I'm Allise Furlani with UiPath's Investor Relations team, and I'd like to welcome you to our virtual fireside chat and product strategy overview. We'll begin with a fireside chat featuring Daniel Dines, UiPath's Founder and Chief Executive Officer, and Raghu Malpani, Chief Product and Technology Officer. We'll then take a deeper look at our product strategy and roadmap, followed by a customer example to bring these concepts to life. We'll conclude with time for questions. You can submit questions at any time during the Q&A session using the Q&A function in Zoom. Before we begin, I'll cover a few housekeeping items. Today's event is being recorded and will be posted to our investor relations website following the session. I would also like to point you to our Safe Harbor statement and remind you that today's discussion may contain forward-looking statements. Actual results may differ materially from these statements as a result of various 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 this is a product webinar, and we will not be taking any financial questions. With that, I would like to hand it over to Daniel to begin. Daniel? Thank you so much, Allise. Hello, everyone. Thank you for joining us. Today, I have the pleasure to introduce Raghu to you. I met many people in my life, but in very few cases, I had really this type of positive vibe that I had when I met Raghu first time. He struck me as a clear thinker, no nonsense, no politics type of guy that we really needed to run our engineering organization. He didn't disappoint. Raghu was basically the architect behind our big push into Maestro, into adopting a new modern workflow engine that is changing the entire company. Raghu, what brought you to UiPath, basically? Yeah, Daniel, thanks for the kind words. Appreciate it, man. Look, as many know here, I worked at Microsoft and Facebook prior to coming here. Worked at Microsoft a few times, left it a couple of times. Worked on what became Azure in my first rodeo there, and then the last time I left, I left the Office organization to join UiPath, to join you and the team here about two years ago. After spending close to 20 years in the industry, I wanted a place which met some expectations for what is it that I work on, who is it that I work with, and how is it that we work together. Daniel, you and I had many conversations before I joined. In fact, we met, I think, two or three times in person. It was clear that the transition from RPA to agentic automation to business orchestration was the need of the hour. The act one and act two transition that you've spoken about for a couple of years now, made this, to me, a resoundingly compelling opportunity, actually, and where my passion and my interest aligned with helping drive a significant transformation, working with people I enjoy working with. More importantly, I think, and we don't talk about it enough in the industry is, what also drove me here is who I work with and how we work together. It was clear that Daniel's way of leading and the culture inside of UiPath best matched my ideal workplace. It's a uniquely customer-centered organization where there is humility all around, with everyone you work with, regardless of levels and titles. Being plain spoken and direct, being humble and decisive, being strategic and being hands-on at all levels, where everyone generally steers in the same directions was an important part of how I wanted to spend the next decade of my career. Working in a fast-paced environment, making meaningful impact, and I found this company, UiPath, to meet the cultural needs for me to thrive doing the kind of work that I was passionate about. I'm so glad to be here, Daniel. Actually, it meets all of my expectations. As we all know, the world of software is changing so rapidly. The winners and losers are to be decided, to be quite honest. The technological mode that we have and we are building, the customer base we have and we are growing, and most importantly, the culture of fast decision-making and the velocity and the customer centricity, I think we have the ingredients to build a great company and strengthen the great company that we already have. I'm excited to be here, and it's been a fun couple of years, and I'm looking forward to many more, Daniel. Well, that's great having you here. You know, you passed the BS test of our engineering leads in Romania. Yes. That was part of the hiring process. That is true. I was impressed that you were the only one maybe, or maybe two people that got a good review from that site. Yeah. It was both hard and easy to pass the test. It was hard the first five minutes, but I knew who I was speaking with, and the connection was deep and immediate, Daniel. I think I understood in those first few minutes talking to some of those folks what was expected and the cultural alignment was clear from the get-go. I'm Yes. You're talking a lot about code in the interviews. Yes. Myself, I have asked you some kind of hypothetical coding stuff. Yeah. Those are good. I think it was really a good lesson how to get someone culturally fit. Yeah. It's super important, Daniel. I think the leadership team that we have at UiPath now, between you and your direct reports, I feel like we have that team where we can speak freely, we can spar openly, challenge each other, and really push the boundaries for what is possible. I feel like we are in a good spot for all the reasons that we just discussed. Daniel, I want to ask you a couple of questions that I'm sure is top of mind of the many investors that are here. There's a narrative that AI could simplify or even eliminate large parts of the software stack. In that world, what moat do you think we as UiPath have? And how does our role become, you think, even more important? You want to take a stab at that and then I can add my two cents? Yeah, of course. Look, as we all know, this is not a new narrative for us, for UiPath. I think we've been on this AI kill list the first, I think, around 2023, and then we've been on and off that list. Sometimes we've been on the beneficiaries of AI, sometimes on the kill list. I would like to debunk maybe a few things about AI versus RPA and versus UiPath. Look, I think it's important to distinguish between using AI during implementation time, during design time, during process requirements definition, and using AI during execution. Because I think first, the main important question is AI capable of executing a task as RPA is doing? Well, the answer, it's very nuanced. In some cases, it's capable, in many cases, to run a complex multi-step task, going multiple applications, completely autonomous. It is not today. Even the most interesting incarnation that we are seeing, like Claude Code, so they are meant for doing ad hoc tasks, and in the presence of the humans. Humans is ultimately what decides if the payment transaction is processed or not. Our business is really about running complex processes, workflows in an unattended, autonomous fashion. I think this is also even philosophically, it's not the territory of AI. AI create a code that is running on an infrastructure, it's running on the framework. But AI is not meant to replace that code, even if it can. In a very simple example, AI can multiply two numbers right now. Look, sometimes there can be errors. If I really put it to test and give it very, very big numbers, AI might not succeed. Anyway, if I have to multiply two numbers in a loop 1 million times, AI will struggle, and it's not going to have 100% accuracy on that one. Nobody ever is thinking that AI should multiply two numbers. Everybody really understands that AI will call a tool there to multiply these two numbers. AI is smart enough to understand, this is a request to multiply two numbers, therefore, I'm going to call a tool. Why it's hard to translate it into automation. Obviously, AI can understand when it's the right context to run an automation, and it's going to run that automation. How do you create that automation? This is where we shine, and we offer an amazing platform that makes it very compelling for most of the enterprise to build their automations to run on our platform. Also, as part of this, it's not as simple as you create one automation, you run it, and you are done. If you look at the landscape of enterprise processes, it's so much complexity there. When we speak of a process like procure to pay or order to cash, we might have hundreds of sub workflows involved that have to be clearly orchestrated by rules, by policies, by human judgment. You'll have always multiple actors. It might be a dozen of people involved in a process. People are not going to let just a black box AI do my order to cash, and people and AI have to reason over a system. Right now, what I'm most excited about is actually the emergence of the coding agents, because this is basically, it's the best of both worlds. Coding agents will help our customers and our partners to build automations, and to build automation at larger scale than we've seen before. This is basically our biggest roadmap change that we had in the last few months. We pivoted the entire company into enabling our platform to be used primarily. We even see the primary person to use our platform is going to be the coding agents. We will support, we are coding agents agnostic. Of course, we work with Claude Code, we work with Codex, and we work with all the best coding agents out there. Our ambition is to have our platform enabled since the inception of an idea of automation to creating a process specification, interviewing multiple stakeholders, understanding the process, the nitty-gritty of a process, creating the solution architecture, creating all the artifacts, including RPA, including Maestro, including Document Understanding, to debugging, testing, deploying in production, monitoring in production, fixing all the exceptions that happens in production. AI will be the main factor interacting. It's going to be a very heavy use of AI, but who runs the pieces, the artifacts that run on our platform are code, are very reliable, work a million time. In the same time, you can reason, same input is going to produce the same output. It's governed, it's secure, it's auditable. In a way for us, now I see that really coding agents, it's an amazing accelerator. Yeah. Thanks, Daniel. Might be a good segue for us to talk a little bit about our product strategy, Daniel. Talk to our investors on how, like you mentioned, how we are going up that value chain from tasks to more complex processes, and then how coding agents make it a massive force multiplier for our platform. Let's just jump right into the product roadmap presentation. I'm going to share my slides. Give me a second here. All right. Can you see my screen? Allise, can you confirm? Yes, we can see your screen. Awesome. All right. Let's talk a little bit about what Daniel spoke at a very high level for the vision of where we want to take our products over the coming months. We'll talk a little bit about our product strategy and how we believe we capitalize on the opportunities that are ahead of us in the coming year. All right. Let's first maybe look at the reality in the enterprise today. There was a survey done with a few CIOs last month, where you'll see on the left that integrations is the number one barrier for transformation, agentic transformation and automation transformation. Nearly 50% of the CIOs say that connecting AI agents to existing systems like their databases, their CRMs, is a large challenge, and it's compounded by all kinds of data quality issues. On the right, you see the emergence of, and this surprised me, too, the emergence of shadow AI, where about 20-odd percent of deployments are already unauthorized. Now, this makes it clear that enterprises need a unified platform that solves both sides, the deep integration into all types of systems, and governance built in. Now, of course, UiPath solves for it, but it also clearly gives a signal that the best way to solve the problem is to really go up that value chain, to take away this complexity from the CIO, the COE, and even the business users, and provide them more turnkey and end-to-end solutions. Now, let's take that enterprise reality and transition a bit to talking about how we are going to take our platforms and our products forward. We want to introduce to you our agentic business orchestration platform. We'll read this from bottom to the top. You'll see we started where UiPath found its first product market fit and defined category leadership, as you know, at the task layer with UI automation and pretty all-encompassing enterprise connectivity. This is the foundation that let us reach into any application and any interface. It's our biggest and deepest moat, as you all know. It cannot be overstated, because the most complex enterprise processes have a mix of legacy and modern applications, and it's critical to have robust RPA support and API support to meet that diversity that we need. As you may know, in the last 12-18 months, we've moved up that value chain to also include process orchestration in our product mix, with Maestro coordinating robots, humans, and AI agents to orchestrate the most complex business processes end-to-end. We've built some of the deepest technological moats here, with support for long-running workflows that run for hours, days, weeks, even months, spanning multiple agents, multiple systems and humans, work that gets interrupted, work that can fail, work that needs to recover and still complete with full visibility into where everything stands at high throughput, auditability, and security, some things that enterprise customers really care about. Further up that chain is what we call agentic case management, where we embrace the dynamism that exists in business processes and the countless variations that they take. These processes are chaotic, to say the very least. Our newest innovation, what we call our case manager agent, can autonomously triage and resolve these complex and dynamic processes. This is our newest offering, launching in May, and I'll talk a little bit about it in an upcoming slide. We are now at the top of the value chain here on vertical solutions, where we offer industry-specific offerings that deliver end outcomes to customers, that implement the highest value business processes end-to-end, and also we'll cover this in a little bit. Now, that's really the value chain story. We're going from tasks to also include processes with our orchestration layer to cases and vertical solutions. All of these outcomes delivered on our secure and governed platform. Now, I want to spend a little bit more time talking about the modern agent-native stack. As Daniel described a bit earlier, and you've probably heard, that the coding agents are taking the software world by storm, and they are totally revolutionizing how software is built and managed. At UiPath, we are going all in and making our platform fully accessible by coding agents. This includes the full life cycle of automations, from building, operating, managing, and obviously, of course, governing automations as well. On this slide, on the left, you see our platform stack, which I just talked to you about. On the right is the key unlock. This is coding agents natively embedded in the platform. They accelerate every phase of the automation life cycle. They will process context, reading process documents, and really capturing the requirements for a real business workflow. They author and deploy, meaning they go from natural language to production-ready agentic workflows with guardrails. They diagnose and repair, proactively analyzing logs, errors, proposing fixes even, and redeploying them in a closed loop. Critically, they support governance and operations, managing machines, and making sure that business SLAs are met and adhered to. The key here is that coding agents compress the time to value dramatically. Every developer, every operator on the platform becomes more productive. What does that mean? That means that more automations get built, secured, and governed. It means that we really can expand the value inside our existing accounts and accelerate new customer onboarding as we add new accounts to our list. Now, there's a segue that I'll present here, which is our developer base expands also. Today, as you know, we target what we call automation developers. Coding agents allow us to target pro developers in the enterprise, as well as those that are less technically proficient. Now, does this mean that our bets and investments on low-code experiences go away? No, we don't think so. It actually becomes more important because it gives people, especially those that have less technical proficiency, the confidence to visually verify and inspect that their natural language intent matches the actual automation suite. We believe that the low-code experience becomes even more critical, at least in the short-medium term, in terms of how people express and manage their intentions. All right. We've introduced the business orchestration platform. Now let's spend a few minutes taking a deeper look at the orchestration layer, the case layer, and the vertical solutions layer. Now, this slide describes the very core foundation of our orchestration platform, the orchestration layer. On the left, you see a real orchestration flow. This isn't, as you can see, a linear automation. Even though it's simplified to fit in this slide, you see that it's a reasonably complicated process with branching logic, with human checkpoints, multiple agents and handoffs, all coordinated by Maestro. On the right, I'll talk to you a little bit about what are the sets of salient capabilities we've built in here that makes this enterprise-grade. First, and probably most importantly, it's an integrated and unified platform where you can model your most complex end-to-end processes, but also implement every single constituent of it. To be clear, Maestro is endpoint agnostic. What we mean by that is you can bring your own systems of record, you can bring your own agents built outside of UiPath. It doesn't matter. The problem we solve is orchestrating these complex processes, spanning systems, agents built on multiple platforms. Someone still needs to move the process along and execute it optimally. Someone still needs to provide you the visibility on how the process is doing, and that's the core of Maestro. It's like that control tower that gives you that visibility that drives the process along to completion. Second, this is our technical mode, which is the durable execution. When an agent fails, when a system goes down, especially for these complex processes, and they happen all the time, the orchestration layer needs to pick up exactly where it left off to ensure that we provide mission-critical reliability built on this engine we call the event-sourced engine. That's really at the heart of this new wave of products that UiPath is building, Maestro, case management, and of course, all of the vertical solutions as well. Obviously business users stay in control. Humans are in the loop at the right moments for triaging escalations, approvals, and so on. Every step of Maestro is audited and governed end-to-end so that, as I mentioned earlier, there's concerns from the CIOs about governance, and this directly addresses that governance concerns that they raised. Last, but critically, probably equally important to all of the rest is Maestro has or will soon have native integration for coding agents. These orchestrations don't take months to build. Developers can use coding agents to author, test, and deploy them, quickly closing the loop on time to value story that we just covered. All right. Now let's get real about what a business process actually looks like. This is an actual insurance claims process and really just one variation of it. Look at the complexity. A claim comes in, an AI agent runs evaluations, it branches. Maybe the confidence is low, it escalates to a human. The adjuster may correct the data. It loops back in, and there's probably some legal and compliance coverage or reviews that need to happen in parallel. Maybe some parts of the process needs to be re-evaluated and so on. Notice the nodes here. There are AI agents participating in this process. There are some deterministic automations, APIs, and RPA automations in the mix. The nodes that are darker, the red colored nodes are the humans in the loop. A single process weaves through all these three components, agents, humans, APIs, and robots, continuously. It's not about really automating a single step, it's really about orchestrating all of these constituents across all of the variations and exceptions and making sure that the work actually completes. This is why naive or simpler workflow tools break down. They can handle the happy path well. The exceptions, the handoffs between AI and humans, the parallel branches, that's where you need a true orchestration platform, and that's exactly what we've built. Now, what you saw in the previous slide was a reality, right? The messy, entangled process. The goal really isn't to create that perfectly linear flow. We know it does not work. The goal is to orchestrate everything that needs to happen, when and as it needs to happen with Maestro's agentic case management capability. At the core of this is our newest technological mode, what we call the case manager agent. This is a foundational investment that makes our push into these complex processes possible. What does it do? It maintains and manages state and context and progression of work across all of the stages that you've seen here. Think of it like a brain that knows where every case stands, and moves it along. As you can see here, the process is broken up into these three stages. The intake step, where a claim comes in, an agent processes it, an AI agent extracts the document. The case manager agent decides the paths dynamically and not using a fixed flowchart. Ditto with the second and third stages. This is the same complex process, same variation, but now it's orchestrated and governed and observable end-to-end because the case manager agent holds it all together, built on top of our orchestration layer. Remember, even this is coding agent-powered, like I mentioned earlier. Now, moving along, and this is a quick glance at how this manifests in our product. We'll see a demo of it in a minute. Now, moving further along the value chain to providing solutions, as I mentioned earlier. This matters because many enterprises do not want to buy AI in the abstract. They really want to buy outcomes. They want fast cycle times. They want measurable ROI. Our solutions are explicitly designed to make value visible in days, not weeks and quarters. This is not a separate strategy from the platform. It is the platform. Every vertical solution is powered by the same underlying platform that I just shared with you. The same governance layers, the same AI trust layers, the same data and integration layers run underneath. It's key to note that we are not pivoting to solutions. We are expanding our addressable market upwards from selling infrastructure to IT, but also selling outcomes to businesses now. It'd be fair for you to ask, what makes us uniquely poised to succeed with this? Our answer is a simple. Our core thesis is actually pretty simple. AI, as you all know, will re-engineer every major business process, and we believe we are uniquely positioned to lead because we combine deterministic and agentic logic in one credible platform with strong governance, supporting complex and regulated areas and systems. What we're building are solutions, not point products. The architecture, as you can see in this picture, includes what business users care about. Domain expertise is built in. Specialized agents that understand bespoke industry-specific logic. Bespoke business- specific logic, workflows pre-configured for use cases, and ROI dashboards that speak the business user's language directly. We're also disciplined about how we will scale. Instead of attempting massive transformation, we start with high-value subprocesses where we can prove impact quickly, build trust, and then broaden from there. Here we are populating that previous picture with a few vertical solutions that we are investing in. On the left, you'll see by industry where you'll see our investments in financial services, healthcare, and life sciences. These are industries where we have a proven strong customer base, and we understand these industries deeply. On the right, you'll see the departmental level use cases, QA testing, accounts, accounting, and procurement. Test Cloud is our beachhead into the QA department. Already a leader in the Gartner and Forrester quadrants, and we continue to see significant momentum there. Now, the key point is each of these solutions is built on the platform, as I mentioned earlier. This really means that every new solution we ship makes the platform stronger, and then the platform becomes stronger with every new solution. It's that compounding model that makes the solutions and the platform stronger over time. Now, I'd like to make a lot of this real to you. I'd like to invite Mark Rubinstein, our Director of Product Management, who'll walk you through a real example of this, a vertical solution that he's helping. Mark, why don't you explain to us the product that you built via demo and then explain to our folks here. Take it away, man. Okay, great. Yeah. Let me share my screen. All right. How's it going, everybody? My name is Mark Rubinstein. I'm helping lead our vertical solutions team on financial services. Over the last several months, we've spent time with dozens of lenders, sitting alongside loan officers, processors, underwriters, QA analysts, watching how the process actually works for loan origination. On the front end, before underwriting, loan officers and processors manually collate dozens of documents. They're hunting for gaps that could stall underwriting, and they're repeatedly circling back to the borrower for more information. On the back end of the process, after underwriting, QA analysts work through hundreds of business rule checks. They're manually leafing through dozens of documents to check and catch compliance or data entry issues before closing. Ultimately, the result is that there's no single source of truth, cycle times drag until one of their competitors end up closing faster and winning the business, and costs spike every time volume surges, and risk keeps accumulating with every file that relies on humans to catch it. Similar to what Raghu showed earlier, the process is nowhere near as linear as it looks, as I showed on the last slide. It's extremely dynamic. It's exception heavy and fragmented. There's loan origination in one system, core banking in another, documents in another, checklist in another. There's no orchestration layer that's connecting them. Humans, these separate teams of humans are the glue that hold it all together, and that's exactly why there's errors, delays, and high costs. This is what their day-to-day actually looks like. It's multiple systems that are open simultaneously. They've got documents scattered across tabs. Data is being manually cross-referenced. I can just feel their pain looking at this slide. This is the environment that our solution must work within. These aren't just operational headaches. They show up directly in the numbers. It's 42 days on average to close a conventional mortgage. Nearly $11,000 to originate a single loan, and 2/3 of that cost is labor. 47%, almost half of critical defects that are found for these loans are directly tied to manual verification and calculation. This is the cost that the process has that really hasn't fundamentally changed for a wide gamut of our customers. This is where our UiPath Solution for Loan Origination comes in. It has two purpose-built modules. We have loan setup on the front end of the process between application and underwriting. It automatically reviews loan data and documents, identifies gaps, recommends remediation, and it helps expedite borrower follow-up. We have the QA/QC module that sits after underwriting and after closing as well that ensures that documents are clean, business rules are applied consistently, escalations can be handled efficiently, and the lender is audit-ready, and I'll demo this module in a bit. Both are connected directly to existing loan origination systems, content management systems, core banking systems. There's no rip and replace needed, which is very important. Together, they're designed to cut setup time in half, cut QA/QC review from hours down to minutes so that they can lead to faster time to close, lower overhead per loan, and fewer defects. All of this is coordinated using UiPath Maestro automations that pull in loan data and documents from their systems of record. We have agents that extract relevant fields and execute hundreds of checks. Then there's people that can operate this solution in a single workflow, which again, I'll show in a bit. None of this was built on assumptions, by the way. It was all co-designed with a set of real customers deployed in real production environments. We started with regional banks and credit unions, some of which are shown on the slide here, so that we could move fast, we could learn, we could iterate quickly. We're seeing the same challenges at significantly larger global banks, and we're working to onboard more of these customers and expand. Without further ado, let me switch over to our QA/QC demo so I can show you a little bit about how this works. In this case, I am the head of lending. I'm responsible for loan quality, and I care about reducing the number of bad loans that were originated due to error and staying in regulatory compliance, all while reducing our overhead. You can see right here, I can view all of the KPIs, metrics that I care about, things like processing time is decreasing, our defect rate is decreasing, our loan volume is increasing over time with fewer errors. Most tactically up here, I can see all the top issues that were found during QA review so that we as a processing team can improve and catch these issues further upstream. Now let me switch over to an individual loan where I as a individual QA analyst would be doing my work. This solution, again, it aggregates all the data, it stitches together our existing loan origination, core banking, and content management systems into one unified view. These systems that were never really designed to talk to each other. Up top, I can see a summary of the loan. If I come right in, I can see where the loan is at, I can see exactly what my QA analyst, my QA agents rather, have already done on my behalf. Below are a series of checklists that I have to work in. Before, these were all reviewed manually. They were tracked in Excel spreadsheets with dozens of documents and windows open on multiple monitors to triage hundreds of different business rules per loan. These checklists are now suddenly smart. All of them are processed automatically using UiPath agents, deterministic workflows, and intelligent document extraction, allowing me to focus just on the issues that need remediation. Now let me go into one of these checklists where I see my review is needed. Instead of needing to manually stare and compare between these two documents on separate monitors and scroll through them to hunt for what I need, the solution automatically extracts the key data points so I can confirm that they're accurate. I see right here the first rule that I need to check is that the name on this document matches what's on this ID. You can see that the agent automatically found that as a match, and I, as a human reviewer, can confirm it for auditing purposes. There's a series of rules here that are designed for this specific document. I see right here that an agent found an issue with one of the rules, and I can see if I zoom in a little bit closer here, that these dates are expected to be within 30 days of each other, and the agent found that they were in fact not. Clearly someone entered the wrong date on the credit approval memo. I, again, as a human, can mark this as a no, not matching. Let's just say that I disagree with the agent's finding. Maybe it got something wrong. I can easily override that and include a note to indicate why the agent was wrong. This is both used for auditing purposes, but it also helps the agents learn and improve over time so that it can get more and more accurate. All right. Let me go back real quick, and I just want to show two actions that I can take now as a QA analyst. One, very often when this is done, I need to escalate back to the processing team so that they can remediate the issues. Before, that required me to collate notes on another screen, write an email, and send it to them. I can do all this automatically. I can see right here that the issues have been all summarized for me. These agents know everything about this loan, and I can easily send an email right here. The other action that I typically do is that I generate a report that can be used post-closing for auditing purposes. Before, this was all done manually, typed in a Word document, for example. Given, again, the solution has all the necessary context, I can automatically generate this report, which before I, again, had to do manually. Everything you saw here was something that used to take me hours, but can now be done in minutes. The solution augments and accelerates my entire team of QA analysts. This was built fully as a UiPath Process App on top of Maestro case management, which Raghu talked about a little bit earlier. Everything from application submission to closing, all of the automations, escalation paths, agents are all defined and orchestrated within. That is our QA/QC module for UiPath Solution for Loan Origination. Combined with our loan setup module, they're designed to accelerate time to closing, reduce operating expenses per loan, and mitigate bad loan risk, all while working with banks' existing stacks. Thank you. Raghu, I'll pass it back over to you. Yeah. [inaudible]. Mark just showed us what our overall platform and product look in practice. Now, let's bring it home, like why UiPath? We opened the session with the reality that CIOs face, that about 50% of them struggling with integrations and data quality issues, all kinds of governance challenges and shadow AI. These aren't just AI problems, they're also orchestration problem, and orchestration is exactly where we're building our moat. We are the category leader in task automation that's proven at scale and in the most complex and regulated industries. We're adding coding agent support, lock, stock, and barrel all throughout our stack to take developers from natural language to production-ready workflows. Obviously, we are building agentic case orchestration and case management with the case manager agent that I talked to you about earlier, that coordinates work across people, humans, and robots, and drive processes, the most complex business processes along. We're delivering out-of-the-box vertical solutions, just an example of which Mark just showed. All of this is built on our enterprise-grade governance and trust layers. Now, each of these modes reinforces the other. No one else, we believe, has this combination, the depth of automation and the breadth of orchestration, and the discipline to deliver these as outcomes, not just tools. That's why we believe we are uniquely positioned to drive a lot of value to our customers upcoming. Now I want to switch to helping bring this to life with a real customer example. We recently sat down with Jason Paris, the CEO of One New Zealand, one of the country's leading telecommunications providers, who's driving a pretty significant agenda to leverage AI as a competitive edge across the business. His organization, we believe, is a good example of what's possible when you combine agents, deterministic automation, and orchestration within a single platform. UiPath is at the core of their transformation strategy. I think they took five weeks to bring their order-to-cash process into production, reduced their cycle time, the processing times, from multiple days, four or five days, to 5-10 minutes. They're now scaling their overall B2B operations with expected tens of millions in savings. What stands out is this isn't a one-off use case, it's the platform that they're betting on for their long-term transformation with orchestration at the center. Let's hear it directly from Jason. Jake, you want to take it away? I'm sorry, Jake and Allise, can you take it away, please? Appreciate you taking the time to share your story. Can you walk us through your transformation goals and how you see One New Zealand evolve as AI transforms our industry? Raghu, thanks for having me, and also thanks for the partnership that you give us. We've been deploying variations of artificial intelligence for over a decade now. Thousands of RPAs in our organization, using large language models, generative AI, and now agentic AI. Our goal is to be the most AI-enabled telecommunications company on the planet, and the only way that we can do that is with pace. We're a small market, the bottom of the South Pacific. When we're working with partners like yourselves, the thing that hopefully attracts you to us is the pace with which we will experiment and that we will deploy the technology. We have a secondary kind of mission, which is AI first, but human where it matters. It's also important to state that AI is going to transform our entire organization, but it's not going to stop human-to-human interaction being really, really important. In fact, what we're finding is that it gives us more time to make those human moments even more important. The way that we can do that is by using a partnership with you to automate at scale. There's pretty much not a single part of our organization currently which is not being process mapped, re-wired, automated, and having agentic tools layered on top of it. Yeah. It's amazing that you've been able to take your employee base along, JP, as you've incorporated AI into your technology stack, into the way work gets done in One New Zealand. Now, I'd be curious to understand, what are the key parts of your AI transformation strategy, and then how does a platform like UiPath, specifically UiPath Maestro, fit into that strategy? I'd be curious also to learn a little bit about specific impact or ROI that you've achieved with the platform. Yeah, that's a great question because I think everyone is deploying artificial intelligence, but very few are being able to bank the cash. That's not the case with our partnership, which is why we are scaling our partnership with UiPath. As I mentioned before, there's not a part of our organization that we're not trying to process map, automate, and re-wire, using advanced artificial intelligence tools. That's an important part of that ecosystem is our partnership with UiPath. Your Maestro tool, we see as an orchestrator over the top of our AI and systems and people. The thing we love about it is that we're a legacy business. We've been around for 20, 30 years. We've grown through acquisitions of different types of businesses. We've got multiple stacks, multiple billing platforms. What we haven't needed to do is a major re-platform or replacement to partner with UiPath. The ability for you to map and then automate and orchestrate legacy technology without having to replace it has been awesome. I'll just give you one example. A business customer wants to replace their handset, either because it's broken or they need to refresh it. That's a path that goes across multiple parts of our businesses using multiple technologies, multiple processes, including external technology and external support. Currently, or it used to be about four to five days to make that process happen end-to-end, and that is not acceptable when your mobile phone is your lifeline, right? If you want to refresh it or you need to get it replaced, you need that replaced within a day, not within days. What we've been able to do with UiPath is exactly what I've just said before. Process map, automate, have an orchestration layer over our existing processes. No change to existing processes, and we've changed that four to five days, to 5-10 minutes. How incredible is that, where you can use this technology with your existing technology, your existing processes, your existing workflows, and move from five days to 5-10 minutes? The ROI on that, of course, is extremely strong, and that's why we're scaling this across the organization. Yeah. JP, your commentary here really resonates. I think the most complex enterprises, such as yourselves, is a combination of modern and legacy technology stacks, and our orchestration layer, as you found out and as you know, incorporates the most modern technologies as well as the legacy technologies and brings it all together. You don't have to rip out what is working for you don't have to forcibly modernize what is there for you, and so on. It's great that our orchestration platform has worked for you in the way that it has. I also learned, JP, that you went from a proof of concept to production grade deployment in just a handful of weeks, like four or five weeks. Mm-hmm. Can you talk a little bit about what enabled that level of speed for you? Yeah. Well, again, we think speed is an advantage for us, not just to attract partners like UiPath to work with us, but also as a differentiator in market. To be fair, we did have prior proof of concepts. As I mentioned before, we've deployed thousands of robotic processes across the organization. In fact, we would estimate that we'd need about 20% more people in our organization we've currently got if we didn't have robotic process automation in place. That's a significant competitive advantage, and a cash advantage just there. The proof of concept that we had with UiPath gave us a huge amount of confidence. It's an integrated platform, AI plus RPA plus orchestration. Again, because that proof of concept worked well, that meant that we wanted to scale quickly. I think we built our very first agentic agent within about 12 hours, and then it took us a few weeks to deploy it because we had to clean the data up, make sure that it was operating appropriately. That's why we've had so much confidence to scale so quickly. I'd say that's not just the mix of the technology, though, Raghu. It's also the subject matter experts, the capability that we've had sitting side- by- side. We have a kind of two-in-a-box model, I think as you'd be aware. We've got our own experts sitting beside your experts working on the issues. It's something that we've really benefited from in getting UiPath's expertise to upskill and reskill our own people within the organization at the same time. Of course, you want to make sure that you test it end-to-end for scalability. Again, the proof of concept did that well. Five weeks, we can see the value, and now it's being scaled across the organization. Yeah, the partnership has been incredible across our teams for sure. Thank you. Yeah. Now, JP, as you scale this technology across your organization, where do you see the biggest opportunities for you next, and how central is UiPath to that longer-term AI transformation strategy for you? There is genuinely no part of our organization that is not going to be transformed through this technology. Your biggest decision is, where do you prioritize first? We're prioritizing where really the biggest layers of volume and complexity and cost sit. Areas like provisioning, finance, risk, fraud, and also even really big, complex programs in IT, like our SAP upgrade. That's just our first bucket of priorities. Genuinely, I can't see any part of our business that's not going to benefit from this, from Maestro and from the technology you're providing us. Yeah. We are looking forward to supporting and partnering with you through this transformation that I know lock, stock, and barrel you're going through at One New Zealand. That's fascinating. Now, I know you've evaluated a number of leading AI and automation platforms at One New Zealand. What ultimately led you to choose UiPath? Then what gives you confidence that UiPath can support that mission-critical execution of your most complex mission-critical automations, not just experimentation, JP? Yeah. Well, I think the first part of it is when you're looking at what technology is available to you want to make sure that the technology partner is agnostic. You need to make sure that it would work across a legacy environment with a lot of complex technology. That was the first tick that UiPath received. Also, you want to make sure that it avoids kind of multi-tool complexity. Again, it's an integrated tool that works in combination, not just as an orchestration layer, but across AI and RPA, which makes sense. When you start to test it, you want to make sure that it's got compatibility, and you can actually deploy it within your organization, just tick. When we did the proof of concept, we could see that not only did it work, but it could scale, and that you can scale it quickly, and you can, as we talked about before, get a cash return on it. A pretty simple checklist that anyone should be going through. Is the platform agnostic and can work within your existing environment? Can it be an orchestration layer which works both with advanced artificial intelligence but also robotic process automation? Can it scale across the organization and deliver the money step, right? Cash that you can either bank or reinvest in other parts of your businesses. All of those have been ticks for us, and that's why we chose you, and we're delighted that we did. That's awesome. Thank you, JP. Great. It's always valuable to hear directly from our customers. With that, we'll open it up for Q&A. Unfortunately, we only have time for one question, so I'll combine a couple of themes that we've been seeing come through. Daniel, as customers begin to deploy more agents, what are you seeing in practice around the need for orchestration? And more broadly, how do you think about adoption of agentic solutions and UiPath's right to win in this space as it becomes more crowded? Yeah. Allise, I would like first to give a quick explanation of what's the difference between agent- to- agent orchestration and process orchestration. Because I think there is a bit of a confusion in the market. I think when people speak right now about orchestration, I think they implicitly assume some kind of purely agent- to- agent orchestration. Like having a swarm of agents. You give them a goal, and the agents will communicate to each other, create the planning, will split the task, something maybe more akin to OpenCrew is happening. When we speak about orchestration, we speak about process orchestration. It means that in order to achieve an enterprise goal that is being compliant with all the regulation or the regulations in place and understanding the complexity and the many actors involved, you need a bit of a different approach. A typical way for an enterprise to solve process orchestration is to have a process view, process description. Many people would use something like this business process modeling notation for showing, depicting the process, the workflows involved. With the caveat, there can be, as I said in the beginning, hundreds of sub-workflows there, and each sub-workflow can have many steps. Some steps can be purely deterministic, and they can be solved by RPA or API automation. Some steps will be agentic. Some steps definitely will require humans in the loop to supervise, as you've seen in these demos. It's kind of clear from all the customers I talk to, in my case, it's almost no exception, that the preferred method of bringing AI into the context of an enterprise process is basically injecting AI steps in a deterministic orchestration and workflow engine. In this way, the AI is limited more to understanding a specific task, work, understanding the work at a specific stage in the process. Yes, I would say that the advent of agentic makes even more compelling for enterprises to have a platform that offers a built-in process orchestration. It's much easier and a more compelling proposition to have in the context of the same platform, the non-deterministic agentic code, the deterministic code, and the humans, and the enterprise workflows that organize, that basically manage all the interaction between these actors. You can apply the same governance, the same security model, you will have the same audit trails, same observability model, same analytics across the entire process end-to-end. This is very valuable for enterprises to be capable of understanding every single interaction that happens in order to deliver a goal across an end-to-end process. Great. Thanks, Daniel. Unfortunately, that brings us to the bottom of the hour. Daniel, I'll turn it back to you for closing remarks. Thank you so much, everyone, for staying with us. I hope that this session give you more clarity of what we are doing. If I have to summarize everything, we are extremely focused on bringing coding agents into the picture. I believe this is going to be a big accelerator into the adoption of our platform. I want to finish saying, somehow under the hood, we built this amazing and unique platform in the market. We are the only platform that is built right now on the top of a new modern workflow engine that is really very good to be used by coding agents. On the top of this engine, we built business-friendly way to describe a process using BPMN. Then we have our proven scalable engine that was capable of delivering for many years automation at scale. We are talking about hundreds and thousands of automations that run in parallel, concurrent, run at a big scale. You need to orchestrate them, to manage them, to feed them with data, to understand analytics. That's not something that you can build overnight, and it requires a lot of deep engineering and architectural thoughts. Then the third important pillar, we have the task automation capabilities. Basically, we have the capability to integrate with every system out there. Legacy system and modern system will continue to coexist for the foreseeable future. It's so powerful to have all of these components in a platform that offer integrated security and governance. With this, again, thank you so much for staying with us. We would like to connect in the next couple of weeks with as many of you as possible. Thank you.