Amdocs Limited ($DOX)

Hi, everybody, and thank you all for joining. I'm Matt Smith, Head of IR for Amdocs, and welcome to today's webinar. So today's plan is to take you on a deep dive to show you just how Amdocs is working to help Telcos accelerate their adoption of generative AI using Amdocs aOS, which is the new Agentic Operating System that we've just launched at Mobile World Congress that's purpose-built for the telco industry. The main part of today's session will include a formal presentation, including some interactive demos to help things bring to life a little bit. After that, the lines will be open for a Q&A session with our sell-side analysts. And for the broader audience, if you'd like to ask a question, you can do so via with the chat feature in your browser. Before we get going, please note that today's session is designed to be educational and insightful. We won't be focusing on the financials or the guidance. That said, some of our comments today may be forward-looking and could include certain estimates and assumptions, which are subject to risks and uncertainties as described in our filings with the SEC. And if you need more information on that, you can find it on the Amdocs Investor Relations website. So to kick things off, let me introduce a familiar face for many of you, Anthony Goonetilleke, Group President, Technology and Head of Strategy for Amdocs.

Thank you, Matt. Great to have everyone online. I just wanted to introduce the group over here. We have Pilar sitting right next to me here, who heads our Global Consulting, who will be sharing her insight around our services space and how generative AI really impacts it. And on the other side, we have Liliana, who heads all of our strategic partnerships for me. And she will share how we work with our different partners to really drive the outcomes that really help our customers accelerate. So with that, let me begin. As Matt mentioned, we will take you on a little bit of a journey through aOS. I think I've probably spoken to many of you over the last couple of months. We did have a major launch at Mobile World Congress in Barcelona that was taken really well by all of our customers with many, many follow-ups scheduled after it. We'll talk a little bit about our outcomes because at the end of the day, this is something we're very focused on. Amdocs historically has really been an outcome-driven company. If you think about what we do, it's been about production milestones. It's been about business outcomes, and it's really been about delivering the end business result as opposed to just another technology project. And so we'll share a little bit about the business results that we're starting to see trickle through some of these projects, and it's very exciting. And when it comes to me and kind of my purview, I always tell our teams like at the end of the day, this is really what we should be focused on because really, this is what will move the needle. Then as I mentioned, we'll talk about our strategic partners. Now Amdocs as a company has many partners. We partner with all over the ecosystem. These are companies we use internally within our software companies we partner with to provide different parts of the system. But there are several partners that really double down strategically. So these are partners we work with all the way from thought leadership and thinking through initial strategic ideas with their R&D teams, all the way to go-to-market to all the way to actually launching out. And we had several of these partners at Mobile World Congress. We even presented at some of their booths at Mobile World Congress. So there's really a good synergy between these strategic partners and us in terms of driving results. And finally, Matt and I will kind of host a Q&A. So keep all your questions for us, and we will set some time at the end of it to take any questions you have. Sounds good, Matt.

Sounds very good.

All right. So this -- for anyone that's new to the company, I'd just like to highlight this is just Amdocs by the numbers. We're a $4.5 billion company with a very, very strong backlog and recurring revenue. As you can see, we have a very, very high renewal rate on our managed services contracts. This really gets down to the point of our commitment to our customers. At the end of the day, this industry is not one that has 10,000 or 50,000 customers. And so we are very, very loyal to our customers. And at the end of the day, that loyalty is returned. And so we're very focused on this. And we have a very, very high success rate in terms of customer transformations. If you look at kind of what we do on a day-to-day basis, this is also very critical. A reminder to all of thousands of people that are developing software all around the world for Amdocs within my teams. And I always remind them, at the end of the day, we are a company that is responsible for developing, creating mission-critical strategic software. If we fail, basically, I don't want to kind of sound glorious, but if we fail at the end of the day, society fails, right? So if your phone doesn't work or you cannot -- data cannot be transferred and you cannot access an application or your enterprise cannot access an application, and this is a fundamental failure. And so this is not taken lightly. And if I look at some of the statistics here in terms of what we're delivering, we are touching around just under 2 billion customer journeys daily from an Amdocs perspective around the globe. If you look at our events that we're processing through our operations and managed services teams, this is over 450 billion monthly usage events. And not to mention around monetization. This is, by the way, the 170 billion are daily rating events. So these are data charging, rating, billing that go through the Amdocs systems. And these are not going away, right? So if these -- if you look at these events, if anything, they are only getting larger and more complex. I think I've mentioned to several of you, if you go back 10, 15 years and you look at my relationship with the service provider, I had one phone, and this is how I connected, one phone, one eSIM and I connected. Today, I have an Apple Watch, I have a phone, I have an iPad, I have a laptop that's connected. And now I have a little travel router that goes with me with a 5G eSIM. And so now suddenly, you have 5 -- essentially 5 eSIMs connected to my service provider. So these events and these usage events and these billing events and customer journeys, we only see this trajectory go up. There is no sign of any of these coming down. And we feel that our job in Amdocs at the end of the day, many people go, okay, so what is your 30-second pitch about what Amdocs does? And we believe our job is really to take the friction out and to make sure that the customer experience we deliver to our customers is frictionless and seamless. And we know there's a lot of complexity in the back end. There's a lot of hybrid networks. There are a lot of systems. There are a bunch of legacy systems that need to integrate. There are so many integration points. But our customers at the end of the day, don't care about this. So our job is really to abstract all of this. So with that, let's jump straight into it. When we think of the Gen AI opportunity around the globe, I think I'm not sharing anything that would be new to you. This is mandatory. We are at a fundamental place. I've been in technology for a couple of decades now. And I think -- I always try to compare like what were the 2 biggest events that really tectonically shifted technology. And I would think the introduction of the Internet and IP and really being embraced or generative AI. And I would say, if I look at generative AI, the pace of adoption far outweighs the pace of the adoption of the Internet, right? If you look at the launch of the Internet and how browsers like Netscape came in and e-commerce came in, you're talking about 10 or 15 years before it became a household name. When it comes to generative AI, we are talking about a matter of a couple of years, and there isn't a person on the planet really that doesn't have access and using it. So I think the enterprise adoption is mandatory. I don't think this is any surprise to anyone. I would say that the major shift really that we're seeing is a focus in terms of outcomes and ROI in terms of what's being invested. So you cannot continue to invest very highly in infrastructure, knowing that you have to do this, but you have to start showing outcomes. And a lot of our customers are shifting their mindset into focusing on very clear outcomes to be delivered to the business. Another thing that's very important to our C-level technology suite is really having the right architecture. Now this doesn't mean that you will not use several different companies and large language models and ecosystem vendors and things like that. But it's very, very important to know what your North Star is and know how you govern this. And last but not least, one of the most important things is really around governance and organizational readiness. We are seeing now that technology is no longer the long pole in the tent anymore. Organizational readiness privacy, when it comes to data and governance are becoming really, really critical milestones. And Pilar is sitting next to me. And although we are not talking about it today, I know you guys are spending a lot of work helping our customers kind of drive this organizational readiness and especially around data privacy and preparing the data. And maybe that might be another session we need to talk about one of these days, Matt. So on to the next slide. And so as we kind of think of this evolution, this is not like Amdocs woke up one day and said, "Oh, we're going to be a Gen AI native company." This has really been an evolution. We have been very much focused on data and automation. And if you look at our strategic pillars at Amdocs, this kind of predates the Gen AI era. And we even partnered with several companies back in 2016, '17 when we saw these models, these language models evolving and really thought about how we incorporate it in our systems. So this is really, I would say, a step function for us, but it's an evolvement in terms of where we're focused on because we were very much focused on the data. Today, you will hear a very common word that 5 years ago, people didn't know what the definition was. But today, people kind of drop it every 30 seconds. It's called ontology in the generative AI space. And Amdocs introduced something called the Amdocs Logical Data model, which was really one of the first frameworks of telecommunications ontology before anyone else did it, really connected all of the database entities, all of the business entities, your pricing, your policy, your customer entities to really give you a logical business view of how your business operates. And we've taken this and really evolved it to superpower, I would say, what we now call aOS. And so just to kind of finish off on the history a little bit, we launched amAIz back in, I think, the middle of '23. And this is really an evolution to that as we kind of formed our -- internally, we restructured ourselves as an organization because one of the things we realized was it's not just about meddling with things around the edges. You really had to reimagine what your organization looks like. So we kind of took everything we knew and we said, okay, like let's start with the blank sheet of paper and completely restructured the company, form an organization that really focused on this, and it's a combination of product and services team that have been brought together. And here's where we kind of ended up launching in March of this year, which was our aOS footprint. Now this was kind of received very well from a market perspective. Our customers kind of like what we've introduced and kind of one of my favorite pictures is down the bottom left-hand side there, you see Satya and Jensen on stage. Talking about some companies that are doing some stuff. And then the bottom right-hand corner is the logo of Amdocs. So this is a very humble but proud moment, I think, for many of our employees to kind of see our logo on stage with these 2 great visionaries of our industry. And even as you look at aOS and you look at what some of our analysts are saying, they are saying some kind of amazing things. If we just kind of jump to the next slide, Mike, Gartner kind of, I would say, summarized it in a very clear way in terms of what we were trying to do. We were really not trying to fiddle around with it at the edges. We decided in the same way that when we built CES around 2016, 2017, we said we need to draw a line in the sand and build a cloud-native suite that is ready for the cloud on day 1. This is really what we're doing in the generative AI space. So launching aOS is our first step in going towards a Gen AI native suite that can deliver those outcomes. And I think I've kind of covered most of these, I think. So let's jump to the next slide here. So I want to spend a little bit of time talking about the framework of aOS and what it really encompasses. So at the bottom, you kind of have our BSS and our OSS suite. These are still really our systems of record. These hold most of our customers' data around the world. These hold the relationship, the policies, the procedures, the customer journeys, the business flows, how you do business. And every one of our customers around the world, believe it or not, does business differently, right? They may use our same systems, but they operate and go to market very, very differently. And I'll come to the middle layer because this is kind of at the heart of it in a second. But at the top is really an opportunity that we didn't play in, but we believe that when it comes to the way the business operates and where the business functions, there is really an opportunity to use Agentic capabilities in terms of our business and networkflows. And also, we will talk about our Agentic services, which is on the right-hand side here. This is, at the end of the day, really how we superpower our services to be delivered under the Agentic Services banner to really give you outcomes faster. The last part that I want to talk a little bit about here before we jump to the next slide is one of the things we allow our customers to do is, look, we are not coming to the market or not coming to the customers and telling our customers, you know, dear ma'am or sir, you need to take everything from us. It's a one-stop shop. We understand at the end of the day that this is going to be an Agentic mesh framework. And there are going to be different companies, different large language models that our customers are going to work with. And so we are allowing our customers to build on top of our ecosystem to really use our components to supercharge and superpower what they're doing. So think about a car being put together. They don't have to reinvent the wheel. They don't have to go and reinvent the engine, right? They can take these key components and put it together. One of my favorite analogies that you guys know is I always talk about the LEGO store, right, where you go into the LEGO store and you pick a bunch of blocks. And at the end of the day, no one's asking a kid to go and build a LEGO block. We are the people delivering those LEGO blocks. But the imagination of our customers and the ingenuity of our customers are putting to these together in the different shapes and forms. So let's kind of double-click on a couple of these layers now. So the cognitive core, which really is at the heart of everything we do, this is the new layer that we've introduced. And this starts from a few different things. So it brings all of the telco context. So you think of Amdocs, think of it as we go from 0 to 100 in terms of super verticalized telco context. It's 40 years of delivering to customers, understanding the context, not just from -- we're not a company that just does a customer management system or just does a workflow. We're a company that takes an order, captures the order, handles the order, puts it in your billing system, sends it out to the warehouse so it can be provisioned. And oh, by the way, provision it on the network and don't forget to bill and charge and rate for it, and you may have to also go through a collections process. So in terms of when you come to the verticalized knowledge of what we do, we have this deep understanding and that is now all taken in and put into our cognitive core. So when we're asking it a task, it understands all of this. I like sharing a small example just to highlight some of these. You take something like proration, for example, which sounds like a very simple thing, right? You need to prorate a customer's charge. But you take proration within a complex ecosystem, there are over 250 different database entities that has an impact on proration. And if you don't know this, you are highly likely to provide information that's not accurate. And so this is really key part of the differentiation. I spoke a little bit ago about the -- let's stay on that slide for a second, Mike. I spoke a little bit about the telco ontology. This is really where I think Amdocs shines. We have been working on the ontology before it was called ontology before people threw it around like hot cakes. And this is really the Amdocs logical data model. This is really how we know how a business operates to all the technical bits and bites on the hundreds -- there are some companies, where we integrate to over 250 systems to provision an order and interfaces to provision an order. And you need to understand this. You need to know how it connects in order to provide accurate results. There are some people going, "Oh, like what is the problem? Like let's just take a bill and feed it into a large language model, and we can get it to explain it." But not what we do. We take context engineering to the next level. So we take all of this information and the information we provide into kind of the nondeterministic angles of the system is next level. And so we believe the level of accuracy we get, the level of low latency we can provide because remember, at the end of the day, we are still working with telecommunications companies. So latency is a super, super critical element that maybe can be tolerated in other industries that cannot be tolerated in our industry. So these are super important things. And then we took all of kind of the eTOM models and we looked at what are all the roles across the entire telecommunications space, and we broke it into agents and subagents and tasks. So we went right down to the granular atomic level to be able to make sure that whatever comes out tomorrow, we will have the components to be able to deliver it. And then as I go up the stack, we communicate in very different ways, right? And by the way, this is also evolving. So whether it's MCP today or agent-to-agent capabilities or whether it's more traditional SDK API connectivity, we are also all over this, meaning we do it today and we provide today as the market evolves. But as we look at open core capabilities and some of our customers looking at how to use this, we integrate these back in. So you have these native connectors that we will be able to connect to our cognitive core. And this is really where I feel like we accelerate and differentiate because we're just not trying to like put a finger and stop the water from leaking. We are kind of reimagining what the telco experience could look like. And this is really why we thought aOS is the right name for it because we're really imagining how our customers could operate in the new world. So I spoke about this. So aOS is really just the next version, I would say, of evolution of the amAIz, whereas amAIz really focused on specific customer care agents, things like that. aOS is really reimagining natively what your experience should be. So now with all of that talking, I'm going to take a break and show you a demo here of how we operate. And I'll step in here just to interrupt and highlight some things. But what we are going to see is really a demonstration of what that end consumer experience could look like. And so what you will see is a customer coming in and having a question or query, I would say, about their bill, and you will see how the agent responds. Now I will tell you that if you -- we had this discussion 12 months ago, many of our customers were looking at having a human in the middle in terms of interacting with this output coming in. But our customers are getting very comfortable of providing this input directly from an agent to the consumer. We are getting a very, very high level of 90-plus percent accuracy, which most of our customers are very happy with. So with that, let's kind of kick off the demonstration, and I'll step in here and explain a few things. [Presentation]

So what you see on the left-hand side is really an extract of what is happening behind the scenes... [Presentation]

You can just pause it there for a second. So if you notice what happened here, you have an incoming call of a customer that's probably a little bit high rate, got a bill shock and we give a very succinct, clear empathetic response in terms of what occurred. But we also use that opportunity for a potential upsell. Now we are able to do this at the speed of light because we're also looking at its entire history, its entire customer journey. We understand that the person is traveling. We understand that this could be a -- it's the right, I would say, the magic moment to interact with the customer journey. [Presentation]

If I can just explain for a few minutes. So here, a few very interesting things happen in the back end. You are looking at the company's policies and procedures. You're looking at a digital twin, and we're just zooming into the back-end systems here. You're looking at a digital twin persona of the customer to look at the propensity of the likelihood for them to accept something or not accept something and you're basically offering them a discount or a voucher based on the policies and procedures of the company, of the mobile plan and of the customer. [Presentation]

So this is the automatic orchestration of it. [Presentation]

And so none of this is guesswork, right? All of this is going down to the system of records, going down to your SOP, Standard Operating Procedures, looking at it, there's a governance structure in place and the cognitive core provides all of these capabilities essentially back to the user. So I think you kind of get the flow and how that works. And maybe let's go back in the essence of time, go back to the presentation. And so I think summarizing this, if you look at the time to value and you look at how quickly this can be done, this would generally be just to get the first response from someone you're speaking to on the phone, it's essentially a 12- to 14-minute call calling a call center. We can deliver these results within 45 seconds. It's very specific. It's a context of one to the customer. And our customers can integrate to the cognitive core at any level that they want, for example. So I've spoken about CES. We continue, obviously, to invest in CES because this is super important in terms of delivering -- running your business at the end of the day, right? This is the engine that continues to run your business. So that is not going away. Now the lines may change in terms of what is deterministic, what is nondeterministic. But in terms of making sure that you have a catalog with your offerings, making sure that your sales process runs the way it's meant to run, making sure that your customer care journeys are defined. Monetization, and you can probably just go to the next slide, Mike. Making sure that these key fundamental functions are delivered, delivered within the policies and parameters and making sure that your business can launch new offers and new products and taking the friction out and how that's delivered is still a core part of CES. So still, this becomes a cornerstone of what your generative AI capabilities are going to deliver at the end of the day. So now we're going to jump -- actually, one more before we jump into services. The Agentic business and network flows is an interesting area for us. This is not an area we've traditionally played in. So this is really an opportunity for us, and we can just go to the next slide, Mike. So these are areas that our customers have where they run operations. And there's a huge labor kind of arbitrage there, right? There are a lot of companies that there's tens of thousands of people that do these functions. We believe that the technology available and some of our Agentic capabilities that we essentially provide directly to consumers can really apply in expediting this, accelerating the outcomes and even providing better outcomes in areas such as order to activation, billing operations. So this is really, I would say, a new opportunity for the company. And in the same vein, if we do one more click and look at kind of some of these network workflows, many of our customers are talking about how do you go towards a dark knock. So we are very focused on -- although we're not talking a lot about it today, we'll show you a demo at the end of some of this. But think about service assurance. Think about having a closed-loop system, where a fault is being found, it's being addressed automatically and a customer is being notified. This is really the future of tomorrow that generative AI can deliver. And again, really taking a lot of this labor arbitrage out of the equation. And so next, we are going -- I'm going to hand it over to Pilar to talk about our Agentic services and really how this is being superpowered by aOS.

Thank you, Anthony. So I'll take some time to ground us in what we mean by Agentic services. And basically, there are service domains that span across IT operations, data and AI, cloud, experience design and quality engineering. And these are activities that are transformed into intelligent, automated and orchestrated workflows that go down to the very, very level of the system of record, which is the unique thing, I think, here. Each domain represents a concrete set of capabilities that we -- where we move from traditional manually effort-driven work towards government enterprise-grade workflows. And what we can see, and if we can move to the next one, I'll take you to how they operate in practice. So here, first off, this is a significant paradigm change for the industry. This is an evolution of what we have done. Amdocs has always been very outcome-based, and we've seen that. This is us taking the services that we have done all along, and we are codifying that in a services and software paradigm, which is IP-based and defensible. We look at these domains and basically, we see how the core components of the complex services. And we do a lot of that. We make it seem simple. Like do you expect infrastructure to work, do you expect your phone to work? And when it's not there, it's disappointing, you don't want to see the complexity. This is taking it to the next level. So what was one's implicit expertise that lives in people's heads with 20, 40 years of experience serving our customers are now being codified in the agents and become orchestrated intelligence. Now that unlocks real value for the customers. Why? Because if you talk to almost every enterprise customer nowadays, legacy modernization is a key thing. They know they are keen to take advantage of Gen AI, but they know in order to get there, legacy modernization is part of what needs to happen. And that's why I think Agentic Services are so exciting because they live in the lane where velocity happens with Agentic adoption. We have structured processes. We have structured data. We know that is where you get ROI. And this is, for us, part of where our differentiation lies. We get to strategic outcomes faster because we have invested over the years, even before AI in building the lanes that will get us there. And we're very committed to doing that in a way that's delivered at the pace of our customer readiness. Anthony mentioned customers are increasingly comfortable putting agent, care agents talking directly to customers without human in the loop. That's not necessarily the case in some of the other services, and we are committed to doing this at the pace of their readiness. Ultimately, what we have seen with Gen AI is that as people get their organizations ready for it, they need to do it at a pace that makes sense for them. And that will be varied according to different customers, different appetites, different regimes. We're a global company. There will be very different data regimes, very different regulatory regimes. And we want to make sure that customers understand that we are there to basically give them as much or as little human in the loop as they need based on their current situation and their confidence and trust in the technology, and they will start putting workloads that they feel comfortable with and move on with that. So if we go to the next slide, you'll see exactly how this works in the demo. But given how important legacy modernization is, I thought I would take you to intelligent app modernization as an example. And these slides basically takes you through the 6 phases that every customer going through intelligent app modernization would go to. What you see, I think, and what we have seen over the years in this type of services, they're heavily constrained by the number of things. Typically and especially in some areas like mainframe, heavily constrained by the talent pool. So very often, as a program leader when I was doing...

And an aging workforce.

Yes. And an agent work for -- aging workforce, and that's aging. And that's been the case, I think, since I've been in technology. So like we had COBOL developers in our back list many years ago. I know, and I'm not the only program director that has done this, I have shaped mainframe modernizations based or migrations based on the available talent. That was my key constraint. And now this is changing, and it's changing in very exciting ways because it's opening up it's opening up opportunities that we didn't have before. So what I'm going to take you through is, I think if we go through the journey, what you're going to see in the next slide, please, is how the collaboration between humans and agents can happen. So what we show here, and I'm going to use the example of workload discovery because I think it's the best one. We have systems of agents. This is unique in 2 senses. The first one is that these are not generic bots. They are not accelerators that are put forward by a platform. They are agents that benefit from both our collaboration in R&D with our partners, which is very deep, and the expertise, the human expertise that has all of that implicit knowledge and all of that explicit data knowledge and structure at the system of record level. And that's all baked in and encoded into our agents, which actually operate as a system and in an integrated fashion, going from task to subagent to agent in a coordinated way. What this allows us to do? Is do what I think is the ideal situation, the best of both worlds. Agents are able to do volume at industrial scale. They can do that consistently, like no human can read like lines of code the way agents can do, and they can do that in parallel. Humans are able to do what we do really well, which is understand the changing context, the parameters of the organization, the risk appetite. We live in very interesting geopolitical times. So your modernization planning might make sense based on the readings of the past, but that's where the human comes in and goes, well, actually, this has just happened. Therefore, we need to refactor the modernization recommendation and that allows you to spend time doing scenario planning, for instance, which was costly and expensive to do before. I always tell people that workload discovery is where migrations slow down. You start and very quickly, you find that you're not going to go to the level of depth that you would like to do if you do it manually. This is actually derisking that for our clients, and it will allow us to focus on where the energy is. So I'll take you to the demo quickly. I'm going to save myself some words. I'll take Anthony's example. What I want you to focus on here is 3 things, I think, that are super important for the Agentic services. The first one is that -- and I'm taking you to the example of intelligent app modernization because I want you to see the depth and the thoroughness of the agents. We're going to focus on 3 areas here. The first one is the business outcomes part of it. This is preconfigurable. And for this type of services, it matters a lot because each of our customers is different. If your app modernization target is to do a data center exit, that's time, time is going to be more important to you than maybe other considerations. In this example, it's going to be time to market, but it could be any of the business considerations that our customers have, and that's how intent gets transformed into outcomes, and it's very important for us to track. Then I'm going to call attention to the left-hand side, where you have the level of depth of the agents and how they navigate all the way down to the system of record, which is very important. And finally, this is the difference from what Anthony showed you before. Anthony was showing us the outcome as it as the end consumer sees that. This is very different. This is the end user experience is the enterprise user view. So that pain there is where humans and agents collaborate, where you will have -- or you can see how we will get you get reports, you get artifacts from the agent. but then the human will evaluate, stress, assess and then decide, okay, yes, go for it.

Let's have a look at it. [Presentation]

Okay. So let's stop here because this is the important moment. This is the human in the loop moment and that's where the appetite of clients is going to vary. You would spend time here interrogating the output that you get and having the organizational conversation. And I expect that there's going to be a range of how much or how little our clients need there, and we're seeing that already in engagement, and we're happy to accommodate their pace and their comfort because ultimately, outcomes are built on trust. And in this type of work, trust is built with the human. So if we can move on, I think you're going to see... [Presentation]

Mike, if we can stop there because I think we have seen the gist of it. What I find is incredibly exciting is how tightly integrated all of the different agents are and that they are very specific to the industry. They really understand situations. If you're doing a migration when a user is trying to buy an iPhone, well, revise that. And this ties back to the policy code. We were talking about earlier, which I think really matters to get to outcomes fast in the industry. And I'll give that back to you.

Okay. Thank you very much. It's exciting. So we're going to very quickly -- we're going to rush through some of these slides just given the timing, but we want to talk a little bit about the outcome here. Let's go to the next slide, Mike. So we always -- everyone knows about the traditional bots, then we had kind of the, I would say, the AI-enhanced era. And now we are really at the Agentic era where really it's a context of one, where you're having a digital twin that knows the propensity that someone will take an offer or not take an offer or how they will react. You intuitively look at the internation on how they communicate and you react your response or change your response based on that. And that is really where the area that we're in. And with that, I want to share some of the outcomes in terms of what we're seeing. So you see things like this is one of our customers that deployed it via WhatsApp channel, 135% NPS improvement. The CSAT increase, something we didn't think faster payments. Why faster payments? Because they understand the issue better. They don't have any complaints, so they pay it and move on with their life, right? 40% reduced waste times. And these are really some of the outcome metrics that we love to look at and love to track. If we jump to the next slide, this is one of our Tier 1 North American providers. When you look at call centers, at the end of the day, there are 3 primary metrics everyone always measures, AHT, SCR, NNPS, average handling time, first call resolution and was the customer happy. And to see all of these 3 things increase with the customer is being in the industry for so long, this is something that's pretty cool. But I also don't want to forget that last point at the end here. From the time we started to the time we end, we ended up with 60% less tokens as we tune it. We understand that it can't just be cost rising just to get a good result. We also have to work on the overall cost structure of what we're delivering. Let's jump to the next slide. And so we've spoken to many of you guys about different POCs, but we have many customers now using our data and kind of AI capabilities in different shapes or forms. And we think we're delivering something that either our customers are looking at or already trying to build or needing to build desperately. So really, it's a place where we feel that we're meeting them. So now I want to introduce Liliana here to talk about our strategic partners, which really is a core part of our strategy because we believe these 4 partners really accelerate everything we're doing, and we work with them very deeply. So Liliana.

So thank you, Anthony. So I'm very excited to be here to talk about those 4 strategic partners. Amdocs has a wide variety, as Anthony mentioned earlier, of ecosystem of partners. But right now, we're going to focus on those big 4, AWS, Microsoft, Google and NVIDIA. So what is so special about this partnership, the strategic partnership that Amdocs is having with them? First, we are sharing vision. So combining Amdocs deep telco expertise and offering that actually we are managing it for, what, 4 decades right now, combine it with the fastest technology that those amazing companies provide us -- we want to lead the telecom industry into -- together into the Agentic era by offering the different unique cross-domain solutions that were presented throughout aOS and the different components of aOS. And why those strategic importance are coming to us with those partners. So as we said, it's the deep knowledge, but our partnership is very intimate and very multidimensional. So with those 4 partners, we're going deep as R&D to R&D. We're developing engineering to engineering solutions. We have a shared go-to-market. We have a shared sales campaign, share offering. And that actually opens up the market share that we have together. We co-innovate more and better together with all the different amazing things that Anthony presented here today as well as we are using them internally. So even Amdocs is transforming and becoming more agenting using some of those AI transformation with our products and our services. And which are the domains we're focusing. So obviously, those companies are broad. And we are trying to focus on domains that might be relevant for the telecommunication industry. So when we're looking at the different domains that when this 1 plus 1, 1 plus 4 here becomes 11 is how we are extending this aOS suite into more Agentic power customer experience into Agentic like we discussed right now, the services, right? We are running together into modernization, different levels of modernization, second and so on and so forth. So now let's see how we're translating those partnerships into live examples of where they're embedded within aOS value proposition. So I'll just share a few examples. So one of them, and we've seen a demo just before, is how we're embedding our cognitive core within the telco contact center that right now, we see a lot of dominant solutions such as Gemini Enterprise for CX for customer experience as well as AWS, the contact center. It's all integrating with Amdocs telco-specific knowledge that provides, as Anthony described before, way more accurate and trustful results, right, using the amazing tools that we are providing with our partners. If we're looking into another example, maybe it's a flagship of an example that shows how deep we are going in our partnership. And this one goes with what we call customer engagement platform. It's a product-to-product integration between Microsoft Dynamics 365 that has Agentic solution for the entire customer experience from marketing to sales to customer care, to CRM, to case management, all of that is actually pre-integrated with Amdocs BSS suite, mainly around the care and commerce as well as CPQ. And those solutions provide end-to-end new customer experience with the agent, and Agentic solution inside to provide new customer experience through B2B and B2C offerings. If I -- we talked a lot about the Agentic services. Maybe just to point out, obviously, what we have heard today is strongly embedded within additional tools that we use, like, for example, the Amazon Transform, right, we use it and embedded when we are actually creating our own tools. Same goes with Microsoft with Fabric IQ and so many more of the latest tools embedding within our different services. I'll touch quickly on 2 more examples of that, and then we can actually see this demo later on around autonomous network, right? As Anthony mentioned, we are partnering here with NVIDIA doing digital twin of the network. We're actually mapping that through the RAN network and trying to predict some faults and actually to prevent them. We can simulate that. We can test that -- we can deploy it ahead of time before it goes into production. So all this preventive care and service assurance and network assurance can be done with our partners here. And the last point here on the shared offering is around Sovereign AI. We are partnering obviously with AWS. They have a very robust Sovereign AI. We are the service arm and sometimes the monetization arm for them as well. As well as AI factory, we see that as one of the most important use cases right now is how we are actually servicing our telco customers with their potential enterprise customer, who wants to use the AI infrastructure. So that's where we are bringing all the stories together. Now I'll finish this one by one sentence here. Amdocs is doing it internally as well. So we are transforming -- Amdocs itself is transforming to become more a frontier firm. And we're doing that hand-in-hand with a very strong collaboration with Microsoft. So Microsoft has this vision of very advanced companies that can transform themselves through the Agentic phases and Amdocs going through this phase with Microsoft, and we are looking and embedding more AI and automation through copilots, through different tools we have out there through our different business units within Amdocs, like sales, like finance, IT and so on and so forth. So now I'll just give a glance of a few real examples coming from our customers. So one case study we see here is Vodafone. So Anthony mentioned how deeply we understand the data and data structure this in the telecommunication industry. So combining Vodafone request to actually migrate a very complex enterprise data warehouse into Google Cloud, Amdocs is the one who actually performed this work and moved everything into the Google BigQuery and that resulted in much faster time to see insight. It allows them to have AI native data foundation that can drive way more use cases. And obviously, it reduced some of the cost of running those data platforms. I'll give another example, and it's similar to some of the examples Anthony shared before, and it's an APAC Tier 1 customer that we implemented with CP that I presented before. It's the customer engagement platform that, as I said, it's Dynamics 365 of Microsoft and Amdocs Commerce and Care combined together. Here in this case, we modernized the customer service and unified all the customer channels into one platform. And it's amazing to see like what we've seen with Anthony presenting some results with another customer and this customer we are improving drastically one of the, I think, a highly ROI outcome is the customer experience. And here, in this case, the case management was improved as well as the NPS that we've seen with the other customers and first call resolution. And overall, those agents, over 4,000 agents could provide services to their large customer base. And then there is another example. Anthony, you want to share with us?

Yes, sure. I think we're actually going to show a demonstration here in terms of -- and Matt, we might actually not show the entire demonstration. I might actually just talk about it given the time because I want to leave it.

Absolutely. We can put this on the website as well, so.

But the demonstration we were going to show was actually our partnership with NVIDIA and Omniverse and how we basically created an entire digital twin of the network and allow autonomous healing, problem detection by using a digital twin. And in the same way, we think this capability is relevant not just to create a digital twin of a customer, but to create a digital twin of towers, to create a digital twin of the network. And really, it's very pervasive in terms of how we can use it. But with that, Matt, maybe we'll just kind of pause for a second and open it up for any questions that people have.

Absolutely. Jill, do you have any analysts on the line?

[Operator Instructions] Our first question comes from Tal Liani with Bank of America.

You actually have Tomer Zilberman on for Tal. Anthony, maybe 2 for you. I wanted to ask, first, you talked about amAIz to amAIz platform earlier. Is this aOS, is it an extension of the amAIz platform? Or is it a replacement of the amAIz platform? And the follow-up to that is, can you give us any commentary around what the traction was or is for amAIz and how aOS changes the adoption rate in terms of Agentic because you had some earlier commentary around customer readiness for Agentic. So does that change the migration path? Does that speed things up? Does that slow it down given where your customers are in terms of being ready for Agentic?

Yes, that's a great question. So think of amAIz as kind of our version 1 entree into this. And what it provided was really Agentic capabilities. Think of aOS and the cognitive core as a complete reimagination. Yes, it incorporated every single thing we learned from amAIz into it. But it basically allows us -- to the second part of your question, it allows us to meet our customers wherever they are at. So if amAIz kind of provided a certain functionality to a certain set of customers, aOS now allows us to help our customers build out their Agentic fabric to help accelerate their transformation, to help in a mainframe modernization. So it gives us, as a company, a lot of different, I would say, handshake points or insertion points with our customers to be able to provide solutions at different places. This is one element. The second element is really just bringing it all together rather than having bits and pieces, so our customers can pick and choose how they integrate with us. So we feel that we've been thinking about it, obviously, for probably, I would say, the last 18 months or so. And aOS was a framework to allow our customers to run faster to integrate at wherever they are in terms of their customer journey and their build-out of generative AI architecture. And the third thing was really to focus on business outcomes. I mean we doubled down on this like we probably never had before as a company and said, you're not building something unless you're going to really deliver a business outcome at the end of the day.

Got it. Maybe as a follow-up, just to that last point, are you thinking about any changes in your pricing model for amAIz versus AOS?

Yes, it's a great question. Look, we are testing the waters. We're thinking about it. As you can imagine, I don't think anyone in the industry has reached any level of equilibrium in terms of what monetization looks like. I think everyone is going, well, how do we charge for it? Is it tokenized? Is it not? But I'll tell you one part about it, some of the savings that these things can be delivered are pretty big. So definitely, we're thinking about what are the different monetization models that can be out there that can basically help us. But we're in the middle of that process at the moment.

[Operator Instructions] Our next question comes from Timothy Horan with Oppenheimer.

So Anthony, obviously, the AI adoption by the carriers can massively improve productivity and quality. Where do you think -- and I would assume they're under a lot of pressure to do so or will be competitively. When do you think adoption really starts to accelerate? And what's kind of the main barrier to adoption?

Yes. Tim, it's a great question. Look, I think in terms of adoption as a technology, everyone is there, right? Like I don't think -- I've met hundreds of customers at Mobile World Congress from around the globe. I don't think there is one customer that isn't doing anything with it or trying to accelerate their customer journey. I think where we are right now is really saying, okay, so we've got this in place. How do we focus on the outcome that can be delivered. That's really, I would say, where the focus is right now. And the second part, I would say, is trying to figure out what that blueprint looks like because you need some resemblance of a blueprint. If not, it just becomes a Wild, Wild West, right? Because everyone is like, yes, I'm doing AI, I'm doing AI. And so you can't add 1,000 different vendors doing 7,000 different things into the equation. So it's about determining who your domain partners are that you want to partner and ones that would really commit to delivering an outcome on this, I would say. You want to add anything on that...

No.

And just on the digital twin, I think you're implying that we're going to have digital twins for every piece of their business, not just the network, which would seem to be a game changer to have a digital twin for the network, but also for the entire IT stack and maybe even customer databases and on and on. I mean, so where are you with implementing digital twins and where are the first places which you see it?

Yes. So I'm a huge fan of the NVIDIA, Omniverse framework and architecture because I think really that unlocks so many different things. We are focusing on a few different spaces where we are starting to get a lot of traction with customers. So definitely in the network space on specific segments. So when it comes to network rollout, when it comes to service assurance in terms of problem solving, where you can kind of do spectrum optimization, where you can do rerouting of networks. I think digital twins can play a very, very big part there. In terms of problem resolution. The second part, which I think like probably got more interest than I thought it would have gotten was when we presented our view of the digital twin persona of a customer. So we trained -- basically, we take all of this customer information and data and propensity to do XYZ and put it into a certain persona. So when you're dealing with a customer, you don't have to guess. You're already going into it going, hey, like I have this marketing offer. There's a 75% propensity that a customer with this persona will actually lead to a conversion in a sale, right? So that part is very, very interesting. And as you can imagine our telcos sit on a lot of proprietary data, and they can use it internally, however they want to sell their own products, right? So there's no privacy issue or anything there. But I think building those personas and using those personas, I think, is a little bit of a game changer versus, "hey, here's a technology, let me hope that this technology delivers the business outcome that I want." So we're pretty excited about this, and we're really starting to incorporate it in many areas of the business.

Our next question comes from Shlomo Rosenbaum with Stifel.

Would you say that the aOS is geared more towards small and midsized clients, larger clients, who is most likely to be adopting this? Would it be the ones that need more help getting usage and implementing Gen AI? Or is it going to be the larger customers that are already more sophisticated over there?

Thank you, Shlomo. I love the question because that's something I forgot to mention. So one of the fundamental principles of aOS is that we have different customers at different stages. So if you take our Tier 0 or Tier 1 customers, they are already way down the journey, right? So they have an ecosystem, either they're building it internally or not. And so with aOS, like we had a meeting this morning with a very -- one of our large strategic customers, and I was in the meeting. And one of the discussion points we had was where they can integrate. So they wanted to integrate at a couple of levels down to compare to what maybe a Tier 3 or Tier 4 customer would discuss with us, right? So we had some smaller customers going, "Hey, that's fantastic. Let's take your entire agent, put the entire aOS infrastructure in and run with it, whereas some of the bigger ones may want to integrate at different points. But at the end of the day, we want to like kind of like that no customer left behind comment, right? Like we want to make sure that aOS can integrate at different places. And that's why by design, we also made it very open. So if you think of the different layers that it comes with, it comes with the fully fledged agents. It goes to the super agents. It goes to the subagents, it goes to the tasks, it goes to MCP integration, agent-to-agent integration. And all of these are very, very different integration points that different tiers of customers will come in, and they are, by the way, we see it even right now. By the way, one of the challenges that we try to overcome with this is that no 2 customers have the same journey or the same experience or even the same destination that they want to get with, with generative AI. So it's not like there's a cookie-cutter shape, and this is really what aOS is also trying to address. So thank you for the question, by the way.

And then so do you envision this pulling along modernization ahead of clients going ahead and implementing aOS? In other words, when I think about the impact on the company, are you going to go ahead and say, "Hey, we've dangled this in front of you, and therefore, we have to do XYZ amount of work for the next 2 years, and therefore, you're going to have this?" Or is this something where you're going to start out earlier on with the clients? Just maybe give us a little bit of color on that.

Yes, sure. So at a macro level, you can think of software really broken into 2 parts, right? So Part A is what we call nondeterministic. This is essentially 100% the generative AI space, right? And there's the deterministics, which are the system of record, which is the standard operating procedures, the catalogs, the policies, the pricing, the billing, things like that. That -- the deterministic part, no matter what anyone says, is not going away tomorrow. Now the lines may change, the lines may be blurred. But at the end of the day, you still need to collect an order, deliver an order, provision an order, operate, bill, collections, do all of those fundamental core mission-critical pieces, while delivering on all of these nondeterministic promises. So yes, we believe that some components will be pulled in, right? So I'll give you an example with one of our customers where they're taking AOS and they're like, well, in order to get the maximum out of generative AI, we also need a modern enterprise catalog, right? So they will go down the journey of modernizing the enterprise catalog because what they have now, for example, it's an older version of a catalog, which takes 3 days to update a product, right? And so you can't really do some of the capabilities like digital twins and propensity to buy, sell, which are all real-time events. You cannot necessarily do that with legacy systems. So you have this ongoing argument, right? I mean, when we look at a macro perspective, do enterprise systems stay? Do they get replaced, things like that. And we believe that there is a world where, yes, there will be deterministic components and nondeterministic. And in our world, we have to balance these 2. And yes, I believe one will also pull the other.

I just had one last question, and I'll hand it off to someone else. Just in terms of the example that was brought with the customer service that was being shown earlier, the Bill Explainer came out a while ago. And maybe you could talk about the adoption on that and whether that would be a good indication as to the adoption on aOS? Or is that not something that I should be looking at in terms of how one is indicative of clients' propensity to adopt?

Yes. So the Bill Explainer and the customer care was really the, I would say, the cornerstone use case that came out of amAIz, right? Now obviously, that's expanded, and we are now cross-selling and upselling and doing all sorts of things. But you're right. I mean that's essentially where it started from. And we've gotten very, very good kind of customer traction and adoption and where customers even want to integrate at different levels of the tasks or subagents to help them. So for example, they might say -- actually, we do have a care Gen AI agent, but we really like the level of accuracy you guys give in A, B, C, D domain, can you incorporate it and integrate it. So that is happening at the moment. When we look at aOS, that then expands it to, #1, different domains; #2, different insertion points and integration points. And #3, kind of what you mentioned before, it kind of pulls in some of the capabilities of the underlying system. So it also creates an opportunity for modernization on some of those components. Like billing, right? I mean, think of monetization -- we have some launches that happened lately about very, very different monetization mechanisms, even as kind of 5G stand-alone and 5G advanced starts to evolve, people are looking at very, very different ways to monetize it. Now these capabilities still need to be built into the enterprise systems in order to be exposed. So yes, that's kind of, I would say, the evolution from amAIz to aOS. aOS just kind of expands that and gives you more depth.

I'm not showing any further questions on the phone line.

Okay. I think we're well over time. This has been a fascinating discussion. I want to thank our presenters for today, did a wonderful job, Anthony, Pilar and Liliana. Awesome. And if you have any questions for us, and we didn't get a chance to address your question on today's call, by all means call out to us here in the Investor Relations department. We'll be pleased to get back to you. And with that, thank you for your time and your interest, and we'll wrap it up.

Thank you very much.

Thank you.

Thank you.

Ladies and gentlemen, this does conclude today's presentation. We thank you for your participation. You may now disconnect, and have a wonderful day.