NICE Ltd. (NICE) Earnings Call Transcript & Summary

Hi everyone, I'm Ryan Gilligan, Vice President of Investor Relations here at NICE, and we are thrilled to welcome you to our 2025 Capital Markets Day. Before we begin, I need to quickly show you this disclaimer slide. Here we go. Just a quick note that the presentation does contain non-GAAP figures. Now we can talk about the great day that we have planned for you all. So in just a moment, we're going to pass it over to Scott Russell, our CEO, who's going to lay out his vision and our opportunity. Scott will then turn it over to Jeff Comstock, our new President of CX Product and Technology. After a quick break, we'll go to Phil Heltewig our Chief AI Officer and the General Manager of NICE Cognigy. And then we're very fortunate to have Nick Allgaier from Lufthansa join us. He is going to share his perspective as a customer. and then we'll go to Neeraj Verma, who is our Vice President of Customer Service Automation. And then lastly, we'll wrap with Beth Gaspich, our CFO, who will provide a financial overview. After that, we'll take another quick break at that point. Those of you that are in the room can step outside just there and grab lunch and bring it back into the room. And those of you that are on the webcast, you'll have an opportunity to type in questions. And we do ask everybody save their questions for this formal Q&A session. So with that, I will turn over to Scott Russell.

Good morning, everybody. Hopefully, you've enjoyed breakfast. Thank you for being here. Thank you to those who are online. It's very much appreciated that you've joined us for the next few hours as we go through an update for our investors in the financial community and the capital markets session. First of all, I do want to just recognize and welcome Ryan. He obviously has only just joined us. We put him under fire. He joined about a week before our first earnings and Capital Markets Day. He is now an expert on the company. But very, very happy to have Ryan join us and lead our Investor Relations as a part of our leadership team. Before I jump into the agenda, I did want to just recognize you might have seen a PR this morning. We've announced this morning a new COO Arun Chandra, who has joined us, who is joining us on first of December. Arun comes from one of our customers, Walt Disney Corporation. But before that, he was an executive that worked with Meta, at HPE is running all of their operations. He also was the CEO of 2 -- 3 tech companies. But he comes to us very much as a partner to Jeff Comstock around how to make sure that we deliver the value and we as a company are an AI company in the way we run and then how do we deliver the value for our customers. So very excited for him to join as a part of our core leadership team, combined with Ryan and Jeff and others who have joined us recently. Okay. So today, I'm going to cover, and if I can ask one thing, one thing, it will be to remember these 3 things. There are 3 things that we're going to go through for the entire day that hopefully will resonate with you and we'll recognize. First of all, we operate in a great market. And I'm going to talk about why it's a great market because I think that sometimes gets questioned. Why is the market that we're in a good one? Why would that be a positive, but I'm going to go into a bit of detail about the trends that are in our favor in the customer experience market. Secondly, we will clearly highlight why we are perfectly positioned to capitalize on that market opportunity. We will not only talk about the AI trends, and we'll go -- you'll obviously hear a lot about AI today, but we'll talk about we as a company, whether it be our financial position, whether it be because of our -- the acquisition of Cognigy, with the AI capability, but also just the broader portfolio that we have as an organization and how that best sets us up to be able to meet the market needs, which is a great growing market. And then last, but not least, is clearly highlighting and articulating how that drives accelerated growth, accelerated shareholder value via some focused investments, and we're going to go into the details of that. Beth we'll speak in a lot of detail about what those growth drivers are, what the outlook looks like, what the growth looks like. We will be sharing midterm guidance for you and indicators of what those variables are -- and then we'll obviously answer your questions later on in the day. So great market opportunity, which I will primarily cover why we're perfectly positioned, and you're going to hear from the speakers. Jeff, Phil, Neeraj, talking about our technology capability and why we believe that we're best positioned and then how that translates into accelerated growth, accelerated shareholder value and by some focused investments. So before I do that, let me just give, for those of you who are not deeply knowledgeable about NICE and who we are and what we do. Let me just recap. And I get asked this question a lot this year. For those of you who don't know, I'm in my 11th month I feel like I've been here for 11 years. I feel like I've been in this organization for a long period of time, and that's in a good way, by the way. I feel like this organization was built for me and I was built for NICE. But I get asked the question, why did I join? Well, I wanted to join a leader. I wanted to join a leader in their respective market. But I also wanted to join a company with the appetite to drive the vision and the willingness to seize the opportunity that AI brings to its respective market. Leadership in its current space but the appetite and the capability and the drive to be able to go and utilize because AI allows technology in humans to converge better than any other, and I've been in the technology industry for over 30 years. We are the #1 company in customer experience, and we'll talk more about that. 27,000 customers around the world, leveraging our capabilities, 10,000 NICE's in different parts of the planet, serving our customers. The majority of the top banks, the majority of the Fortune 500 or Fortune 100 and Fortune 500 leverage our company every day. And while I'm today is going to be particularly geared to CX, it's a worthwhile reminder that we are not only leaders in customer experience, we're also leaders in financial crime and compliance, protecting $5 trillion a day of transactions that happen through our Actimize our financial fraud and compliance portfolio. We have 30 million pieces of evidence supporting first response also for those who are in need with 99 recording and being able to support in the Public Safety and Justice business. So we are a market leader in our own rights, and we are well set up. As I say, we lead the market today as an organization. And clearly, we do so in a very financially responsible and attractive way. So the massive CX opportunity. Why do we believe this is such a great market. Well, I think in the hype of AI, a few things get lost. Every customer we speak to starts with this. They start with what is the business value for their company. Now it could be triggered by problems or issues, but fundamentally, companies or brands that invest in customer experience outperform their peers. You can do any research you want. We obviously have done research from different categories. So whether it be about increasing your top line, whether it be about reducing your cost whether it be able to get operational improvement, whether it be about delivering a higher customer set. Businesses invest in CX, not because of AI, they invest in CX because it delivers business returns, period, and that is still true today. I'll talk about AI in a moment, but companies that invest in CX perform better than their peers, and it's proven and they're able to showcase that. For example, just a simple example. If you take bots, -- they're a great company. You might use their technology in your car or at your home. They leveraged CXone, they consolidated all on our platform. They increased their customer set by 30% and -- they reduced their operating costs in their core volumes by nearly 20%, and they've increased their CSAT so the happier customers by over 20%. So they were able to achieve through focusing investments on their CX platform they're able to get a better top line. They doubled their sales during the holiday season as a result of having a better customer experience, increase customer set, reduce cost, better returns and better revenue. So I say this because sometimes we can get caught into the hype of what AI means in this market. Companies are investing in CX and they will continue to do so because it's good business. it is good for their business to do so. And it's a market that is naturally growing, we sit here today, coal volumes, digital interactions, AI interactions are naturally growing. Consumers are interacting with their brands more than ever, and it will just continue to rise. So just as a natural market where businesses are investing, they're seeing more and more volumes coming through. And our data reinforces that, not only because we're acquiring winning new customers, so our volumes, but if you take NICE in the last 9 months, in the last 9 months, our AI volumes have grown by 65%. Our digital volumes have grown in the mid-40s and good old voice has grown in the mid-20s. So not only are we winning in the AI market, it's not at the expense of others because the whole volume of interactions between consumers and brands are increasing. And here's the thing -- this -- that number doesn't include some of the things that you're now seeing in the market. And you'll hear a little bit, and I know Nick will talk about the Lufthansa example, where, by the way, they were able to automate, I think, 16 million-plus interactions on their Cognigy platform. But this doesn't even include where you think about how consumers are going to interact more with their CX platform rather than websites or apps because they're going to -- they're just going to chat. They're going to converse, they're going to interact on a conversational platform that will increase the amount of volume that goes through their CX platform. That doesn't even include here, machine-to-machine, where you tell your agent on your phone, Hey, I want to call my bank. I want to be able to increase my credit limit. And at the same time, can you contact my airline because I want to rebook the flight with my family. All of those interactions, the volume of interactions are often limited by the timing capacity we have as consumers and as humans, a lot of those constraints go away. That's not built in here. This is just pure volume of interactions that naturally is happening between consumers and their brands. But if that market was great, well, it just got a whole lot better. It got a whole lot better. This is a massive market. The AI potential, the transformation that it brings, the opportunity it brings in CX, has got a whole lot better. Why do I say that? Well, first of all, you're going to hear a lot today about fully autonomous resolution. So I think we've all had the experience when we've called in or we've chatted to our brand of choice, and they use words like this. Look, let me just put you on hold for a few moments, if I can, please. And then you don't know what's happening. But right now, that agent is pivoting and they're going and chatting and interacting and conversing with different parts of their organization to resolve your requirement. And it might happen multiple times because they might have to operate with multiple individuals in the organization. So unless it's a knowledge-based request such as a password reset or checking a balance initially on those easy automated plays. Then the ability for organizations up until the use of AI was pretty limited. You are still response, you still needed a human in the loop, well, we can do that automated with AI, and we can do it again and again and again at scale. As I said before, the Lufthansa example is a great one, but it's not the only one, and I'll talk a little bit about our customer references. The second is our journeys are customer-driven -- most enterprises, when they talk about AI, they're talking about how they can replace what humans do in the enterprise. Our focus is the customer's journey. It is very different lens on how you use AI to deliver an outcome. When you're trying to use AI to complete a task that an otherwise an internal employee would do, yes, sure, that's useful. And we do that as well for the front office. We'll get to that, but our emphasis is the customer journey. So you do get full resolution in an automated way, and all of the interaction needs, whether you ask Florida's consumers or whether the brand wants to contact you to be at to fulfill an outcome. It can all be done automated autonomously on the platform. So that going from reactive to proactive, using the data of the intelligence and being able to drive a proactive model rather than a reactive one, which has historically been in the world of CX. I want to highlight the seamless human and AI experiences. Every company, even if they want to go to a full automated model, we'll not do it in one go. And I would contend that there are many businesses, and I'll highlight this that don't want to go to a fully automated model. They want you to contact and talk to somebody at the other end. Why? ability to upsell and cross-sell ability to be able to provide different levels of service. There are some businesses, particularly in life sciences and health care, that they want the first interaction with AI, understanding intent, understanding your needs, but they will intentionally take you to a human agent because your needs give them an opportunity to perform a task for them, for you as a consumer, better than what NII would be because they want that human touch. But even then, even then AI will play an active role because we will be supporting assisting using the same model, the same data to be able to converse with that consumer through the human agent. So AI is a tailwind for NICE, for this market. I'm talking about the market, it is a tailwind. There is no doubt the market is great with CX Interaction volumes continue to rise, and we have got a market shift where the value proposition back to the core business drivers and the opportunities that AI brings are continuing to expand our opportunity, and I'll talk about that in total address at market terms as well. So it not only gives us the ability to automate to automated self-service but we also have the ability to do assisted service with humans in the contact center as well. But here's the thing. While we are incredibly bullish and we are, we are incredibly bullish about the opportunity that AI brings to be able to automate low-complexity transactions. And there is you can debate what low complexity looks like. But there is a large volume of interactions that are happening with contact centers today that can be easily automated on the NICE Cognigy platform today. Today, you can do that and you can do it in a really scalable way. But we also recognize, to my earlier comment that some of the high complexity transactions the things that are really difficult to be able to serve and deliver or, as I say, where they are intentionally wanting a human in the loop, there is still a lot of interactions, a lot of interactions that businesses will serve, so this is in a market where it will replace all of the human work. And even if it ultimately gets there, the period of transition is a long one and a one where to deliver the full CX need you need both, and you know where I'm going with this. But as I highlighted before, even in that human in the loop, those interactions will still be AI-assisted. We do not see a world where AI does not play a role in the customer experience market. So either fully autonomous self-service or human and AI working interoperably or human engaged service where your customer service representative is interacting, but it is assisted by the AI platform. This is a market that is fantastic. Great business value, great business value, interactions volumes continuing to grow, clearly a market where AI is giving a significant shift in boost and a tailwind and an opportunity for us -- and then ultimately, it is a market where it is not one or the other. Human engagement in the contact center, AI, automation and assistance in that platform, end-to-end, it is a growing market from both perspectives. So why are we positioned to win? What is the strength of NICE to be able to capitalize on this? Well, let's just start with our current momentum. These are numbers today. 20 billion-plus interactions, 20 billion interactions across all of the major industries, AI interactions, human interactions, combination thereof that are coming into our platform every single day around the world. I mentioned the growth of voice, I find it remarkable because humans still like to talk and chat and sometimes they want to talk to somebody, whether it be an AI agent, but we had over 1 billion minutes of voice of voice in September, over 1 billion minutes. So there is a lot -- and that is with average handling time and call times continuing to reduce because businesses are better and better, they're getting the AI assistance to be able to make call shorter, faster, better containment. We are a company that has had 40 years of experience in this market. We understand customer experience better than anyone. We understand the intent, the needs, the contextual, the behavioral. We understand the skills that are required. We know what a good interaction looks like and what it doesn't, all of that knowledge, all of that experience, all of that logic, all of that capability is core to the platform that drives all those interactions, drive those engagements and helps businesses deliver on the business promise that I mentioned at the beginning. But we're not just getting started in AI today, and you saw the numbers in Q3 that we reported last week, 49% growth in our ARR and self-service effectively, we are growing, and there's over 6 billion augmented interactions on that platform. So of the ones that are coming in, there is a significant number that are already AI assisted or AI delivered in a self-service way or in an assisted way. And clearly, the traffic growth continues to rise and grow. As you could appreciate, for us to be able to continue to grow the revenue line, we've got to have the volume that is growing and then we're able to monetize that volume. What's interesting about the AI traffic growth is this one interaction can lead to many, many AI sessions. Just think of the flow. You first call in -- you have an interaction, you've got a need. Maybe you've got a -- well, an example that Neeraj is going to talk about later, is a dispute on a credit card, you call in and you're interactive by an AI bot. That is trying to determine, number one, what is your intent? What are you there to solve? Number two. And usually, it will already have the prebuilt logic about who you are, how you like to interact not only the language, the style, the behavior, the sentiment that you have and be able to change based on the tone and your interaction. But then that first session says, "look, we need another bot that needs is specialized in dispute. And so we'll go into details and into another AI agent that will be triggered. That AI agent then says, "Look, this requires someone -- this is a complex one. We now need to get a human to be able to step in. So they hand over to a human agent. A human agent then has a copilot where they're interacting and there's another session. That copilot then gives assistance to the human agent about how to resolve the task and then it gets handed back to an AI agent. So one interaction, which historically you would have seen in our commercial models and Beth will talk about commercial and pricing that would have been a user-based human where we had seat-based pricing. But in the AI world, it's not one-to-one replacement because we're able to increase the volume of interactions and not only those interactions increasing, the number of sessions and AI services that we can run can then be multifold. And that's what's driving a lot of that traffic growth above what you're already seeing in the momentum in the transition to self-service. And that clearly translates to a financial model that is strong. Not only is the AI growing at 49%. We're nearly it's $3 billion in 2025. We updated our full year guidance and increased our outlook for 2025, but even more importantly, we're a cash, free cash flow, we're a cash generation machine. So our ability to be able to generate real value for our customers is also translated into a financial performance where not only we're growing our top line, but we're then generating true value for the company that we can then transfer to our shareholders. But we don't believe ourselves only the market also recognizes us -- it's not many times where a vendor can get up and say they are the #1 undisputed #1 according to all of the industry analysts. This is in our data. This is theirs. Cognigy was ranked #1 for conversational AI for CX. The NICE business was ranked both highest in vision but also highest in ability to execute in the Gartner Magic Quadrant for Contact Center as a Service. Clearly, proven leader is the 1 ability to execute and vision. We're the #1 in intelligent self-service. We're the #1 in intelligent contact centers. You can see the list. And all of these analysts spend their time and effort, and I know you interact with many of them, their job is to analyze the industry in who is best placed to be able to serve this market deliver on those outcomes and advising companies who are to go to. What does that mean? We get a lot of inbound demand from our customers that has already been prequalified, validated by these industry analysts. These industry analysts are constantly assessing the movement and the change in the role AI plays. And so they are very deep in our industry and the knowledge of what we serve, but also what we need to be to continue to be the best. And if that weren't proof enough, I guess you can just look at the world's leading brands that have chosen us. Every one of these companies, and there's 27,000, I couldn't put 27,000 names up on the slide. 27,000 companies that have chosen NICE their brand of choice, their CX platform of choice -- every one of these has a business story of value. So whether it be an espresso that implemented our platform and they were able to increase automated payment completions, automated payment completions by nearly 30%. Every one of these scenarios, and I'm so pleased, Nick is here to talk about the Lufthansa story, but there are so many stories. Every one of them driving business value. Every one of them are clearly looking for the outcome, the benefits and the results and every one of them improve the capability of our platform because we're able to then serve more needs more data, more capability that we can then reinvest back into an AI-led platform. So how do we do it? How are we the market leader, validated by the industry analysts, [ Valero ] by the customer market and in total revenue terms we are the market leader as well. Well, we are the only company that has a purpose-built platform for CX. And you're going to hear this a lot from us today. What do I mean by purpose built? Well, number one, it is a platform that is only focused on CX. There are many other technology companies that want to cover tasks, workflows, cases. processes. They want to cover all of your different organizational things. They're trying to be experts at everything that happens in the enterprise. In my view, that's not possible. If you want to go deep and you want to be rich and you want to have a great customer experience, you've got to have the logic, the knowledge, the capability that is purpose-built. NICE Cognigy was purpose-built for using AI in customer experience. Now you can use AI for for all sorts of purposes, you can use it in your supply chain. You can use it in HR, you can use it in your finance, you can do it in your operations. And you can do self-service in those scenarios. They built it specifically for CX, and you, of course, NICE in our history, whether it be in workforce management, whether it be in the contact center, the contact center as a service, our whole company was geared around CX. So we're the only organization that has the full suite of capabilities. The AI platform is native, and I'll come to this later, but let me just say this, the other contact center as a service players do not have an AI-native platform of their own. They have to use somebody else's and embed it into their platform. They don't have their own. We do the advantage that, that brings us to win in this market is immense. And Jeff and Phil and Neeraj are going to showcase how that will play out, why a combined platform where you've got native capability all in the one stack matters. But it's also a platform that has all of that data, that 20 billion interactions per year that's all -- and it's repeatable value. Every interaction adds to it. We get more context, we get more knowledge. We get more insights. We can build AI, CX specific models that is solving for those scenarios, and it's transferable. We can do it by industry -- we can do it by horizontal. We've got the ability to use that interaction data that leading volume to our advantage to ultimately deliver a better experience for customers. It also is a platform that allows for that end-to-end journey. So we can get better resolution. And what I mean by that is we own the point of engagement. So when it comes in, whether it's a chat, whether it's an e-mail, whether it's a text, whether it's a call, whether it comes from an AI Chat GPT or whatever platform they're coming in, once it hits the enterprise, we are the platform of engagement we are the customer engagement platform. It's a moat. It goes into that, why does it go into that platform, where there are rules guardrails, knowledge, systems to interact with. You're not going to let your consumer interact and have a poor experience. So not only does it need to be responsive and scalable and knowledgeable but it needs to operate within the framework that the enterprise has set. And that's not a static requirement. Businesses are constantly evolving, what they want their customers to see what services they want to render. How they're going to deliver against that. So that platform becomes the gateway of value for a company. Now historically, in the contact center, a lot of those calls were simply contained. Anyone can cry a bot that says, "Hi, Scott, how are you? what's your request? I say, look, I want to dispute something that's on my credit card. And they say, fantastic. I've got that for you. I will get a service representative to contact you shortly, finish. That's not resolution. That's simply containing that first request and then you sit there as a consumer waiting for that call to come back, that interaction to come back. With our platform, we take the call, we interact we're able to resolve it real time, including performing tasks in the mid- and the back office through AI agents, and we're able to deliver end-to-end resolution. So we own the point of engagement -- but we also then turn that intelligence, that knowledge we have with customers, and we turn it into autonomous interactions on autonomous actions. I can't tell you how important that is Contact centers were often built because they had certain levels of constraints. How many humans, how many people could be staffed and they were never staffed for peak volumes. So when an event occurs -- you can't possibly , which means that you then they also manage the handling time. How long are people waiting and interacting on that call, and they are constantly trying to reduce it. but they're also then measuring that first contact resolution. So can they resolve it now with knowledge ways that the agents can, but there's no -- if you're a large organization, -- and you have got tens of thousands of different types of intent coming into your business. I don't care how smart the customer service representative is they're not going to be able to be specialized. So you needed specialization in your contact center. All of that knowledge and how that gets delivered, that is now being embedded and used from an AI purposes in our platform. We use that same data, that same knowledge, what a human agent does, how they interact, what the and then we look to do it in an autonomous way. So the -- every channel, every journey, every interaction AI in the loop, human in the loop, it does not matter. It all goes into that one platform, and we're able to then learn and use that for our customers' benefit in either a human way or an AI way. and it's built on the industry's largest CX data foundation that the market has. And then clearly, we can then do autonomous delivery and being able to get a better outcome, higher resolution rates, reduce cost to operate, improved customer service through CSAT. So that platform then gives ongoing value, whether it be an assisted or an autonomous way. And this -- and I think you've heard us talk about this before. But this is why when we talk about the mid and the back office and the role we play, this is what we mean. Our platform, a contact center by its very nature, the platform was built to serve that first contact. It was the first brand guardians to be able to take that, that inbound digital, the chat, e-mail, text, call and being able to find a way to resolve it usually through knowledge or specialized tasks -- but if they weren't able to resolve it, that they would then contain it, so then there was a follow-up activity afterwards. But with and the build of AI agents, our platform able to build AI agents that can solve the tasks that you need as a consumer, and we can do it real time. So no, I'm not trying to pivot our company to be a mid- and back office player. We're not going to be an ERP. We're not going to be any sort of back office enterprise HR finance systems, but we will build and deploy AI agents that will perform the tasks that they do in the mid and back office for customer journeys. Remember, we're purpose-built for a customer journey which means I'm not interested in trying to replace what a billing Clark does. What I am interested in is that there is a task to be able to approve a build, a charge on that bill back to Neeraj's example later, I want to be able to automate that task. We can do it from our platform -- you know why? Because we interact with every business system that is out there. Every CRM, every ERP, every back office banking system, insurance systems, every back of airline systems, all of them have already got the prebuilt interactions. We've already got that connective tissue. We simply leverage that to be able to create AI agents to be able to solve the consumer journey. And I highlight this because AI can be used for many reasons. But just because you've got an efficient back office doesn't mean you've got a great customer experience. In fact, I would argue there are a lot of businesses that might experience that pain because they're looking at how do they reduce the cost of somebody in finance or in payable or in credit or in claims rather than thinking about what's the customer's experience and how do I make it better? And how do I use AI to fulfill against that? So again, we are purpose-built for CX. But what it does mean is it gives us adjacencies. And Beth is going to talk about those adjacencies -- we've lived and breathed in a world of customer service for a long period of time. We will continue to do so. But we will largely contain to the front office. To deliver customer service, we're able to now move into the mid and back office to complete resolution fulfillment of customer needs on this platform. But we're also then able to move into sales. In the marketing, organizational internal boundaries are being blurred. Why did you have a specialized sales outbound team that was different than your customer service inbound team. It was largely the skills and the capability and the needs that you had to serve to. It was based on the human constraint. But if you think about it, if you're interacting, which is why, by the way, many customers still like to have human-assisted in customer experiences, if you've called in about your pharmaceutical, you're making sure that you're your drugs that you need for your life needs and the delivery of that is on time. But if you're interacting, hey, there's also some some herbal remedies or other products that you can take, you can proactively do you can assist them you'll be able to upsell on the same platform. So customer experience is not customer service. We say it intentionally. It is customer experience, fulfilling all of the needs of service, but then the opportunity to go beyond that into sales, marketing, commerce, from a customer journey perspective. The last thing I'll say about this is it will increase our market immeasurably. So in '20 -- in interactions for those who joined me, you would have seen that our total addressable market is about $31 billion in the market that we operate today. We estimate based on the opportunity that AI brings both identically and conversationally, but also the expansion of the CCaaS market. This market continues to grow or come to our growth drivers in a moment. And the ability to go into the mid and back office, we will more than double our total addressable market within the next 3 years. This is a great market, of which we are well positioned to capitalize on, and if we do our job well, we're able to then go. And last but not least, if you weren't convinced up until now, we did some research or more importantly, BCG did some research, and they were able to assess and they went to NICE customers, non-NICE customers. They went to a broad section of different customers. And it was a really interesting bit of feedback around how companies are thinking about who they will buy their AI platform from in the context of customer experience. This isn't a -- who will I use for AI. It is who will I use for AI in customer experience. So let me just go one by one, if you don't mind. 40% of the respondents said that they would buy it by AI from their CCaaS provider if that CCaaS provider has a world-class AI platform. Why? Well, it makes sense if you think about it. Why would a company that's already invested all of that capability and logic into their CCaaS platform. If it's got an AI-native capability, 1 of the first things you'll do is you'll do human-assisted. When we first launched our AI capabilities, you could appreciate the majority of our early AI platform was assisted. It was co-pilot. Because you're trying to make your human agent, which was under a lot of pressure, high turnover, really difficult skills to be able to recruit and then retain high-pressure environment, inbound, unhappy customers, and we're trying to make it easier for them, better for them, so improving the productivity. So real-time assistance, contextual assistance using that data on the contact center is contact center as a service platform. But then it expanded into auto summary and insights from the different calls. It changed how quality management was done. So instead of getting a call recording and having to decipher in a supervisor, you would then be able to do real-time insights that made it easier for managers and supervisors to be able to assist and train their agents. But then naturally, as you can appreciate, our autopilot then become more -- because we already understand what the interactions are, we already understand as a contact center platform what can be automated. Again, we've got great data, and we can already tell them using automated insights. Hey, these interactions are purpose-built for self-service. And so of course, that self-service autonomous play became part of that CCaaS platform. So no wonder 40% of the respondents said that they will buy their AI from their CCaaS vendor. Now NICE up until the ninth of September -- that was where we got our growth from. Did companies come to us when we weren't their CCaaS provider. No. And by the way, they didn't go to any of the other CCaaS vendors either. Because if you don't have an inherent AI platform to be able to deliver to it, then why would you go to your CCaaS? It's got to be and what the market's now decided is you can't just have average capability. It's got to be great. It's one of the reasons why Cognigy was so important because we understood what their needs were. We didn't want to OEM a third-party product. We want to have our native capability to be able to serve those customers, those installed base customers the best possible way. And that meant we needed a significantly improved AI platform to do it. NICE Cognigy delivers against that. The second most -- the second highest amount of choice was to go to a CX or so an AI-specific vendor. NICE Cognigy is one of those. And you know about the other ones is a ton of capital going into these companies in the market, you know them core AI, poly AI, yellow AI, Sierra, Deca, all of these companies are built where a business says I want to go to an AI-specific player to do my AI service for my -- in the context of customer experience. Well, now we have got market leader in Cognigy, with both conversational genetic AI to go compete with that market. And it doesn't matter. It does not matter that whether contact center or not. In fact, I'll tell you this, we are going to aggressively go after every every, every company. We're going to be explicit to go after everyone that uses another CCaaS platform because what a great opportunity for us with the richness of knowledge that we have to be able to have an embedded Cognigy is fantastic as it is, it's going to get a whole lot better on our platform, but we are not going to say to those customers, you must use our CCaaS. We are simply going to go to those customers and say, use our AI platform that is a market leader in its own right. And by the way, the journey will likely get to there in consolidation because we will then have the ability to show a better together story versus not. So the first, the highest places where companies will go for their CX AI needs is the CCaaS vendor and a purpose-built player. We are now able to natively capture that. And you can see there that, yes, this market does sometimes go to a hyperscaler. Now what they usually do there is they'll do that because they're trying to build themselves and they want the inherent use of the underlying technology. And yes, they'll sometimes go to their CRM player. But here's the thing. We get the feedback. Why would you go to a company that is focused around the internal employee usage of a CRM platform when the whole goal is customer experience from a customer journey. That's what we do. That's what we've always done. And so a platform that delivers great customer journeys is very different then a CRM platform that is about the business value that you can get from your internal business outbound to customers, very, very different. To give you a simple example is Sony, so Sony hit all of these identified measures. They had a challenge. They were a company that has got a lot of different sub-brands as you know, whether it be the TVs, different devices, PlayStation, et cetera, et cetera. And they had a whole lot of inbound where it was not easy for consumers to be able to be navigated to get the right response at the right time, the right service. So the first thing they did was they consolidated all of that customer experience onto CX1, put it all on to the platform. Once they were on the platform, what they then did is they used all the data, which was primarily human-assisted interactions, so you would call or chat or whatever with Sony, and we use the data, and we identified the top self-service scenarios. And then we purpose built it on the platform, so that they were able to create all of those self-service scenarios notably and then could interoperate with the humans that we're assisting. And so what did they deliver? They delivered an increase in customer sat, they delivered a reduction in cost because they were able to contain more of those interactions via self-service and autonomous channels. And they were able to increase their revenues because they were able to get better feedback, better responsiveness that was then able to get better performance from the top line because they were able to get more upsell through the same mechanism on that platform. increased revenue, reduced cost, better customer set. So again, I'll just repeat why we are poised to win. We are the only player in the CX market that is an AI-native CX platform. CCaaS players don't have it. I'll say that again, CCaaS players don't have it. We are the only ones that do. We're also the platform that owns the engagement. CRMs don't own the point of engagement. It has to get into the enterprise before they can do anything with it. So our platform, that first point of engagement is what we're able to contain no matter how and when those interactions, 24/7, 365, never down, being able to make sure we deliver against that promise. We had the CX domain expertise. Hyperscalers don't have that. Hyperscalers do not have CX domain expertise. They've got great technology. And same with the LLM, by the way, the big AI players. We leverage those. We use the generative AI models. We love the large language models, and we're very agnostic about which ones we can use, but they don't have the -- they don't understand the guardrails, the rules, the knowledge, the insights, the small models, by the way, to make it cost effective as well. You don't want to be going always to your large language models where you don't need to but we're able to deliver upon that natively out of our platform. And the human and AI engagement is going to be an interoperable one for many, many years to come. It's not going to be AI only or human only. It will be both. Well, that's native in our platform. So those AI domain players that used to be nice Cognigy and is now part of us, they could only do the AI piece. They can't do all of the other voice and all of the other channels and all the other interactions that requires human in the loop and enterprises, especially large enterprises must have the ability to do both. They want the ability to do both. They need the ability to do both. And so the native platform that brings that all together is something only we can offer. I can tell you all of those 7-figure deals that we present each quarter and it keeps on growing. Quite often, it's just a consolidation of fragmented stack. They've used different technologies for different pieces, and they consolidate on the platform. Now that we've got an AI-native platform, our ability to do that is better than ever. So we feel that we are in a great market, and we are really well positioned to win. So what does that mean for growth? Well, let me start by saying we have a number of growth catalysts things that we see here and now that will drive our midterm growth. Let me quickly go one by one. The first is AI growth across every touch point. So clearly, yes, our NICE Cognigy platform will be taken to every installed base customer we have. You can be assured of that. Every one of those customers has AI needs. Some of them might have already implemented something basic, something advanced doesn't matter where you're going to make sure that we leverage not only that 40% preference, but all preferences because we've got a full platform where we're going to take it into that market, and it is a large market. But we're also going to make sure NICE Cognigy is a winner in the AI CX stand-alone market as customers decide that's the way they want to go. It is a market leader already, and we will continue to make sure it is. But we will also, number two, we will automate our genic AI on our platform. So our NICE Cognigy platform and the Agentic capabilities, combined with the CX platform combined together into one offering a compelling offering using the data, the knowledge, the insights that becomes better together, we will then be able to provide better automation, better offering, something unique that the market hasn't -- isn't able to do -- and we're going to do so quickly. And Jeff will talk a bit about what our focus areas are from an engineering point of view to capitalize on that opportunity. The third growth catalyst is the one that you all know very well, and that is continuing the CCaaS jumps. Our win rates are improving. They're getting better. We see that. We see our win rates, but it's also the market continues to move across. So this is a good market. About 40% has moved. You can debate about the stats on that, but about 40% of the market has moved to a contact center in the cloud. So there is a significant amount of jump balls that we are competing for and win. And let me assure you, when we go for those CCaaS moves, we're going to be using our AI capability as a differentiator from what we've already got. We've already got the market-leading platform. It just got a whole lot better. I think you can see, and my emphasis on international and partnerships is a key growth driver. International expansion is tracking really well. I've got to acknowledge that the company had invested heavily and continues to do so in sovereign clouds and capacity around the world, U.K., Europe, Asia, we're able to then get greater expansion with our market-leading platform. And because international hasn't moved as quickly to the cloud as what it has in the U.S., the opportunity for us to be able to go straight to that combined platform has become even more compelling, and we're seeing that such as what you saw with DWP, they're originally chose purely on the CCaaS platform and then quickly say, "Look, we'll use all the sell the AI capabilities that you've got at nice to be able to then extend and then transform even faster in delivering to the U.K. citizens. And then last but not least is a growth driver is going beyond the contact center. I mentioned going into the mid and back office and doing more customer resolution, automation, from our platform rather than purely the front office. And I also talked about going beyond customer service into sales, marketing and other areas. That growth driver, what you're going to hear is not really -- this is more an engineering growth catalyst that will drive into the sales. So it's not really a significant number in the midterm guidance that you're going to hear, but clearly, we see a tremendous opportunity, particularly with Agentic AI to be able to grow that with this platform. So we're on the road to doubling our cloud revenue. You can see the numbers, and we're going to go from $2.2 billion to $3.5 billion by 2028. We're on the road to $4 billion plus, and that road is based on those growth drivers based on a great market and based on our ability to compete and win. But there are a few things that we need to continue to invest upon and Beth will go into more details about the financial outlook, including 2026. So first of all, we need to continue to innovate. This isn't a market where you can sit still. As great as our capability is, this is a fast-moving market. There is a lot of investment coming in. So we've got to accelerate our innovation whether it be about consolidating and bringing the Cognigy platform and the CX1 platform together to become a better stack, more complete stack to be able to offer to our customers. whether it be about innovating an orchestration or automating resolution with the Agentic capabilities, there is a lot of innovation needs that Jeff and the team will drive in the coming years. The second is to be CX on that growth catalyst about international, we've got to continue to increase coverage, capability, assets, service to be able to serve that market. It's not a case of just asking if we're going to open new capabilities in new countries, we've got to have a localized capability to be able to support that. It also means recruiting enabling supporting partners who are going to be able to serve and be able to help us serve locally in those markets. And then last but not least is you could appreciate the operation and the delivery foundations, so whether it be about reducing the time to deploy, we've made significant improvement in our deployment time frames of AI. It was initially quite a long period of time. We've been able to reduce that, and we continue to invest in the capabilities to be able to serve and get deployments faster, more valuable, more capable in a shorter period of time. So it isn't that we're just doubling our revenue without investment, we will be investing and those investments will be able to deliver significant growth and shareholder return. So let me recap again. Number one, great market. Number two, we're in a really great position. And number three, that will deliver growth. And I know that the expectations are there will be a lot of interest in what that top line and margin and what that looks like, the growth is real. I think you saw in the sentiment that I talked about in the earnings call I was very clear we needed to see the proof of the data and the plan that we've built to achieve this is rock solid. We've looked at all of the things that could happen that could potentially derail or impact that. Does the AI movement happen as quickly? Have we got the ability to deliver what if the competitors step into different spaces and moving to us, we've looked at all of those angles. This is a bottom-up rock solid plan that we are executing on. I'm not giving a guidance from the midterm that has speculation. It is based on real data, real understanding of the market, real pipeline, real customer feedback, real analyst feedback, real knowledge of who our competition in the evolving competitive landscape. We believe that we will achieve that mid-term outlook and top line growth and double our revenue over the next 4 years based on all of those variables. We are ready to run. And really, it's just about execution against the plan from now on. So what we want to be able to do is be able to share with you on a quarterly basis. And you saw, for example, in Q3, we shared our cloud backlog. Our cloud backlog growing at 15%. So we clearly are seeing both pipeline and the underlying proof points of a future revenue that we get that is growing and is going all in the right direction based on executing against this plan. Hopefully, that makes sense. Hopefully, the context of who we are as a company, the market that we operate in and the top line growth that we are expecting to achieve is very clear. For the rest of today, what we want to do is quantify it. We want to take you through the real technology stack. What it really looks like. We then want to go through the financial models and what it really looks like. And then obviously, at the end of today, we will come back and answer your questions where we'll have the whole group or the presenters up on stage to be able to answer questions that you have. And with that, I'm now going to welcome on to stage. He's been with us for 6 weeks, but it feels like 6 months. I'm probably not as much as mine, but Jeff Comstock, Jeff joins us from Microsoft. For those who have not had the chance to meet Jeff, he was the leader of all of their CX portfolio, the CRM, the CX his company. He led the build out of a contact center platform ground up. He obviously led the CRM portfolio for Dynamics. So he understands this space exceptionally well. We're very lucky to have him and he will be able to share with you the outlook and where we're going from a product and technology direction. Jeff Comstock, thnak you.

Okay. Good morning. It's great to be here with you. Great intro. Scott. I appreciate that. So you've got a lot of context on what I've done before joining NICE, I was with Microsoft for 25 years. And for many of those years, like Scott mentioned, I was building CX related products. And I got the opportunity to scale those products through the last few platform shifts that we've seen in the industry. And what became really clear to me at the very beginning of this platform shift, which is a Agentic AI is that things are going to be different. CX is going to fundamentally change and how CX has delivered is going to fundamentally change. And NICE is incredibly well positioned in the face of this change. So in this session today, I want to go through just a few things. One is what are the trends that we're seeing in the marketplace already underway? And how is NICE positioned with those trends and in that context? And then I'll cover our product investment areas like Scott mentioned, right? So how are we going to leverage that position of strength in our unique CX assets to lead and win in the Agentic era. After my session, hopefully, that will be super clear. So let's start with the trends that we're seeing. I don't think I need to convince anyone in this room that consumer AI has gone mainstream, right? There's over 800 million users of chat CPT alone, hundreds of millions of users of other tools like Gemini, Cloud, what that means for CX is consumer expectations have skyrocketed, right? And as we know, unfortunately, most enterprises aren't meeting that expectation. And the gap between expectation and what enterprises are able to deliver is widening by the day. So this is obviously a key impetus behind our acquisition of Cognigy, our market leader in Agentic-AI and conversational -- and you're going to see that in action when fill comes up and demos the product. It's really helping our customers close that gap and then some in terms of those expectations. Second, the AI that augments the workforce in the flow of their work has really gone from market buzz to strong customer demand. And of course, NICE is really well positioned here with specialized copilot that's out of the box. So we're seeing a surge in usage of surge in revenue from that in best session, you're going to see how that shows up in our revenues. The third big trend is platform consolidation. In the CX space, it's highly fragmented, like Scott mentioned, it has been for many years. And unfortunately, enterprises have just had to a la carte, pull together these best-of-breed capabilities, integrate them together in a very fragile way just to support a basic CX function. But now we're seeing a real big push by customers to accelerate that consolidation. And why is that? Because customers even realize that to get the most out of AI, they need to consolidate and simplify their data estate. This is where NICE is really well positioned with the CX1 platform. It's arguably the broadest and deepest CX platform on the market. And as Scott mentioned, we're seeing customers as they come on board to see X1 they're consolidating a number of other solutions on to CX1. Now a story, and that value proposition is just going to get that much better as we bring cognitive on and we expand those platform capabilities. Next is the fact that Agentic AI is really enabling us to leverage that point of customer engagement that Scott talked about and automate more of the tasks and jobs that need to be done to deliver incredible customer experiences across the enterprise. So we can automate those tasks, we can orchestrate across the enterprise in a way that just really wasn't feasible before Agentic AI. So this is a huge focus area for us. There's a lot of strong customer demand. At the end of the day, they want more automation. They want better customer experiences. And we're going to have a really big opportunity to expand our value proposition to our customers. And lastly, we're seeing a really strong demand for proactive experience. Scott gave a really good example in his walk through. Right? The best consumer experience, if something is going to go wrong for example, is for the brand to notify you that something went wrong and here's what they're doing to resolve it. right? That's the better customer experience. We've got great solutions at NICE for proactive engagement, and we're seeing a surge in usage there. I'll also say this proactive engagement capability is going to play another strong role as we build out our Agentic AI orchestration capabilities, right? Because the direction we're going is supporting more holistic customer journeys and that's going to involve proactive engagement, reactive engagement. So we have all the component parts to that. So we are really well positioned from a platform perspective. We have a platform advantage. Now I want to focus on how are we going to expand that, how are we going to leverage that advantage and differentiate even further. And to do that, I want to show you the most common point of fragmentation in the enterprise. This is essentially every single enterprise contact center on planet Earth. -- right? They have one system, one stack for human-assisted service. This is the land of CCaaS, right? This is where NICE has been a market leader for a long time and continues to lead the market. And then this is a simplistic view. Most enterprises have a collection of capabilities, digital channels, bots, virtual assistance, right? And this has long been a real big challenge for enterprises. Fragile integrations, each stack is operating on an incomplete data set. But these challenges magnify as organizations try to advance with AI, right? So as they're going on that transformation journey, this becomes even more of a problem. As they go to automate -- they've got to automate it in 2 different stacks in 2 different ways. That automation drifts over time. So even though we're seeing from customers a really strong motivation to transform with AX with AI, it's fragmentations like this that are standing in the way. So how are we going to help in this particular situation and others and further differentiate our platform in the process. Well, we're going to do that by bringing Cognigy onto the CX1 platform natively. And that is going to have game-changing implications for our customers because now they'll have best-of-breed capability for self-service with Cognigy, best-of-breed capability for human-assisted engagement and market-leading agentic-AI capability. But that's not where it stops, right? When we bring it on natively, what that means is we now have one data layer for customer engagement. And think about that data set, right? That's every single intent, every customer need all the steps that have been performed, whether by automation or through human-assisted engagement and then the outcome that was achieved. Across the entire enterprise across all interactions across all channels. And I will tell you that is by far the most valuable data in CX. This is the most valuable data, right? We will have it in our data layer, we'll apply our unique set of CX specific purpose-built AI to drive insights. Right? How do we get better outcomes, including cross-sell upsell, like Scott mentioned, right? And then we'll take those insights, and we will feed a common AI stack that is supporting both self-service and human-assisted service, right? So we're learning from all engagements, whether it's automation or human-assisted service, everything we learn feeds the AI stack. That then improves both self-service and human-assisted service. So this creates a very powerful compounding AI learning group that quite literally with every interaction, it gets smarter, automates more as it goes, right? So this is our platform advantage taken to the next level. No other vendor has the best-of-breed capabilities for one, as Scott mentioned, let alone all integrated into one platform powering this AI learning loop. That's what we're building, that's where we're going. So I've talked about this platform advantage that we already have. So let me just briefly review the CXone platform today. So it is composed of 3 distinct product areas on the left-hand side, and I'm talking about that white arc across the top is automated experiences, right? So this is where self-service is. This is where we just upgraded in a big way to best-of-breed capabilities with Cognigy, then we have orchestration of workflows. This is classic CCaaS orchestrating customer engagements across all channels of engagement. And then on the right-hand side, the augmented workforce. Another area NICE has led for many years in the WEM, WFM space. All of these product areas sit on the same stat, right? They're powered by the same customer engagement data the same purpose-built AI models, but they're independently adoptable. We can sell them independently. They can be adopted independently. But as customers adopt more they get compounding value as a suite. And this is very important because, again, as Scott mentioned, this is a very fragmented market. Customers want to consolidate, but we've got to meet them where they're at. They will start where they have the most urgent need and they'll expand from that. And we've got the CX1 platform as a distinct advantage that we already have and that we'll be building on. So in terms of that fragmentation, meeting customers where they're at, the NICE team has done an incredible job over the years building adopters so we can land in any environment in the enterprise, highly fragmented customers don't need to do a rip and replace. On the left-hand side, we have a ton of adapters where we can link right into the channels that they already have, right? Even other CCaaS and ACD systems. On the right-hand side, we have hundreds of adapters and connectors to CRMs, other mid-office, back office systems. And this helps us bring in context from the enterprise, this will also be a key facilitator for us as we expand beyond the contact center and orchestrate more workflows across the enterprise. All right. So let me kind of close here on our investment themes, our investment areas. First, no surprise, I talked about it, is to accelerate Cognigy. And our focus there is very specifically to accelerate Cognigy stand-alone value, right? So Phil and the team have done an incredible job getting Cognigy to a leadership position in conversational AI, self-service and Agentic AI. We're just going to pour a whole bunch of feel on that. accelerate that road map and extend that leadership position in the market, stand-alone. The second vector of investment around Cognigy, as I mentioned, is to bring it on to the platform natively. I've already touched on how that is going to take the CX1 platform, take it to a whole another level, incredible platform benefits. But that's also going to accrue benefit back to Cognigy stand-alone because as it operates on the CX1 platform, we can easily bring in market-leading capability like proactive engagement that I mentioned earlier. All of a sudden, Cognigy will have a world-class knowledge management system and expert because we can just bring that along for the ride. And all the AI models that we have that are CX specific also goes right into cognitive. So in multiple ways, we are going to accelerate Cognigy stand-alone. It's a key land and expand for us. This is an area where a lot of customers want to start their AI transformation journey, and we're going to be there for them. The second place we're going to focus is extending our enterprise leadership from a platform perspective. So we partner with some of the biggest, most complex brands on Planet Earth, and we treat these customers as design partners. So they want -- they need a further feature functionality. We build that for them, but we built them -- we build those at the product level. So other customers can benefit. The second place in the platform, we're investing heavily is to ensure that our platform has plan at scale, resiliency, availability and performance. As Scott mentioned, we're getting tons more interactions on the platform as we bring Cognigy natively on to the platform that's going to drive more interactions. And of course, we're planning to execute many more agentic journeys on the platform. So we're getting ahead of that, getting ready for that surge of volume. And then the last place the third sort of big investment area is this notion of orchestration. So we have a number of capabilities in the platform we're going to leverage there. As you can guess, Cognigy again, is going to play a role from an Agentic capability. And then we're making net new investments to enable these types of orchestrations. So in a little bit, Neeraj is going to come up and really bring this to life for you. And what that looks like when we're orchestrating more holistic customer journeys across the enterprise. So we'll get more into that. So I'll just close with this. Agentic AI, it is going to fundamentally change CX. And NICE is incredibly well positioned. We have the specific and unique CX assets, and we have the plan to not only lead, but to win in this new era. So with that, I believe we have a break coming up 20 minutes, 15 minutes, so be back in 15 minutes, and then Phil will come up and demo Cognigy live for you. All right. Thank you. [Break]

All right. Welcome back, everyone, and thanks for coming today to listen to us. Really good to listen to Scott and Jeff about where the future is taking us and the market opportunity. And I'm here today to give you a bit more of a look at Cognigy and what we're doing at nice Cognigy and the future that we're seeing as in the better together story of NICE and Cognigy together. Now for those of you who don't know so much about Cognigy, we thought we put a quick slide up to give you an overview of who is this company that NICE acquired. Now we were founded in 2016 in the beautiful city of Dusseldorf in Germany. And we have since emerged as a market leader in what used to be called conversational AI now is called Genifcustomer experience. We are processing by now. So this was in 2024. But by now, we are processing billions of user interactions every year. We have the highest recommendation rate in the industry. As you can see on Gartner Peer Insights, we're servicing brands all around the world. I think it's around 1,000 brands that we are servicing. And we're also praised essentially by any analyst in the market. We're a Magic Quadrant leader. We are a Fortier. We're an IDC leader, we're an ISG leader, et cetera, et cetera. And we're very proud that we have attained this position as a small start-up from Germany growing into this market leader globally. Now to give you a bit of background where we came from. This is getting a little bit technical, but we emerged in an era of the so-called NLP frameworks. NLP frameworks back in the day 2015 were coding frameworks that analyzed user inputs and attached a label a so-called intent to them, right? So a customer would come in and say, I want to refund for my ticket. The NRP framework would then detect, okay, refund request and then would give you that result, and then you had to write code to do something with that, okay? So what do I do next? We saw a gap in the market where we saw that large enterprises don't actually want to write code to then determine what to do, but we want to have a graphical tool set what is now called a low-code tool set to define the process that would happen afterwards. This is what would then be called a conversational AI platform, and that is what Cognigy built and emerged as a leader in. Overtime then, of course, with the emergence of large language models, we first enhance the conversational AI platform with large language model capabilities such as generating these NLP models with large language models, testing, et cetera, et cetera. And then last year, we created what is called the AI agent orchestration platform. So an LLM native platform inside of Cognigy, that makes use of all the enterprise features that we already had. And still incorporated large language models and by the way, any large language model, not one specific model to enable our enterprises to make use of this amazing technology. And the next step for that is nice Cognigy, and that's what we're going to be talking about here today. The platform is a holistic platform that allows you to design, run and optimize AI agents at scale for large enterprises. -- with various components, very subproducts like AI copilots to help human agents with agentic-driven experiences, knowledge, process knowledge, et cetera, in real time, during calls and chats -- we have a live agent product, which is a digital channel product. We have the AI agent platform that we're going to look at more today, voice gateway to connect the Cognigy agents to any ACD on the market. So this means whether it is CXone, whether it's Genesis, whether it is Avaya, whether it's 8x8, we can work on top of all of those ACDs and CCS players. And then we have a very advanced insights product as well, which is specific for insights, for agent AI for customer experience. A very important Cognigy is not just an Agentic-AI product but it combines a gene with logic guardrails and prebuilt system integrations that we have built out over the years because hallucinations remain a fundamental challenge in the AgenticAI world. But by embedding it into a structured workflow as you will see in Cognigy, we actually get a handle on that. plus agents must be deeply aligned with the company's logic, data flows and value creation. And a genetic AI can't really do anything unless it is connected to back-end systems, to booking systems like in the Lufthansa case, to CRMs, to ERPs, et cetera. These AI agents need access to these systems to the data in these systems to the APIs in these systems in real time in order to be able to help a customer. Now I can tell you a lot about the platform, but we thought you're seeing slides every day, you're seeing videos every day on the Internet. Why don't we do something different and just show it to you live. And that's what we're going to do now. If we could please switch to the laptop view. There we go. Okay. This is essentially how you set up an AI agent in Cognigy. You can see you can pick a an avatar, you can define a name, you can define a description, et cetera, et cetera, the style of how this a agent speaks and so on. This is what we call the persona of an AI agent. You can have one persona for all the things you agent does. You can have multiple personas where you say, okay, let me hand this call over to another AI agent and this other agent might have a different persona or not. What we then do is we assign this AI agent a job, okay? So this agent, Jennifer, has a job here, she is a virtual service expert, and she has a job description and specific instructions and knowledge for this job that she needs to be able to do. We're also giving this AI agent so-called tools. In this case here, an authentication tool to authenticate a customer, make payment tool and an updated account tool. So these are the things that this specific AI agent can do, and it's all done in this low-code platform so that enterprises can build these AI agents themselves. Now Cognigy AI agents are multimodal air agents. That means we can interact with them both in chat and also in voice. This is the chat interaction here in what we call the interaction panel. It could also be on SMS. It could be on WhatsApp, it could be on our website widget. And we can just have a chat with it. So here we say, so the agent says HelloPil. Thank you for reaching out. My name is Jennifer. I'm a virtual service actually with Nexor. How can I help you. I could say I want to make a payment -- and then she says, Got it Phil, I can help you with that. First, we need to authenticate you, shall I go and send you the verification pin.And so I can just have this chat with this AI agent. But in Cognigy, you cannot just set up the AI agent like this, and then it's kind of a black box, and you don't really know what's going on, but you can actually look under the hood. And that's what we see here. We can take AR agents. So in this case, Jennifer and embed her in a structured workflow. So we can do things before data hits the AI agent and these things are required in an enterprise context. You filter out PII data, you make specific settings, et cetera. And we can also see the processes that happen if Jennifer wants to do a specific thing. So for example, here, we have authenticate. So when Jennifer wants to authenticate a customer, she will say something specific and she will say exactly that. So that is important because LLMs, you can't force them to say something in an exact way, but he in cognitive, you can then she will send out an SMS via Twilio and continue with the flow or with the payments -- in this case, we have integrated with Stripe. And you can see we can do that in a very low code way in a very easy way. Now as I said, these agents work in both chat and voice. So what we're going to do now is we're going to call this AI agent, and we're going to use something in cotton we call life follow mode. I've now activated the life follow mode for this phone number that I'm going to use, and we can see what is happening in real time. So let's kick that off. [Presentation]

All right. So what we saw here, we saw a real-time interaction with 1 of Cognigy AI agents. This is not a pre-rendered video. This is exactly the type of experience that our customers can create with Cognigy. You can see you can use this low-code interface for debugging purposes, but this voice experience, the very natural voice, the very faster responses whilst interacting with back-end systems, is not fake. This is reality, and this is what we have deployed at scale for hundreds of thousands of brands out there already, and we are going to have Lufthansa talk about that in a second. If we could please switch back to the slides. Great. Now what we saw was the Cognagy interface, right? The Cogni studio that we can use that our customers use that enterprises use to build out these types of experiences. But what I also want to share with you is another experience, which is a front-end experience. This is the type of experience that can be created with the cognitive platform. Earlier in the demo, we saw a chat, and we saw a phone call that was made using a regular phone through the phone network, so connected to a CCaaS and then against the cognate AI agents. Now what we're seeing here is the next level of experience. It is a proactive outreach by an AI agent that knows that this customer has to renew their mobile phone plan. This customer is then sent a widget -- and using this widget can then using what is called WebRTC communicate with the AI agent in real time. So here, the widget comes in via SMS -- they click the URL, they go in and then a voice conversation starts afterwards. So let's take a look at that. [Presentation]

I know everyone wants to erupt in cheers because this is exactly the type of customer experience that we would all like to have because it really is -- and what we are seeing here is not the future. This is reality right now. This is not a video that was produced by our marketing team and some video cutting -- this is reality. This is a straight off recording of a phone. Our customers can and are producing those types of experiences at this point in time. But there is something very, very important that I want to share with you. Everyone can do a great demo these days. We have LLM technologies. We have speech technologies and text-to-speech technologies, like 11 labs. We have the open ALMs everyone who can quote a little bit of Java script can put a cool demo together and blow you away with the demo. But for the types of customers we are dealing with enterprise customers, this is not near enough. There's a lot of stuff happening under the waterline that is even more important. And without these kinds of things, our customers could not even think about going live. There's omnichannel routing. We are supporting more than 30 different channels in the platform, whether it's voice channels through the contact center, whether it's voice channels on web whether it's web chat, whether it's Water, whether it's WeChat, whether it's line Messenger for the Asian markets or Kakao Messenger in Korea, we are supporting a large number of channels, and you don't have to build this experience for each channel, you build it once and then you deploy it on a channel and it immediately works. And you saw that earlier, proof in point I chatted with the AI agent and then I call the agent and the experience was equal. Plus there's a genetic orchestration. We have the tool usage, the planning and reasoning baked directly into the platform. We did not tell the AI agent, okay, now you use the authentic tool. Now you do this right? We just provide the tools and the reason and capabilities of the LLM take care of the rest. We can build end-to-end workflows, where we have a visual flow builder that you saw, we can have guard rails where we can make sure that the AI actually stay on track. We can have fallbacks because sometimes LLM APIs are flaky. Right? Maybe sometimes the response time of a certain API goes up and then the LLM don't respond to 200 milliseconds anymore, but they responded in 3 seconds. That is too long for a voice interaction. We measure these kinds of things in Cognigy and can then fall back to another LLM that is faster to maintain a very good customer experience. We have more than 100 different system integrations with CRM systems, ERP systems, et cetera, so that our customers don't have to build them, plus the contact center integrations Cognigy is integrated into the NISeXOne platform and will be even tighter integrated in the near future, but we also remain a stand-alone platform. And I can't stress that enough. A large number of our customers at NICE Cognigy, are using a very wide variety of CCAR systems, right? Be it a genesis, be it Avaya, we work just as nicely based on those contact center through our integrations, both on chat and on voice. We have the handover to life agents. We didn't see that in the demos here, but the AI agents, if they get stuck, can also say, hey, I think I want to love a human in either by communicating with a human in the background and then providing this information to the customer or by handling the full conversation to a human agent because human agents are not going away. And when it's handed over to a human agent, we can use embedded agent assist. And then we have all these boring things that are still extremely important for large enterprises, versioning, auditing, who did what in the platform, role-based access control, data residency controls with sovereign clouds around the world, GDPR, observability, et cetera, et cetera. So it's much more important to have a holistic platform rather than just a prompt wrapper that allows you to show a nice demo, but in an enterprise scenario, you will not be able to go live with that. So to summarize all of that, how do we differentiate as Cognigy. We allow for hybrid AI agents. That means AI agents that are both Agentic and based on traditional conversation as to more deterministic, especially important in regulated industries. We have AI operations and orchestration, the fallbacks, the observability, the orchestration of different AI models. -- native contact center connectivity to pretty much any contact center out there. multimodal experiences like we saw in the second demo, we can have chat experience. We can have voice experiences, we can have voice experience with multimodal widgets and graphical interfaces, which we believe, and by the way, many of the analysts believe to be the future of customer service and customer experience interaction and of course, all the enterprise readiness with our certifications, security, compliance, data privacy compliance, et cetera. And that really led to us being loved by many of the largest brands in the world. If you look at Gartner Peer Insights, we have the highest ratings in the world and the most ratings in the world compared to the likes of core AI, Emilia, Sierra and many others. And we picked out 3 quotes here that make me as the founder of Cognigy, specifically proud while others do the slides, Cognigy does the work. Surpassing expectations and agentic AI teams with easy use. And the other thing that is important when you want to make a customer successful. So on the one hand, you have the product that needs to be outstanding. But on the other side, you have the company, Cognigy, that makes you successful, right? And this is something that we also, I think, share in vision with NICE. We don't just provide the technology. We provide the experience and the knowledge in the industry that makes our customers successful. So our secret to -- or one of our secrets to the success of our customers is our customer obsession. We work very closely with our customers. But in the end, we are delivering real impact. We could have an amazing product. We're going to have amazing customer obsession. If we don't deliver impact, it's worth nothing, but we are delivering real impact. Core containment, depending on the use case between 60% and 90%. Call containment means that the call does not have to reach a human agent anymore afterwards. If it reaches a human agent, average handling time reduction of 25% or more through the AI capabilities, first that come before the interaction with the human and then how we are supporting the human agent during the conversation with the customer. And it's truly a win-win-win situation, CSAT improvement. Customers love it. Nick is going to talk about that in a second. Customers like being held by the AI agents because they're being helped quicker. They don't have to wait on the phone lines to get done what they want to get done. So we are seeing also a CSAT improvement here. And I could tell you about this all day long, but you might think, yes, of course, philosophy of Cognigy. And I am but there are also other fans of Cognigy. So I thought I would like to bring on stage here Nick Allgaier from Lufthansa, please, everyone give the hand to Nick. And he will tell you firsthand about his experience with Cognigy. Thank you.

Good morning, everyone, and thanks for having me. My name is Nick. I've been with Lufthansa Group since 2014. And my teams and I were in charge of the conversational AI developments for our B2C space. So if you ask me one of the most exciting spaces to be around. Today, I would like to show 3 things. First, I would like to show how we are leveraging conversation at AI to address real operational challenges at scale. Next, I would like to show how conversational AI has become really mission-critical asset for us. And third, I would like to point out how our partnership with now NICE Cognigy, is of strategic importance to us as we move into the next era of customer interaction. But first, let me fill in on who we actually are at Lufthansa Group. We're around about 100,000 employees, and our mission is to connect people, cultures and economies. We do that around about 3,000 times a day on one of our flights. So we bring our guests to more than 300 destinations across 12 airline brands, with more than 700 plates. And all of that summed up in the year brings us to 130 million passengers, bringing us revenue of round about EUR 38 billion. That scale is great until it's not. And why is that? Our business is not a linear business. Our business is pretty peak heavy. So let me take you on a little trip with a bit of a twist of a perspective because many people might not have looked at this challenge from that angle. So it's July. A beautiful summer day. You're at the airport, mid-90s degrees outside. And the airport is full of people. So you see that a couple about to go on their honeymoon crews. You see that family about to depart on their beach holiday. And you see the grandparents excited to meet their grandchildren. But then at the back of the skies, you see dark clouds. And you can hear roaring thunder coming in, and you see the truss lightning. There is a thunderstorm right above the airport. So what is happening? Unfortunately, due to the thunderstorm, the first slide is not about to go out. The slide from Frankfurt to Paris in this example, can leave. What does that mean in turn? If the flight to Paris is not leaving, it can also not bring passengers back from Paris to Frankfurt. Unfortunately, the Thunder some is sticking around for longer than expected. So also your third flight and fourth flight of the day leaving for Zurich is delayed. And unfortunately, due to the delays throughout the day, also the last -- the fifth and the sixth flight, they're not going to make it on time. So lots of passengers will have to contact us that day. And that is not because they love to do so, but because they will have to. If you run -- the numbers here real quick, and this simplified example, we're talking 0.5 million passengers in a day easily. For our passengers for our guests, this is a massive inconvenience that situation. For us, that means suddenly 0.5 million of our passengers need answers. They need support. And that support can look very different. Some people might just need a new flight. Others might want to get their money back because there is no point in taking that trip anymore. Others might have missed their connecting flights. So they need a hotel, they need food. They need compensation. So it's a wide range of customer intent and requests that we'll have to serve on that day. And let me assure you such peaks no matter how big the human workforce, there is no chance to handle that peak, that spike in demand reliably at scale. So this was a summer day. So now imagine a full pandemic is about to hit you. And we're not talking on airport closed for a day but we're talking a country shutting down. We're talking an entire fleet of 700 planes on the ground for days and weeks and your passenger is reaching out to you. And that's exactly the situation that we were in. That was the reality that we were facing and that made us start looking into conversation at AI before it became so fashionable. So we realized if the future for us continue to be so peak driven. Our customer support model, how it was no longer had a real future. So we saw this would not be a simple task, but we realize there's 4 key areas that we had to focus on. The first one is we needed to automate whatever we could. So we had to reduce the manual the manual contact volume. Second, for what's left, we have to prioritize. If your flight is leaving in an hour, you should be first in line because you have no time to wait. If you're contacting us for a seat reservation for your next trip 2 weeks out, you'll probably be okay waiting for a couple of minutes. Next, what we still need to handle manually, we have to look at efficiency drivers here and improve the time spent on each case. And lastly, we want to gather insights into the first 3 areas and see what is working, what's not working and how can we get a continuous improvement cycle going. So we saw 2 things here. The first is there would be a wide range of use cases that we'd have to look at, starting from simple FAQs ranging to complex end-to-end rebookings, deeply integrated into our legacy systems, our booking system and other legacy. And also, the more we lean into automation, the more important the scale would become. Now if you start implementing automation on several channels, that wave of contact is going to hit you on all the channels at once. So scale really becomes important. So the question for us was now who would be the right partner to pick in that endeavor. So we sat down with our experts, and we're imagining what platform would work best for us. So we came up with all sorts of requirements. First, the first attempt 150 requirements. Then we cut it down to a short list of 30 requirements. And I would like to point out 5 in particular that we then decided to focus on. First, we wanted to have an agnostic modular platform. We wanted to have the opportunity to replace certain components with others if we saw the need for it. We wanted to have a platform that not just our technical experts, our developers could use where also the business experts could contribute to creating the experience. As you saw earlier, we needed a platform that could handle the scale required, and we wanted something where we could create the experience once and then implement it for all of our airlines. Otherwise, we'd have to create one board for each airline in each language. If you're serving 4, 5 airlines in 6 different languages, you could imagine the work that would go into creating each of the experience separately. And lastly, as I pointed out, we wanted to get analytical insights into what works, what doesn't work and what needs improvement. So we screened the market. We started with hyperscalers, and we saw that wouldn't work for us. very technical solutions is what we saw, but we realize we need to keep looking. The second area was CCaaS providers. And I believe it's fair to say, have we found something that worked for us. I would not be standing here today. The third area was specialized AI, conversational AI vendors, and there we were on to something. So we did an RFP. We looked around and then out of that RFP Cognigy came out as a winner. The first reason was Cognigy provided a platform that matched best with our requirements. But then we also saw that the team of Cognigy they understood what challenge we were facing, they did not just understand the challenge. But together, we've been working on that challenge. And they have supported us in tackling that challenge since then -- say, this was the world before identic AI. So if you were to look into platforms now, you might have different arguments. So I'd like to have a look at the differences of the world before Agentic AI and agentic AI and point out what I would still say is important to look at. First, how do these systems work. In a deterministic conversational AI system, you tell the system what to do. In an agentic AI system, you tell a system what to do, but also you have to cater for what not to do. And the problem here is there is an infinite number of options for the what not to do. So the decision-making on the left-hand side is pretty clear. It's if than else. On the other side, the Agentic system has much more autonomous decision-making power and ways to decide, leading to outcomes on the one hand, deterministic, so consistent and reliable. On the other hand, probabilistic outcomes. So the decision-making was autonomous, but the outcomes can be inconsistent. The flip side of it is the deterministic system can feel rather concrete can fuel rigid and scripted while in agentic system there, the conversation can flow much more natural. It can feel much more flexible and smooth. So if we were to look at platform selection again, I would say to those dirty criteria. I would add 2 more as very important. The first one is as a business, I would like to have full transparency over what's happening underneath the hood. And I want to be in a position to control where I want to use what -- if this process is important to me, I want to have consistent outcomes. So 3 points that I would like to point out here. The first one is, if you ask me, is sorry, I can't answer that, can be better than a Roke AI agent. And sorry, I can't answer that. It's very different to sorry, I don't understand. I think the times of sorry, I don't understand, they're over. They are no longer accepted by the customer. But I can understand what you mean, but for whatever reason, decide that I don't want my AI system to answer that. That could be because it's a very sensitive topic or because I'm not confident enough that the AI system has all the relevant information at hand to autonomously answer that question. Think back to that couple about to go on their honeymoon crews. If their flight was canceled and they missed their crews and now they ask for a refund, imagine how they would feel -- if we hallucinated their refund, gave them the wrong amount of money and would give them the wrong timing of when they would receive the refund. I'm not sure that would fly with us again if this is something that we mess up back big time. So for us, it's important to be able to blend these 2 things. We would like to have the control of certain things and have a deterministic system where needed in place, but complement that with an agentic AI experience where it's meaningful. Fast forward to 2025. Where do we stand now? Actually, conversational has become the most popular channel through our Lufthansa Group. Those experiences are the #1 contact channel for us now. We are integrated in chat, voice and agent assist. And we're making use of generative AI in more than 50% of our conversations actually in voice, we're beyond 90% of conversational AIUs. And we're in the process of rolling out more and more agentic AI -use cases. This year, we will probably end up with around about 12 million conversations being facilitated through the Cognigy platform. And we have had record days like the ones that I pointed out earlier, where we had around about 400,000 sessions in a day and more than 10,000 concurrent conversations with our customers. Now we see that millions of customers, customer contacts get prioritized. So it is now a reality that if you call us and we can identify you and your flight is leaving in an hour from now, you will be first in line, and we will tell you that you're first in line because your flight is leaving soon. We see shortened AHT across both chat and call and we're saving a good amount of money every single year. So where do we go from here. What we want to create is Lufthansa Group's digital go-to person. And I think it goes along towards what Scott is saying that companies are now not looking at ways to reduce customer contact, but we want to find better ways to serve our customers. We want to embed our conversational experiences across different points of the customer journey. And overall, we want to create the best conversational experiences in the tourism industry. For us, that means practically, we will embed our conversational experiences in more channels. So you will be able to find us hopefully soon, then also in messaging apps, the more touch points on our website. Wherever you're going to call us, et cetera. Then I also strongly believe that channels will blend. And Phil also showed that earlier. I believe as as multimodal experiences are starting to mature now, customers will think less in this is chat, this is voice. This is call, but we'll see those experiences planned and customers will want to interact however, how natural it now feels in that very moment. And that can mean you might call us for a seat reservation, but why not select this seat on your phone? Because whenever you've seen a seat map, it's much easier to pick where you want to sit than having an agent walk you through your options or think talking to the website and telling the website what you need to find out and then the UI will serve you in a dynamic fashion. And lastly, this is not where our collaboration with NICE Cognigy will stop. Aviation will always face volatility and unpredictability, but dealing with that volatility and managing that volatility, this is really where customer value is created and where competitive advantage lies. So for us, AI is more than NICE to have, how we handle volatility creates true customer value. So AI enables us to manage unpredictability to scale reliably and to improve customer experience in a time where expectations have never been higher. And with our partners like NICE Cognigy, we're not just waiting for this to happen, but we're actively creating the next generation of our digital customer experience, one that weather storms even in the most literal sense. Thank you very much.

Thank you, Nick, for this amazing presentation. I think what this presentation really shows is that what we can create with our type of technology a true win-win-win situations. Now what do I mean by that? Because usually, it's win-win, but here it's a triple win. Firstly, the enterprises deploying our technology are winning because they are saving, as we could see in this example, multimillions of dollars every year whilst creating superior customer experiences. And that is the second win. Okay? The customers of our customers are also experiencing the benefits at scale because if such a storm hits and 500,000 people have to call the contact center, they're going to be waiting for hours on the phone lines before anyone can help them unless they have deployed AI agents. And then the third party to win is us because we are the software provider, and we are generating revenues in that way. And I hope what you can also take away from this experience, this is not just about putting an IVR phone bot in Yes, we can do that, too, and we are doing that. It's not just about putting a chat bot on the website. It is really about revolutionizing the customer experiences that can be created and not limiting the customers. With Cognigy, if a new LLM comes out tomorrow, you can embed that in your customer experience flow. It's an orchestration platform that allows you to be open new voice models are coming out. New speech detection models are coming out, et cetera, et cetera, you can use all of that. You can use different models in different markets around the world. And what all of this means is that our customers can grow together with us. they can power all of their customer experience, whether it be customer service, whether it be marketing or sales in one platform, and that platform is NICE cognitive. Now we've spoken a lot about Cognigy and what we've done in the past and the position to which we've gotten. But of course, the really big news is that NICE has acquired us because otherwise, we wouldn't be standing here on stage today. And the analysts around the world, industry analysts really welcome this. Many of those have said that an acquisition like this would happen. And some of them called it the biggest news in the CX industry of 2025. an industry leader has a nice coming together with an a genic AI and conversation with [ EYLEA ] as Cognigy, it's really the perfect union. Now one thing to highlight again, and I know I've mentioned a couple of times, whilst we will be integrated, and I'm going to be speaking about that a little bit more in a second, we'll also remain available as a stand-alone platform. Now bringing those 2 together, what is that better together story. Why is it so unique? Why does it bring such tremendous value -- so on the one hand, we have Cognigy, which is a genetic AI and orchestration of AI agents at scale. We bring a world-leading platform with all the systems integration, et cetera. But what does it mean financially? What does it mean GTM wise? It means that now NICE cannot just sell AI solutions to their existing customer base, but the whole CX market. We have a stand-alone solution that can be adopted by anyone in the market, no matter where they're using CXone or not. On the other hand, we have NICE, which is, of course, vastly larger than Cognigy, has a much bigger scale, global GTM teams in place all around the world. I have already visited our teams in Singapore, in Australia, all around Europe. So massive scale compared to what Cognigy has, 27,000 customers that NICE Cognigy can now be sold into. So a massive upsell opportunity on that end. But those are the GTM portions. But in addition, there is this amazing fit that is the product fit. So again, we have Cognigy with the AI agents, you can build, deploy, operate and optimize -- and then we have the CXone Empower platform with all of their capabilities. And we worked on this slide a lot and we try to do it justice of how amazing this integration is, but we couldn't really because it is much bigger than this. But if we wanted to show you how good this integration really is we would need a slide that's 10x the size. And what do I mean by that? At Cognigy, we had all these plans when we were still stand-alone. What are we going to build in the future? We knew it would be about orchestrating customer experiences across the lifetime of a customer, not just singular interactions. It would be about not just handling inbound, but also outbound so that we can expand into the sales arena. It would be about intent mining taking customer call records or transcript and mining them for what do customers actually want and then building AI agents based on that. It would be about analyzing transcripts after the fact. So really in-depth genetic analytics -- and then when NICE approached us to acquire us, we saw that all of that was already there. the world's leading outbound dialer, the world's leading intent mining. All of these components are already in the CXone Empower platform, and we are integrating with those now in Cognigy. And we are going to create the leading CX AI platform on the market, and we are very well progressed in that journey already, and we can't wait to show you what's going to happen here over the next couple of years. I really believe that with those 2 companies coming together, we possess a strength that no one else in the industry has no matter how big the competitor is. All of them have some components of this, but really bringing it all together in one is extremely unique and is extremely exciting for us here at NICE and NICE Cognigy specifically. Now I would like you to introduce you to 1 of my colleagues here at NICE. Neera, who is going to show you a little bit about that future, about the progress that we have already made in integrating those 2 platforms, and I couldn't be more excited than to hand over to Neeraj. Thank you very much.

Thanks, Phil. I was going to use my Starbucks name. I'd like Nick better than years. Never no one pronounces it the same. Okay. So let's talk about what it takes to achieve consumer intent resolution right? When we talk about intent, it's a really interesting story. And this is going to get deep and technical. This is what the consumer expects. When they call a business in -- they expect they have a problem, they have a need. They expect that they're talking to a front office agent or a chatbot, and their problems are resolved in real time. The reality is slightly different. The reality is that it's a -- we call this intent resolution journey. And this journey goes across, and Scott's mentioned it, it goes across front, back and mid-office workers. So it's really disparate labor forces that exist in most organizations. When you think about these disparate labor forces, oftentimes, you've got back office workers that work completely different hours in front office agents that are 24/7. You get this asynchrony and resolution. The customer calls into the front office, they handle all the conversations. Well, someone in the back office might be fulfilling a request to actually complete the customers need, which creates once again, this necessity for a case management system to manage this asynchronous behavior. It creates a really frustrating experience, right? Everybody is called in and they've -- I don't know you call in a business about a problem, and they give you a case number and maybe they'll follow up with you later. This is the reality of intent resolution in a lot of organizations today. But when you think about the unique advantage of NICE has, we are the customer engagement platform. And that doesn't mean we just sit at the intense generation, right? We're not sitting at when the initiation of the intent happens. We sit across from the initiation of the intent all the way down to the fulfillment and resolution of that intent across all of these disparate workers. And it's really important. We always talk about this concept of our data layer and this data lake that we're. All these conversations were mining, all these screen recordings are mining. But the reality and the moat that we've created is -- we have a really, really interesting operational view of how an intent is resolved in every organization in the entire world. We understand exactly what each of these workers are doing, how they're doing it, what applications they're using, what tools they're using and how long it takes. It's really, really important. It creates a really big moat for us. AI agents are, you can -- just like Phil said, you can create an the agent for anything using open AI. But the reality of AI -- has that actually solve customer service problems is super complex and our data allows us to get there really fast. Now I just talked about us gathering data points, right? Now we know what every single worker across the entire enterprise is doing to resolve consumer problem. We're gathering tasks. We're gathering what tools they're using. We know the channels and we know the integrations. So what does that look like to you? What is an AI agent, right? AI agent is just a description of a job. What job are they performing? What tasks are they doing? And what tools are they going to be using to complete those tasks. We have all of this data on the platform. So now we can translate the human workers across not only the front office, right? This is what everybody thinks about when they think about automation, automating the front office, customer chat bots. But the reality is that we can expand our TAM and market all the way down into the full intent resolution journey. It's really, really important. Automating back office, knowing what those workers are doing is going to be the future of customer service. Now what happens when you do that? The reality is that as a customer engagement platform, we operate on top of all ecosystems, right? Everybody thinks about CRMs or case management systems as being the customer management platform, but really intended resolution sits across all of those ecosystems and platforms. And as a customer engagement platform, we understand the entire journey. And once we add AI agents into this journey using Cognigy, it creates this intent resolution journey that's in real time, it's hyper-personalized and allows us as nice to really expand what we're doing today in the front office all the way down to journey resolution. So we'll see a quick demo. Let's talk about what this demo is going to be. You're going to see a consumer. We're going to join a call. We're going to join a conversation with a consumer and a business. And this consumer is chatting with an agent or a front office agent, about a credit card dispute, right? They've seen something on their bill and they're going to chat to an agent. You're going to see this customer is not -- or this prospect is not on CX1 today. So you're going to see what their agents are doing today to handle these types of conversations. So you could see as the conversation goes on, this human agents got SOP pull out. They've got billing systems. They've got CRM systems. It's a pretty complex process, right? In general, agents have something like 8 to 12 applications open at any given time. It's really, really complex. And this is the world today, right? Everybody thinks it's super streamlined. -- 1 application to handle everything CRM. No. The reality of customer service is this. It's a really, really complex process. Now if you looked at the SOP of this particular agent, right, the procedures that they're using to solve this consumer intent, their entire job is to talk to the consumer, takes a couple of notes. -- create a case and then say, we'll follow up with you later. The conversation ends right here, and it's super frustrating, right, non-real-time resolution for the customer. Okay. Now several days later, right, that same case goes to a back office worker. This is really important. A back office worker. It's got a case management, ticket management system up. They've got a lot of tasks. Some are CX related, some are not. -- and this disparity in what they're doing is a big problem, right? They're going up, opening the ticket. They're helping us resolve it, done. Now if you really think about this journey, and by the way, when we think about the moat that we have today, -- just last week, I had my team pull up 1.8 million of these journeys across the top 10 banks in the U.S. This journey on average today across those banks takes 3 to 5 days to resolve. It's a big problem. This is not fake data. This is not made up. We understand these journeys really well. It's a 3- to 5-day journey across the front, all the way down to the back office. Now let's talk about how nice solves this problem. We're going to join a CX orchestrator right? Their entire job is to look at customer journeys, solutions to consumer problems from a journey perspective. This orchestrator is an AI-powered tool that's looking at the conversation that you just had and thousands of other conversations. Putting together these disparate pieces in what we call, once again, the intent resolution journey. Of course, there's a lot of front office journeys that we're already automating, things like FAQs and just like Scott said, password resets, there's a ton of these, easy, easy to create agents were already doing that today. But a lot of journeys that are -- they cost a lot of money. They cost a lot of frustration from a customer perspective are journeys that go across the front back and mid-office. Now this orchestrator, as he's analyzing these conversations, right? It's using AI agents to do analysis on screen recordings and SOPs to see what tools and integrations are agents and our workers are using to resolve this issue. I can see that the dispute charge is being highlighted by the orchestrator. Let's drill into dispute charge. This is really interesting. This is the journey that we just noticed. The really disparate journey, right, you've got a Tier 1 agent that's using all these tools. And then you've got a case being created by that agent and then you've got back office workers executing those cases. The reality is all the data that we've gathered allows us to orchestrate and put this journey together, put together a picture of what's happening in the world today. And if you notice on the right side, we've got very specific descriptions of exactly what these agents and workers are doing to resolve the problem, right? We've been able to take conversations and screen recordings and tasks and integrations, convert them into descriptions of what an AI agent should be doing, which allows us to really quickly take the next step. So it's recommending that, hey, I can analyze these screen recordings. I can analyze these SOPs that are already within CX1 expert and empower agents in all of our call recordings and it's going to recommend something really interesting. You can see that it's taking this really synchronous journey across all of these workers and recommending that we create AI agents to replace the tasks that these workers are doing. It's really, really interesting. We have the descriptions. We understand what these agents are now doing in the front office and the workers are doing in the back office. We can use those tasks and descriptions and data that's coming from the front office and the back office to create AI agents to replace the humans and the human different tasks in the front office and the back office with AI agents created in Cognigy. This is really a data-driven AI approach. Creating AI agent is just like I've said, it's super easy, right, type in a couple of descriptions. But the reality of creating AI agents to solve customer service is really complex. It's this journey and being able to translate that into AI agents that drive value across the entire NICE platform is really difficult. Now I'll highlight and hover over a couple of these agents, and you'll see the tools kind of blink through right? This is what's happening. These agents are using these tools and they're assigned these tools, and they're working together using the agent to agent platform. Now let's click on Sam here. As soon as I click on SAM, it's recommending that I create a dispute intake agent, and this is directly within the Cognigy platform. I've taken the description, the system-generated description based on all the recordings from all the conversations and screen recordings that we have from the front office. We've created this agent automatically within Cognigy, using the right tools that are already available in the enterprise platform. But the reality is that having this enterprise platform that we have within CXM Power, we have every integration that you can imagine. So as the agent, the human agent is opening up applications. We can monitor what they're doing, translate that to what an AI agent should be doing and create the AI agent income. And this is the power of the platform combined with Cognigy. Now let's go ahead and deploy these agents. This is how easy deploying an agent on the CXOne platform is. We understand the channels already. We understand what the journey looks like. Now we're going to deploy this AI agent across our platform. And you're going to see a conversation, right? Let's talk about the original disjointed asynchronous conversation we saw that was super frustrating for our customers. Let's see that being replicated within the platform. with AI agents. You'll see the nice reasoning capability of our agents right here and how our primary AI agent, SAM, is utilizing other AI agents across the enterprise in order to complete his tasks. [Presentation]

Wow, what a great experience, right? The reality of the world is orchestrating these types of experience takes an enterprise platform and creating these types of agents isn't a single prompt. It's a data-driven approach that takes the entire customer intended resolution journey, convert it to AI agents and turns 3 to 5 days into 1 to 2 minutes, right? That's the reality of the world that we're living in today. CX1 is in the ideal position -- you cannot expect case management in our system -- CRM systems to understand these journeys. They do not lie at the heart of the initiation of the intent all the way down to the resolution. It's really, really important. Having this data is what gives NICE the advantage. Now we've been managing human-to-human interactions. -- for 40 years. We understand how human agents work operate, how they resolve issues, how they're optimized. I mean, it's really, really interesting. We've got 40 years of data on this. Converting that -- and using that expertise to manage AI agents is just the obvious next step. I mean it's really, really obvious. And here, you see a unified agent management platform that manages intent resolution AI agents and humans working together, right? You've got AI agents that are reaching out to humans for help. And you've got entire fleets of agents that are not generic out of the box agents. They're working together to resolve intent journeys, all the way across the front to back office. And you could see how much. How many operational dollars you're saving, how many agents you have deployed. It's really, really important. And some of the reality of having all this data is optimizing AI agents and testing and evaluating AI agents is really difficult. It's actually an open research topic. Are you going to use goal-based evaluations? Are you going to write all the goals yourselves? Having this 40 years of data on what great conversations look like, what resolution looks like allows us to create AI agents that actually resolve customer issues and not have to create them manually, right? I think in this sort of near-term future, you're going to see there's going to be a lot of adoption in AI agents, but creating those agents is difficult and being able to automatically create some gives us a huge head start. So once again, the unmatched domain expertise that we have in resolving intent is really the key here. You cannot expect other organizations that don't live and breathe customer intent resolution to understand how AI agency to solve problems. And you see this across the market today. Our platform is purpose-built. We have tens of thousands of integrations across the platform. hundreds and millions of journeys flow across the platform every month. We understand these systems really, really well. We have thousands of AI models that understand what good conversations look like and what resolutions look like. It's unmatched expertise and our sort of transformative approach going from conversations and journeys to creating AI agents to solve those journeys automatically is truly unique in the market. And with that, I'd like to introduce Beth. Everybody knows her.

Thank you, Neeraj. And just in case everyone doesn't know me, I'm Beth Gaspich, I'm the CFO at NICE, and I'm pleased to be here with all of you today. I'm going to close out our prepared remarks. And I want to assure you as well that I'm the last speaker before you in lunch in case you're hungry. So I hope you've heard throughout all of our speakers and presenters today, just how excited we are about the future of NICE with our CX AI platform and what it means in the next wave of transformation in the CX and customer experience era. Every time I see the demos from Phil and Neeraj earlier, it reminds me of just how I'm increasingly impressed with the opportunity we have in front of us. This is -- it's important to highlight that is real technology. NICE Cognigy is deployed in large enterprise customers, global marquee brands like Luftansa Group that you heard about from Nick. So it's really exciting. So what I want to do is start back with where Scott began and started earlier today talking about the massive opportunity that exists in the AI market and how we are going to capitalize on that with our leadership and the strength of our assets. After that, you've heard from our product leaders with Jeff. You heard from Nick, you heard from Neeraj, you also heard from a customer that our innovation together with Cognity is perfectly positioning us to win in the CXI era. So now in the next several slides, what I want to do is share with you how we are also perfectly positioned to win from a financial perspective and capitalize on this massive opportunity. So I want to start with talking about building on our profitable foundation. NICE has a proven track record of strong financial performance. You can see that since 2020, our revenue has increased at a compounded growth rate of 13% and we exceeded $2.7 billion in total revenue last year in 2024. If you look on our profitability and our operating income, you'll see that we're growing even faster at a 16% compounded growth rate over that same period. And importantly to highlight the great track record of our operating margins throughout that time, exceeding a 31% operating margin last year. This healthy financial profile really demonstrates the great best-in-class operating leverage we have at NICE, but as well as a testament to the scalability of our CXone cloud platform and the ongoing financial strength and discipline we have at NICE. Further looking at the strength of our financial foundation and the health we have at NICE free cash flow, it's one of our greatest strengths, you can see over the same 5 period that I talked about previously, we had a 15% growth annually in our free cash flow, and we generated more than $700 million in 2024. Throughout those 5 years, we have consistently shown and delivered on very healthy, best-in-class, very strong cash generation from our operations. And of course, it's important to highlight that, of course, during this time, this has allowed us to make strong, bold strategic investments like the one that we just did of Cognigy. In the third quarter, we spent about almost $1 billion acquiring Cognigy. In the same quarter, we continued the strength of our buyback program. And in parallel, we paid $460 million of debt, ending the quarter with no debt on our balance sheet. And we did all of this through this strong free cash flow generation and exited the quarter still with nearly $0.5 billion in cash. So our strong cash flow generation really sets us up well to step into and capitalize on this next opportunity in front of us. And really, it demonstrates both the financial resilience and agility we have in our business. Next, I want to share with you a little bit about how year-to-date we have consistently delivered on our revenue and profitability targets. You'll see that through the first 3 quarters of this year, in total revenue, cloud revenue and EPS, we have exceeded the midpoint of our guidance consistently across all 3 each and every quarter. And the other important area to highlight here is the stabilization of our cloud revenue. And you see that 12% plus growth -- the first 2 quarters, of course, coming, excluding Cognigy, and achieving that 12% in Q3 also excluding Cognigy, with a 13% growth overall in the third quarter, inclusive of Cognity in our cloud. This growth that we're seeing and the impressive results that we're putting on the board here are really being driven by the success we're seeing in AI. Our capital allocation approach as well uniquely sets us apart at NICE. So I talked about the significant amount of free cash flow that we generate and the strength of our cash and our balance sheet altogether. It's important that we always continue to deploy our capital in a disciplined way. We've done that historically, and that is our plan looking forward as well. We're going to continue to propel the business forward and we're also planning to deliver significant shareholder value. Our approach is really 3-pronged. So it's focused on these 3 different pillars, and I want to talk a little bit about each one. The first is strategic and disciplined investment, and we prioritize investing organically. The areas that we're focused on and we'll talk more about organically are around, of course, driving our product innovation, both fuel and Cognigy as well as our other AI product road map. The second is around increasing go-to-market efficiency, which, again, I'll talk more around the strategic partnerships and how that's positively impacting that growth and expansion, and of course, AI and cloud delivery. All of our organic investment is also allowing us with that free cash flow to continue to look at acquisitions as well. With respect to acquisitions, typically, what we're looking at is technology tuck-ins. So we look for tuck-ins that fit and complement naturally our CXone AI platform. So while we are primarily focused on these types of tuck-ins, we also remain open to larger acquisitions as well as long as they're meeting our strict criteria, both financially and strategically, they continue to drive us on that road map and the overall AI strategy. Finally, the expanding share buyback program, you've seen that we've increased our buyback throughout the course of this year. At the end of the third quarter, we had increased our buyback by 18% on a year-to-date basis. And we have a tremendous yield of a buyback yield of greater than 5% over the last 12 months. So we continue to prioritize in addition to the spend we're doing organically as well as for acquisitions in our buyback program. Finally, our rock-solid balance sheet is something we're highly proud of at NICE. We continue to fund all of these capabilities that I've highlighted as well as those share repurchases. We announced a $500 million buyback program earlier this year, and we're also continuing to make that a mainstay of our program. So these 3 pillars and the strength that we have in our capital allocation with great ample flexibility to fund on growing top line growth as well as drive increasing shareholder value and returns. We've built NICE both on resilience and flexibility. And we have an incredibly strong financial foundation that is going to allow us to continue to accelerate this top line growth. Our plan is to accelerate that top line growth and shareholder value by making some targeted and strategic investments in the coming year. These investments are going to be highly aligned with the growth catalyst that Scott talked about earlier today, and then I'll share a little more about to ensure we win with precision and purpose. So these are the 5 growth catalysts that Scott highlighted earlier today. What I'm planning to do is actually show you some financial metrics and data behind each of these that demonstrate the great positive momentum we're already seeing in each of these growth catalysts. So all of these 5 growth catalysts, of course, are overlaid by the incredible cross-sell and upsell motions that we have at NICE. And so we have multiple different levers that we are using to accelerate top line growth. And it's important to highlight as well that this cross-sell and upsell motion, we now have even a greater customer opportunity by also cross-selling into that Cognigy installed base as well. Growing demand for our AI is our #1 driver of our top line growth at NICE. We're seeing rapid adoption of AI across all facets of our business. And you can see we expect Cognigy to further accelerate that growth. Customers are choosing nice because they look for us to lead their AI transformations. They see the positive impact that we have on the customer experience, that we understand the customer journey. And you can see and financially quantifiable metrics and outputs like you saw from Phil earlier today, that this is real, customers can measure the positive impact in ROI that they see from our technology. So our customers are choosing our CXone platform. And this is allowing them to augment their workforce, allows them to orchestrate workflows and and of course, to automate with AI and our cognitive and AI as well as our organic NICE AI as well to automate those experiences using AI Here, I want to share a little bit of in numbers how we see the acceleration of AI and our business. So from the start of this year, we began sharing with you our AI ARR growth on a quarterly basis. You can see throughout the course of this year, our growth is getting stronger and stronger. And this is even before the acquisition of Cognigy. So in the third quarter of this year, just a few days ago, we shared that our year-over-year growth in our AI revenue grew 43% and that further increased to 49% when you add Cognigy in that picture. And we remain confident that our CX AI ARR is expected to grow more than 2x by the end of 2026 as we exit the year. We're on a great path. You can see that based on the growth. We see that in places such as our pipeline, the RFPs that are coming in. The recent bookings where Scott shared that we had AI in every single 7-figure ACV deal. So we see it in the numbers. And of course, we expect further growth acceleration from all of our AI offering beyond 2026. Earlier, Scott also mentioned that I would talk to you a little bit about the pricing model. from -- over the last several years, our pricing model has continued to evolve, and it has continued to evolve to really ensure we are monetizing the opportunities and this continued shift towards AI. So I'm showing you here a very simple look of what our pricing model is composed of. There's primarily 2 key levers. They are users on the platform and sessions, which you can consider as interactions. So we are monetizing across both of those. So regardless if you are a user, human-led on the platform or if it's sessions, the pricing model will monetize and accelerate the growth from both of those. What we've seen over the last year is the growth in the sessions is greatly outpacing the number of users. That means that the AI that's coming through and the shift to AI and automation, we see that in the volumes, which I'll share a little more with you. So as customer automation is increasing, we're seeing the positive impact of that. And you saw that in the prior slide with the increasing growth in our AI revenue that we're generating. Finally, regardless of the mix, whether a customer is using our copilot to augment their users, and drive a better user experience or if they're fully agentic and using AI for automation only, our model is designed to accommodate and drive growth in any of these levers or a combination of them as well. So here, you see more evidence that our pricing model is working is exactly as we designed it. We're seeing that the growth acceleration that Scott talked about, which has been phenomenal growth in the agent and agentless automated AI is really taking off the volume interactions that are AI-driven is really accelerating. And you can see that there is a parallel growth as well in our AI revenue. So the pricing model is working exactly as defined that as automation and AI use increases, we're seeing that direct parallel correlation into our AI revenue. So now I want to shift away from talking around the pricing model and talk more about what we're seeing from customers. You heard earlier from Nick from Lufthansa Group, but I'm going to share 2 other real customer success stories with you. The first customer success story is a Fortune 500 media and entertainment customer, and this customer started their journey with NICE back in 2023. This customer initially adopted our core CCaaS offering. So that included also digital channels. And as they stepped into using our CXone platform, they created a really strong operational foundation for them with the customer experience. But in 2024, you'll see that they came back and purchased additional AI capabilities. At that time, they purchased both our autopilot capabilities as well as knowledge management and that allowed them to automate their front-office interactions, but also guide their agents in real time. And you see that increase in revenue that we received as a result of that additional adoption during the course of 2024. But they've had a great experience. This customer continues to come back to us. And most recently, they have further that by now also deploying a lot of our CX AI models. Earlier today, Phil talked about the strength of the data as well as Jeff, that we have at NICE. For decades, we have been delivering intent-based outcomes to our customers. So we have proprietary data that allows us to drive the best resolution for our customers. So our customer and this success story came back, took on that additional CX AI models in addition. And you can see the great trajectory of growth we've seen over this period. that in 20 -- between 2024 and 2025, the AI growth in the recurring revenue for NICE was 71% year-over-year. And the growth in the overall revenue and spend at NICE increased 45% year-over-year to $9 million. So you can see that we -- it's a great growth that we're seeing in the overall AI. And what's important to highlight about this story is the transition and the transformation the customer is going through. You'll notice that the seat count initially increased in 2024, and -- but then as they adopted more of the AIs and they become further embedded in their environment, you can see that they actually successfully reduce the number of human agents in 2025. So they were able to do this. They have reduced costs. We've delivered a strong ROI. And of course, it's a win-win for us at NICE as we've seen this great accelerated recurring revenue growth. So I'm going to share with you a second customer success story as well. So this is a very similar pattern, where initially this very well-known Fortune 500 U.S. utility company came to NICE in 2023 and initially also purchased our core CXone offering. That customer very quickly came back and added more of our capabilities due to the depth and breadth of our platform. They quickly added virtual agents. They added predictive analytics, and they also added automated self-service. What you've seen in this customer is that they have been able to maintain a steady level of agents in their normal environment and in their business, they would have continued to add more and more human agents. But with the adoption of our CXone platform and our AI capabilities, they have used our AI and Agentic AI capabilities to be able to maintain that steady number of seats. And they've done that with feedback to us that they've improved their customer experience because they're getting a much faster resolution and they're much happier in terms of the interaction with the agents, which are now being guided in real time. So once again, you can see our economics worked perfectly as designed. You'll see that on the AI ARR, we've seen a 40% increase year-over-year. And in the total spend, we've seen a 26% year-over-year increase. And again, this is just in the last 12 months. And this is just the beginning for us. We're seeing many, many customers that are on this path initially adopting the core CCaaS capabilities then starting to get more and more familiar in adopting our other AI solutions that are allowing them to really drive ROI and further our ARR spend with NICE. So the other growth catalyst, the second growth pays is about the migration of the CX customers from on-prem over to the cloud. And Scott talked about that this morning that the estimate is that there are 15 million human agents on a global basis. But it's still expected that there's only about 40% penetration of those customers moving that have already moved from on-prem into the cloud. So this leaves a massive number of 9 million seats that are still available and that we will use to continue to cross-sell and upsell and bring in those new logos onto our platform over the next several years. So there's a great runway ahead that this will continue to inject growth into our cloud. And this is an area where at NICE, we shine. I've shown you some of the strength that we've had in those customer success stories. But you can see we have an increasing number of 1 million plus ARR cloud customers. We operate in the large enterprise, more and more. We are bringing on further large global brands. So this will continue to be a tremendous growth catalyst looking forward for NICE. And today, these 444 customers already represent more than 50% of our cloud revenue. The next growth catalyst. You've heard us talk about quite a few quarters over the past year. We've seen enormous success in our international business. We've won some great deals, both large deals over $100 million in TCV, but many other smaller deals where we continue to gain momentum and grab further market share. You can see this reflected in the 36% compounded growth we've seen from the third quarter of 2020 and -- and our international revenue and the contribution from the cloud is now 57% of of our total international revenue. So more and more of this business is coming in the cloud internationally. This momentum reflects the opportunity that we have as well when I shared the 9 million seats that are still yet untapped to move to the cloud, the biggest areas of opportunity there both are in the large enterprise and as well as the opportunity for us internationally. The next growth catalyst is accelerating our ecosystem. This year, we have both added many new strategic partnerships. We have also expanded a lot of key partners that we work with at NICE. You can see that 73% of our new large enterprise CX1 ACV year-to-date at NICE was led by our partners. We're seeing really great success. And through our integration with AWS sales force Snowflake and ServiceNow, we're expanding our reach. We're accelerating our time to value, and we're driving strong customer success together. This will continue to be a great growth catalyst as we continue to strengthen these partners and use this opportunity to tap into further market share across the globe. Finally, when I turn to our last growth catalyst, we want to talk about expanding beyond the contact center. First, I'll start with where we work today. And today, automating customer service is orchestrating workflows, which is really our bread and butter at NICE. And this perfectly was demonstrated by the demos that you saw earlier from Phil. So regardless of whether it's human-led or fully automated, automating customer service is the core of what we do. So fulfilling customer service intent is really the next frontier for us at NICE. We have spent years mastering interactions in the front office between our customers and consumers. And this was demonstrated in the example that you saw earlier today from Neeraj, and it shows really just a glimpse of what's possible and how we're continuing to extend the customer journey. And then finally, when we look beyond the customer service, this is the TAM that today is sitting in front of us, but we have an immense opportunity for new incremental revenue. And this new incremental revenue is not included in any of the financial models that I will share with you. So this is an opportunity that exists going beyond customer service. This is areas where you've heard Phil talk, for example, around proactive outbound capabilities and allowing customers to do more selling to their customers. So this is really just an area that is the tip of the iceberg that we haven't yet even really started to tap into. But the combination of all of these different areas provide us with incredible confidence and excitement about the future as we start here in the core of our growth where we're already seeing great indications of that accelerated growth, moving into the mid and back office where we can naturally extend the orchestration of workflows and then, of course, the further capabilities outside of the customer service arena. I talked earlier about our cross-sell capabilities. This slide really shares just the strength of what we do at NICE, but it also demonstrates the intense depth and breadth that we have in our platform, which is now even further enriched with the addition of Cognigy. You can see that our customers routinely come back and buy more and more from us at NICE. And we're seeing that our CXone customers are increasingly adopting multiple solutions. So the deepening adoption we have of all of these solutions and our advanced AI capabilities in our platform is one of the reasons that we feel highly confident about the growth path and the acceleration that we expect to see looking forward and also will provide long-term, durable growth given the stickiness of how deep we are actually embedded within those enterprise customers. So to summarize for the growth catalyst, you can see we're actually executing on multiple levers. There are multiple growth catalysts that we have in our business, and we have many ways to win. We are seeing momentum across all of those, and we will continue to fuel them, which is what I'm going to talk about a little bit more now. So with the strength of our financial foundation, the healthy free cash flow generation we have and this immense opportunity that's in front of us with the wave of CX transformation, we are extremely well positioned with our AI and our CX AI platform, we plan to make some strategic targeted investments. So here is what it looks like for 2026. In 2026, we plan to spend an incremental $160 million to seize the opportunity that's ahead of us now. You've heard about how excited we are, how confident we are that at NICE, we have the assets, we have the market leadership, and we have the financial foundation to fuel and win that opportunity. So the time is upon us, and we expect to seize it by really making these strategic targeted investments. These investments are designed to unlock that accelerated future revenue growth. So I want to walk through each of these a bit. The first is the investments around cloud and AI delivery. And the second is around R&D, where we've grouped these together, where we plan to spend about $95 million incremental spend in 2026. And then also in the go-to-market area, where we plan to spend an incremental $65 million in 2026. So let me talk a little more specifically about where that spend will be going. In 2026, under the cloud and AI delivery, we are going to continue to fuel the delivery of the cloud by optimizing compute, which is needed for the very large enterprise customers where we operate, also to expand our regional infrastructure and resource capacity. I showed earlier that we've had great growth internationally, and we're seeing continuous momentum with a market that's still largely underpenetrated. We've invested in the past couple of years for sovereign cloud. We will continue to do so that and further fuel it from here. And then finally, we will also build AI centers of excellence that will help us drive that important time to value for our customers. And this is all part of the delivery. Then when we look on the R&D front, for R&D, we purchased Cognigy. We're very excited about what agentic AI means to the inclusion of our platform as well as on a stand-alone basis, and we plan to fuel it further. So we are going to continue to invest in Cognigy as well as our AI road map for our other AI capabilities at NICE. Then finally, on the go-to-market expansion, I've talked a lot about the success we've had this year with the partners that we've either brought on board or that are new for NICE. We will continue to focus on those and use that go-to-market to further enhance and increase our global reach. And then finally, the last thing on the go-to-market is around the AI-first sales strategy. We are going to bring in tools and more focus on subject matter experts as part of our go-to-market that will also allow us to seize this opportunity. So this is all incremental revenue spend on top of what you would typically see for us. And in a few minutes, I'm going to show you how that all comes together. First, I want to share what the impact will look like on our gross margin. So in 2025, you see where we plan to exit 2025. And the expected and estimated impact in 2026 is about a 200 basis point impact on our gross margin. It's important to highlight that, once again, these investments are strategic, they're deliberate, they're intentional, but they're also time bound. So while we will make these significant investments in '26, we will also continue to invest in the course of 2027, but you'll see those margins start to recover in 2027. And then you'll see in 2028 that we're returning back to our expansion path. So this is going to shift a little way from the internal investments that we plan to make. And I want to focus here around the operational rigor at NICE. One of the areas that we pride ourselves on at NICE is the rigor that we have in driving operating leverage. Today, we are already using AI tools internally at NICE across all of the different domains that you see here. And we've deployed them in highly measurable ways. So what's important to highlight is that we are going to maintain that continued strong rigor in our business, that operational excellence that we've been known for. So while we're making those strategic investments, we are also putting in more and more AI embedded capabilities within our company to drive ongoing operating leverage long term. Next, I'm going to move to our operating expenses, where you'll see that we have increased go-to-market and R&D investment to accelerate our top line growth. First, I want to highlight, it's important to note that the percentage of revenue that we're showing here for OpEx is actually inclusive of R&D capitalization. We wanted to show you the full spend that we have on the R&D front, but that should be taken into consideration that the portion that you see on top of these bars is what we're capitalizing and putting back on the balance sheet. But this represents the full spend we have. So the plan that I talked about, where we're planning to target these deliberate investments, are showing up here. So you can see that a big area of expansion and these deliberate investments first is in our sales and marketing, where we expect our expense ratio to move from 20% to 22% in 2026 and to continue to invest, and that sales and marketing ratio will increase to 24% by 2028. For R&D, you can also see this combined spend of about 15% of our total revenue estimated for this year, and that we will continue to further fuel that over the next few years as well. So these investments are going to allow us to really seize the opportunity to make these intentional investments to be able to drive that growth momentum we see in the top line and to really capitalize on the opportunity both with Cognigy, but more broadly, the CX AI transformation that is happening. So now I want to bring everything together financially and what you should expect. We expect revenue growth to grow from high single digits, which is what we expect this year and the 7% to 8% total revenue growth that I mentioned just a few days ago as we reported our third quarter earnings. And we expect to see that growth acceleration continuing to extend first into 2026, where that range is now moving from 7% to 9% in 2026 and to further accelerate to an estimated double-digit growth in 2028 between 12% and 14% on our total revenue. This will all be driven by all of those growth catalysts that I talked to you about earlier. The cloud revenue growth is going to be driven by those cloud -- those different catalysts that I mentioned and of course, with AI being the #1 key driver of that growth. Our expectation for cloud revenue growth is a range of 12% to 13% for this year in 2025. And we just increased our cloud revenue expectation coming out of third quarter. I want to reiterate that expectation is that we have maintained an expectation of a 12% cloud revenue this year, exclusive of Cognigy and incremental growth as well coming in the Cognigy acquisition that we just recently closed. Moving into 2026, we expect Cognigy to add about 150 to 250 basis points of incremental growth in our cloud. And during that time, we have seen already the stabilization of our existing cloud revenue, where we also see many indicators of accelerated growth. With an expected outcome by 2028, we will see a 17% to 19% expectation in our cloud revenue growth. So the investments that I highlighted earlier are intentional, and we feel that we want to move now to take time during this wave of transformation to really capture this growth and fuel it here at NICE. So now beyond the top line, I also want to share with you what it means for our margins. So the margin profile with this incremental investment injection, we'll expect to see a shift from where we expect to outcome during the course of 2025 with operating margin this year of about 31%, a free cash flow margin estimated to be about 19% to 20%. So we expect that additional investment to result in a 25% to 26% operating margin during the course of 2026 and an 18% to 19% impact on the free cash flow. Again, you'll see that return to profitable growth and the operating leverage in our margin coming out in 2028. And that will happen gradually throughout the course of 2027 as we step into 2028. Earlier today, I mentioned the strength of our financial foundation and our healthy free cash flow generation. So throughout this time, while we make these incremental investments to drive acceleration in our top line, we also plan to continue to return to our shareholders through our buybacks at least 50% or greater of our annual free cash flow. So in any scenario, we are going to maintain a balanced approach where it matters, keeping a disciplined yield return profile that we expect to yield significant shareholder value as we capitalize on this opportunity and drive this accelerated growth. The cloud backlog is something that we just shared for the first time in the last quarter and a few days ago. And we've seen that the cloud backlog increased 15% year-over-year. Excluding Cognigy, that same cloud backlog grew 13% year-over-year. The expected duration on that is approximately 24 months, and that gives us really great confidence of this acceleration of our underlying business. And we're -- of course, that will be amplified further by the addition of Cognigy. AI is driving the next phase of our cloud revenue growth. This year, of the $2.2 billion cloud revenue that we expect to achieve for 2025, AI represents approximately 12% of that. We expect as with AI as our #1 growth driver that AI will expand to approximately 30% of our cloud revenue in 2028, and we expect to achieve a $1 billion AI revenue in 2028. $1 billion of AI revenue over such a short period is, of course, a major milestone, both for NICE, but frankly, the industry as well. So we're extremely excited about this path and the indicators of growth that we see that we plan to capitalize on and further fuel. So before I wrap up today, I do want to summarize and share what our value creation playbook looks like at NICE. It's composed of these 4 different areas of focus. The first is our cloud revenue growth, where we continue to be laser-focused on that acceleration. And we are perfectly positioned to win in this CX AI market opportunity. Our assets and our short-term investments that we plan to make will allow us to get to this 17% to 19% growth by 2028. The second focus area is our operating margin. So I've shown you our great track record that we have at NICE of driving great profitability, and we have the operational rigor that we will use to continue to drive that leverage in our model and deliver on this expectation with a return to margin expansion in 2027 and rising margins in '28 and beyond. The third is the health of our free cash flow. With more than $700 million in free cash flow that we generated in 2024, we run a very healthy and profitable business. We have tremendous retention rates from our customers. You can see just how sticky our customers are. As you saw how many different solutions as part of our CXone platform, our customers are using that are very embedded in their day-to-day and their mission-critical customer-facing opportunities with those consumers. For free cash flow, you can see we expect the margins to be 20% to 21% in 2028. And then finally, capital return, where I've mentioned that throughout this period on an annual basis, we expect to deliver at least 50% of our free cash flow back to our shareholders through our share buyback, where we have a $500 million share buyback program in place for us to utilize. So bringing this all together, we have the perfect playbook, the financial foundation. We are positioned to excel and win in this next era. Together with Cognigy, we couldn't be more excited here at NICE to drive forward and be the market leader in this enormous transformation that is happening in customer experience, ultimately, leading with strength, investing with purpose and delivering value. So thank you. That concludes my remarks. We are going to now break for lunch, and then we will come back for Q&A. Thank you. [Break]

Okay. So before we kick off, maybe I thought I would just say a few words because you've seen the whole morning in the presentation. You've seen, I guess, the overall vision that we have, the product capabilities, the financial outlook. So I just want to set the tone and set the scene. We are in growth mode. Now I sort of opened that up this morning. But when you're in growth mode. When you've got a market that is -- has got a growth opportunity, we are unapologetically driving long-term growth. Unapologetically driving long-term growth. The vision for this company is clear, long-term profitable growth, but the time is now. This isn't a market where I can sit there and say, oh, let's acquire Cognigy and let it run as it is and try to capitalize. We're going to double down. So a lot of the investment that you saw is on the back of seizing the market opportunity. A lot of it is related to making the acquisition of Cognigy and the AI capability live, real winning for this company over the long term. We are clearly pivoting ourselves in a growth mode of this business. And so it is about the long term. We're driving that long-term growth. When it looks at the investment and Beth has had the opportunity to present it, but it's not a long window of time where we're that additional $160 million, that is not -- we're not going to go continually in that -- going down the margin. We're going to lift the margin back up, and it's going to be scalable, profitable growth as we continue to expand. But we need to seize the opportunity now. This isn't a company where I can say, let's just try to continue at the same margins and expect the growth profile. There is a lot of companies investing in the AI market in this space. We have an opportunity to lead and dominate this market, but we need to move. So that's the reason why you saw the implications on 2026, but I hope you would have confidence that through that, we're able to then drive long-term growth because I would remind you, even without those investments, we're already back to organic growth of our core, accelerating that growth. So we're reaccelerating our core growth. Then you add the Cognigy and the AI impact, and then that growth becomes more long-term sustainable. So I wanted to highlight that because I didn't have the opportunity in the beginning. You can see the product capabilities where we want to go. You can also see what we're trying to drive in terms of that high-growth balanced business. And the last that I will say is you will see the margin profile is a very favorably comparison to most of our peers, and we will drive that margin growth as well as the top line growth in a more balanced way. So hopefully, that answers, I guess, a couple of questions that I had immediately after the session. Happy to answer any more in that regard. And I'm not sure who's controlling the...

If everyone could just raise their hands, we'll call on you and we'll get you a mic. So why don't we start with Tyler?

Maybe a multipart on the big highlight, obviously, investing for growth. So Scott, you came in earlier this year, I guess, it feels like, as you said, longer than the year. NICE, as a company, has historically been a very financially disciplined company, right? Beth showed the chart of kind of consistent expanding operating income. We've seen really good EPS, growth CAGR. Like what specifically were the biggest areas that you kind of came in and said, gosh, like we're massively underinvested here. And I guess for Beth, I mean, as I look at the free cash flow margins, last year, I think you did about 27% free cash flow margin.

26%, but close enough.

Yes. The mid-20s. Even as I look to 2028, it doesn't look like free cash flow margins are getting back to that. So just help us understand the free cash flow dynamics there, too.

So I'll answer the first one on the investment and what was missing or it's probably not about missing, but it's more about emphasis and capitalizing on the opportunity. So 3 parts. The first is clearly a native AI capability. So if you look underneath our platform, we worked with a lot of partners that we needed their AI platform embedded and underneath to be able to deliver to an AI experience. The market is pivoting. I've presented the numbers. If anywhere between 30% and 80% of the volume of interactions can be delivered in an AI native way as this context as the center of engagement, the customer engagement platform, we have to own it. We have to be at the center of it. That is a core part of what we offer. That's not something. So that was a core capability and Cognigy was the perfect mix. And I would highlight Cognigy. If you heard Nick, scale, it's a platform. You don't need forward deployed engineers. We don't use those terms because it's already built. So an analyst, you could sit on there, create agents, low-code platform is a great platform view. So AI is one. The second is international markets is not just about sovereign clouds, but it's also about feet-on-the-street partner ecosystem, our own teams of sales covering those markets. And that market is not well penetrated from a CCaaS to the cloud. So we've got to move faster and more aggressively to be able to seize on that opportunity. We were already doing it, but you don't want little increments. You got to go and both because once they've moved to their CCaaS cloud platform, it's pretty hard to change. And then the third thing that I would say is the underlying core AI platform when you combine this together in CCaaS platform, we believe it needs the opportunity to accelerate that, that Jeffrey is going to drive that native AI CX platform and the way the market is going to require that -- requires that as well. So winning the AI, including in a market where we have 0 participation that drives tremendous growth; International markets, given the size and scale of the coverage; and then last but not least is a core stack that we're able to then expand into those adjacent markets. So core platform going into the front, middle and back office, fulfilling journeys, going into sales, going into marketing, those expanded areas. I think the way I would best describe it, Tyler, is we are really driving to capitalize on the market opportunity, but I can't sit and wait. If we sit and try to hold on and then manage -- we didn't buy Cognigy to run it as it is. We bought Cognigy to be able to really double down our investment in it to continue with leadership so we can -- because the market will move, it won't wait for us. In fact, we will lead the way, and it will actually cause our competitors to have real challenges because we'll be able to dominate where we currently don't participate. Beth, do you want to answer that?

Yes, sure. Let me take the free cash flow. So as you can see, I mean, we consistently generate significant healthy amounts of free cash flow. When you look on the free cash flow we generated in 2026 relative to what we're seeing this year, first of all, I would say last year, we generated more than $700 million in free cash flow. On a year-to-date basis, over the last 12 months at the end of Q3, we had almost generated another $700 million. So continuing to generate profitable cash that we invest into the business. When you look on the free cash flow margin as a percent of revenue in last year relative to what we expect in the current year 2025, there's 2 key factors which are different year-over-year. One, of course, is Cognigy and the impact of that from the date of close through the end of the year. At the time that we announced the close of Cognigy, we mentioned that we do expect that it will be dilutive in the near term. Over the first 18 months is the estimation based on our financial models that by the time you get to around the 18-month mark from the date of close, you'll see a return to accretion from coming from Cognigy, both in EPS and cash flow, by the way. So that's one of the factors you see in the current year. The second item that you see in the free cash flow impact this year is a nonrecurring onetime item. if you look back on the second quarter, we had an issue of a tax matter that settled. That was a onetime nonrecurring item in the second quarter. Those kind of items are -- you can't anticipate the timing of the close, but that happened during the course of Q2. It was actually quite a favorable outcome. And you can see that because if you look at our effective tax rate during Q2, it wasn't changed. So we had more than adequately reserved for an expected outcome there. However, it did impact the free cash flow impact this year. So if you normalize those 2 impacts, Cognigy, together with that onetime nonrecurring item, you'd see a similar margin to what you saw last year. I think more importantly, as we look forward, we're all very excited and sitting here about the opportunity that's ahead of us. And so we want to fuel it. And so I've talked about the investments that we plan to make to seize the opportunity. And when you look at the expected free cash flow margin even during the course of this fueling where we're fueling the road map of Cognigy and our AI road map and everything we're going to do to capitalize and seize this opportunity, it's an important part that we feel is highly necessary to drive that accelerated top line growth and durable sustained growth over the long term.

Your name and your firm, please.

All right. Wonderful. Rishi Jaluria, RBC. I really appreciate all the details and greater transparency. Definitely something I think a lot of us will go back and really appreciate as we rebuild our models. Maybe 2 questions. Scott, I didn't -- obviously, I can understand why today is very CX focused, but I'd have to imagine there's probably a lot of excitement around the FCC business as well, especially if we think about the -- maybe I don't want to call it opportunity, but the risk that AI agents create more fraud, more complexity in the financial systems, which probably creates an opportunity for you, and maybe even leveraging AI within the FCC platform to make it easier for firms like ours to stop fraud and really get just more insight into what's going on. So I would love to hear a little bit more of your thought process there. And then one that would be, I think, both for Beth and Scott is just around the gross margins. Totally appreciate that you're bringing down gross margins in the near term. It makes sense. Totally get that. It seems like you have confidence in that bouncing back pretty quickly in 2028 in spite of the fact that your AI mix shift is going to go up pretty dramatically, right? And obviously, cost of inferencing might be coming down a little bit here and there. But maybe walk us through what gives you confidence in gross margins rebounding back, especially as those -- that AI mix continues to go up and up and up, and we're going to keep having more complex models that just add more drags. So maybe walk us through that.

Yes. Great questions. So you're right. We put a great emphasis on CX today. I mentioned the FCC or the Actimize as most people know it and the public safety business at the beginning. And obviously, the CX business, more than 85% of our revenues is the primary, and we are very focused on capitalizing that opportunity. Having said that, we are the market leader when it comes to financial crime and compliance. We have a great track record. And the beauty of that business, as you described, is AI from a machine learning point of view is entrenched and deep and really domain specific. It's actually very strong with -- in a highly regulated way. Businesses are clearly the bank, the financial institutions are working with us in using the generative models, but there are 2 factors that continue to be -- to play a role. I think in the usability of what we're seeing in that space is companies where you've got your investigators are using AI to be able -- the generative AI platform and more on the investigation and streamlining and shortening cases and the investigation. But explainability matters. I use -- I like the example that Nick used in the CXone going from deterministic versus probabilistic. You can't use probabilistic when you're talking about fraud. It has to be something that's explainable. So what we see is the continued investment of that platform. So it's a great growth opportunity. As you know about the Actimize business, it has not -- it's got a lot of runway in terms of the on-prem customer base and moving over to our cloud. So it's a real growth driver. So it's a great business in its own right and opportunity there. And Beth, if you.

Yes. For the gross margin, I would highlight a few things. I think, first of all, it's important to highlight that we are in the early adoption stage for many of our customers, both as you think about a lot of the very large international customers we've added over the last 12 to 18 months as well as many of the AI customers that they're in the early phases of adoption. So we've already embedded a lot of those fixed costs without giving the benefit of the ongoing ramp that we'll see in the cloud, that's going to be further accelerated that growth trajectory as we see ongoing adoption of AI, and we showed you a lot of that in the interaction volumes today. So we see the signs out there that give us the confidence that we've made these investments. But on the other side of that, you're going to see the cloud growth accelerating and driving accretion to the margin. Similarly, on AI specifically, we see that our AI solutions and Cognigy are going to be accretive to our gross margin. We've done a lot of the AI development of our AI internally over the years. So in some -- to some extent, it's some cost. Of course, we continue to invest, but a lot of the AI is our own technology, the AI proprietary Enlighten models as an example, a lot of the AI other solutions that we've built. So today, we integrate with a lot of the LLMs, but the LLMs are agnostic. So it also gives us the flexibility that if we find that we're partnering with a third-party vendor that is more expensive, then we have the option to switch. We're not wedded to any particular vendor. So I think the combination of those things, the top line where we have early days in terms of the cloud revenue contribution, together with the accretive nature of what we offer and the meaning and value that it brings to customers from these AI capabilities and more agentic AI, while they're getting a strong ROI. So they're willing to spend more with us to save significantly more on their end.

Second row, please.

Sorry for being inconveniently placed. Samad Samana from Jefferies. So just if I think about the disclosures around where AI revenue is in terms of cloud today, just back-of-the-envelope math, I apologize, I'm doing this in a notebook. It's about $260 million of AI revenue that goes to $1 billion in 2028. Can you help us maybe understand how much of that is NICE pre-Cognigy? How much of that is Cognigy given that you gave us the $85 million end mark for '26? I know, again, I'm not trying to make you do back-of-the-envelope math without a pen and paper up there, but just help us think about those building blocks. And I have one follow-up.

Yes. I would start by saying that the Cognigy contribution, we expect it to add about 150 to 200 bps -- 250 basis points of contribution to our cloud revenue each year on an annual basis. So that's the contribution from Cognigy. We broke it down this year as well in 2025. It was about a 50 bp impact positively to our cloud growth in Q3. We expect it to be about 150 bps to our Q4. That's for 2025. And then as I said, extending the 150 to 250 as you look forward to next year. So we expect it to continue to amplify our growth. And this, of course, is already on the stabilization of the cloud you've seen on the core CCaaS side.

Yes. What I would just add a couple of things, if I can. So Cognigy, it's pretty hard by the end of next year, it's going to be pretty hard to distinguish. There is no other AI platform. It is the Cognigy platform embedded on the CXone stack. So there's no separate. We're using the agentic capabilities, the conversational capabilities. The models, the Enlighten models, the CX data, all those things obviously leveraging on the CX platform, but it is one AI platform. But what I would say is if you think about where our growth to get to that $1 billion, it will be CX customers, CCaaS customers of NICE, where we cross-sell Cognigy capabilities at scale. We will win stand-alone with Cognigy as a stand-alone player where we do not get reached today. That is all incremental. And then we're able to go into the mid and the back office and do more things with our customers' AI capabilities beyond pure conversation, more use cases, more scenarios. So those 3 under the AI banner are the growth drivers. So it's an accelerating. So as you can see, our total growth rate going up to the 17%, 19% in the cloud, but the AI growth rate, obviously way more than that. But you don't really distinguish post -- once we've gone through the acquisition and the integration, and we're investing a lot of short-term impact on that margin is very much around the acquisition of Cognigy and seizing on the opportunity in front of us. But once you get through that, it is NICE's.

One follow-up. Everybody describes AI as a gold rush. So I think that NICE is showing their actions are behind it, right, whether it's the acquisition of Cognigy, now accelerating investments for next year. Just maybe help us think through though, what are the guardrails that you put around in terms of seeing. Should we accelerate even the investments you announced today based on what maybe the proof points of success are? Would you overshoot it and/or on the other side of that, what are the guardrails on the other side? So just help us think about your framework around this is the anchor point going forward.

Yes. So first and foremost, being -- it's easier to chase the puck. So if you noticed in the short-term guardrails, we presented 5 growth catalysts. 4 of those are very clearly associated to the midterm guidance that we gave. AI everywhere, automating AI creation, CX market jump balls, international and strategic partnerships. So you are clear and using those and having multi pivots, whereas I would argue 2 years ago, it was the CCaaS move that was our primary growth driver. So we've clearly put more arrows in than we can fire. The contact center beyond the contact center is really exciting. And what Neeraj presented, the opportunity for us there is immense. That is more about an engineering road map, but we're not getting ahead of ourselves around trying to chase those end-to-end scenarios because there is a big CX in customer service and automating that without needing to go too broad or too wide. So one of the guardrails is be clear and sharp in our purpose and being maniacally focused on that. So as these new things come up, we're able to then absorb. So as the market, this will continue to evolve, we focus on those CX journeys, which means those adjacencies will be very purposeful in investment and very purposeful in outcome that we expect to deliver. And then the other one is on that international expansion, as much as I'm very excited about, we're not going to go into every country in every geography where we don't need to, where it has a high cost to serve. So we're really targeted about which markets. So obviously, with Cognigy, Beth really talked about it today, but we get a tremendous uplift from the go-to-market that they have with their European presence, their relationship and their knowledge. If you go into Germany, when they think about an AI market-leading company, Cognigy is at the top of the list. It's -- and so we're able to use that in the European market and be able to expand there. Same when we go into Asia, it won't be to every single country. We're not going to 50. It will be really purposeful on the big markets. So I guess what I'm highlighting is our growth is really purposeful and targeted. Our innovation is really purposeful and targeted. And so we are delivering value that we can resonate as a market leader, as a CX market leader rather than being an AI player that can do a lot of different things. My analogy is many AI players are trying to be an inch deep and a mile wide. We are deep in the CX market. I believe you will find in the different domains, you will get leaders in each of those domains, whether it be in HR or finance or supply chain or procurement, there will be leaders that will go deep in those domains. We will be deep in the AI CX market.

Great. Elizabeth Elliott Porter from Morgan Stanley. So first question, just on the hybrid pricing model strategy. The growth in sessions outpacing users makes a lot of sense. We're seeing AI drive a ton of acceleration, more interactions. And so 2 questions. One is, are there any implications for the revenue visibility as customers shift more towards pricing per session rather than pricing per user? And then second, one of the pushbacks that we often hear from customers is as we price by session, by interaction, costs can quickly get out of control. So is there anything that you guys are doing in terms of guardrails or holding customers' hands in order to help manage the transition between users and sessions?

Yes. It's an evolving one. So first of all, we wanted to present on the pricing because it is a changing buying mix. When you do those 7-digit ACV deals, the proportion that is under the AI that is session-based or interaction-based versus seats. So customers are being very purposeful about buying where they're ultimately able -- if they're going to get efficiency, productivity, maybe even seat reduction, they're reinvesting back into the AI. Again, with Cognigy, we are able to win in both scenarios. So the perfect one is you keep or you keep on increasing seats, interaction volumes increase and you have the AI sessions or not. But we've proven and we know with customers such as the media company that even when seats reduce, i.e., so we can see that reduction, the AI sessions more than compensate because you're able to do more of those service requests, you're able to have more of that, so you're able to then drive that certain mix. And I expect that mix shift will continue where customers will initially look to use productivity and keeping their seat count similar. That's what we've seen so far. But ultimately, once automation really kicks in, the seats will reduce, but they will correspondingly increase their AI side. So now the second part of your question is about the value and maybe those -- we're able to -- one of the reasons why you use a CX-specific platform, you can go to an AI and LLM, start doing this natively yourself and realize your compute is out of control. So Phil presented and Jeff presented observability, analytics, insights about what the ROI is, what your cost to serve, but also what the return is. So we're able to give them that observability in the context of CX and so they can thoughtfully manage that without blowing out their costs. It's one of the reasons why you will come to a vendor like us. Last but not least is I will say this, we are very optimistic about value pricing models. It's early days. But because we've got the data about where you get value, and for example, on proactive, we know we've got examples with customers where they will pay a significant amount of more per session on some of the outbound scenarios because of the value drivers that it will bring. So they'll spend a lot more on those and then they get the return on it because it's increasing top line, not just reducing cost. So that gives us future opportunity to be able to use pricing power, using our knowledge about the value drivers and being able to step up to that. This market, most customers ask us for a value driver proposition, but they don't necessarily want to pay for it or price it in. They want to monitor it under a consistent pricing model, but that could evolve over time.

Let's go over here, please.

Tom Blakey from Cantor. Thank you, by the way, for a great presentation and lots of data here to digest. Maybe a multiparter, but I'd like to hear from maybe Scott, Beth and the panel. The platform consolidation examples that I think the gentleman here at the end gave was great in terms of that credit card fraud example. You're turning 3- to 5-day resolutions into minutes, but there was a lot of different software packages that had to be touched there. So along the theme of platform consolidation, is there enough investment here with this quick hit, if you will, investing in calendar '26 where margins start expanding again to maybe shift in terms of your competitive landscape more on the workflow side and those adjacent market side. I'd like to hear from everybody about are you now competing against ServiceNow and Microsoft? And is there enough investment here that Beth's outlined very articulately to compete at that level going forward?

Maybe I'll just kick us off. I think our focus initially is going to be just that CX workflow perspective. So helping customers from that point of customer engagement and automating the tasks and orchestrating across the enterprise. So it isn't about taking out those applications, it's about orchestrating on top, right? So that's how we're going to start. That's where customers want us to focus. And then we'll just see how it plays out, right? Does it mean those other systems just have less value to those customers? Do they replace them? We'll see how it works out. But we're going to be very CX driven in that regard, if that makes sense.

Yes. What I would add to it, though, and I guess this is very important because it's very purposeful from our point of view. Those systems in the mid and the back office are largely built around human usage internally. So whether it be use of cases, processes, transactions, it has a flow of activity that is driven by what a user or a department or an area does. We are very purposeful around a customer journey. Now that doesn't mean that they -- the Lufthansa example, they were really clear about what journeys, what scenarios, what use cases and what it took to fulfill those to achieve the outcome. You extrapolate that out and what the platform is able to do. We can be super sharp on building out those journeys, those AI agents on our platform that interact with those platforms. And it may mean that you don't need to raise a case or you don't need to initiate that task because it's automatically happening from our platform. I say that because it is an evolving mix. As Jeff mentioned, and you know this better than anyone with your background at Microsoft is other vendors have got their own focus around whether it be CRM or finance or billing or those different platforms, they're looking at it from the internal usage. Our focus is from a customer journey. That will distinguish. And when that all comes together, I suspect what you'll see is our focus around strategic partnerships, for example, how we're building collaboration with ServiceNow because if you are going to build in internal workflows that can be used for customer service, we'll use it. So we want to help customers capitalize on that back-office investment and being able to seize upon that, but we'll do it from a customer journey context, not from an internal workflow or ticketing context. Hopefully, that makes sense.

Let's go here.

Alex Rocha from Guggenheim. I know you started to disclose AI ARR earlier this year. But if you could just provide a little more color on what actually makes that up? I know it obviously has to do with AI solutions, but I'm just curious if there's like a list of products. Just trying to better understand how do you define AI ARR.

So AI ARR, we're very specific. It, ultimately, is a set of SKUs that are AI products either through a direct interaction or a product that is used as a facilitation of that interaction for customers. So very specific, very clear and transparent and tangible that we can then track the growth. And so as we're tracking the volume of usage, whether it be an autonomous one or it's an AI agent that orchestrates that journey or it's an AI product, for example, Copilot that is helping assist, any of those are our AI products. So it really does cover automation, orchestration and assistant augmentation that covers that portfolio.

And I would just further clarify that it is CX only, the AI that we report and it's advanced AI as well. So we have some kind of early machine learning-based AI that is in CX that we exclude from that. So it's what we characterize as more automation-based AI.

Okay. We'll take this next question from the webcast. The question is on expanding the contact center, can you do this alone? Or do you need to make any acquisitions in this space?

So sorry, what was expanding the contact center?

Beyond the contact center.

Beyond the contact center. I sort of come back to the point -- the question that was asked around where we were going to invest. We are very purposeful around seizing the market opportunity within the current portfolio. Cognigy was a big acquisition that opens a market opportunity that is immense for us, both in capturing the automation play on where NICE doesn't play, the obvious installed base opportunity in bringing that together, but then under Jeff and Phil's leadership being able to bring it under a single platform. That platform allows us to go into the market adjacency into the mid and back office and that platform allows us to go beyond customer service into sales, into marketing, into other scenarios. Having said that, the amount of innovation that's happening in this market that can be leveraged either through partnerships or through potential acquisition, Beth made the comment that we would look at primarily tuck-in -- technology tuck-in acquisitions to expand in adjacencies that would support us. So we're always open to that as long as it delivers long-term shareholder value. And if it's going to be large strategic ones, it really needs to be justified. We've got very high bar threshold about the return that we would bring. So everything that is being presented does not assume inorganic moves. It is very much around our current capability, obviously, with significant organic investment that we've talked about to be able to seize upon it.

Let's go right here, please, in the second row.

Sam Brandeis, Wedbush Securities. I have a 2-part question regarding competitive landscape. This summer, Salesforce and ServiceNow announced a $750 million joint investment into Genesis. Well, I know you guys made your own deal with ServiceNow and actually tightened with Salesforce recently. What does that joint investment signal to you? And how do you guys view your positioning versus Genesis specifically? Then Scott, I know you touched a little bit on like hyperscaler competition. How do you see those players, I guess, evolving into 2026? And as they talk about AGI, ASI, if they were to achieve that one or multiple of them, would you see -- would you view that as a risk to you guys and overall industry?

Okay. I'll let you sort of cover the second part. So on the first part on the investment. Look, I think there's a couple of things to note. And it's not just the investment that Salesforce and ServiceNow made in Genesis. I would argue the amount of the investments that are going into CX AI players and pure play that are trying to -- it is significant. It's a great market. This is a great market. I can't stress that enough. This is a growing market in its own right, even without because you've got increased demand of volume of interactions, you've got a volume of -- again, I'll just go back to Nick's presentation where he talked about, hey, we're going to -- the way consumers interact with brands, they use websites, apps, mobile, the convergence and the opportunity for us to be a primary engagement platform drive the volume, irrespective of the competitive landscape. So number one, I think those investments, and I can't speak on behalf of those 2 companies, but I think it recognizes that this is a growth potential. From a competitive landscape for us, all I can tell you is at the same time they were making those investments, they were aggressively pursuing expanded partnerships with NICE because of our market leadership, our platform. So they need our reach, our engagement platform, and they don't want to be limited. And I guess we use it. We're very happy about the competitive scenario because let's face it. We've got a native AI platform. The others don't. You're going to have to use either ServiceNow or Salesforce's AI platform in the CCaaS space. We don't have that concern. We're able to give the complete end-to-end on our stack, including integrating natively with ServiceNow and Salesforce and Amazon and the others, and we're able to do so in a proactive way. So I believe it actually strengthens from a competitive standpoint, but we keep a close eye -- close watch on that. I'll comment on the hyperscaler and then I'll quickly get. On the hyperscaler side, look, we collaborate with the hyperscalers. I mean, Amazon, we've got a great partnership. We've got hundreds of millions of pipeline being generated through the Amazon relationship because they've got a reach and a go-to-market that they can bring. Our Cognigy business actually works openly on the Microsoft -- sorry, the Azure on the GCP or on the AWS platform. So it's got a public cloud opportunity to be able to work there. What I would say is this is those organizations clearly want to be able to advance their technology on a large number of use cases and scenarios. So we're obviously aware of it, but they are -- the depth and the breadth that you need to be purpose-built in this market requires very targeted investment that goes over and above what you're sort of making publicly available. So I guess what I -- available across all of those different spaces. So we're using precision and focus to be one of our competitive moats. Speed is a choice, but also speed is a moat. If you're able to be deep and targeted and specific, then even if those platforms are or those companies are getting there, you're able to then still get competitive advantage. But do you want to talk about anything specific?

On the AI side, clearly, these models are advancing very quickly. And we see those as very complementary to what we do. Because those models need to execute on a CX contact center platform that has compliance, security, observability end-to-end. So again, massive advancements, just even OpenAI has the real-time GPT or GPT real-time model that's voice to voice, super complementary, right? So we'll drop that in where it makes sense, but that's going to run on top of that substrate that is hardened for contact centers and CX. So we keep on top of the latest AI. Phil and team are already working with the GPT real-time model to see where it makes sense to use those kinds of frontier models, and we'll just continue doing that. We'll stay up to date. We'll stay on top of the frontier, and it will be complementary to what we're doing.

Let's go to the front.

Willow Miller for Arjun Bhatia, William Blair & Company. So thinking about one of your more historical and ongoing growth drivers, migration to the cloud, can you help us understand what the ARR uplift from on-prem to cloud looks like now, especially the migration includes AI or even Cognigy at the get-go. In the past, I believe you framed the uplift as 2 to 3x.

Yes. So make sure I just understand your question, the ARR uplift that's already embedded now?

Right. So in the past, you had mentioned when a customer goes from on-prem to cloud, you could see a 2 to 3x uplift in ARR. So I'm curious to hear how that could trend now as customers think about AI with that migration.

Yes. Okay. Thank you for clarifying. Yes. So that's correct. When we look at the transition and the migration we've seen of large enterprise, as they migrate, first, if we talk about our own installed base that were prior and previously on our legacy on-prem solutions, as they've migrated over to CXone and our cloud AI platform, I would say, on average, we see about that 2 to 3x uplift of ARR for a typical customer. But we actually have quite a few customers that have given us 8, 9 and 10x uplift. And if you recall, I showed you a slide earlier today about the number of applications that the customers have deployed, the solutions that are embedded within CXone. They're all seamlessly integrated. But you could see on that slide how I showed that routinely quarter -- each and every quarter, we've seen that customers are kind of cross-selling and buy-selling. And so that just demonstrates the opportunity that we have there. And during the break, actually, someone asked me as well about some of the customer -- or 2 of the customer success examples that I shared earlier as well. They noticed that not only did we see this great expansive growth in the AI, we also saw it in both of those customer success stories on the non-AI portion. So once again, it's demonstrating that our customers are coming back. They're large enterprise. They have complex needs, and they're buying more and more of our capability. So when we first introduced CXone, it was the seamless integration that was native to the cloud of workforce engagement and analytics and CCaaS, the ACD. And so that all still exists. And many customers continue to buy that, of course, in the core capabilities we have. So then our AI capabilities, Copilot, Autopilot, Cognigy, of course, the Autopilot, all of that is now further incremental revenue that they're buying. So further deepening of their relationship with us and extending the customer lifetime.

Just to be clear, we haven't actually modeled out what is it with AI. I think it's a little early to be able to say exactly what that ratio is because what we've seen with AI, and Beth mentioned this earlier, most customers start with a number of use cases and then they build out. So you get natural expansion as more and more scenarios, then they start doing auto self-service, they do with assisted, they'll do orchestration journeys. So it's not that they implement everything all in one go, it's an incremental build-out. But we can probably relook at that and maybe we'll give further update. It's actually a great question.

Yes, it is a great question. And I mentioned it earlier, but also you should recall that our AI is still in fairly early days when we're talking about our AI ARR. So many customers are still in the infancy in terms of adoption. And so it has a natural expansion in the path. And of course, you've seen that those interaction volumes we've shared are just growing tremendously. So that will continue to feed into our AR and AI ARR revenue.

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This is Nick here for Pat Walravens from Citizens. Scott, you mentioned earlier today that you were working on decreasing the time to implementation. I was wondering if we could get kind of a baseline for, like say, timing on that right now and maybe ideally what a potential goal is in terms of implementation?

Yes. I'll say 2 things. So first of all, we have internal metrics. We haven't actually -- we don't show that, but we have increased or improved our time to deliver. I think it's maybe 30% or 40% we've already improved. I'm sorry. Importantly, though, it does really change depending on what scenario a copilot where you're doing assistance using the data on the platform and you can do -- those cases have a much shorter time frame versus maybe when you're introducing your self-service for the first time, you're putting the checks and balance the guardrails. Cognigy has a proven method and way of being able to roll out. We are very focused on the assistance that we provide for them. So the first deploy use case might take a little bit longer, but then after that, then they're rolling out and they're implementing themselves. So it does depend a little bit. There's not like one model. I guess what we are focused though is to make sure we're assisting customers to be able to do it as quickly as possible. What are the issues that we're actually doing? We've already got the prebuilt connections. We've already got the data. We've already got the knowledge. We already know what the agents do. But putting that into that platform where you build it, where you've got the same or better quality level, so you're testing to make sure that the AI agent doesn't hallucinate, delivers on the service, can manage to at minimum equal or better than what your human agents were doing, those sort of things you need to make sure that you configure and get it right upfront. Once you do, then you then go and scale the deployment. So yes, this year, we've seen a significant reduction because we did invest in the AI center of excellence, and I spoke about that. So we have seen an improvement by about 30%. But maybe I can give more information in the future -- in future earnings calls about what we're seeing there. It's definitely a focus.

And it continues to be one of the areas that we highlighted that we're going to fuel the investment as well. As you said, Scott, we already did a lot of improvement there with like around a 30% improvement, but continue to be one of the things I highlighted as part of the incremental spend looking ahead as well.

Thank you, everyone. I appreciate your time today. Those who are online, I really appreciate it. Hopefully, you got a lot of value. We're in growth mode. It's exciting times, but I look forward to answering any other questions following on from this event. Thanks, everybody.