UiPath, Inc. (PATH) Earnings Call Transcript & Summary

Hi, everyone. Thank you all for joining our investor event today. I would first like to go over a few housekeeping items. Today's session is being recorded and will be webcast live. I would also like to point you to our safe harbor statement. And I remind you that today's discussion may contain forward-looking statements. Actual results may differ materially from these statements as a result of various risk factors, including those found in our SEC filings. We may disclose information related to development and plans for future products, features or enhancements, which are subject to change at our discretion without notice. All statements are made only as of today, and UiPath undertakes no obligation to update any forward-looking statements and makes no assurances and assumes no responsibility to introduce future products, features or enhancements described today. Additionally, we would like to note that we are in our earnings quiet period. And as such, we will not be taking any financial-related questions. In terms of today's agenda, we'll begin with our CEO, Daniel Dines, who will lay out his vision for the company. then Graham Sheldon, UiPath's Chief Product Officer, who will give a review of recent product updates and demos. This will be followed by a customer panel hosted by Ben Feichtner, who leads our go-to-market efforts in Americas with IAG and Dentsu. We'll discuss how they are harnessing the power of AI and automation together with a brief Q&A session for those attending in person. Following the customer panel, we will take additional product-related questions. Afterward, we will move to another room for a cocktail reception, where you will have the opportunity to interact with additional management -- with additional members of our management team. And with that, I'd like to introduce our CEO, Daniel Dines.

Well, hello, everyone. Thank you so much for being here with us. I know many of you for many years so far. And you've been with us for good days, sometimes for bad days. But today, tomorrow and day after tomorrow is going to be good days, guys. I promise you. Basically, we are announcing the second act of UI path. And I want to give you a quick glimpse into what's coming and why we believe it's our second act. So in the past 10 years, we've been building our platform, business automation platform, to go after rule-based processes. So as long as in a process, the information was structured and the steps that a person should follow were governed by rules easily to express, then we could have go and automate the process. And I think we did quite a good job. We started from like a small apartment, our equivalent of a garage and we emerged like $1 billion company going into, I don't know, more than 30 countries with offices and everything, going public on New York Stock Exchange. So that's really a good ride. I think we made a platform that was capable of delivering really reliable automations. But it was limited, and we always knew it was limited. Many business processes are not really comprised only of rule based or structure information. There is a lot of unstructured dynamic steps in a process that we couldn't automate. So our strategy was always to kind of entangle what is structured, what is unstructured, automate what is structured and let people handle the unstructured part. And it works well, but I think we left out a lot of processes because think about our customers, if we get into a process where maybe 20% is structured and 80% is unstructured, there is little point to go after the 20%. It's not enough value that you can get there. But now we are in a fantastic moment when the technology to deal with the 80% unstructured is emerging, and we can tap into it. And for the past 18 months or so, I think us, many other companies, many of our customers, are trying to figure out how can we get the power of generative AI and deliver it into an enterprise automation scenario, deliver it in a reliable way. Because in a way, this is the crux of the issue. This technology is really powerful, but it's unreliable. And you will need to find a way to make it reliable. And I think a lot of progress happened in the industry. And this concept of agents are all the rage now. And for us, it's really a big moment because I will make a point that UiPath is a company that is really well positioned to deliver on agents, on agentic automation, to deliver on a technology that can grow also to address the unstructured part of the process. Because in a way, it's a natural progression of RPA. RPA it's a natural progression from rule-based automation into a more dynamic unstructured type of automation. So we are going to introduce the notion of agents. And an agent is basically a combination between an instruction set to an LLM and the set of tools that the agent can call dynamically. These tools are, in fact, robots in our platform. So I want to give you a very quick example that we can all relate to. If I want to build a travel agent, right, I have to -- I will give a tool to the agent to retrieve the company, my company travel policy that can be updated from time to time. So it's dynamic in nature. So agent will retrieve the company policy, and then I will give agent a tool to retrieve my information from Workday, what's my position in the company, so the agent can understand what kind of allowance do I have, what kind of flight tickets can offer me and hotel rates and everything. And then I give the agent tools to find, to search for flight, to search for hotels. And then I give the agent tools to book a flight and book a hotel, different airlines, different type of websites, aggregators, everything. Now the key is that this agent is capable of dynamically choosing the tools based on the input. So if I'm going and I say "I want to book me -- I'm going to go to -- I'm in London, I'm going to New York next week, and I want to book me a flight and a hotel and return in 2 days". All in natural language, agent is capable of understanding intent and choose different tools that has its disposition. The tools are reliable. This is a very key aspect. You can rely on the tools and tools obstruct the way -- the information in a way to the agent because you don't want to give an LLM agent your credentials to work with. Because you don't know what's going to do there. But if you give a tool that is very precise and reliable in nature, the agent can use the tool and you have the certainty the information that is going there. It's only the information needed for the particular task. You go there. Now with UiPath has created these integrations with different platforms. This is our specialty. We are a Switzerland of business applications. So we have all these reliable tools that can help an agent and really go and fulfill the goal that someone is given the agent. And we have all the governance and security to run the agents as much as we run our own robots. And we are creating -- we are letting our people, using our low-code platform, to create this agent. So we tap into our big community of people. Like, we have 3 million people training our platform to also build these agents. So they are a natural part of our platform. Because in a way, in Workday, you can have low-skilled individuals, you can have high-skilled individuals, but they are managed by the same platform. It's kind of natural to have our platform managing robots. In a way you can see them as low-skilled employees and agents, which are more high-skilled employees, and the agents who take advantage of the low-skilled robots in order to perform their high-level job. And everything it's surrounded by our orchestration engine because we have built orchestration since the beginning. There is no way you can deliver robots in a reliable way without having orchestration between robots and humans. And now we are extending our orchestration to have agents talking to humans, talking to robots, and everything in this workflow, enterprise workflow, that is really reliable. In the past like 6 weeks, I travel the world, I was -- a week ago, I was in Singapore and then Dubai, London and then here in Vegas, and prior, to Tokyo. So I talked to a lot of our customers. It resonates so much. This agentic automation thing opens the discussion. They are really open to all these new use cases. They are open to test. They are open to co-innovate with us. And it really makes sense. When I mentioned to them, guys, in order to deliver AI in an agentic shape, autonomously unit process orchestration, and you need to have robots talking to agents and to people and ground the agent choosing robot in the enterprise data, it all makes sense. I've seen CIOs saying, "Daniel, there are many companies that talk about AI and automation." But now I see UiPath in a different light, process orchestration plus AI plus automation is the key to deliver. And again, we are the Switzerland of platforms. We connect agnostically to all the business applications. We have connectors. We have robots that can go to all of these platforms equally. Salesforce can build agents, but they will build agents that live and thrive into Salesforce. We can even connect to their agents like we use APIs to different platforms. But think about most SMEs, don't use one single business platform. They use multiple business platform. So this is why an agent that mimics an SME that use multiple business platforms will need to have robots that are capable of connecting to all of these platforms. This is where our unique advantage is. And I think Graham will show you practically what it means, where we are from a product standpoint. But I want to finish saying it really brings a lot of energy within UiPath. Everybody right now has a singular focus. Let's bring agentic automation to the market. It's really our biggest opportunity, and it's the beginning of our second act. Graham?

Thanks, Daniel. So for those of you who I haven't met, I'm Graham Sheldon, the Chief Product Officer. So the product vision, strategy and road map are things that I get to think about all the time. And I get to talk with Daniel about those big ideas and how to make them real. So mostly what I'm going to do is show you what we mean by agents and robots working together. So what we are investing in as a company to make this agentic automation strategy real are four key things. The first is bringing to life enterprise agents. There are a lot of agents out there, as Daniel described, but for ones that you're going to actually deploy for your mission-critical business processes for these end-to-end workflows, you need to be able to trust them. And there are things that we are going to do specifically to bring those agents to life in a way that you can guarantee that the decisions that they're going to make are ones that you can trust and that they make these decisions independently so that you can remove as much as possible from the workflows that the humans are currently still doing today. That's something that is a big advancement for generative AI, and it's kind of like -- I think it may actually be helpful if I start by describing why this is such a big leap. I mean for those of us who are in it, I think it's useful to level set that like there's massive gains in productivity when the PC came out, we digitized a lot of the physical work that happened. There was another inflection point and a disruptive point when the Internet came around, and you had the democratization of things. And then with mobile productivity, mobile applications, putting all of that in your pocket, just at your fingertips, I think we're all going to look back at this moment and think about all the work that has been automated in the past as being only half of the problem. It's the left brain stuff. So think about your work, like the things that you do that are based on rules. If I get this kind of a case, I do that with it. If I get this kind of an invoice and it matches, great, I'll send it straight through to process. The other half of that problem, though, is what happens when you don't have a business role to describe it, where you can't ask a developer to sit down and code all of the different variations in all of the different error cases that you might run into. So you need to have the right brain stuff, the more creative things, the more adaptive, the more flexible. And so what UiPath is going to be really great at is combining the left brain stuff that you're doing with rules, that we will continue to invest in best-in-class automation for, with the best of what generative AI can now do with large language models and large action models that can help you anticipate what's going on and apply these things in the more nondeterministic cases, where you're applying a policy, like Daniel described, for travel or a policy for taking on mortgage statements and you're trying to approve a loan. Those are cases that apply a little more judgment. And there are a lot of these cases where today, those use cases -- there's many, many use cases. I'll talk about one of them in a little bit, but many of those use cases are things that today, you have to rely on people to do. You have outsourcers who you are not willing to go pay a full-time employee for, but you think that you can't just write business rules to do it. Those are the kinds of things where agentic orchestration comes in play where the best way to solve those problems, again, end-to-end will be to put the robots to work on the tasks that are deterministic, that are based on rules, put agents to work on the things that are based on the goals that they're trying to seek and obviously, people for the most critical end decisions. So we're not going to completely remove people from this, but they're going to be put to use for the right problem. We're going to continue to invest in our core automation platform because those agents, this is a really critical part of the strategy, those agents are only as good as the tools that they have. For many of you who played around with ChatGPT, especially early on, you know that you get somewhere and then you've got to go copy and paste that data into some other system. And you are going to go copy and paste that data back into ChatGPT to get anything really done. That's not useful to have yet another tool in this panoply of enterprise ecosystem. You want that stuff integrated. And so that integrated value and what the automations can bring is a way to do that and a way to do that securely. So those automations are built against your ERP system, your CRM system, your HRM system, in a way that you trust it to do the right things. So if you give the agents those tools, they can be the best agents. And because, again, we are the Switzerland of those, we've invested a lot, both over the last years, and we'll continue to, to make sure we have the best automations, which become the best tools for those agents. And then, of course, we continue to work on making sure that we meet all of the highest level of rigor in terms of regulated industries and customers who need to meet their compliance, very high level of compliance, and governance with our cloud. So a lot of this innovation is obviously happening there. And that's where we're seeing a lot of growth. So it's the combination of these things together that will help us be in the best position to help our customers build agents that they can trust to do a lot more work than was ever before possible. Our first best agent that we created last year and announced at this conference was Autopilot. And Autopilot took flight earlier in June, and it's seen a lot of great adoption and success. I was just talking today with a whole bunch of customers who came up to me and said -- they were telling me about use cases that they were using it for, "Cisco is one here where for test, it's resulted in a huge reduction in manual effort and yearly savings." I had another customer come up to me today and say how it was helping them with filling out employee self-evaluations. Who loves filling out employee self-evaluations? It's better if you could just take all of the content for the stuff that you've been working on and have Autopilot help you put it into the right format, so you can put it into Workday instead of sitting in front of Workday, filling this stuff out. These are just a couple of the use cases that were really -- Flo is here from Dentsu in the back. Hi, Flo, it's nice to see you. They've done some really exciting stuff applying some of the HR policies and helping exactly in that scenario I just mentioned. WEX is doing some really interesting work where it's taking different sources from Google Docs and from Google Calendar and some of the transcriptions and things and helping people prepare for their meetings. I know every time I go get to meet with Daniel, he asked me 100 questions. So it's great to know beforehand what it was that we were talking about. So having -- in WEX's case, they're using Autopilot as a way an agent to help them prepare for those executive meetings to prepare the action items and key points. And then the last one I want to show you is one that's kind of interesting, not only because it's saving time and employees experience is getting better, but also because it's saving lives. So CSL is a company that bearing in particular, is one that is -- they're working on taking in donors of plasma. And so we've been working with them to develop Autopilot as a way to make it easy for the physicians to help triage the patients who want to be able to donate and are using a cross-section. The folks -- I mean it's amazing. They have to sit down with a huge long document, like hundreds of pages, that have all the medications and types of complications and things like that, that govern whether or not they're allowed to take your donation. And that's critical because when they deliver those to their customers, they ultimately have to ran -- out in the field. If they get any of that wrong, someone transcribes it incorrectly, they have to get rid of that plasma. So that's a person's life that's now at stake by getting -- making sure that they don't have to do that. And I actually want to show that to you directly. So with Autopilot, it combines basically the best of what Generative AI can do in terms of understanding natural language plus what UiPath is doing in AI called specialized AI models. So in this particular case, if you're sitting down with a donor who has psoriasis and has also taken Accutane within the last 3 months, the doctor might remember that, but there's obviously a limit to what they can remember. So this is going and checking against all of the documentation, those sort of standard operating procedures to see whether or not that's someone that they can take. And indeed, it comes back not only with an answer, but with the documentation behind it. So you can take a look at that and that the doctor can actually refer to the source data, the citation from that source data, to be really sure that they're about to make that critical decision. It's a good example of where you are developing that trust. When I'm ready to, I can then go and onboard this donor. This is where we're using specialized AI. So there's a specific model for ID cards that Autopilot has that it's going to make use of to take out the information from this driver's license. And for those of you who are on the earnings calls, I've shown this particular one before where, in this case, if I was entering this all by myself, then I'd have to be waiting like the patient is sitting there, wondering what's happening, it would take me 20 minutes to enter all this information in. Instead, this is going to -- Autopilot prefills out an automation that's going to drive the UI. So because Autopilot is built on the UiPath automation platform, it can now drive this whole experience. So you can see that it's doing this while we're waiting, and it's just doing it all for me. Because I'm in the loop, because I'm watching it happen, if there are any mistakes that get made, I can catch those mistakes as we go along, but it's saving me a ton of time, saving me some of those clerical errors that would ultimately end up in maybe some of those -- that plasma being discarded later on down the line. Because many of the doctors do this very often, there's a component built into Autopilot, and this is one of the bigger differentiators for Autopilot, that remembers how I did it before. So that memory component allows Autopilot to learn what the next step is. As you probably know, doctors don't always remember to put their notes in or they wait until the end because it takes a long time. In this case, because Autopilot has the whole chat history, it summarized all of that. It suggested that I enter it in and voila, it's entering all of those notes in for me. I didn't tell it to do that, it remembered and prompted me proactively to do that work. So we're driving the business process in some of those best practices by using Autopilot and its capabilities. Autopilot also has this ability to understand unstructured data. As you know, this medication list has a bunch of like handwritten stuff in it. So it's not just structured data, it's a little bit unstructured too. This is critical information to grab from the patient because these medications have cross-effects in them that might affect their eligibility. And so I need all of this critical data in a form that we -- last year, Clipboard AI is something that we announced and won the Time Innovators award. And as part of that, what Clipboard AI can do not only is to understand the data, that I'm putting in here in structured data, but it also understands screens. So in this particular case, I'm going to use Clipboard AI and Clipboard AI is going to look at the form on the left-hand side. I have not built an automation for the thing on the left-hand side. I could have, but I didn't. What Clipboard AI lets me do is it allows Autopilot to examine what that form actually has, understands what it's asking for and translates the data I have about the medications into that form. So now I'm going to be very precisely be able to fill this out. So when I hit paste to put that data in, it will allow me to do that. And obviously, at least in my household, we have lots of medications because I got three kids, and if I'm going to put them all in there, then you tend to have a lot more than even just. So this is a massive time savings for someone to use Clipboard AI within Autopilot to be able to paste this into the medication form. So there's a lot of these types of processes that you can obviously imagine that this would be useful for. But we're not stopping there. This is a sort of reactive digital assistant kind of experience. But we want -- we're talking today about Agentic AI, independent autonomous agents working on my behalf. I just did a whole bunch of work there. But wouldn't it be better if I didn't have to do that work in the first place? Maybe it would be better if we could actually build an agent to help me process the easy cases so that I could only -- I could focus on the ones that are -- that are harder, the ones where real judgment is involved. Well, in this case, we're going to go further with Autopilot to ask it to actually generate agents for me. In this case, so it's going to use what it's done in the past, the memory that I've -- that it's got of what I've been doing and create a new agent that will go through my donors. So you can see it proactively came up with the donors that are waiting for my action. And it's going to create a flow for me that this agent is going to follow. So I'm confident that it has the right flow, that it will use this basic loop to go through and go through my donors that are open, and then it lets me basically just use the automations that I have to approve the ones that are good, reject the ones that aren't and leave the ones for me, waiting with a recommendation that it's not clear about. And then the last step in this is I can actually go ask for Autopilot to schedule this for me. So just imagine, you're walking in your first cup of coffee, Autopilot has already completed for you most of this work and you're left with just the ones that you need to pay the most attention to. The rest of this demo, I'm not going to be able to show you in the interest of time, but as you can see, it's spawned a new agent on the side that's going to work for me. I can multitask. I can keep talking to Autopilot if I want to, but it's going to see those three agents, and it's going to start processing them on my behalf. All right. The last thing, and Ashim asked me to try to get this short and I'm going to fail at this. But the last thing I want to give you is a use case for invoice processing. And this is where we're going to create a new agent. So this is where customers -- in the past, for those of you who don't know about invoice processing, robots have been super helpful. They've taken a lot of the pain out of doing the two-way matching and entering the data into the ERP system. But what happens if there's a mismatch? There's still a ton of human work that has to happen. Now I've got -- if there's a mismatch, now I'm going to go back into the parts database, I got to go see if this is a substitute for that part. If they returned it, maybe they did it within the 30-day time frame that I gave them, or if it's over 60 days, then I'm going to have to charge them for a replacement. There's many different policies at play here that a human has to do. Then you go back to the supplier and you're back and forth with the supplier, "Hey, you charge me too much. You've added an extra 0. Can you revert these things?" There's a ton more work that still is manual in this process. That's where we think agents can really play a big part. Applying those policies, communicating with the supplier and making some of these decisions automatically so that the humans don't have to. The humans are just there to do the final approvals and to check that the right decisions are being made. So here's how this is going to work. This is our new experience for building agents in Autopilot. And we'll obviously build -- this is a catalog of ones that we think are great that we'll have out of the box. This one happens to be a dispute investigation agent, which I can open up in the agent builder. But I've already done a little bit of work here and specified some things. So this is the agent builder experience. In the agent builder experience, there are some critical things that I can do to build, test and deploy these agents at scale and in my mission-critical things. As I define it, there's some basic stuff I need to do like a prompt. The prompt defines what the role is that this agent will serve and the goal that it is seeking to accomplish. The second thing, and this is really critical are the tools. And I told you before that the tools really matter, the agents are only as good as the tools that are in there. In this case, it has access to SAP, and it has a web reader to research about the parts that the supplier has given me. But those tools are not limited to those. It's every single automation that this customer has access to, every activity, every API, every agent -- every process and other agents, ours and other agents from other ecosystems, that are now available as tools for this agent. That's super powerful because now I can say, "These are the specific tools I want you to use". And I can say, "This is how I want you to use them, and this is how I don't want you to use them," so that you're more likely to get an accurate response and one that you can rely on. Importantly, there's context. So in this case, there's a policy document that we've indexed and used the enterprise contact service for. And then crucially, and this is another unique part of the UiPath approach to agents, I'm defining an escalation. If the agent needs help, and it can't find the supplier's e-mail, it needs that information to kick off the next step. If you can't find that, we've built this in so that you can go to action center and ask someone specifically for that piece of information, so it stays on the guardrails. All right. Now we're going to give this a run. I've specified some input data, a couple of things that don't match. And now it's going to go try to figure this out. What the agent is doing right now as I'm testing it, is it's coming up with, first, a dynamic plan. It happened to have picked a couple of tasks to do with the web reader to go get information about the mismatched parts first. Then it's going to go to the contact -- the policy document, and it's going to go figure out which policies apply and which ones don't apply. The last thing it's going to do is go into SAP. In this particular case, we did this as a UI automation, but it could be an API automation. And again, this is unique to the UiPath platform that it's going to have access to these particular tools. And last, but not least, it ran into a problem. It didn't find that e-mail address for the supplier. So what's it going to do? It's going to escalate. Now the agent is paused. It's waiting for a critical input before it moves forward. It's going to go to action center, and it's going to assign this to me. We've done this in action center, but these will show up in Teams or Slack or wherever or an e-mail. And here, I'm going to give it the reason for, here's the thing you should do in the reason. And down on the bottom, it says, add to agent memory. It went really fast. Sorry, you didn't get to see that, but that memory thing is what allows us to learn -- it's that memory part. When I give it that explanation, the next time it sees a case like this, it knows what to do. So now that we've done that we really wanted to figure out that it's great that it works in one case. We want to figure out if it works in a lot of other cases. To have real confidence in this, I need to be able to evaluate this on a whole bunch of different cases and make sure that it's going to meet my criteria. I want -- for this kind of thing, I wanted to meet 90% score. So over time, I can see we will run these evaluation sets and give people the ability to see how well it's doing. And then it can also capture all of the examples that it is seen so that I can actually go back and inspect the kinds of decisions that it has made and tweak it as I need to. These are critical capabilities in order for you to build these things that actually will work reliably and with high confidence. Last, but not least, we're going to go create an experience. So there's two ways to do that. One I can create an app, so that's that digital chat bot kind of experience, or I can include this agent in one of my existing workflows. So because we have these structured inputs and structured outputs from the agent, I can include it into these workflows. So this is my invoice dispute agent workflow where I've included this particular agent in there. So any existing workflow, any existing automation, you can now extend with these agents to take care of those particular decisions that need to be made. All right. I think I'm going to end it there. We've got -- a lot of this stuff is new, the agent builder experience, the agent catalog, the ability to build apps that use the agents, the agent service that does the planning, the context and learning. And we're obviously quite -- we're here during this conference to figure out which use cases this is going to apply best to. We've got a lot of good examples. But the reason that we win on this is -- and the reason that we think that we're in a unique position to drive a lot of this AI transformation through agents and agentic automation is because we have the best tools. The automation platform is best-in-class. The second big piece is that we're doing this orchestration across robots and agents. We are Switzerland. So Daniel said it, we are ecosystem-agnostic. We integrate with everybody. We do the long tail of integration. And then it's on top of a system that is secure and compliant and enterprise-grade, so that the accuracy and reliability that you get out of it is top-notch and that you can run your most critical business processes on it. With that, I'm going to turn things over, I think, to Ben. And hopefully, that was -- I'm sure there's a lot of questions. I went through a ton of material there. Happy to stick around a little later and answer any questions you might have. Thank you.

All right. We have two of our esteemed customers here today and clients. So we'll have them introduce themselves quickly. So we're going to go through this. We're going to answer -- I'm going to ask them a couple of questions in a fireside chat format, and then we'll hopefully have time for one or two questions from the analysts, and then we'll hand it over to management Q&A. You guys want to take a seat. Thank you again for joining us. So I'm sure many of the folks in this room have heard about both of your companies, but I would love maybe to start with a brief introduction. We know we have Dentsu, the global advertising powerhouse; and then we have IAG, one of the world's leading airline companies and parent of companies like British Airways. So maybe, one, you just give us a brief introduction and then share a little bit on the industry dynamics that's going on for each of you right now.

Sure. Let me start. So my name is Alex Bedier, CEO of Dentsu. So as you know, Dentsu's is operating in the media and creative business. It's a Japanese company, headquartered in Tokyo, which has more than 100 years, I would say, tenure. And we've always been working around innovation and trying, I would say, to solve challenges for our clients. in a sustainable and, I would say, an ethical way. And more recently, we've really reorganized our overall promise to become fully integrated across our customers. And by taking the power of all of our agencies, we are now operating as a single, I would say, group across the world in 120 countries, 71,000 employees. So it's a people business that's trying to solve, I would say, the media challenge, the experience transformation through creativity, an interesting challenge. And as you know, in terms of industry, it's a very challenging industry. I would say, fast moving very, I would say, disturbed by technology generally speaking. So we have to be at the forefront of every single challenge basically.

Thank you. Luis?

Yes. Hi, everyone. My name is Luis I look after Intelligent Automation for IAG. IAG is a group of airlines. We're better known by our brands, British Airways, Iberia, Aer Lingus and a few others. And we operate in an incredibly competitive space, as you know. So innovation for us is incredibly important, whether that is investing on a panel of startup every year to develop new technologies for us or finding sustainable fuel. We are the first group that commits to Net Zero, which is a really tricky thing to do for an airline -- and obviously, automation and AI plays a huge role for us. So we're investing quite heavily on that to make a difference for our customers going forward.

That's fantastic. And two very distinctly different businesses in a lot of ways. And maybe we'll start with this, Alex, because I know you guys have been on the journey for a few years. But as you've started with the core platform, you've moved into intelligent document processing and automation cloud. I would love to hear a little bit about some of the use cases you guys are solving.

Yes. So you're right. We've been partnering together since 2017. We've been through that whole journey of taking the benefit of the cloud. 700, I would say, automation live today, so I won't go through all of them, obviously. Maybe just it's 800,000 of recurring hours say, for our business so far. Maybe one or two interesting use cases, one which is more related to our client work, where we've -- by leveraging both, I would say, Generative AI capabilities, so contact grounding, document, I would say, understanding and automation, we've been able to automate a lot of manual processes around, I would say, e-commerce, KPI calculation, media optimization for dedicated clients. That's one example. The second use case, which is a POC still that we mentioned in the introduction is how do we make the life of our employees. We are a people business. Sor for us, it's very important, more easy when it comes to year-end processes. And again, full context rounding, document analysis, the teams, and that's important. It's an interesting point, build a process to augment the process helping, I would say, our people to gather information from our policy, from their goals, from how they've been performing. So I'd like to name that new approach. You talked about Switzerland, but for me, it's the Swiss army knife, but you can, I would say, put that in the hands of end users, our people in departments to really transform at the department level this automation.

That was awesome. And Luis, I know, similar to actually been on the platform for a minute, a bit longer than a minute, I suppose. But when you look at it, too, as you've expanded into, I believe, document understanding, communications mining, and even our process and Discovery suite, you've done some cool stuff. So I'd love for you to share some of the end?

Yes. Yes, of course. So airlines, as you know, was one of the businesses that went into complete shutdown during the pandemic. So for us, coming back to pre-pandemic volumes, automation, UiPath in particular, more of a survival tool more than anything. It was really, really important to get some process automated. But we're automating processes, not just on your traditional finance, HR procurement, but very deep niche aviation processes that have to do with how crews are allocated to aircraft, how engines are repaired. And I'm sure everybody has experienced this, when you're checking in it takes a long time, you don't know what the heck people are typing in, but they're typing in for a long time. So we're working quite heavily on that to try to make that process easier for our staff as well.

That thought crossed my mind on my flight here on the way yesterday. So I appreciate it. But it wasn't one of your airlines, I will say that yours were automated. Well, that's great. And I think Luis, maybe I'll come back to you. We hinted on this a little bit, right? And you guys are both doing things with automation and Generative AI. I'm sure if you're in the audience right now, you're wondering, does this complement? Or does Generative AI potentially replace automation? I love your take and maybe some of the use cases you're working through there.

Absolutely. And we take a very special view of this. We don't really see as automation and AI being two different things. We do see -- the approach we take is we look at an operation, we look at a specific business process, and we break it down. And we realize that this particular business process has, I don't know, 15 different tasks. And then what we can do with UiPath is to say test #1 is about extracting stuff from a document. I have a tool for that. And step #2 is processing and email. Step number #3 is making sense of a particular piece of text, et cetera. So the way we look at it is rather than thinkng about it as two different technologies, it just becomes tools in our arsenal that we can use to automate processes. And we're doing it today for engine maintenance where we started with LLM to digest very, very complex PDF document manual to repair and engines. I don't know if any of you have seen a manual to repair a Rolls-Royce engine, but you're talking about documentistic. And LLMs are incredibly powerful to summarize and answer questions quite accurately. But what we're doing now is it's okay for an engineer to find the answer much quicker than before. But what if the answer is you're going to need this rubber gasket, which is part #23, and wouldn't it be great if we can allow that engineer to say -- to allow the technology to say, "Would you like to order that part? We have 10 in the warehouse." So that to the engineer is just part of the same process even though to us in this room, you're talking about RPA now helping the AI. What we're trying to do is just to use those two technologies to complement each other and automate end-to-end.

I think that's a very, very cool use case that clearly defines it for me of the differences between how you can leverage the power of an LLM and then also automate that action to SAP or whatever your ordering system may be. That's great. And Alex, how about you? How do you look at these things as complementary?

Well, I look at really definitely complementary and I think we shouldn't the complexity of this new world. So I'll take my personal example in Dentsu. We are running today 59 different LLMs from the very small ones. And there's not 1 single answer to any, I would say, single problem. Each one has its advantages. Each one has its complexity. Each one has its cost also. I think that's an important element. At the end, most of the time where we want to use that it's like you Luis, to deliver an outcome on the business process and trying to augment that as much as we can. So for me, yes, it's totally complementary. I'll take an example maybe to illustrate that. We've been working hard with our legal teams to see how we can really optimize all the journey around contract creation. Again, we are a professional services firm, generating tons of contracts. It's a heavy workload. It's painful for the clients, painful for us. So here, we are using a combination of a niche LLM technology, which is really, really powerful for contracts and safe, I would say, that always gives you the same results when you're asking a question, which is for legal person, very important, and automation to bridge the gap. So automation typically will help us analyze and extract data and tack it into the contracts, query that on the LLM to ultimately, automate redlining, automate a lot of stuff. The two are working hand in hand. And I would say one is helping the other to achieve again an outcome, which is optimizing our journey, I would say, to contract certification. And I've got many other ones, but that's an interesting one, I guess.

No, I think those are great use cases. And also just to highlight kind of Graham's point on bring your own, bring the third parties, use our specialized model. So it's very cool that you guys have thought through some of these use cases already. And I guess that ties me to my last question before we open up to the audience here. So when you look at agentic automation and kind of the future long-term, Alex, maybe you want to start with how you guys are thinking about this now, but also in the foreseeable future with everything going on in the environment?

Yes. So I guess a lot has to be done, but it's a great opportunity, I guess. If I talk about one of my core businesses, which is the media supply chain basically, from buying to planning to scheduling, not only this is a very complex supply chain, meaning that in each country, it's very different. We've got many, I would say, channels, social networks appearing every day, digital TV media. And to add on that, clients want a bespoke solution almost every time. So I really see this -- so obviously, we do automation already in a lot of areas. But I see agentic really has the secret weapon to help, first of all, going deeper into those automation scenarios; help us, I would say, really manage all of the edge cases and there are many that cannot be would say, hold based to your point, I think that level; and when we have to make changes because a client decides to operate differently, to do it very fast without rebuilding absolutely everything. And I'm really positive that Agentic AI and automation together can really crack that use case and I'm pretty sure we're going to do some very interesting work around that.

That's awesome. Luis?

It's incredibly important for us. And that's reflected by the fact that in our group, the Head of automation and AI sits at CEO level minus 2. It is not a back office function. It's really important to our strategy. And I'll give you another stat that is really different in our case, 70% of the EBIT impact that we have had through automation this year is new revenue. So this is not about finding FTEs that we can remove from the operation. It's about scaling our operations. So this is incredibly important. And we want to transform the user experience. The interaction with an airline tends to be stressful. The last thing you want to hear, your flight is delayed, your flight is canceled. What did we try to help that using agentic automation to say, "Your flight has been canceled. But here are three options where we can rebook you. Would you like to choose one of those?" That sounds super simple for an end user. What happens behind us is incredibly complex. So by using these technologies, we think we can transform and obviously get competitive advantage with that.

Thank you both. So I think we have time for one or two questions. I can't see name tags, but if you get a mic to -- so there we go.

Scott Berg with Needham & Company. Thanks for joining us today. We really appreciate it. How do you think about using platforms like UiPath in conjunction with these AI technologies versus trying to take the AI technologies in custom build youe own applications through some other means to accomplish the work tasks that you're actually doing. I think there's a pretty big debate at our world around how much companies will use platforms like UiPath specifically to aid these processes versus you all trying to do this on your own through some other internally developed tools.

So we think that it is perfectly positioned to bring AI and add value. Let me tell you why. We did deploy a couple of years ago, an AI team to do AI specific solutions. And one of the things that we're working on, for example, was delayed prediction, can we use all the data that we have to predict whether this aircraft is going to be there on time, yes or no. When we tried to deploy it in production, we struggled because, in order to put it in the production, as good as the model was, you need to think about the two steps that happened before the model is used and the two things happen afterwards. So we had to do a lot of development to stitch it together as part of the business process. Where UiPath excels is that all these technologies, including LLMs, et cetera, it's already part of that orchestration. So it makes it really easy to say, automate the process using AI, not to think about AI in isolation. So I think that's -- in our case, that's where it's been a differentiator.