of the platform is insane here. And the example Beth gave earlier, Beth I'm going to do a tiny correction to what you said. It's not that their AI that they built themselves failed, is that they built it, it was perfect for them. It worked really well, but the CIO said, "I don't want to maintain this. My god, as I said to Scott, a pet's not just for Christmas, it's for life." And he's like, "I don't want to be in this business." I want -- and the software companies, that's what they do. So increasingly, we see organizations playing with homegrown solutions, but then realizing that actually that's not a business they want to be in. And so they want to outsource that to specialists like NICE that do it. And then with the point solution as well we come across, yes, you can get these amazing point solutions that are very good at very specific thing. AI-based selling in insurance at a certain particular segment, amazing. Again, because it's trained on specific data for that particular segment. The problem with that is, you need 5 or 6 of those solutions. You need AI governance, 5 or 6 different times. You get disconnected consumer experiences that work one way in one situation, and a way in another situation. You end up with an AI Frankenstack that's just like the CTI Frankenstack that we just got rid of. So I strongly believe that our heritage around Enlighten, that differentiation matters for the vast majority of use cases and delivers value. And the auto discovery is a great example of that one, but also platform wins. And we'll go through another year probably of customers thinking you can build it. But as always, as every single wave that's come, we all know whether it was the Internet, whether it was mobile, whether it was cloud, once you go through a year or 2 of build, customers realize, I don't want to be in this business. I need to outsource to a specialist.

I would add 2 more things just to round that out. The first is, the pivot and the branding that I provided and the pivot that we've announced and owning AI, if we didn't have that historical capability, I would have been more measured taking my time to do that. Be assured, I got up there and I made a bold statement to the market. We will be the AI company that will humanize and create reimagined customer experience. And that that's a lofty ambition, but one that I feel that we're able to step up to because of that history. And then the second is, I believe strongly the market will continue to see domain, deep categories where you need the context matters and horizontal players where it's good and that is where the build is because there are use cases across an enterprise, where building yourself is okay. And that might touch into our world. There will be an overlap and it's also okay. But when you've got a consumer that has high expectation about the accuracy, the timeliness of the response and then the fulfillment of what's going to -- you can imagine in 5 years' time, no 1 is going to sit there waiting on a call. We think of it now because we've lived that life. Consumers will not, and that cannot be delivered through generic platforms. It's got to be domain. And that's not just in the CX world, I believe that's in other domains as well. I would highlight that doesn't mean we've got all the pieces to the puzzle. There is a lot of work that we are going to do organically, and I believe there is also opportunities for us because of our financial strength of Beth and the team have delivered over a long period of time. But again, strategic value, long-term shareholder return. That's what we're after.

I will add one other thing or two for the CCaaS question and AI. This is an anecdote. But we had our ECAB yesterday, our Executive Customer Advisory Board, big companies in there. The CIO of a Fortune 50 company, who was part of a ECAB, used the term and other AI companies like NICE. We're not a CCaaS company. Our customers perceive this as an AI company.

Yes, it was very intentional. So yes, it's -- by the way, go to market, quick response. It's a pivot of our company. When you've been living, breathing, thinking, working around CCaaS, and that's been your livelihood for 30 to 40 years, and you then become -- it's not immediate. And don't worry, on the go-to-market side through our partners, who we partner with again, the signaling of partnering with the AI of Amazon, of ServiceNow, of Snowflake, that's intentional because of that pivot.

I have a question here on the web. This is for Barry or Scott. I guess you mentioned data has to go through CXone Mpower to power the AI-driven outcome, but that is tapping into other systems of record. Can you or do you need to replace these systems of record to truly empower your AI self-service platform?

The answer is no. So I mentioned the hubs when I did my presentation. Those hubs include an integration hub which actually reach out into any kind of system of record, be it a Salesforce, a ServiceNow, and Epic, you name it, those systems. So we're leveraging that data alongside the conversational data to do that now. Another thing we have, and we announced this today -- yesterday, and it's going to hit a subset of the market. But we use Snowflake internally within CXone as our data lake. It's a technology that we use. Now a good portion of large companies also use Snowflake. And with that, we have new innovation, new technologies called Zero Copy, which basically extends the data model of CXone into all of the Snowflake-driven applications in an organization. So that allows us to seamlessly access that data and use that in fulfillment and for our AI as well.

I have one more question from the web here. Scott, please explain why exactly NICE has the surface area to win with agents relative to Salesforce, which seems to be the main panel in the front office?

So first of all, you've got to break down agents. So there is no doubt that there is a role that enterprises -- and it's not just Salesforce, Salesforce, ServiceNow, Microsoft, Google, Amazon, and I think you'll see more and more of players like Open AI and others where they will have AI agents that are easily created just like ours is, but it will -- to be to serve a lot of tasks that cover a lot of different human tasks that happen within an enterprise. Now there's a couple of things to remember. Number one, if they are incorrect, and it's inside the enterprise, it's okay, you imagine your own experience where you're using your own Copilot and it's not quite accurate enough. It's not quite detailed enough. It's not quite context-sensitive enough. That is back to the models. It's back to the question of the ENLIGHTEN models, the context, the CX specific insights that we have that are already in our models. So when you're using an AI agent, in the role of customer service, where accuracy is a vital importance -- you might be interested to know by the way, enterprises are looking at the accuracy of human agents and then they compare that to our AI models and our Autopilot and our Copilot and how accurate that is versus when the human sells. And you wouldn't be surprised to know that when they do that comparison, the accuracy that we have now with our AI is even higher than the human side. But the generic players are an inch deep and mile wide. We are the opposite. We are deep in domain. And I want to come back to the comment that was made. We are not a CCaaS-only player. We are a CX AI player, and we are not limited to the CX and AI because I do believe our total addressable market will expand. We are an unavoidable contact, that single pane of glass, why would you go to Salesforce for that? Why would you go to ServiceNow for that? Because they can't deliver all of it. If you say, oh, I want to use voice, "Oh, well, I have to go back, if I'm going to use my ACD." Does that mean there's no way a customer is going to say, well, I'm going to use a different platform. So if we can be the best and own that and then be able to build out more and more actions, more and more flows, more and more tasks then we go way beyond the contact center as we traditionally think about it into being an enterprise platform that delivers AI to ultimately fulfill customers' needs and intent, but we can go way beyond. Now I haven't stated that anywhere, but you can clearly see the expansion opportunity. We've got to earn the right, though, and the way we earn the right is where we are domain deep and the best and that is in CX.

Anybody else in the audience here? Catharine?

Catharine Trebnick, Rosenblatt. So on the large enterprise, you talked a lot about large enterprise. Can you explain where your headed with the mid-market because it does seem the concentration of the discussion on large enterprise that you might be abdicating that mid-market 500 seats. But before I do that, I have to say congratulations on the new branding, I do like it.

Thank you. Thank you. It was very exciting inside of NICE, the reaction to the branding. It was, it was great, she was amazing. But interestingly enough, I've been -- it pains to remind our team again and again. It's not just the branding. This is a statement of vision and intent of where we're going. Look, to answer your question and I smile because Beth and I often talk about when you share one piece of information, the byproduct of what you haven't shared and what's the implication of it. We are clearly highlighting our strength in the large enterprise, that has clearly been something that we believe will give us a huge uplift of growth potential because all of those large enterprises, and you've seen it. They're in their early days of AI rollout and adoption and expansion. We've just launched AI agent, CXone Mpower agent, so the potential growth there on the upsell and cross-sell. So that's why we highlight it because we see it and it's a larger and larger proportion. You know when it comes to the mid-market, interestingly enough, that's where the point that we made about the 110 additional partners is critical. So doubling down, and I guess I do have a lot of experience of doing the best of both, getting a volume, low-touch-no-touch capability through the ecosystem that is able to expand and scale. So our international expansion, you'd be interested to know is nearly exclusively through partners. But even here in North America, our ability to have a platform that is able to meet the needs of those customers that is able to benefit. The platform actually helps in the bid market because it's easier for them. They don't want to have -- they can't manage 4 or 5-point solutions. They don't have the IT shop. So helping our partners be more enabled to be positioning. So I don't need the feed on the street in the sales side, they can do that, but we support them. That is a key element. And things like the AI center of excellence that we announced is a key enabler to support that partner community. No, we definitely see that.

I was just going to add as well. Of course, the small and mid-sized segment of the market is still attractive for us. And I can tell you, I actually had breakfast this morning sitting with one of our long-standing customers, and they said they have 650 agents in their contact center. They're distributed across the U.S. But one of the key reasons that they're here is to learn about Copilot, Autopilot and proactive AI agent. So it's applicable across all segments of the market. As Scott said, we emphasized it more because the opportunity ahead is still as penetration on the large enterprise, but we have significant number of customers. And I'm sure if you walk around this afternoon and you talk to some of them, you'll find that the mid-market is also extremely excited about everything that we're doing.

That's exactly what I was saying. I was about to say like this is the record conference we've had. We have more -- we have 20% more attendees here than ever before. They're not coming from the high end because the high end has only so many customers. They're not coming from the SM, small market, because they don't travel to these things. It's coming from mid-market. The people we have there are from the mid-market.

R.K. Mahendran with HMI Capital. Maybe 2 questions. One on partnerships and one on AI. On partnerships, there's one notable name missing from the recent announcements. I'm just curious what that implies, given their ambitions on agents. And when we, as investors, analysts, et cetera, can start to see some of that flowing into the financials? So I'll ask that one, I will ask the next one.

Okay. So I'm sure we don't need to guess who you're referring to. Two comments on this. So clearly, Salesforce, you'll see, if you're here tomorrow, you'll see the presentation with Barry. We're already a great partner, deep integration. I think we do more with them in this space than anybody else. We've already got a great relationship that's already there. But I can tell you, I've been very clear about these partnerships. They've got to be incremental for both of our customers. So announcing a partnership that we're integrated -- well, I could have done that day 1, but each one of these partnerships are very much about incrementality. So there is more benefit mutual value and we've been working diligently with Salesforce on that. And I look forward to sharing not just with Salesforce but with other key partners. So don't read anything into it other than it's a critical relationship that we want to make sure that it's going to deliver true incremental benefit. And that's why I said, hey, we know there's more to come because there is. And when you see that -- so first of all, remember that every one of those partnerships is required engineering build. So we've announced it, we know what we need to build. So Barry's team, so whether it be with ServiceNow, connecting to the workflow and the AI engine of ServiceNow using few business and bedrock of Amazon or with Snowflake in that Zero copy data. There's some engineering work to be done. It's a matter of months. Then we start deploying and rolling that out. I probably won't be calling it out separately because it will be a part of our AI growth, but I do see as an accelerator. And obviously, inside of the management, we've got clear measures of success in growth targets with those partnerships.

Got it. Helpful. And on AI, it was really helpful to see the chart, albeit no numbers, but of the volume going exponential, and you've sort of talked about how there's an interaction, sort of usage-based component in the pricing. I know it's evolving. How should we think about how much of that $208 million of AI that you guys disclosed is coming from that volume metric growth versus more per seat or fixed basis?

So I specifically called it out that actually the majority or more than half of that revenue we're showing you in AI and self-service is coming from those consumption-based models, so whether it's based on sessions or interactions. It's more than half already today. And of course, because we're in early days with AI, the consumption is going to continue to expand, and you saw that in some of the data that Scott shared earlier this morning. And so that's a great opportunity that we look at for the potential upside looking ahead as well.

Billy Fitzsimmons from Jefferies, here for Samad Samana. This one is probably for Beth. I'll expand on kind of the metrics question. Several metrics were disclosed. Obviously, it stood out that AI and self-service ARR accelerated year-over-year. And then the 33% AI and self-service penetration stood out as well. And that metric struck me, is impressive and maybe even higher than I would have thought it would have been, given how early the opportunity was. So first, just want to understand what's in that metric in those AI and self-service metrics in general? I know in the past, you guys have talked about AI and digital. And I just want to make sure if there's any nuances between those 2 or if it's just naming. And then second, I just want to understand for that 33% AI and self-service penetration, how is that measured? Like if a customer bought one AI product, would they be included in that? And so if that were the case, I'd imagine the spend penetration would be like a totally different number, right? There'd be a lot of opportunity to expand there. And so just how should we think about the runway for AI and self-service adoption over time?

Yes. So you asked quite a few questions. So let me make sure I touch on all of them. I think I'll start with what's inclusive in AI and self-service and the revenue that we're reporting. I think, first, it's important to highlight that we've been doing AI for many, many years. [ John ] asked about that earlier. But what's not in that number is some of our machine learning-based AI. This is really looking at our next-gen AI. It is inclusive of our digital channels, as you highlighted. That's what we called out last year when we talked about the disclosures that we had. And of course, with the 33% of our customers, it is actually being pulled together data that's down to a SKU level of what did we sell to a customer. So yes, it's based on if a customer is buying one self-service and AI application or digital channel, that's inclusive in that AI revenue. But given the stat that I showed, 25% of our new bookings recently were coming from those AI and self-service. We expect that to continue to have that strong momentum and continue to compound that growth that you're seeing. That along with is what I mentioned a few minutes ago around that most of it is consumption-based. So that's another element that most of our customers are still in their very early days of adoption of AI. So as they continue to roll it out, you'll see that play out and come through in the growth.

And maybe just to give you a bit of a context on the timing. This is like any other major technology shift and change. If you think about it, and you just go back to what you heard from [ Alaysha ]. [ Alaysha ] has a lot of brands, a lot of service professionals and they started with knowledge. They didn't start with agents, they didn't start with workflows and knowledge and they only used Copilot for a certain location and for one brand. And so what they're wanting to do because you can't afford to get it wrong. You can't guide the agent incorrectly, the knowledge and the way that it's surmised and the guardrails that you put in there, there is a bit of work there. And it's not technically the work -- it's the accuracy, it's the change management. I do believe that we're not over that hurdle where it's then, okay, I get it. Now I scale it. So we're seeing a lot of customers use, trial it and they get success and they are able to do so in a matter of months, the scaling opportunity excites us the most. Because once you do it for one use case, your ability to do it with 100 or 1,000, let alone what you see with Mpower Agent and others, obviously, that gives us strong view that we've got a great growth appetite with our customer base that they will do naturally. And that they will do it. They've already done it in other parts of the business. So I'll just give you an analogy. No one in my engineering teams even thinks about using AI tools to help them and be able to generate code and test and things like that. All of Barry's team, it's pervasive. It was one of the first things that was rolled out with AI. Now getting them to use it really, really, really well, we're still on that journey. CX is the same thing.

Clark Wright with D.A. Davidson for Gil Luria. Historically, you have noted that there's been a 2x uplift when switching from on-premise to CXone. However, in the presentation today, you noted that they're directly now switching from on-premise directly to an AI-enhanced platform. So what are you seeing now in terms of the uplift? Has there been any changes in that regard? And I have a follow-up.

Yes. No, there hasn't been any change. In fact, we've talked about we have customers that have seen an ARR expansion up to a 10x multiple for an existing customer of NICE, that's a legacy workforce engagement that's moving over to our CCaaS platform. So we continue to see a great uplift as those customers move. I think what's interesting, and I heard Scott say it just a few minutes ago, is that when you think about the opportunity in the market, we are out there to really grab those new customers, bring in the new logos, and we have a tremendous track record of those customers being customers for life. So with our -- the existing customers that we do have, most of those tend to be in the very large enterprise that have been doing business with us for many, many years. And so that's part of the customer base that looking as part of the cloud migration in the next few years will likely look to shift as all large enterprise makes that move. So the multiple and the opportunity ahead is still continues to be quite significant.

And I don't think we were pretty accurate in the on-prem to cloud and the multiples and what we saw. But that was largely because we already knew what the revenue was. The multiples on the AI side, I think you'll see that evolve. You would have seen -- I presented a chart, I should have mentioned it. You would have seen agentic AI at 45% compound annual growth. I think that is grossly understated, grossly understated. But no analyst has gone out there and said, in the CX market, what is the agentic AI growth opportunity because no one's really -- we haven't yet seen that materialize. So -- and that's just on the Agentic side, let alone on GenAI, which does have market data that shows high compound growth rates. So we'll keep you updated as we see more measurable expansion opportunity for $1 of on-prem and what that means. But even though a cloud as it is today versus AI as well.

And then additionally, last year, you noted that NRR for CXone was 113%. How does that compare today? And how do you think about kind of the growth mix between existing spend versus new logo?

Sure. Well, the NRR that we called out last year, importantly, the 113%, we have just recently started introducing NRR last quarter, the 111%. That NRR, just for transparency and for clarity, is representative of all of the cloud business of across NICE. Of course, CX is such a significant portion of that, that the CX Mpower platform is driving the bulk of that NRR. So we haven't broken it out separately. We will continue to provide that NRR information, but we consistently have a very high gross revenue retention of all of our customer base. So our customer base, I highlighted a few times, we have a great track record of those customers continuing to stay with us.

So we have time for 2 more questions.

This is Nick Lee from Citizens Bank on for Pat Walravens. Beth, we saw cloud grow 12% last quarter versus 27% a year ago. Can you walk us through some of the factors that contributed to this slowdown?

Yes. Sure. On the face, it's comparing apples and oranges. The 12% growth that we're talking about for 2025 is an organic growth. We did an acquisition of a company called LiveVox, which is focused around outbound. We did that right at the very end of 2023. And so when you look at the growth that we had in 2024, that was inclusive of a significant portion of this acquired cloud. So really, you have to take that into consideration. We provided some data last year around kind of sizing that so you can look at it on an apples-to-apples basis. So that's the reason for the change is that the 12% is organic for this year.

Next question.

This is [ Sam Kogan ] on from Barclays. Just curious like what the key driver is for the on-prem to cloud migration? And if the unlock is just the macro environment? Or is there any other accelerator like AI that would drive that over time? I think it's natural to see a little bit of a slowdown in a tougher macro environment, but any sort of background you could.

It's a good question. Still the most -- as it's always been the most common driver is a burning platform. Out of support, needing to then renegotiate a new 3-year deal, customers don't want to do that. So that's still the underlying thing. I've got to get off of Avaya. I've got to get off Cisco. I've got to get off my Genesys on-prem or renew for another 3 or 4 years, stuff that doesn't work anymore. Increasingly, we've got customers that also need to leverage AI technologies. They see others doing it in their market, taking advantage of that, and they can't do it on an on-premise as well. So that is a big catalyst as well. But that's basically it. That's very simple.

And is there a breakdown you guys provide maybe on the on-premise base from U.S. and then international? Like is that international expansion opportunity primarily driven by the idea that there are more on-premise contact centers internationally compared to the U.S.?

No, the expansion internationally, I believe, is basically down to a great success we've had with our partner strategy in international, a big focus for us last few years. If you look at both of those big $100 million-plus deals that we closed, both of those got closed through partners that have been partners for NICE for 3 years or so. So in certain territories in international, we've got the machine working. And we've got partners working for us when we're not, and it works really well. That's the biggest thing. I wouldn't say in terms of the on-prem to cloud migration, there's nothing materially different between Australia, the U.K., Europe and the U.S.

But what I would add to that, though, is for our international team, the U.S. was quite advanced. I mean we've seen a lot of -- they were often the first movers, the early movers. But our international, you're seeing more and more. So we still have -- I don't know what the percentages are, and Barry, you might -- if I broke down that 35% to 45% where does international versus North America sit there? I think proportionately, we could probably provide it. The first...

[ Failure ] is actually the first -- most established. Then it's probably the U.S., then it's probably the U.K., then it's Europe, all in the same kind of bound...

So -- but I don't want to miss the point that I wouldn't call them laggards, but if you've been holding off because of whatever factors, whether it be macroeconomic, whether it be competing priorities and you're now looking at it, those customers are no longer saying, oh, I just want to move to a CCaaS or to the cloud. They're very much saying, I want to go to an AI platform. And they're viewing it very much about leapfrogging the innovation agenda that they've got because they don't have to do a 2-step move, they can go straight to that. And that's the beauty that they have, obviously, with the CXone Mpower.

Thank you, everybody. So that will end our Q&A session. I'm going to ask everybody next 20 minutes at 2:00, let's meet outside these doors, and then we'll take you down to the innovation hall. Thank you.

Thanks, everybody. Appreciate it.