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

Thank you, Mounish. This is amazing. Yes, we strongly believe that you are at that in 2026, this modern AI native case construct is the way how cases would be built and process will be executed and managed. Thanks, Mounish. All right. Actually, let me go back a slide. So Daniel introduced the concept of the map of work earlier. As he mentioned, process context is scattered around everywhere in the enterprise. The map brings that structure, the meaning so that agents, humans can reason and understand over the process and have the same understanding of how a process actually works in the enterprise. The map is living, meaning as your business changes, as your clients' business changes, the map also changes and keeps there. And the map is 100% owned by our clients. It is governed, meaning changes are audited. It means that they're versioned. So let's talk a little bit about what the map has. It has structured knowledge. It has the case plans, what Mounish showed a minute ago. It defines how work is structured and how business processes move from the beginning to the end. It has policies and rules. What's allowed? What are the thresholds, who approves who are, that type of thing. And then it has the ontology. And ontology is really a way of saying that there is a shared description for business objects that our clients choose, things like customers, things like their orders, things like their invoices and their relationships. Having this ontology allows every system, every agent to understand the business in the same way. This is how you take all of the unstructured scraps of information with the Cartographer agent, which I'll talk to you about in a minute, and convert that to something structured that agents and humans can reason together. The next bit -- the next piece in the map is what we call operating knowledge. This is what experienced people in the enterprise know. This is institutional knowledge. These are the people who provide precedents. These are the people who provide worked examples. These are the guidance that people carry. And now when these experts leave, many enterprises lose that knowledge. But with the map of work, we build the systems and capabilities to capture as much of this operating knowledge as possible, and you will see. And then finally, the decision ledger is where human -- the decisions and judgment that human makes and the rationale and the reason for it are captured. This becomes the gold mine, which our specialized agents can use to drive that continuous improvement for processes and this is a magical thing that the map of work can provide. Not only do you have that shared enterprise context or process context, but you can use that process context as a living, breathing artifact that improves as your process knowledge improves and drives continuous improvements to how your automations are built in the enterprise. All right. So you might wonder, how does this map get drawn? It sounds like a very complicated thing. And it really is not. I think I'll tell you why UiPath is uniquely poised to solve this very complicated process context problem. So how do we go from this unstructured siloed information? I'll even use the word chaotic artifacts of information into something that is more structured. So there's 2 answers. First, a person. We're defining this new role that we call the Cartographer. Someone who owns the map, someone who curates the map and someone who keeps the map up to date in the enterprise. Now you might wonder, is this a new hire? No, it isn't. Our clients already have business analysts. They know how the business runs. So they already know how to translate business requirements and business lingo to technical teams that do the actual implementation. This is the business analyst role elevated. And what elevates this business analyst to be a Cartographer is this new product we're launching called the UiPath Cartographer. This is the product. It is really a copilot that guides the Cartographer to pull all that context and give the structure that helps drive a shared understanding of processes. The Cartographer, the human is in charge of defining SLAs, in charge of defining the outcomes. And the UiPath Cartographer, the agent, the copilot assist the cartographer in gleaning that context and helping create this map in a manner that is shared. Now -- the Cartographer alone cannot possibly map a very complicated business process. What Mounish showed is not something one human being knows about. So the Cartographer -- UiPath Cartographer natively supports collaboration across multiple stakeholders. And this is critical. This is -- we've learned over the last decade how businesses actually manage and map their processes, and we've taken that understanding and brought it into how we've built the UiPath Cartographer. In addition, our customers, business analysts, they think about process reengineering all the time. It's not about just mapping how processes work, but they are the ones redefining how process should work to get that next level ROI. UiPath Cartographer has in-built capabilities to guide the cartographer to help assist with process reengineering. So take these couple of concepts. You drive process reengineering, you map all of the work in one shared artifact and then you drive a collaborative experience with which you glean and gain this enterprise context that helps you drive this transformative value that mapping can help provide. Now I'll put this all together, so I think you can all reason through the massive opportunity we at UiPath see in communicating with our clients and having shared this vision with them. Step one is cartography is what I just described to you, what Daniel described to you a few minutes ago, is we capture that process context with UiPath Cartographer. Step 2 is you take UiPath what Mounish showed you, you take -- the map is fundamentally written in a way that agents can reason with, which means that even UiPath for coding agents can take the map and build out these automation artifacts. This is transformative. Our clients are finding that both the examples that Mounish showed and a few others from our clients, 40, 50 minutes of work where the agent goes off on the side and just builds these things out is truly transformative, and you'll see a few demonstrations of this over the next couple of days. And then step 3 is Maestro. Maestro in its various incarnations orchestrates these very complicated processes in a governed, auditable and safe and secure and resilient way. And finally, I think the step 4 is an important step that we're also adding to our product vision. And that is this concept we call the decision ledger. Human decisions, human judgment is captured. Whenever a human does an override to a policy or makes a decision, we capture that in this thing we call a decision ledger. And the inputs in the decision ledger are fed back to the map to drive continuous improvements through a process. We have specialized agents that can reason over the artifacts that are in the decision ledger and assist the Cartographer in curating that information and drive this improvement. So that is the cycle. You capture with the Cartographer, you build with UiPath for coding agents, you run your complex process with UiPath Maestro. And then finally, you improve the processes with the decision ledger. -- the map of work at the very heart of all this. So you're probably thinking how do customers get started? It's almost never from a blank slate or a blank map. We have hundreds -- I mentioned to you earlier, one of the things we have embarked upon over the last year is this verticalized use case selling. So as it becomes us, we've built about 100 or more of these prebuilt maps that we'll show you in a little bit. Now where you start depends on your process. There are some processes that run about the same way in every company within an industry. They have very little variability. For those, we've actually completely prebuilt out the map, but we've also built out all of the automation constructs that go with the map and curated business user-specific experiences. But many do not. Many processes have much in common, but there's enough variability that we can't possibly prepackage them. And we support that too, and I'll explain in a little bit. And then there are a few processes that are completely bespoke to a company. That's what maybe makes the company as unique as it may be. So there's kind of 3 places to start with the map. The first and the best way is what we call the prebuilt maps with UiPath Solutions. Some processes, as I mentioned, look about the same way in every client, and every customer in a particular industry. So we've drawn that entire map, and we've implemented all of it. Every single agent, automation, any automation artifact that is required for us to operate the process, we've built it out. And we built curated experiences for the business user. These are our turnkey products. Now what you see in this slide is a little bit of eyesore, but what we want to tell you is that we built these verticalized processes for these prebuilt maps for a very few industries, healthcare, which is where we have some significant strength in financial services also and utilities and a few cross-industry use cases. We've chosen these use cases and industries very, very specifically. A, we have the strengths in these industries, like I mentioned. Our teams have done TAM and SAM analysis for where the highest growth and revenue opportunities for UiPath is. So we've used that to select this list. And finally, we had customer demand within our own customer base that we've used to see which ones to see these prebuilt maps with. Now as I mentioned, these solutions are completely built by UiPath. Our clients manage their business and we take care of the rest. There's only a few configuration artifacts that they have to provide things like their rules, things like their policies for which we provide curated experience and they capture this. Martijn is here on stage, and he'll walk us through how this actually works. But I'm going to maybe leave you with one important point before I hand it off to Martijn we picked a process we call the Source to Pay, specifically because we expect most of you here to be reasonably familiar with it. So you can learn about how the platform makes this prebuilt maps with UiPath solutions possible. We have more complicated processes, especially one on denials, which has, I don't know, 400,000-plus payers, payer policies across 100 payers. The reason we didn't pick that process is because it would be far too complicated. You would probably -- we'd spend more time explaining the process than really the concept of the prebuilt maps. So with that, I'll hand off to Martijn to use this simple example as a demonstrative use for how we think about prebuilt maps, UiPath solutions and printing these processes. Take it away, Martijn.

Thank you, Raghu. So let me set the stage here. At Cobalt Ridge, the source-to-pay process was a complete mess. maverick spend was out of control as people kept bypassing procurement and we're not making use of the negotiated discounts and contracts. This also led to a host of problems downstream in the accounts payable department. The team was being swarmed with invoices that are littered with exceptions. Now most of these problems originate at the beginning of the process, the request intake. So what about you, Raghu? Do you ever create any purchase requisitions?

Several and not my best days.

I hear you, man. Creating purchase requisitions in a legacy SaaS procurement system can be a painful experience. Let me show you what we do in the UiPath source-to-pay solution instead. So I'm not sure, should I switch? I should be seeing my demo screen. Awesome. Okay. So let me show you how we do this in our source-to-pay solution. We make buying as easy as asking. So as a manager in Cobalt Ridge, what I can do now is ask what I want. I want 5 laptops for developers. Now in the background, the solution is using the business ontology to understand my intent. It knows what I need and who I am. And it's using that to gather this data from our business systems, from our SAP, from our Coupa, from our Workday. But that's not the only thing we're doing. We're also checking my request against the Cobalt Ridge purchasing policy to make sure we only surface the products that I'm allowed to buy in this situation. This laptop looks great for my use case. I confirm the shipping address and boom, I'm done. It's that easy. From here, Maestro will take over the approval process. It will start reaching out to the users that need to approve on Teams and Slack. So we get a response fast and keep the process moving. So how about that, Raghu? That made the purchase requisition for us.

Yes, yes. So Martijn, this looks almost too simple. And I'm assuming -- I'm guessing the investors have seen this and like many have already seen this, what's going on here. So show us what is turnkey? What is the power of the platform underneath that makes it possible to build something like this in like an afternoon of configuration?

Yes, absolutely. So like any UiPath solution, the Source-to-Pay solution comes with a completely prebuilt map of work. So here, you see that visualized. It comes with all of this out of the box, a case plan for Source-to-Pay, a business ontology describing the concepts, business rules and policies. It's all there from day 1. Now that's not the only thing. It also comes with a complete user experience, agents, dashboards optimized for all of these personas based on years of experience. The only thing the business team now needs to do to make the solution their own is to configure the last mile, their own rules and policies. I want to see how that's done. Let's take a look. So here, as a process owner in procurement, the only thing I needed to do is to upload my own purchasing policy. It's a 30-page document describing everything about who can buy what at Cobalt Ridge. The solution already understands the principles of source to pay. So use that to translate this written document into a set of deterministic rules. Here's the rule we just triggered for laptops, describing what kind of laptops can be bought for engineers at our company. And when this rule run 100 times, you'll get the same result every single time. Let me see. Okay. So it was really easy to get this set up as a business user. Now let's move over to the accounts payable team because they were being overwhelmed with incoming invoices before. But now they're back in control. Here as a process owner, I can see all of the invoices that are coming in. I can see what my team is doing, but also what the agents are up to. They're doing a great job actually. 88% of invoices are now being handled completely straight through. Here's one for our laptops, for example. You see the agent picked it up or the system picked it up, I should say. It extracted the data from the document, matched it against our systems, performed all the necessary checks and posted it for payment. So no human action required whatsoever. Let me -- wait a minute. There's one invoice there that's blocked right now. So the only thing that matters now is how fast we get it moving again. Let's see if we can resolve it. It's an exception of maverick spend. We're missing the purchase order. And also there's no requester listed on the invoice. So I cannot start the approval process automatically now. Luckily, the agent uses business ontology to find out who could be the requester. It's suggesting it might have been Kate. She's in similar requests before in the last 6 months. It's also coming with a set of suggested actions. We could go to the experts, Dana, or go to Kate directly. I'll do that. Again, Maestro will chase Kate on Teams right now to make sure we get this invoice in blocks fast. So that was the exception. Now let's move over to the decision ledger. You heard Raghu tell you earlier, this is where we're capturing all of the decisions made by the end users. We're using that to optimize the solution over time. So here, I can see all the exception rules, and this is the one we just triggered when an requester is missing. It's not performing well. 42% of the time the people chose the suggested action. For the rest, they didn't. So how to improve? Well, the ledger already provides a suggestion of what we can do. It proposes that if there's one likely requester, I should go -- we should go directly to that requester instead of bothering me. So that's a real improvement, saving me precious time.

So Martijn, what's -- from the business standpoint, what is the ROI that a customer sees with this?

Great question. So Raghu, all of our solutions -- let me stay here. All of our solutions come with a set of prebuilt KPIs that matter for each process. For Source-to-Pay, it's all about reducing maverick spend and increasing our touchless rate. And I can see here that the process is performing much better than it was before we got started with it. We also saved over $300,000 already. So that's an outcome. Now here's what we just saw. The Source-to-Pay solution that Cobalt Ridge rates started in days instead of months. It allowed them to get fast ROI and outcomes that matter. And that's essentially what all the UiPath solutions do. Over to you, Raghu.

Thank you, Martijn. So as you saw from Martijn, the ability to use the UiPath platform and the concept of a prebuilt map to make wholly built complete products and solutions to customers to solve their process use cases that they really care about. So they did not buy the components. They did not have to hire developers to build these things out. We had prepackaged maps, fully built automations for them to go use. And that's what you saw. All right. So what do we see here? All right. So on the other end of the spectrum, where a customer may have a very bespoke process that is uniquely just their own -- our customers can build their own maps. I introduced you to the role of a cartographer. The cartographer would build out these maps from scratch, of course, with the cartographer agent doing the heavy lifting. So there's another way for clients to use cartography and the maps. But there is a middle path, and we are proud to announce Process Atlas. Process Atlas is a collection of maps for a set of processes that are common enough that we can give it some shape, but have too much variability for us to complete those maps. So we give the maps the domain knowledge, which is drawn by experts. And then your cartographer adds that institutional knowledge to complete the map so that it captures what is unique to your implementation. This is a long list of pre-mapped processes that we're going to launch with Process Atlas tomorrow actually. We chose these processes again very deliberately based on our assessment of SAMs and TAMs and where we found the best product market fit for our existing customer base. All right. With that, I'd like to invite Anvita, our product management leader, to walk you through how customers realize value with Process Atlas and cartographer. You'll see Anvita show how our history and strengths with mapping and building business processes really sets us up to collaboratively build these first-class assets and build out these maps in ways that we believe only UiPath can. And you'll see through the demonstrations how that works. So Anvita, please take it away.

Perfect. Thank you. Let's ground to yet another complex long-running use case. Large manufacturing equipments, when they break down, need to be covered for insurance, need to be contained for a repair or replacement, and that costs manufacturing companies a settlement amount. Cobalt Ridge, one of the illustrative manufacturing companies, needs to pay about $42 million a year in coverage cost. Here's the problem. Their mean time to resolution, call to close is about 11.4 days and their target for next year is under 4 days. And here's where the problem really lies. Here's how a single case of warranty resolution gets operated end-to-end. A version of this for every case and thousands of them operated by Cobalt Ridge every single day. There are people across teams that are contributing at different stages of this process and no single person or team within Cobalt Ridge might have seen this entire picture. You just heard about the concept of a cartographer. Me as the cartographer on this engagement is tasked to map how this process runs today, find how a reengineered process should be implemented so that we achieve our business goals of reduced call to close time and reduced coverage costs. Let's see how we do that. UiPath Cartographer allows my ability to build and maintain this map with an accelerated time to value. Here is where I begin, UiPath Process Atlas, hundreds of complex processes mapped by expert attestations with deep domain knowledge across the core 7 industries we operate in. They're all mapped across business areas and personas. And for my work, as I pick warranty resolution in the aftermarket and service business area, I can see a whole map built about how warranty resolution operates within a manufacturing industry, sample business KPIs followed by the complete case plan. multiple stages from intake to triage and doing the diagnosis and eventually doing restoration and closing the case, along with conditional paths when a case goes sideways and evidence that never arrived, having engineering exceptions to deal with or substitutions of parts that need to be reviewed or product quality escalations need to be managed. Every stage marked with a human role on who should be taking decision at every single stage, along with standard business ontology of which are the typical systems and business entities that go in managing this process. It's all interlinked and underpinned to SLAs across stages for the entire process. And it all underpins with standard coverage rules and policies that govern the entire process. This becomes my starting point. As the cartographer on this engagement, I built on top of this to complete this map to give it context about how we at Cobalt Ridge will add more information on how we manage our systems, our handoffs, which systems do we track, so we -- I can complete this map and build out my map for warranty resolution.

So Anvita, the 45% number at the top left there, I think it indicates the domain knowledge, the starting point that our experts, our attestations got us to. But there's a sea of red here. How does the cartographer agent help you, the cartographer into accomplishing the -- how do they prioritize it from the set of actions that it can possibly take to help you improve that number to a higher number?

Yes, you're absolutely right. So the 45% coverage got me from the process Atlas skills, the baseline of how a process typically runs in an enterprise setup. A bunch of reds indicating clarifications that I need to provide. And my starting point with UiPath Cartographer is to initiate a conversation providing it all the manual discovery documents that I've had, sets of process interviews, workshop transcripts, sample architecture diagrams, even full sample claims with hours of hand annotated description about how lines that were down for hours were dealt with.

So this is literally the scraps of unstructured information that we're going to provide structure for it.

Essentially. And I can simply prompt the UiPath Cartographer agent to document what I provided it, where the sources contradict, highlight it for me, and I continue to be in charge of approving all of those deviations. One such that it will highlight will be about when there are discrepancies observed on processes that were not observed within the UiPath skills. I continue to spar with the agent to provide it an input on how to continue building this map based off of how we at Cobalt Ridge operate this process. It also highlights inconsistencies what it observes between the context I provided and the skills it already has. One such where it mentions a couple of stages were missed out in my base map, claims denial and claims withdrawal. And upon my confirmation, it is able to add that into the map of work, so it continues to hydrate it with the context relevant to my enterprise. A few iterations out, we reach at a higher coverage, 45%, moved up to about 68%.

Yes. So a lot more greens, but still some reds. I'm still curious how does the agent help you prioritize from all of these unfinished tasks?

So UiPath Cartographer is a goal-seeking agent. It starts by listing out what the process KPIs are at the moment as a baseline, so it can figure out how the process reengineering solution should work. 11.4 days are call-to-close time, the baseline has been mapped, and this becomes an anchor for UiPath Cartographer to provide reengineering solutions. It built out the entire case plan based on my manual discovery documents, was also able to add the additional stages it observed is missing from my skills, but is relevant for my company. It also looked at mapping all the individual data entities from business -- for business ontology along with the core systems that we at Cobalt Ridge touch in, Helios CRM, AssetVault, systems and more. It brought in all the rules that we at Cobalt Ridge operate across individual stages and steps within this process, along with mapping individual departments and people and personas of who does what and at what stage. But as Raghu noted, there's still a bunch of reds. At this point, me as the cartographer need input from my business teams. There are a bunch of rules and exceptions that I need inputs from my business teams in the warranty resolution department to confirm how some of these exceptions have been handled. UiPath Cartographer supports that collaboration natively. Let's see a quick example. Here's one exception on how the UiPath Cartographer mentions that high-value claims over $10,000 need a VP approval, but there are open questions on how these exceptions are really handled. I need input from my business teams to give me insight on that. Ideally, show me how they do some of these exception handling. From within the tool itself, I can initiate a handoff. The agent is able to draft out a sample question with evidence attached and the explicit question that I need, and I can simply create a handoff to my warranty officer, Scott in this case, who can receive a notification on any communication channel for Cobalt Ridge his teams. This provides my -- me to really scale building this map of work through collaborations with SMEs. A few days out, as I start receiving inputs from business teams, all of those inputs get captured as feedback coming within individual sections that the map continues to build. The agent is able to take all of those inputs so it can add it to the map, redlining the sections that were updated based on the SME input that I received and is able to continue hydrating the map based off of all the inputs with SMEs that keep coming to me over the next few days. Just like that, mapping scales for me. From the 68% coverage, I reach a number which I feel confident about to hand it off to my developer team to go ahead and implement the reengineered process. What used to take me months of manual discovery, chasing teams, identifying what the exception handling paths are has shrunk to weeks, if not days, with UiPath Cartographer. And this all resides in a living governed asset within the map of work. The case map built out with individual stages capturing how work happens today and a reengineered view of how work should happen, anchoring to the expected benefits that we started with, layering on what the baseline of mean time to resolution of warranty is and what is the FY '27 target. This is how cartographer scales, building process automation opportunities at scale.

That's awesome, Anvita. Thank you so much. As you saw from this demonstration, our strengths and heritage with understanding business processes allows us to use Process Atlas to help photographers like Anvita map out their most complex business processes and not only map it out, but also reengineer them because of, again, our experiences with what the best ideal use cases and processes should be. All right. So UiPath Cartographer is generally available. We're going to announce that tomorrow on main stage. UiPath Process Atlas, the 100-plus skills that we have to hit the ground running is also going to be announced as generally available tomorrow. UiPath for coding agents is generally available. Now this is what helps our clients automate their processes, but across -- manage it across the whole life cycle from build, deploy, govern and even continuously improve, as you saw in a couple of the demos. And then an important observation that we found is customers of ours who are using UiPath for coding agent are finding about a 59% increase in their developer productivity. And then we're finding that those same customers are finding about 3 to 5x increased deployment velocity. So throughput of the developers is increasing almost by an order of magnitude. I want to transition to our test section now. So you saw Cobalt Ridge in action. You saw cartography mapping of work and building out automations with coding agents. What this really means is the power of cartography and the power of coding agents really accelerates the time to value for our clients. They can get a lot more done. Lots more automations get built, lots more agents get built, lots more things get orchestrated, lots more lines of business applications are also built. But these systems are all built on critical mission-critical applications like you see here, ServiceNow, Oracle, SAP and what have you. And the resiliency of these applications are fundamental to ensure that all your line of business applications, your automations don't break. Because if they break, obviously, your business comes to a cross. What we're finding happen is because of the proliferation of these coding agents, the volume of what developers are able to produce has increased manifold, more code, more change, more automations. This is causing test debt. Test teams are struggling to keep up. And the gap is showing in application quality. It's declining, as you can see here, even as developer productivity is improving. And for that reason, at UiPath, we're taking a novel AI-centered, agent-centered, coding agent-centered to providing the power of automation, the power of coding agents, the power of transformative AI value, just as developers have it for building software to also testing software. And for that, Ingo, I'd like to invite Ingo, our VP of Product, to walk us through Test Dark Factory. Ingo, take it away.

Thank you so much, Raghu.

Clicker is yours for a minute.

Here you go. Raghu, is always spot on, ladies and gentlemen, to put it plainly, software development is accelerating, but our ability to test isn't. So that means we are creating risk faster than we can test. And that's exactly where UiPath Test Cloud comes in. That's our market-leading offering for all things software testing here at UiPath. And today, we want to take you inside one specific capability of Test Cloud, the dark testing factory because the dark testing factory is the solution to fix the challenge Raghu just outlined for us. It is where software testing stops being something you run and starts running for you. And that shift towards more autonomy in software testing has to happen. And you do not need to look too far to see why. Just look at how software testing typically happens today. First, testing isn't just one task. It's a whole designing, automating and executing test and everything in between. And in many organizations, much of that is still done by hand, entirely manually or at best supported by traditional rule-based automation. And that is the manual grind that typically slows us down in software delivery. And while rule-based automation helps, it only gets you so far because not every task in software testing can be reduced to a fixed deterministic rule. That's where agents come in. So think of conversational agents like delegate or autopilot and suddenly, more becomes possible. But here's the thing, one agent here, another agent there, these are all disconnected pockets of authentic intelligence. And that's not enough to keep pace with software development. So the real shift happens when you start connecting these islands of intelligence into governed autonomous flows, flows that spin up and tear down and tire test environments that execute test cases across stages and analyze results all autonomously while humans stay in control. And this, ladies and gentlemen, isn't a future vision. That's what our customers are already doing today. So the lights are already dimming. Our customers are making their testing more and more autonomous or progressively darker, if you like. And every step up in autonomy brings another big jump in testing speed. And that's the journey we've seen our customers take so far. The dark testing factory brings that journey to its next level. You can think of it as autonomous testing on steroids. Here, autonomous testing is no longer a point solution. It becomes an operating system, Raghu, an operating system that allows our customers to turn their testing from something they run into something that runs for them.

Ingo, this is amazing. I share your intuition. How does this work? Let's show that.

Absolutely. Let's see it in action. Here you go. For this, ladies and gentlemen, let's go here into UiPath Test Cloud. That is the place where you design your dark testing factories. And let me show you one we specifically built for SAP S/4HANA here at Cobalt Ridge. And let me start where the work actually begins. So what you're seeing here is the blueprint of the factory. You see how work enters the factory, how it flows through and how work gets done. And remember, you do not run the factory. The factory runs itself. Now how does it work? Well, the factory is constantly listening to anything that changes your software application that could be a new transport entering SAP or a user story closing a Jira or a commit directly coming from your deployment pipeline. So a change is flowing into the factory. And from there, the factory takes it over. It creates a plan to test the change, breaks that plan into actionable work items and then distributes those across all the tools available to the factory. And Raghu, that's the big picture view of how the dark testing factory operates.

Do you mind double-clicking on one of these, like maybe the risk assessor and actually show what the value that, that provides? And also maybe speak to our investors here on sort of the differentiators here. What was the pre-dark factory cost of developing the risk assessor capability and post dark factory we are doing it.

Absolutely. Let's first dive into the risk assessment and then let's focus on the value add. So let me zoom into one of the tools the factory has available to get its job done. Let's dive into this one, the risk assessor. And as you can see, this tool is not a single agent or a single automation. It's a broader agentic workflow, an agentic loop, if you will. So this specifically is here to identify the business risks identified or associated with an incoming change. It also consolidates those risks with the factory's broader risk model to determine what needs attention first. So here, autonomy is always risk-driven. And here is where it gets interesting. You can even see these agentic workflows at work. So for example, here is the risk assessor currently busy processing an incoming change. You get all the live execution details, and you can see exactly where the factory stands. So you can literally see the factory thinking and working in real time. And importantly, those agentic loops that do not improve themselves. So at the right moment, it's either an agent or it's a human, as you can see, that jump in for review and approval. And if a person is needed, well, they are looped in right here in the inbox, where they, for example, provide missing knowledge to the factory, they help clarify the exception or they help the factory to make a trade-off. And that is the role of people in the factory. Humans do not run the factory, they teach the factory. And every time humans teach the factory, the factory improves its memory. And this memory doesn't just get better from what people teach it, it also gets better by learning from its own mistakes. And this is what turns the factory into a self-improving system. Raghu, this is what turns the factory into a system that gets a little smarter, a little more autonomous or a little darker over time.

Yes. Ingo, I totally see us bringing the concepts that we've learned with developing software to testing software, testing dark factory. Now tell me, just as a developer of software, the human is in charge. AI proposes but the human actually is the ultimate decision maker. How does that -- how do you manage costs? How do you manage governance, controls, guardrails? How does that work?

Absolutely. That's exactly it. Autonomy without control is not the goal here. That is why the dark testing factory is not only deeply embedded in our platform. It's also deeply embedded into our AI trust layer, where you can, for example, decide how much autonomy you want to grant the factory and all of its components. You can also define cost and budget controls down to the very last level. And you can also, of course, wrap policies around how the factory operates. So that means the factory doesn't just act autonomously, it acts securely too. And not even that is the goal, ladies and gentlemen. We are not making software testing more autonomous here at UiPath just for the sake of it. We are doing it for a reason. So what this is all about is helping you to answer one question we all have to answer at the end of the day, are we ready to ship the software application? And as you can see, the dark testing factory helps you to answer that question with evidence. No gut feeling, no intuition, facts. And the factory doesn't just help you to answer that question for one single application, but for every application in your entire enterprise. And that, well, gives you the confidence you need to ship fast at scale without leaving quality behind Raghu.

Thank you, Ingo. Thank you very much. All right. We are at time for the product section, but I'll quickly recap. I think what we showed you is our momentum with orchestration. Hope you saw that, and we can take questions a little bit later. You saw how we are going all in, in this model we call the Cartographer with the cartography agent to build out the map of work, which then is fed to coding agents to truly transform how process context is captured and how automations are built and managed in the enterprise and with the continuous learning loop where you can continuously keep this map up to date, but also your automation is up to date and ever improving. And finally, I think as more software and more code is being built in the enterprise, UiPath Test Cloud enables us to actually rein in the risk that comes with more software being written. So this is our product strategy. This is our vision. We hope to launch it to the world tomorrow, and we're very excited for it. Thank you for joining us. And then I think we -- I hand it off to Ashim, I think. Or do we have a break. So we have a break now. Thank you. [Break]

All right, everyone. We're going to get started with the second half of the session. We had technical difficulties in the first half of the session, and we just wanted to let everyone know that the first half will be posted to our Investor Relations website in about 30 minutes. With that, I'll turn it over to Ashim, who's going to run us through our go-to-market strategy.

Thank you, Allise, and thanks for everybody to come here to Las Vegas. I was just thinking about it. It's been almost 10 years for me at UiPath. And when I look around, it's amazing to feel like you're talking to friends who have talked with each other for so long. And as you hear the company's story, I'm just thinking as I was in the break of just reflecting on Raghu's pitch, the sophistication of the platform versus 10 years ago is incredible. I remember meeting Daniel somewhere around 2017. And there was really a box on the page called RPA. And today, when you're looking at cartography, you're looking at agents, you're looking at orchestration, it is incredible about how far we have come. In some ways, during the 9 years, we've had both an evolution, a period of evolution and a period of revolution. What I would say for UiPath is right now, we are doing both simultaneously. And for the next 25 minutes or so, I want to show you how that's transforming over to go-to-market and to show you what we're doing, how are we approaching our customers to capture the opportunity. The opportunity. So I think Daniel talked about $142 billion TAM. That is one frame to see the massive opportunity that's sitting in front of us. What we see, you can see in the back, you'll meet a lot of our sales leaders here. Over the last 9 years, we've actually seen an unprecedented level of activity, whether that's POCs, services, driving think orchestration into production, customer inquiries or just the sheer amount of work in enabling our sales team to be talking and responding to the customer requests that we see. That is a little bit of execution in terms of just the incredible way that the teams have become deep with our customers, but it is also because of the confluence of tailwinds that we see before us today. Some of these, I know that we know already, right? Agents. For the last 2 years, I think everybody has seen the swarm of agents coming, have been predicting it. And in some ways, we've overestimated it. But what we have underestimated is today, there is an incredible call for governance and incredible call to be able to orchestrate those agents and enterprise processes. What I do feel is something that is often left under the covers, but is just emerging is the tokenomics of it all. So I remember about a year ago, Daniel talking about why would you use an agent when deterministic automation can do the same job. Today, 30x higher cost. You hear CFOs coming out and CEOs coming out about how budgets are being exceeded. That is a real call for the RPA portion of our platform, the deterministic pieces of our platform. And so the AI wave not only has a great pull for us in terms of activity of orchestration and agents, but also into the deterministic parts of our platform. And when you look at what's happening in the world around engineering and what we're able to produce and how fast applications are able to come through, the application spaghetti that went through enterprises, that is only going to proliferate. Not only does that have an orchestration impact for us, an opportunity to be on top of those systems and processes to bring order and efficiency, but it also allows us to do and really grow our application testing business that you see. So when you look at our platform, there are multiple vectors of growth. The one thing that is interesting is that in some ways, the technological moat is becoming shallower with the pace of innovation that is there within the engineering realm. But what is very different is the need for depth and expertise. So when you look at expertise for us, whether that is in our people or whether that is in our product, UiPath has been investing in this for the last 2 years. And you'll see that throughout this presentation, whether that's cartography or as we go through the go-to-market areas in which we were talking. So when you look at all of this together, what is this activity that we're hearing about? What are they -- what are customers asking? They actually aren't asking about agents. They're not asking about RPA. They're not asking about the technology itself. When they ask about what UiPath does, it's very simple to say that we solve the hardest problems for long, complex workflows that every enterprise is battling with. That is a battle that has been there since I was an intern at General Electric in a manufacturing plant. It is the same problem that existed when we IPO-ed when we had a broad AI -- a broad story around our platform, and it is the same story that exists today. What is interesting as you look through the pages here is you can see the breadth of what we're able to do, right? So Daniel talked about the openness of our architecture, model agnosticism, the ease of use that we are able to go and put into enterprises, it means that we can conquer a broad set of problems. What is often missed is in the last 2 years, internally, we have been transforming ourselves to be super deep in specific domains and verticals. This slide, probably if you go back to our last Investor Day, it was just giving you a demographic of our ARR. Today, verticals is the way that we are organizing, the way we are going to market, the way that we are approaching our customers and the way that we are training our teams. So I look -- Eric Bouchard, who you're going to hear in the panel in just a little bit in our customer panel, he has been in the financial services area. I wouldn't call him an automation expert per se, but he's super deep in financial services, right? Joe Tafe runs manufacturing and our Summit business, which is our broad industrial base. He's talking about procure to pay with CFOs. He's not talking about the core technology itself. That happens as you go further into the processes. So this is not just about a technology area, right? So Raghu talked about the technology components, cartography, getting deep into this, Atlas, very specific processes that we're going. But this is how we are now organized, how we're running and how we are training and recruiting teams. Our Brandon Deer, who is our go-to-market COO, he owns enablement for us. When you look at enablement, enablement is as much about the breadth of our platform, but it is also beginning to drive depth across every function, sales engineering, sales, customer success, services across the board. That depth translates to our customers. So where we are fortunate sitting here at this moment is all the work that was done by the leaders that preceded us in UiPath. We had an incredible period where we were able to penetrate and move into the deepest customers, and we're still winning them today, which I'll talk about in a second. But when you look at this, we have 9 of the top 10 energy utilities. When you look at financial services, 70% of the top institutions are on UiPath. That has a twofold effect. One, we get a ton of information as we are deepening our expertise by listening to our customers. So when you look at Atlas, that is not our technology team in the back room or in the back office, so to speak, hands on keyboard alone. That is our FTEs deployed in our top accounts. That is our teams and our product managers having access to this customer base to hear it. What it also means is we have an incredible starting point of credibility and a starting point of workflows that we can go and drive against. When you look at this, though, our strategy still has 3 components. We are still a land-and-expand business. I think a lot of times when you look at companies, they've moved into this area of giving up on the land, but going through expand. And if I'm candid enough, the hardest question that we always get or the most frustrating question is around our customer count. We're not -- we don't look at the total number of customers as the primary metric. We look at the quality of that customer base. So when you look at the quality and you look at our financial services sector, through very intentional deep analytics and through making sure that we are extremely focused, we said federal credit unions above a certain asset value. Those are the type of logos that we want to go through. And you'll see that translate into Hitesh's section in terms of the economics for the company. But you can see the amount of increase that we are getting on new customers that are starting out with us at $100,000. And you're looking at that because their ASP, even if they don't reach that $100,000 right away, the average ASP around our new logos has doubled. So I can't resist the CFO part of my side. That is efficiency, right? Go-to-market unit economics, the cost of acquisition can go down and every dollar that you're putting in is returning more, both on a dollar value basis as well as a time basis. That is something that is not there sitting in our headquarters. That is something that is there in every vertical leader that they're thinking about. They're going after and targeting intentionally the ICP, the ideal customer profile. That is where we're channeling our marketing dollars. That is where we're channeling our people. But expansion is still going to be the fuel that will drive the company. And as we think about our ways to approach our customer base, we really started by acknowledging one really important fact, and that is our customer base in itself has expanded. When we first started, I think UiPath roots were a line of businesses. And over time, we kind of -- we invented the center of excellence, so to speak, within many customers during that era. So when you look at a certain period of a time, the center of excellence became our primary customer that we would sell through, that we would talk through. There are still huge importance to us. We are still well connected. When you go around and meet customers in this -- during this conference, you're going to find many COE leaders. Many of them have broadened their scope, including AI capabilities. But what you're also going to see is as we go into orchestration, we are now moving in and talking to AI architects. As we go into Agentic and governance, our profile of who we are selling to is changing. As we're deepening with the vertical solutions that Raghu talked about, line of business buyers. We're back into talking to supply chain leaders. We're talking to procurement leaders in the office of the CFO. We're talking to Chief Risk Officers, whether that's with WorkFusion or other products that we have. And when you think about what that means for us, that is an entirely larger market and a new set of budgets that we can go after. So as all things within software, you can go and say, okay, I know the customer, and you can unleash the team. Daniel was very conscious to say, what are the 3 motions that we want to invest in. It gives us focus. It gives us prioritization of where our capital will flow, and it also informs us in terms of what are the types of skill sets we need to bring into the customer. So we have 3 motions that really are driving our growth that you're going to see some of the impact that, that will have or that has had when you see Hitesh's section. Those are, one, orchestration and automation, really building on the foundational base that we have of customers and centers of excellence and moving up the stack, and I'll go through that in a minute. The second is selling verticals, and the third is application testing, which you saw Ingo's incredible demo that he saw that he gave everybody here just a few minutes ago. So I want to start with the horizontal. Very specific in terms of a customer journey. You can land with a simple use case that can have deterministic automation. And what's very interesting for us is when you go to public sector in Europe, deterministic is in. They're not -- they don't have high-risk appetites to go through. So landing with deterministic in many regulated industries is not -- is actually a structural advantage to us versus a disadvantage. It is an accelerant in the discussion versus a deterrent. So when you land with that customer, our biggest thing is land with the right use case. And you saw that with the ASPs and the quality of the new logos, land the right customer, land the right use cases. And then you move up to the stack to say, how do we go into higher-quality workflows that we have. And in some ways, moving from task to process, what Raghu talked about, that is embodied in that motion that has been there throughout for our customers. What is newer is continuing to now scale from the deterministic side to the other parts of our platform. So if I have a workflow of 20 components, now you can go and say, 10 of those are deterministic, 5 of them could be agentic. When you put it all together, it needs orchestration, and you start moving up to the stack. So when I think about customer metrics, customers greater than $100,000, when you listen to our earnings, they're growing substantially. Customers greater than $1 million. We continue to see double-digit growth across those categories of customer bases. One of the reasons is because we are providing higher and higher value. It's where we're focusing on, moving from task to process, moving from process to impact across everything. And in doing that, 90% of our $1 million-plus customers are pulling multiple elements of that platform together. This motion is actually our second nature. It's very much into our DNA. What we have -- when you look at this from what the impact is from a customer base, you can look at a key insurance company that we have within our portfolio. They landed and built a multimillion-dollar foundation. That was built on deterministic automation, claims, underwriting. These are things that we were already involved in. As they know our technology, as they know our team, now they're expanding into IXP, they're expanding into Maestro. What is also lost is their deterministic base is also expanding. So this chart is actual data. And what you can see, and I would just point to 2 things that I think are super important. One, you can see the continued expansion even on the deterministic side. The second point is when you hit into that area where you could get into that enterprise architecture for orchestration and broaden the scope in terms of AI, it has a twofold effect. One is you get a pretty good surge of growth just from pulling in the new products and new capabilities for the outcome that's there. What's lost is look at the curve for the deterministic automation. That's the tokenomics coming into play. Customers know us. They know when to use deterministic automation. And as you get into larger workflows, it pulls in multiple elements of our platform. The second piece of our motion is verticalization and driving vertical solutions. And I give a lot of credit to the Americas sales team around what they drove over the last 2 years and our international base with Alexandra and Matus, who are also here in the audience. We kind of said, look, for the last 7 years, we know the processes that are driving the highest value in approaching it in a horizontal way. When you see 7 of your banking customers going after loan origination, you can go and say, how do I start selling loan origination right off of the bat. And what that does is it flips the curve in a different way. It packages it from multi-steps into a single step and a single outcome. And when you do that across the industries and you infuse our company with good expertise, the right analytics, you can actually go and put this together in a way where you get multiple vectors of growth, financial crimes. Organically and inorganically, we approach this, right? And we're getting incredible response from our banks. Here, we are selling to our Chief Risk Officer, Healthcare RCM, Office of the CFO, which Raghu gave you the procure-to-pay example, and you're going to see that actually in the next slide. But what does it bring for people? It brings them a fast time to value. So whether it is the components or a productized solution, they're able to plug it in and get ROI in a much faster way at a much lower TCO. The second piece is you're able to differentiate because we've invested in industry experts. We have people who've lived their life, both on the product side and on the field side, invested in understanding what a healthcare provider has to go through, what problems are they solving? And we are able to infuse that as we iterate with the customers. And the third piece is it gives us flexibility on pricing. When you're pricing per widget, it's very different than when you price per outcome. When you're able to say you're going to save $100 million, it gives you a very -- a much better discussion when it's something tangible and it's something that you can implement within the next 90, 120 days, and you don't get shifted over to procurement. It's a real line of business leader discussion in which we're facing. So let's make it real. This is this year, Fortune 500 automotive company. And you can see they were a good deterministic automation company, $100,000-plus customer. They continued to scale. They expanded with their platform. And then what the team went in with is they said, they actually met with the CFO and they said, let's show what we can do around your toughest processes. Procure to pay, no matter how many vendors are out there, you do not hear efficiencies of really transforming procurement for major companies. Manufacturing, retail, that is 80%, 70% of what a finance operations sometimes has to contend with. We were able to show the solution that was built by our product and engineering team. The CFO, he didn't have to be technical. He didn't have to ask, can you tell me the difference between agentic and deterministic. He went and saw that, that creates, that solves my problem and my need, and it allowed us to price and move that customer from a great customer in our $100,000 club to $1 million-plus customer in terms of where they are today. And that motion is incredibly important to us. That's the second motion. The third, I would call an adjacency, but it's really hard because it's not parallel play. It's synergistic play when you're talking to the CIO or when you're talking to the COO, CEO or CFO of a company, you're able to go and bring other value drivers for what we have. So with application testing, Ingo showed you the -- like we are, to me, leaps and bounds ahead of what I would consider a lot of legacy applications within that space. So when you sit down and say, here is modern technology to plug into a problem that has been there where there is still a ton of manual testing or inefficient applications, we are landing application testing focusing on specific problems, S/4HANA migrations. We can go and land in that area. It develops a credible landing point for our application testing business. And then from there, we can expand meaningfully into other applications or other areas. And then lastly, it is reenergizing our GSI partnerships. GSIs, application testing, systems implementations, this is a massive issue that they are dealing with today. As much discussion as there is with AI, we're forgetting how many companies are migrating from on-prem to cloud ERPs. Application testing has a massive tailwind for us to be able to go and take advantage of that. GSIs know that, and that brings into a scale play for us within GSIs. That is another vector of growth for our customer base. So here, I'm not going to go through the orange and blue, the deterministic side, move them with AI products. AI products pull forward deterministic. That part we talked about earlier. But what you can see is there is another burst of expansion that comes from application testing. So now you're not competing against an automation vendor. We are solving multiple problems for our customer. That is hard to compete with for one-on-one competition, and it deepens our differentiation, and it gives our sales team another avenue of expansion and our customers another avenue of value for where we are. All of that comes with incredible demands on delivery. And I would say, as much as we have transformed our go-to-market, we are transforming our delivery centers as well. Forward-deployed engineers, I know that's become a term in the industry. What I would say is we are authentically evolving our FTE model in 2 ways. One, bringing forth our services and our engineering teams to really make sure that the first wave of implementations go well. How many implementations have fallen short in the AI realm, falling short on ROI, falling short on expectations of implementation time line or delivery. Our delivery method is improving every single day, and this is an area that we will continue to invest in. The second piece is our partner ecosystem. I remember 3 years ago, when you walked around, you can see and talk to our partners. And you can feel a little bit of -- you felt a little less energy, you felt a little less connectivity. I'm excited for you guys to meet our partners right now because they are a channel to market, both on the implementation side and within specific segments of where we have. And then professional services is a differentiator for us. Because being a smaller company, that linkage and the ability to move with speed enables us for faster and faster implementations. So if I go back to those curves that we showed you in terms of when you put forth deterministic to AI, AI to test, the faster that we can get those in production, it gives us the next set of burst around expansion. So we take delivery and the focus on adoption has been a major focus for us for the last 12 months, and we will continue to double down into this area. So that is our strategy. Our 3 motions: expand horizontally, expand vertically and the vertical depth with vertical depth that we have and frankly, take the opportunity of what is a differentiated application testing platform and deepen our roots with our customers and being able to expand the value that we provide our customers. So how do we operationalize the strategy? This is a standard pyramid chart. I know for everybody who sit here, sitting in here, I'm sure every single event that you go to, you're going to see this. The difference for us is in that strategic and core business. What you usually hear is large enterprise, enterprise, commercial. For us, what we've done over the last 6 to 12 months is really look at our strategic accounts and say, we're going to change the way we support and deliver them to be very specific, tailored. It's not about a ratio there. It's about the skill sets that you're needed. If you're a healthcare provider and you're going through a massive implementation with an orchestration, we probably need more people to help on the engineering side to ensure the proper governance, et cetera, that is moving into those areas. If you're attacking a case within procurement, we want to make sure that the product team that is associated with there is really working through that area and supporting them in the right way. So the top level of this pyramid is not about pyramids. It is not about ratios. The top part of this pyramid is driving value and expansion through really customized support. The middle level for enterprise is where we are running programmatic plays across our customer base. So when you listen to our sales leaders and you interact with them, that's where they're putting and where we talk about our ratios of deploying enterprise reps in terms of how we're approaching it. It's where we're going through the 3 motions in a programmatic way, aligning marketing, aligning delivery so we can scale across the thousands and thousands of customers that exist in that space. And then commercial, it's all about ROI. Commercial is important to us. But how we support it, how we deliver it, channel supported with the right approach and go-to-market and the right digital support. We cannot physically be in all of the locations. So building our digital capability in this area is very critical for us. And you can see that we've expanded the concept of segmentation around our customer base to the global footprint. Three points of excitement for me on this page. The first is we are truly global. We have capabilities in Australia, in Northern Africa, in India, in Korea, in Europe, in Germany, going across and obviously, in the United States and Latin America as well. So our -- as we talk about operating leverage later, the foundation of where we're playing has been set. What we're doing now very deliberately is saying, where do we want to channel from a prioritization standpoint, our investment? What are the largest markets to go after? So it's not about segmenting your customer alone, it's about segmenting the geographies. That has a specific area of making sure that we can channel the right amount of investment to win in the markets that we need to win in. And then just the last 2 areas, pricing. Our pricing is always evolving in a certain way relative to the market, but we have a foundational set of principles. We price the platform. We price consumables. Our consumables are really around our orchestration and our agentic capabilities, and we price robots and users. And each -- all 3 of them have tangible value to our customers. And as you look at that, that really has protected us from this question around attrition of the user base because really robots is the lion's share of that robots and user base. And our pricing is moving towards pricing the platform and the consumption of what we're selling to a customer. They have the ability to understand what they are buying from that perspective. And lastly, our partner ecosystem. It is vibrant. It will continue to improve. We're never satisfied with it. But you can see that we have -- we've kept one of the calls that has been there from our partner ecosystem is to be stable. This is a partner program that we launched 2.5 years ago or 2 years ago. We've kept it very stable amongst our partners. But what's great about it is we are also developing deeper and deeper partners, partners like Genzeon, healthcare providers, deepening with them. That is very important as we're moving and evolving our program. And testing is bringing a new suite of partners into our base, which is super exciting. And we will continue to invest in technology and go-to-market partnerships. We are more focused. We are deeper and we're more intentional in terms of where we are going. And that element of focus, it's something that we're never satisfied with. If you sat in our meeting 2 days ago, you'd say, hey, we want to be even more focused. We want to be even deeper with these partners. But going across go-to-market, what I hope that you'll see as you listen to our customers is that customer centricity, the focus and the energy around higher-value processes and outcomes is what's really driving our company. So with that, I'm super excited to bring to the stage Eric Bouchard and an incredible customer panel. I've sat in your seats. I've been on the outside of the software world. When a vendor is saying something, it means something. But when your customers are saying something, you know that, that is true. And I'm super appreciative for the time and the expertise and frankly, the quality and the caliber of customers that we're able to bring on the stage today. So Eric, I turn it over to you.

Thanks. Well, first and foremost, thank you all for joining us today. I thought we could start out with just a round of introduction. If you don't mind introducing yourself, your organization, your role, and then we'll jump into some Q&A. Srini?

Good afternoon, everyone. Glad to be here in this panel. Srini Nanduri, I'm the Vice President and Head of Data and AI at PPL. We are an energy utility organization, 100-year-old organization and really looking at the transformation of AI across the board and how do we build the utility of the future. If you look at it, my team really is looking at AI and automation, how do we help our customer, our field, our grid operations and really helping our employees having those AI tools so we can serve our customers and really serve the community that we serve today.

Jairo?

Jairo Quiros. I'm SVP, Global Business Services. I'm also responsible to drive automation and AI across the enterprise, leading the Center of Excellence for automation. And in my global role, this is what we do day in, day out.

Hi, everyone. I'm Pramod Dibble. I lead AI and automation for USAA's Bank. So USAA, you're probably familiar with them, have an insurance company, property and casualty, life insurance company and a bank. As we start to think about how we deploy AI and automation across all 3 of those lines of business, that's really where my role gets involved. So with a heavy focus on anti-financial crimes, that would be anti-money laundering, bank fraud, customer disputes and escalated complaints.

Good afternoon. Glad to be here. I'm Paru Puttanna with Voya Financial. I'm Senior Vice President, heading the architecture as well as the Agentic AI delivery. As you guys know, Voya Financial is a Fortune 500 in retirement, employee benefits and investment management. So my focus is going to be across the board, helping the organization in both in the architecture space as well as having the Agentic AI and automation delivered.

Thank you, all. So I thought maybe we'd start out talking a little bit about the journey, your automation journey over the years and more specifically how that's changed in the past 2 years. So Jairo, maybe I'll start with you and the journey Equifax has been on from its inception, but with a focus on really what's changed in the last couple of years.

Super. So I think at Equifax, we've been committed to improve the way we work. And 10 years ago, actually in 2016, when UiPath was ramping up, we also made partners with you guys. And since then, we've deployed hundreds of automations across the enterprise. Our role is global. So when you think about the breadth of the type of work that we've done with you guys going from deterministic automation to integrate Gen AI to adoption of intelligent automation and all the flavors that we discussed here today. When we think about Agentic and AI, for this past year, I think the impressive aspect of how the technology has evolved and the relationship also has evolved as a customer of UiPath is that we strategically have partnered to provide our feedback to help build our requirements within the product. And I think I speak the name of Equifax, but I think it's a common sense that you guys have been open to integrate the new features into the product. And in this case, particularly over the past couple of years, AI has been an inflection point for us, of course, as many companies. But our focus has been not to really prove the technology works. It's actually thinking about how do we integrate a new operating model, how do we redesign the processes now that we have the technology available in order to do so. And utilizing capabilities such as Maestro and the orchestration has been key to us for the last year or so as we're designing the future around automation at Equifax.

I appreciate that, Jairo. Paru I thought maybe we'd ask you a similar question. Obviously, past couple of years, you've been very engaged. Maybe talk a little bit about that journey, where Voya was maybe 3 years ago, how that's changed in the past couple of years and the things that we're accomplishing today.

Yes, absolutely. So I think like everyone else, so I think they've jumped on the Gen AI and Agentic AI journey in the past few years following the industry trends. We also learned from some of the initial Gen AI experiences, build versus buy, what is the best way for us to move forward. And it became very natural of using some of the UiPath capabilities. To name a few, so we are heavily using the platform using the communication mining, Maestro for orchestration, IXP from a document processing perspective. and definitely the unattended and serverless robots. And then recently, we've also started to use the coded apps. So I think we are touching all the capabilities of the latest and greatest from UiPath. And it's been a great journey from the past couple of years and where we are headed, and we are using UiPath capabilities for our strategic use cases and implementations.

And Paru, maybe staying with you. So you're using the whole platform. What are some of the use cases that you're most excited about? Have you organized some of those things? And to the extent you can touch on it, what sort of value are you getting with some of these workloads in production?

Yes. So we view AI as not necessarily a technology initiative. We are using it as a catalyst to really transform or reimagining how we serve our customers and partners. We also have a cultural shift for our employees, they keep our employees to do the better work. So from the value perspective, some of the use cases, I'll say I'll come to the value next. So we have multiple use cases which are in production, whether it should be the claims processing, analyzing the fraud and also improving some of the business processes automation. From the value perspective, I would say, I think the value comes in the way I look at it, whether it's a multiplier of -- you look at the model capability, multiplied it by your workflow, multiplied by trust and multiplied by governance. It's an exponential factor of the human ingenuity. So I think if you -- any one of those factors are 0, your business value is -- you're not going to achieve the business value. So the way we look at it, how can we reduce the business processes or reducing the time and we'll also improve from the accuracy and serving our quality to the customers and keeping the customer experience and customer obsession in mind, then you automatically your business value is -- you will gain the business value. Yes, I think from the finance perspective, you do have to show from the ROI. I think these are the factors that we are including when we are providing the business value.

And Srini, maybe over to you. I know we've obviously spent a lot of time with Paru and some of our forward-deployment engineers and insurance experts. We've been talking a little bit about some of the opportunities within utilities and specifically meter to cash. Where are you most focused? Where are you guys getting value? What are you most excited about?

Yes. I think as most of us on the panel, like we started our journey with task automation, right? We looked at employees and said, what are the tasks that you're working today? Can I automate those tasks? And we soon found out that the business doesn't work in that way, right? It's a business process that you need to look at from a transformation perspective. So we step back, we worked with our business and really define what is that end-to-end meter to cash process looks like. This is when you set up a new meter, the billing system that needs to be onboarded and then you finally get the bill, right? And one of the things we found out as part of the billing process is it's called the high-low issue or we build an Agentic solution on high low, where you get a bill at home, you look at the bill and why is my bill high compared to last month, right? You don't know why -- that's a simple question that you should be able to answer. But as a company which has a legacy around so many systems, we're still in the process of transforming some of the legacy systems to the cloud, we found out that, that is a pretty complex question for us to answer, where we need to take a question. We need to look at multiple systems. We need to go manually find out why the bill is high by touching the billing system, by accounting system and some of those areas. So there's a lot of steps that needs to be followed to actually make that happen. And this is where -- I mean, one of the great examples we have, we automated the whole thing. We call it like the Agentic solution around high-low bill analysis, where we have agents actually look at the billing processes. The RPA bots actually go pull that information, provide that information to the agents. And Maestro is really kind of orchestrating that end-to-end process, right? Looking at why is the bill high, is the weather-related issue? Is it something else? And really can provide a much more detailed information back rather than a simple automation that you would have done. And this also gives an example of how do you look at that end-to-end business process that you can really transform rather than those individual tasks, right? And now the process becomes really intelligent and then you can fully automate the process as we go through. So this is one of the great examples of looking at the end-to-end business process, identifying those tasks and really automating some of those steps as part of the meter to cash process, where the whole process becomes integral.

And Srini, maybe continuing in that vein, I think you said that you guys have mapped 50-plus of these processes within the organization. Are you looking at these? And at what point are you saying these aspects can be deterministic. This is where we need agents. And how are you making those decisions as go through? And then how is that resulting in a build and maybe your use of Maestro orchestration to stitch that together?

Yes, I think that's a great question. And I think the way we have -- we normally start off by not looking at whether that's an agent or an automation bot. We actually take a step back and say, what's the business process that you're trying to automate? Is it something where you need to bring some reasoning into the mix? That's where the agents are really good at. If it has to go -- and as a regulated organization, we need to make sure there are certain aspects which are highly deterministic, right? When we are pulling information from a billing system, when we are adding details back to the billing system. So there's a lot of SOX and other compliance that we need to go through. So we have to look at it as an end-to-end business process, identify those different tasks that you need to follow, right? As part of the process. Some could be agents, some could be deterministic RPA automation bots and some could be human judgment, too, right? At some point, if the billing exception, the agent or RPA is not able to solve that problem, the Maestro kicks off the task to a human. So the human actually goes into a workflow, figures out what happens. So there's a human judgment as part of it. And this is where really -- I think the future of automation would be a combination of those 3, as was said before in the previous presentation, is really around orchestration around humans with agents, with RPA bots, right? You need to bring all the 3 things together. And based on that, you can figure out which is the right process for the right task.

So maybe staying in compliance and regulated industries, Pramod. So you're using some of our financial crimes and compliance vertical solutions today. Talk a little bit about how you guys are actually using that at USAA and any ROI or value that you are able to share.

Sure. I'm going to avoid getting my hands slapped by the lawyers, so I'm going to stay a little vague about figures here. Let's just say that we're comfortably in the black on the engagement. We started as a WorkFusion customer. And of course, with the acquisition, now we're part of the broader UiPath family. There are a couple of processes that if you're a bank, you must do. So if you don't spend your Friday evenings with a glass of wine in the Patriot Act or maybe Bank Secrecy Act, then you might not know that you're required to file a suspicious activity report, which is called SAR. SAR is you've observed something in a customer's behavior that you need to then refer to the federal government, to the Treasury Department. And this is one of the highest risk compliance processes that exist in banking. So 99% is not good enough. It has to be 100%. If you missed a few, that means you're going to get a consent order. And part of a consent order means you can't sell checking accounts, and that's bad. So we try to avoid that as much as possible. We're leveraging WorkFusion's solution to perform some of the research that goes into compiling the packet that then goes to FinCEN over to the federal government. In the before times, this process took about a day or 1.5 days to do one, and we do a lot of these because we're a big bank. So that's one space where we're leveraging this. It's a combination of a variety of different modules. One of the advantages of using a vertically integrated solution is I could go out and build this probably. The issue is it's a complex multistep process, and there are different technology modalities that are baked into each one of those subprocesses. So what's going to happen here in reality is that my business is going to have an idea, and we're going to talk about it for a year, and then we're going to go build code about it for a year, and then we're going to validate it for 6 months. It's the problem that you've solved 2.5 years later doesn't even exist anymore, not really. So that's where the vertically integrated solutions really speed up your value -- time to value. Secondary process where we're using this is you're required to review all of your high-risk customers once a year. You risk rank your customers based on what you think they're about to do. And then every year, you look at them to make sure that they didn't do those things. That process takes about half a day, again, in the before times, now substantially less than that.

Jairo, let me come back to you. You have a lot of big technology partners and vendors. Why UiPath, right? Where you're using us? Why us versus others? What capabilities? What's driving you guys to make those decisions?