Amdocs Limited (DOX) Earnings Call Transcript & Summary

Thanks, Bella. Hi, everyone. I'm Matt Smith, Head of Investor Relations for Amdocs. And welcome to today's webinar, the aim of which is to recap and go behind some of the buzz coming out of the Mobile World Congress, which took place in Barcelona last week. If we can get going. Just a reminder that today's discussion will include some forward-looking statements, which are subject to the risks and uncertainties outlined in the SEC -- in our SEC filings. We may also reference some financial measures that are non-GAAP in nature. And you can find the reconciliation of those measures to the most comparable GAAP provisions located on the Amdocs IR website and also in our 6-K filings with the SEC. So with that, let me introduce our host for today. I'd like to welcome Tal Liani, who's data networking and cybersecurity analyst at Bank of America Equity Research. And Tal is going to be joined by Anthony Goonetilleke, who is the Group President of Technology and Head of Strategy for Amdocs. Tal and Anthony are both freshly back from a long week at Mobile World Congress, and they have a lot of great insights to share. So with that, I'm very happy to hand everything over to Tal. And Tal, the floor is yours.

Hey. Thanks, Matt. Thanks, Anthony, for doing this.

Thank you.

Yes, Anthony, it was a great, great week last week, a lot of vibe, like the vibe was great. And you've been to many Mobile World Congress shows over the years. What was new this time? What was new and interesting? And how can you describe it? What do you describe as attendance, vibe, level of excitement? Where is the focus this year versus previous years?

Yes. Look, I think we -- Mobile World Congress is definitely back. I think the vibe was good, as you said. I think it was pretty much crowded everywhere. My litmus test is all the restaurants in Spain, they seem to be pretty booked every night. But look, I think it was very, very hard to go past any stall, any booth, any floor without talking about AI. I think I'd call it a sprinkle of AI, everything, everyone, I mean, this was the hot topic. And there's a lot of excitement, there's a lot of buzz around the world. So I think rightly so. Of course, you have your main themes like connectivity, which still is pervasive at a conference like this because that's what everyone is there to talk about at the end of the day. Interestingly, we're starting to talk about, I think I had an interview with a magazine and they wanted to know about 6G and what we're thinking about 6G. So we're thinking about connectivity, but maybe a little bit differently this time. I think we're -- I call it ubiquitous connectivity, meaning like, hey, like I don't care what it is. Just connect me, just give me the best speed, whether it be LTE, 5G, 6G, fiber, whatever it is, just give me the best connectivity. So I think connectivity was still pervasive. I think there were some very interesting themes maybe kind of on the peripheral, which I thought was very important as well. Security continues to be very core focus. So when you think about things like how do you secure your data, not just as you're transferring it but also in memory on CPUs. So there were a lot of discussions about that. I know there was some kind of legislative discussions in the last couple of days where even the government was thinking about how do you secure data in memory and things like that. So I think as, you know, communications industry, I think a lot of people are very focused on this because you're held to a much higher standard, I would say. And of course monetization, at the end of the day, all the vendors there, all of the companies, the service providers were about monetization. So how do you continue to monetize the services? How do you continue to monetize connectivity? There's a lot of CapEx investment that has gone into fiber rollout, that has gone into 5G. How do you monetize these? So of course, there were discussions such as network exposure and things like that. But I think predominantly, if I had to say one thing, I think it was mainly around AI and generative AI. And that would, I would say, it was the big theme.

Got it. And specific to Amdocs, what are the key themes? What's the message that you had to investors and customers? And what announcements did you make that you think is worth highlighting?

Yes. I think a few things. Clearly, all of our customers, every meeting, and we had probably 1,000-plus meetings with various customers. And it's all a great opportunity for us because we get to meet with all of the C level of all of our customers from around the globe. So at least for the Amdocs management team, it's a really nice place to get to meet and interact with our customers all in one place without having to put thousands of miles or frequent flyer points. But the main theme again seem to be how do I use AI, how do I use generative AI to generate business results? How do I drive results out of it? Now, I mean, to be fair, I would say the majority of discussions that we have and the majority of thinking was around efficacy of costs, right, cost leadership, operations, how do you drive costs down, how do you do things better. But there are, I would say, kind of side benefits for this because a lot of the ways when you introduce AI to drive costs down, you're also introducing a better customer experience, more contextual customer experience. So for example, rather than being on a phone and talking to someone for 15 minutes, you start to kind of focusing on resolving a problem at the first attempt, understanding why the customer is calling, using all of that information to provide a very direct answer and resolve it very quickly. So a lot of the discussions we had were around generative AI and driving costs down and running POCs. So I have to tell you, I don't think there was one meeting that I went into that any customer said, hey, we are not interested. So every customer, first of all, was interested. This is number one. Second, every customer is experimenting, doing something in some shape or form. Thirdly, I would say that we are fast approaching the crossroad of trying to show business results, right? So let's say, 2023 and the early part of 2024 was around like experimentation and pilots and things like that. I would say the second half of 2024 and going into 2025, we are really going to prove the technology out, show real results, show how concepts of trusted AI is going to work, how security is going to work in AI because these were all very, very big topics. And talking about kind of, I would say, key topics, the cost efficiency of AI. This was a big topic that came out. Clearly, we have -- as Amdocs, we have a lot to say in this because everything that we do when we look at -- we're trying to work on a model that allows our customers the optionality to use different large language models, to use proprietary models, to use open source models because we know in the long term, you're going to have to kind of balance these out rather than maybe double, triple down on one and be locked into it. And this is also some of the announcements we made were kind of an enhanced partnership or extending our partnership with Microsoft on not just the customer experience stuff but also around the generative AI stuff. Expanding our partnership around NVIDIA, so we can bring what we call a large language model garden to our customers so that they can have optionality on these things and allowing them to kind of architect solutions that gives them the best of both worlds because this space is moving so fast. I have to tell you, Tal, I kind of pride myself of trying to keep up with technology and what's happening. This is probably the fastest pace I've seen in anything in the last 20 years. Like you wake up, everything is moving. A couple of weeks ago, I'm sure you saw the results around Sora and all the excitement around the world. Tomorrow, it's something else. So I think our customers are trying to get a grip, trying to get a handle on where all of this is moving. But at the same time, they also realize that they need to start showing some results and delivering some results on this stuff.

So I want to take a step back maybe and just ask why gen AI is important and not regular AI, like the traditional AI we had before? What -- how do you define gen AI? And what's the difference really?

Yes. At the end of the day, we can probably chat for an hour about it. But if I had to kind of summarize the benefits of generative AI versus kind of traditional, machine learning or AI, think about -- look, one of the best examples I can use to articulate it is, I am surprised at some of the emergent behavior once you feed in all of this data, right? So for example, we have models where we take large language models. We have our telco taxonomy. We feed in real-time data of customer history, customer bills, customer transactions. We put all of this information together. And then you essentially go and you give it a problem to solve. You ask it a question. And how articulate the information is, how precise it is, the emerging -- some of the emergent behavior that you didn't expect to come out of it. I have to say sometimes we're showing -- everything we showed, for example, at Mobile World Congress was live. So these were live large language models that we were showing. And I have to say, I always hold my breath, right? When you're doing these pilots with live models in front of customers, you're typing questions, it's returning response, you're like, oh, my gosh, I hope it works the way we expect it to work. And sometimes, I would say 6 out of 10 times, I was even positively impacted going, wow, this is a really good answer. I didn't think of that. So I think this emergent behavior, this 1 plus 1 equals 3 is some of the wow effects of generative, right? And the leapfrog, like even when you take a step and look at some of the stuff that Sora is doing in terms of text to video, for example, right? This is a leapfrog. This is not -- you're going into a video editing program and it can assist you and help you do things better. You're now jumping a couple of categories here, and you're going from text all the way to video. And think of these capabilities. Like one of the things I think was also pervasive throughout the conference was the angle of generative AI coupled with physical devices. So I think in the next 6 to 18 months, we're going to see a lot of physical devices coming into the space built in with generative AI, right? So you can think of -- you've heard of a Humane coming out with their little lapel device that you can put on that basically, if you think about it, it's taking videos of the world, coupling it with large language models or multimodal large language models, and giving you analysis and results on the fly. Like this is groundbreaking in any shape or form. And then I was playing around with the Apple Pro Vision, for example, and someone the other day, got a large language model and ran it on an Apple Vision Pro. So this is the ultimate edge architecture and you're running all of this technology. So I think the innovation that's coming out of it, the speed and the results is very different to traditional AI. Traditional AI, you needed a bunch of scientists. You needed a bunch of technical people. Today, a 14-year-old kid sitting in Vietnam can create something amazing using generative AI. And the same technology, the productive emergent behavior that we're seeing, I think, is groundbreaking.

And what's your business model around gen AI?

Yes. So if we can go to jump to Slide 5 here, Matt, just we'll illustrate it just a little bit better here, so we come at it with 2 approaches. So our first approach is, clearly, we have a product suite, our CES suite. And every one of our modular components are infused with generative AI. So that's kind of the left-hand side of the slides. So if you take something like our catalog, it comes out of the box with Copilot capabilities, with guiding capabilities. So yesterday, if you had to go for a 2-week training course on how to use our amazing product catalog that we felt so good about, tomorrow, you could maybe have 1 day training and you can come and say, hey, create me a 5G plan, and look, I want to target people in Dallas, so I want to target millennials in Dallas, and click return. It will give you 5 sample plans and you can pick it and you can edit. So all of our products now are rolling out with AI capabilities. Same with our CPQ product, our billing products. So you will see in the next kind of starting of earlier this year, it started to roll out. On the right-hand side, what we kind of traditionally or internally, I would say, call type B is we've come with our use case factory or our app factory of capabilities of we think you can put on top of any system. So we're system agnostic. It doesn't matter if you have an Amdocs system, if you have a non-Amdocs system, basically, using our amAIz gen AI platform, you could leverage the abilities of generative AI and bring it directly to your business operations, to your call centers or even to your customers, and expose it. So if you look at our models, it's yes, we're going to double down on our products and they're going to come rolled out. So if you have our products, fantastic, you're going to benefit from this. But on the right-hand side, even if you don't have our products or if you have some of our older products, you can still benefit from the generative AI capabilities.

Got it. And about 3 quarters ago, you launched a platform. You called it amAIz, I think. And can you go over what's the platform? How does it help you? How does it help your customers?

Yes. And if we can just jump on to the next slide there. So this platform is very telecommunications focused. So we took our decades of history of logical data models of how entities relate to each other and we kind of infused it into training a model to be able to understand the world of telco or the telco taxonomy. So for example, take something like proration or early termination fee that telecommunications companies are very familiar with, but one of these simple business ideas have so many different correlations that relate to it, and our model understands this. So for example, when you ask it a question, when you feed it information, it understands the impacts of all of these entities. The second is we come with a set of use case kits. So it allows our customers to build these use cases very fast because it will be fantastic and it will be great for me to say, hey, Tal, like I have 40 use cases, which I do today, by the way. But in 2 weeks from now, maybe there is something else that you want to work out. Maybe there's a combination. So our use case kits are very modularized so it allows you to pull in components together, stitch them together, create your own use case components. The third part is around trusted AI. So we provide a governance framework so it gives you the guardrails because the last thing you want here is to break your policies, your procedures, your rules in the catalog on what discounts you can give and things like that. So we infuse all of these together, together with what your customer really wants to achieve. And the final thing I would say is we allow you the optionality, as I spoke in the start, to integrate to any of the large language models. So whether it be OpenAI, whether it be Meta's LLaMA 2 or Mistral, allow you this optionality to use different large language models. And in some cases, by the way, in our use case and on our apps, we use multiple large language models even within the same transaction. And the reason for that is, again, leading to its cost, right? So it's one thing of saying, hey, like I just want to go and ask something a question, which is fantastic. But imagine now the repetition of trying to do these thousands and tens of thousands of times over a whole year, your cost can go up very quickly, very fast. So this is something that we're very focused on. And something that we also bring to the table is the ingestion of all your data. So you can think of it as the "plumbing" of all of your real-time data, how you access it. All of this needs to be done and be orchestrated in the correct way. And if we have time here for a second, I'd love to like jump to a little demo here to show you very quickly, all of what I've spoken about in action. So if you see here, actually, let's just go back to Slide 7 here just before the video starts for a second. Can we just stop the video? Yes. So if we go to Slide 7, here we have what is traditionally what looks like a customer bill. And you can imagine there's all sorts of charges on the bill going from what you pay for, bandwidth increase. I would say, as an industry, we probably still need to do a better job to make this better. But this is what customers usually see, right? And what we do is we did an analysis, for example, and we saw that about 30% of people calling in, are very simple, hey, like there is something wrong with my bill? Or why is my bill so high? Or why was my bill higher this month than last month? So if you look at these 2 things, even if you are an agent, you're sitting there, you're comparing bills, you're trying to figure out what's going on, you're trying to figure out what changed, but let's jump to the video for a second now. So this, by the way, this process, even for the best agent, it would take approximately 15 minutes to answer the question. But here, we have a simple question where you come in, hey, why is my bill higher than the last one, right? So we plugged this information into a large language model. We go to the Amdocs amAIz platform. We look at real-time data sources, including the last 6 months history of your bill. We look at the current offers, and we say, hey, your bill is high because due to a few factors, you exceeded your plans data allowance. This was an extra $15 charge. Like it's very simple English. It's very clear, but it also gives you opportunity to potentially upsell here, right? Because then the question was, hey, are there any more plans with an increased data allowance? So straight away, we look at the person, we go to the product catalog, we pull out offers from there. We combine it with the contextual information that was provided. And then we come back and go, yes, hey, we have some plans for you. There are 2 plans that we think is relevant for you. And here is the magic, right? Like the click to action here, like right there, you can say, hey, buy this plan now or change to this plan, you confirm it, you're done. So here you have within like 45 seconds that we can -- yes, we can just stop the video for a second. Within 45 seconds, you not only -- you had a customer that was irate calling about a bill, not understanding it. So you didn't take 15 minutes to try and understand the 2 bills and explain it to him, you also created an opportunity to upsell a new product. And probably the customer -- the chances are the customer walks away fairly satisfied, number one, getting the correct information on what happened; number two, with the opportunity to upsell; and number three, you just said, oh, yes, this looks good. Let me do this, and it's orchestrated. So Amdocs really is probably the only one positioned in that way that can do all of those things, right? Understand the information, provide it, understand the contextual nature, but then also orchestrate it and have that click to action, right? We just click it and make all of that magic happen behind the system. So that's what kind of amAIz brings together.

So you said in the past that your gen AI platform is geared towards the telco market. Why can't telcos develop the same thing? Why do they need your help?

Yes, a few different things. First of all, of course, they can. But it's going to take them time and it's going to take them money. We took decades of information of our logical data models, of our telco taxonomy, of our experience. We also -- once we ingested the data, we also trained it. Like we saw some behavior that we felt needed to be fine-tuned. So we did all of this fine-tuning of the model. We did all the inferencing, right? So when we come to the table, we've done all of that stuff for you. We've invested in R&D, and we bring all of this to the table for you. And the only thing you need to do is to make it contextual to your customer base. So you are now leapfrogging easily 12 to 18 months' worth of work, tens of millions of dollars, and decades of experience across the globe that even if you had it, I'm not sure how you're going to stitch it together. And I feel like this is the value proposition. But then there are 2 other little things: The ingestion; and understanding of real-time data. There's a terminology called RAG, which is really the augmentation of real-time data into a generative AI model. We bring this to the table and the know-how, and we even have some patents around it, for example. So when we're ingesting, for example, information from a bill, we just don't take the bill as a PDF and shove into a large language model. We take the source files. We understand the detail XML files. We take the proprietary information. And we know what the changes to look for and how to ingest it. Same with the pricing components, the discount components. You add all this together and you improve the level of accuracy, right? Standard GPT model that's available in the market, we did some side-by-side tests. And even the best ones are providing 60% to 65% accuracy. Currently, we're achieving levels of 93%, 94% accuracy, and we believe this will only go higher. And I think this is the differentiator of using Amdocs, number one. And just the acceleration. I mean we are doing pilots like I tell my team, like I don't want to do a pilot that's going to take more than 90 days, right? We can do pilots to show you actual business results, including integration, ingestion of data, the orchestration, tuning it or fine-tuning it to your customer base in 60 to 90 days. And I don't think there is really anyone that can achieve kind of that level of productionization of generative AI in the telco space today, at least not without giving time and money.

Yes. Are you worried about competition in the space?

Yes. Look, I mean, I think competition -- first of all, competition is healthy. It pushes all of us. We are waking up, I would say, for the last 12 to 18 months, nothing else but focused on this in every aspect of our business from front to back. And one thing that really was led from Shuky, our CEO, all the way down was this mantra that embracing the technology in all aspects of the business, right? And this is not like a little side homework project that someone needs to do. This is the CEO, our CEO of the company, standing up and saying, hey, this is something that's pervasive. This is something that's going to change the way we do things. And I want our entire business to change and do a paradigm shift to align to this. And this is what we did very, very early on, and I'm very grateful for that. because now we're starting to see the result for us. So are we worried about competition? Always because we look at our competition, and I'm sure they're looking at us. But on the other hand, I think we're very focused on us. Meaning we are focused on delivering customer results, we're focused on increasing our accuracy levels, we're focused on giving optionality for large language models. So there are a lot of things that we need to be focused without looking at our competition and maybe they can be focused on us, for once.

Yes. Now I said it before, in your -- when you presented it, you said it's focused on telcos. What are the opportunities to expand it to cable, cloud, maybe even enterprise?

Yes. Look, when I say telcos, I kind of talk about telecommunications in the broader aspects, right? So cable, wireless, we're agnostic to that. Definitely, we have many of the world's best cable customers. We have dual play, triple play, quad play all over the world. So when I talk about service providers, I'm talking about the entire gamut here. Definitely around enterprise as well. So we're seeing some early use cases around private networks and doing some kind of spectrum adjustments for that and some use cases. And of course, our framework potentially could be used in other places. I mean that's not what we're focused on right now, meaning other verticals outside telecommunications, but it could be very easily adjusted to that if we so decide to go down that space. But right now, it's around communication service providers in the broader context, MVNOs, cable providers, wireless providers, multi-play providers and enterprises that provide connectivity.

Got it. Is this -- so gen AI could be both an opportunity and a threat? How do you view it? Like does it provide tailwind for your growth? Or is it a potential disruption to your business model?

Yes. Look, I think -- first of all, I think the disruption to the world, right? Like let's start with that. I think there are going to be people that embrace it, and there are going to be people that are sitting on the sidelines, and there are going to just be people that are talking about and doing nothing about it. So first of all, I think the key thing is it's here. It's real. It's something that people need to kind of embrace. So absolutely, it will be a disruption. And I think disruption is not a bad thing, right, if you're on board and if you're on board to grabbing it and you understand it and kind of that's where you're going. I think disruption is a bad thing if you're trying to push against it and say it's not going to happen. So I think from this perspective, I'm not worried, necessarily worried about the disruption of it. I think everything that we build because we have a very, very solid generative AI focus, all of our product managers, for example, in my group, we have a big product management organization. Every one of them know that if I have a review with them, if I have a meeting with them, the first thing I'm going to ask is, okay, tell me how you're infusing generative AI in this product, right? Like tell me how you're capitalizing it. Tell me how the business is going to benefit from having this capability, right? Like if you take one of our products like CPQ Pro, one of the core things we bring to the table is of revenue acceleration opportunity for our customers. So it's not just about Amdocs benefiting. The first and foremost, the question I ask is how our customers benefit from that. So I think from that perspective, I'm not necessarily worried about it. I think definitely, it's an opportunity for our industry because I think you have connectivity, you have generative AI. At the end of the day, generative AI also needs to reside on top of connectivity, right? So there are opportunities around the edge. We see distributed generative AI becoming a big thing as generative AI starts to move to the edge. So definitely, architectures and monetization models will need to change. So overall, when I think about it, I think about it from a positive perspective, I don't necessarily have any negative connotations attached to it.

Got it. And is it an opportunity within -- is it an upsell opportunity within existing customers? Or does it open up for you kind of opportunities with net new customers?

Yes. So I look at it -- I kind of look at it maybe from a couple of different perspectives. I think from the first perspective, our customers should be happy and we'll be thankful that if they're choosing Amdocs products. The road maps are already coming with generative AI capabilities built in. And every quarter, as they get different releases, they ingest all of these generative AI capabilities. So we tick the box. And that's -- like I hate to even say this, but that's almost BAU for us, right? Like that business -- that it's already become business as usual about the capabilities rolling out within our products. I think the upsell opportunity for us comes with customers that are either on an older version, customers that are not on Amdocs or customers that are thinking of doing something completely different and reinventing their business model, right? So if there is a customer out there saying, hey, like, I'm going to create the next-generation telecommunication service provider. And I'm going to really rethink how I'm going to do things like, boy, like would I love to have a discussion with you, right? Because we have some really cool things that if I was designing the next-generation telecommunication service provider, like it could be built on a very different perspective, a very different foundation. You can think of customer experience very, very differently. And so I think this provides us an opportunity and definitely some tailwind to have some very interesting discussions with maybe a variety of customers necessarily that maybe even didn't use Amdocs heavily in the past before.

Yes. And also, you announced strategic partnerships recently. Two of them with actually the 2 leaders of the space. One is Microsoft and the other one is NVIDIA. What exactly are you doing with them?

Yes. So the Microsoft partnership, I would say, can be split into a couple of different perspectives. So first, we have the partnership with them over the customer experience platform. We're working with them together with them on integrating dynamics or using the dynamic infrastructure, integrating it with all of the workforce components like Teams and things like that to just provide a better holistic experience when it comes to sales force automation, all the way from the front end, all the way to network provisioning and all of our components. So this is the first part with Microsoft. The second part, of course, is all the components around generative AI, starting from Copilots, all the way to leveraging their OpenAI partnership and we're in discussion with the OpenAI guys as well on how we leverage those capabilities inside our amAIz framework. The NVIDIA partnership brings, I would say, there is a crossover component to Microsoft and a different component. So clearly, NVIDIA is the leader in the space when it comes to GPT and generative AI infrastructure. But the partnership with them also allows us to give optionality into different kind of large language models. We're working with them also when we look at on-prem infrastructure versus cloud infrastructure. So the combination of both NVIDIA and Microsoft allows us to bring -- you combine that with our amAIz platform, you combine that with our products, you combine with that with our services in terms of joining all the dots, ingesting the data, orchestrating it, it gives you this kind of one-stop box, right? So if you want to embrace generative AI and you want to execute it in production, you don't have to go and do more research. You don't have to go figure out all the way from GPUs to cloud to kind of the integration work, all the way like we saw in the little demo there, the button that says, hey, give me that other plan and doing all the plumbing inside, it's kind of a one-stop shop.

Yes. Got it. Last week at Mobile World Congress, you also announced new capabilities for gen AI platform. You unveilied something called CES24. Can you take us -- this is the customer experience suite. Can you take us through it? And what are the new use cases and advanced copilots that are driving this new customer experience suite?

Yes. So one of -- I would say one of the big things is we see a great revenue opportunity for our customers around the enterprise space. So the enterprise space, I would say if the consumer space was accelerated and digitalized over the last, I don't know, X years or even the last decade, I would say the enterprise space has lagged behind a little bit. And so being able to, number one, starting all the way from the sales process. So if you take the average time it takes for an enterprise agreement to be put into place and executed, we're talking like 1 month-plus, right, the average time. We think we can significantly reduce this using our CPQ Pro platform. Framework -- 60% to 70% of the framework agreement can be created with the sales rep sitting in front of the customer versus going back. Like today, the way it happens is the sales rep goes out as a discussion says, hey, I want this, I want a T1 Line, I want VPN, I want a firewall. Takes all the information, goes back, speaks to a few people in the office, comes back and says, "hey, you really need this. How about this? This to and fro takes weeks, right? But when you have CPQ Pro, you can just verbatim, enter it all in, and it basically spits out, looks at your inventory, looks at what's available, looks at your backhaul capabilities and spits out a framework agreement in a matter of minutes. Now you can at least verify it with the customers, hey, I just want to verify, this is what we're doing, this is what we can do. And then it just accelerates the closing of that and the provisioning of that. So what took maybe potentially 1.5 to 2 months to reach a close, you can start recognizing that revenue much earlier. So that's something we're really excited about in CES24, bringing these enterprise capabilities to market. The second thing is around what we call freestyle billing. We think that consumers and enterprises are really wanting to charge customers and do monetization the way they want to do that's contextual to the consumer, that's contextual to the market, that's contextual to the country, to the industry. And so we provide a vast array of optionality from real-time sources to different methods of payment, to different bifurcated billings. So you may, for example, want to split your bill by an enterprise picking up some of the tab because you're doing some sponsored activities in stadium versus you paying half of the bill, or a car manufacturer may want to say, for example, that, hey, the first gigabyte I will cover, after that, you may want to pick up the bill. So all this type of optionality comes together in freestyle billing and just allows our customers to choose what they want to do. So those, I would say, are some of the exciting things, but of course, we're beefing up our security portfolio, what we call our Microservices MS360 portfolio. So as these releases come out, we also beef up not just the functional components, but also the nonfunctional components. And this is kind of what we're excited about.

Got it. And what's the reaction you're getting from customers? What they're saying and how incremental could this be to revenues in the long run?

Yes. I think our customers are very, very focused on 2 things: Number one, top line monetization opportunities because clearly, everyone is using the network in every shape or form. How do I get some of that revenue? How do I monetize some of that revenue? So that's kind of one discussion we're having. Second discussion is about cost optimization of all of the operations they run in the call centers, and things like that. And how do we drive a better customer experience versus driving standard KPIs, right? Like average handling time and things like that call deflection. So the basic KPIs, we believe, are doing a better job. So the discussions with customers kind of center around business KPIs and very quickly moved to how can we leverage this technology to help us deliver better business results, whether it's doubling down on generative AI, whether it's focusing on -- so we had a customer etisalat that we kind of shared of doing the next-generation retail store, right? So this is where you just basically walk in, you pick something up, you walk out and it's fully automated, just provides an amazing customer experience, all the way from people that are looking at a very focused vertical to complete modernization, right? So some of our customers are saying, hey, like, we believe that we want to go down this path, i.e., focus on enterprise customers or focus on the specific areas of the business versus others saying, look, we have a couple of different businesses coming together, whether it be through a merger or whether it be through an organizational reconciliation, and we want to change the way we work. So these are really the types of discussions we're having. One thing that I don't hear is any customer saying, yes, we're good. There's nothing we need to do, right? So this is a good thing. I think there's definitely in the industry, there's still a job to be done. Broadband and connectivity is still pervasive across everything we're going from, I call it the COVID recalibration, right? So if you had COVID and you had this new realization, then you had a post-COVID world, we are now up to the recalibration stage where we know things are never going to go back to the way they were. We know that maybe we are not over-correcting. We're coming back to some type of like equilibrium, right? So yes, it may not be 5 days in the office and maybe 3 days in the office. But people also realize that connectivity needs to be ubiquitous, right? So it doesn't matter if you're going on a holiday, it doesn't matter if you're at home, if you're at work or if you're in the carpool lane with your kids, you still need to ensure that you have the right connectivity, so you can take a meeting, right, no matter where you are. So I think this isn't going away. And we're in that era. And I think we're just touching the surfaces because we're also seeing new technologies like the kind of some of the LEO, MEO, GEO satellite technologies coming into play around rural broadband and around augmentation of broadband. So I think there's definitely a lot of work and a lot of activity. And as you said, we kicked this off by talking about the buzz and the hype and things like that. I think you add devices and how devices are now getting connectivity, all sorts of devices coming out with built-in connectivity. You talk about the level of security we need to provide, you talk about ubiquitous broadband no matter where you are. So I think you start to add these things together and all of the technology elements, I think there's still a lot of work to be done.

Got it. Switching to margins. In the last few quarters, you have seen margin upside, and it partially was because of the internal usage of gen AI. So how do you lever gen AI internally? And how does it help the margins?

Yes. Look, I think it's not something that we just work up yesterday and said, hey, like we're going to do this, and it was magic. It was really a long-term people strategy, a long-term resource and site strategy, coupled with generative AI. So there's multiple vectors from Amdocs perspective that we look at. Clearly, from a generative AI perspective. We believe there's a lot of promise. Now I have to say, I have to caveat that with saying, it's one thing to say, hey, I want to create a cool iOS app and do this. It's another thing when you're dealing with complex enterprise software. But definitely, we see a value there, and we see a benefit there. We see a benefit in the services space. And all of our technology teams are embracing this. So we are comfortable with kind of the guidance we've given, and we feel that with all of these levers that we have on the table, we think we can deliver on that promise. I don't know, Matt, if you want to add something to that?

Yes. No, I think you said it right. I mean it's not just gen AI, but I think what you're seeing in the margin expansion is really just the cumulative impact of all of the operational excellence initiatives that we've implemented and deployed over the last couple of years, right? Automation, AI, machine learning, is the way in which we manage the global talent pool, the resource management around that. So plus, in addition to that, we've now got generative AI as an added driver of cost and efficiency this year. So I think when we look at our margin, we, in Q1, delivered 18.1%, which itself was up 40 basis points year-over-year. I think, 30 basis points sequentially. And the guidance range we provided for the year is between 18.1% and 18.7%, and we'd expect to continue seeing some gradual improvement in the operating margin as we go through the year. Maybe tilted a little bit more towards the second half, but nevertheless, some room to run in terms of raising the margin this year.

Got it. Then do you need -- so you provided targets for the year. You said that you'll grow 10% in the midpoint. Do you need AI contribution this year to hit your guidance or AI -- you don't see AI contribution this year?

There is nothing in our revenue outlook for generative AI this year. So we've guided the Street to constant currency revenue growth of between 1.2% and 5.2%, 3.2% at the midpoint. There's no generative AI in there from that. Yes, we're getting some benefit on the margin, but -- and that's definitely contributing to the EPS growth, but on the top line, no gen AI impact.

And one thing, just to kind of add a little color to what Matt said. I think also you need to think about it from the perspective of generative AI is pervasive in terms of our entire suite, right? So for example, a customer deciding to buy C1 or do a transformation may do it because we have the generative AI capabilities built in to our suites. So when I spoke about this kind of type A and type B kind of generative AI-powered CES suite versus a use case library, like I don't see ourselves -- because this is so pervasive and our entire organization has grabbed it, including our product and services group. It's not like there's like 1/4 of the company, and this is our generative AI thing and the rest of the company, and this is a non-generative AI thing. It kind of -- everything that we do as we roll out our products, it's going to come with generative AI.

Got it. Anthony, another question is when we talk about gen AI, we talk about extreme efficiency. So the flip side of the coin of efficiency is less manpower, you automate things, right? Amdocs is a technology company, but it's also a service company. And I'm going back to my question about revenue contribution. Is gen AI a risk or an opportunity? And the question is for that kind of services, which is basically integration services and deployment and consulting and a lot of -- you're not a software, like you don't send a disk and you don't sell a download link to customers. You sell a whole service. So with that in mind, does gen AI reduce the price, increases the efficiency, which the flip side is, reduces the price so much that you would need to sell more things in order to grow rather than no gen AI scenario?

Yes. Look, I think the way to kind of think about it is we have releases coming out. For example, if I go back 5 years ago, for example, right, we had a product release that came out every 18 months, right? In 2024, we have a product release that comes out every 3 months, right? So our customers -- so think about it from a customer perspective, right? If one of our customers said, hey, this is fantastic. We also would like this, and we want to make sure it's part of your product roadmap and part of your platform. I would say, no problem. It is, and you will get this information -- product because this is what we're working on. You can have it in 18 months or 24 months. This was a discussion 5 years ago. Today, it's a quarter, right? It's 3 months, right? Spring summer, winter, autumn release, and you benefit from it. So the acceleration of what we do, we didn't triple the size of the company or anything like that, right? So I think our customers will start to really have an upside in terms of the benefit of the productivity, what they get, how they get it. And this will continue to increase. Like Matt said, we didn't just kind of wake up and wait for generative AI. Clearly, our testing suites are very, very, very highly automated. Now it's going to even be done better. So this is like a gradual improvement. But I think what our customers will start to see, and I think this is also what they're demanding, is an acceleration of capabilities, an acceleration of functionality, sometimes even more in terms of what they were getting than they were getting before. Because on the flip side of that, the market moves very fast, right? So for example, if you could wait for 12 months before launching a new product, you need to react now within a week, you need to react within 3 weeks, right, because everything is kind of changing and shifting. But clearly, your point is a good point. And this is why I think if we did not embrace generative AI, if we were still working in a traditional model, it would be a lot more difficult because at the end of the day, our customers are looking at value. The second component on how to kind of think about it is when we kind of take accountability for a transformation or a model, it's many, many components, right? It's integration, it's enterprise software. It's helping our customers think of what the journey maps look like, for example. It's production. So there are many, many elements. There's the managed services components. So it's not like we are doing one little thing, and that's what we do. And so that's being disrupted. So there's a plethora of activities that we need to still bring together, we still need to make sure they stick together, and it still works. So I think between a combination of the 2 things, I still see a strong demand for what we do.

Got it. Okay. Maybe I'll finish with last question and Matt or Anthony, for both of you or one of you. How long -- so you said, Matt, that AI is -- gen AI is not part of your guidance for the year. It's not part of the numbers. How long does it take -- right, how long does it take gen AI to translate into revenue contribution? Or by the way -- sorry, I'll extend it, or cost benefit?

As we discussed from a margin point of view, it's beginning to contribute already today. But I think we're -- and probably Anthony is better to talk about this, but I think if last year was the year of experimentation, as I've heard you put it, Anthony, this is the year of productionization. So we'd be looking, I think, just to continue advancing all of those POCs that we're working on with our customers. And hopefully, we can get to a point where perhaps it's contributing to revenue in the next fiscal year. But Anthony, yes, maybe I'll let you finish.

But I just want to make sure I'm clear on this one. Like I don't see it as A or B, right? Like I don't see as traditional Amdocs and AI, generative AI revenue. At the end of the day, our products are going to -- they have it today, right? So the question is, if someone is taking a catalog, you -- and it has a Copilot built in with generative AI, do you call that generative AI? Do you not call that generative AI? Are they taking it because it's generative AI? It kind of becomes a little bit gray. So I don't necessarily think -- and I don't even see how you could do something like that potentially come in and say, this is a generative AI revenue, this is non-generative AI revenue because everything we do, everything we've built is starting to kind of build in it. But to Matt's perspective, in terms of initiatives that we're doing in generative AI use cases and pilots, almost every one of our customers are either talking to us or thinking about or in the middle of executing some type of generative AI pilot. So this will lead to them potentially buying the capabilities, buying the amAIz platform, buying our CES24 suite because it comes with generative AI capabilities. So it's this continuum, right? Like it's not -- I just don't want to set the stage that all of a sudden, there's going to be like one day, you're going to wake up and there's going to be A or B.

Got it. I think this is it. We ran out of time and out of questions. Thank you. Do we have any questions from the audience, by the way? Is there a way for people to ask questions?

There is a Q&A chat box, but I'm not seeing much.

Got it. Okay. So with that, maybe Anthony and Matt, I want to thank you very much for this, and I'll pass it back to Matt. I don't know if you have any closing statements, but this was very deep and thorough, and I think we all understand now how you participate in the gen AI space. Thank you.

Thank you, Tal, for hosting. We really appreciate it. I think it was a great discussion and look forward to catching up. And if anybody is listening and has any questions, just reach out to us here in the IR group. Thank you.

Perfect. .

Thanks, Anthony.

Thank you.

Take care. Bye-bye.