UiPath, Inc. ($PATH)

Hi, everyone. Thank you for joining us today. I'm Allise Furlani with UiPath Investor Relations team, and I'd like to welcome you to our virtual fireside chat and product strategy overview. We'll begin with the fireside chat featuring Daniel Dines, UiPath's and Chief Executive Officer; and Raghu Melpani, Chief Product and Technology Officer. We'll then take a deeper look at our product strategy and road map, followed by a customer example to bring these concepts to life. We'll conclude with time for questions. [Operator Instructions]. 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 [indiscernible] 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, and hello, everyone. Thank you for joining us. Today, I have the pleasure to introduce Raghu to you. I heard many people in my life. And -- but in very few cases, I have to really this type of positive that I had when I met Raghu first time. He strike me as a clear thing or no known sense, no politics type of guy that we really needed to run our engineering organization, and he didn't disappoint. Raghu was basically the at behind our big push into Maestro into adopting the new mode of engine that is changing the entire company. So, Raghu, what brought you to UiPath, basically?

Yes, Dan, thanks for the kind words. I 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 Azure -- worked on [indiscernible] became Azure in my first studio there. And then the last time I left, I left the office organization. To join UiPath, to join you and the team here about 2 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 want? Who is it that I work with? And how is it that we work together? So Dan you and I had many conversations before I joined. In fact, we met, I think, 2 or 3 times in person. And then it was -- it is clear that the transition from RPA to Agentic automation to business station was the need of the hour. The ACT I and Act transition that you've spoken about for a couple of years now. Made this to me a compelling -- 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. But 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 plainspoken and direct being humble and decisive being strategic and 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. So working in a frustration environment, making meaningful impact. And I found this company, you had to meet the considerable needs for me to thrive doing the kind of work that I was passionate about. And I'm so glad to be here, Daniel. 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. But the technological mode that we have in the 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. And so I'm excited to be here. It's been a fun couple of years and I'm looking forward to many more

That is great helping you here. And you pass the best test of our engineering is in Romania, that was part of the hiring process. So impressed that -- you were the only one, maybe or maybe two people that got a good review from that side.

Yes, yes. It was both hard and easy to pass the test. It was hard the first 5 minutes, but I knew whoever and the connection was deep and immediate, Daniel. So I think I understood in those first human it's talking to some of those folks, what is expected and the cultural alignment was clear, clear from the get go.

And you were talking about the [indiscernible] code in the interviews.

Yes.

Myself, I have asked you some kind of hypothetical coding staff because good. So I think for -- it was really a good lesson to get some more [indiscernible].

Yes, it's super important, Daniel. And I think the leadership team that we have at you have had now between you and your direct reports. I feel like we are -- we have that team where we can speak freely, we can spare openly, challenge each other and push the boundaries for what is possible. So I'm I feel like we have a good start 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 many investors that are here. There's is a narrative that AI could simplify or even eliminate large parts of the software stack. In that world, what moat do you think [indiscernible] UiPath have. And how does our growth become you think, even more important? Do you want to take a step at that and then I can add my two cents.

Yes. Yes, of course. Look, as we all know, this is not a new narrative for us for UiPath. So I think we've been on this AI list, the first, I think, around 2023. And then we've been all know that left sometimes we be on the benefactors of the sometimes on the queue list. So I would like to bank maybe a few things about what AI versus RPA and versus new UiPath. So look, it's [indiscernible], 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 of the main important question is capable of executing a task as TA is doing. Well, they are -- it's very new. In some cases, it's capable in many cases, to run a complex multi-step task, growing multiple application completely autonomous it is not today. So even the most interesting incarnation of that we have seen, like [indiscernible], so they have meant who were doing at hog tasks and in the presence of the few. So 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. And I think this is also able to 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 it's not meant to replace that quote. If it can, in a very simple example, I can multiply two numbers right now. Look, sometimes, there can be errors. If I really put it to test and give it very big numbers, I might not succeed. And anyway, if I have to multiply two numbers in a million times, I will struck and it's not going to have 100% accuracy on that one. Nobody ever is thinking that AI should multiply to numbers. everybody really understands that I will call the tool that to multiply these two numbers. It's worth enough to understand this is a request to multiply two numbers. Therefore, I'm going to call a tool week, week. So it waits hard to translate it into automation. Obviously, I can understand when is the right context to run an automation and is going to run that automation. How do you create that automation. This is where we shy and we offer amazing platform that makes it very compelling for most of the enterprise to build their automations on our cloud. And also as part of this -- it's not as simple as you create one automation, you run it and you are at. This is -- if you look at the landscape of enterprise processes, it gives and 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, like human judgment. And so you'll have always multiple actors. It might be a dozen of people involved dealer process. It's not -- people are not going to let just the black box say I do my order to cash and they people and I have to raise over the system. So right now, what I'm the most excited is actually the emergence of the coding agents. Because this is basically -- it's the best of both worlds. Coating agents will help our customers and our partners to build automation. And to build automation at a larger scale than we've seen before. This is basically our biggest road map change that we had in the last few months. So we pivoted the entire company to enable our platform to be used primarily. We even see the primary person to use our platform is going to be the coordinated. I think we will support a coding agents agnostic. Of course, we work with Cloud, we work with Codex, and we will work with you know all the best coding agents out there. But 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 mitigate of the process, creating the solution architecture creating all the artifacts, including IP, including document understanding, to departing testing, deploying in production, monitoring and production, fixing. You know all the exceptions that happens in production. So I will be the main factor in direct. So it's going to be a very heavy use of , but who runs the pieces, the artifacts that run on our [indiscernible] our code, are very reliable. Work million time in the same time, you can reason same input is going to produce the same amount. It's [indiscernible], it's secure, it's auditable. So in a way for us, I know I see that really coding agent, it's an amazing accelerate.

Yes. Thanks, Daniel. So how about -- it 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're going up that value chain from tasks to more complex processes and then how coding agents make -- is a massive force multiplier for platform. So let's just jump right into the product road map presentation. I'm going to share my slides. Can you confirm?

Yes, we can see your screen.

Awesome. All right. So let's talk a little bit about what Daniel spoke at a very 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. So let's first maybe look at the reality in the enterprise today. There was a survey done that a few CI was last month, where you'll see on the left that integration is the #1 barrier for transformation. Centric transformation and automation transformation. Nearly 50% of the CIOs say that connecting AI agents to existing systems like their databases, the CRMs is our challenge and it's compounded by all kinds of data quality issues. And on the right, you see the emergence of -- 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 serves both sides, the deep integration into all types of systems and governance built in. Now of course, [indiscernible] for it, but it also clearly gives us 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 genetic business orchestrator platform. We'll read this from bottom to the top, you see -- we started where UiPath founded its first product market for and defined category leadership, as you know, at the task player 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. And 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. Now then, as you may know, in the last 12 to 18 months, we've moved up that value chain to also include process orchestration in our product mix with Maestro coordinating robot, Siemens and AI agents, to orchestrate the most complex business processes end-to-end. We've built some of the deepest technological motor with support for long-running workflow, that run for hours, days, weeks, even months an agent, 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 our throughput, auditability and security, some things that enterprise customers really care about. And then further up that chain is what we call attic 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. And our newest innovation, what we call our case manager agent, 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. And then we are now at the top of the value chain here on vertical solutions, where we offer industry-specific offerings that deliver and outcomes to customers that deliver the highest value business processes end-to-end. And also, we'll cover this in a little bit. Now that's really the [indiscernible] story. They're going from tasks to also include processes with our orchestration layer to cases and vertical solutions. All these outcomes delivered on our secure and governed platform. Now I'm going 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 quote engagements are taking the software world by storm, and they are totally revolutionizing. Our software is built and managed. Now UiPath, we are going all in and making our platform fully accessible by quoting agents. This includes the full life cycle of automations. From building, operating, managing and obviously, of course, governing automations as well. So on this slide, on the left, you see our platform start, 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. The world trust is context, reading process documents and really capturing the requirements for a real business workthrough, the also and deploy, meaning they go from natural language to production-ready get workflows with guardrails. And then they diagnose and repair proactively analyzing log setters, proposing fixes even and redeploying them in a closed loop. And then critically, they support governance and operations, managing machines and making sure that SLA, business SLAs [indiscernible]. The key here is that coating 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 bit 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 work for this. Now there is 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. But 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 cohort experiences go away. But 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 so down. So we believe that the low core experience becomes even more critical, at least in the short, medium term in terms of how people express and manage their intentions. All right. So we've introduced the business orchestration platform. Now I'll spend a few minutes to taking a deeper look at the orchestration layer, the case layer and the vertical solutions player. 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 the 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 saline 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 into in processes, but also implement every single constituent offer. To be clear, Maestro's end part agnostic. What we mean by that is you ran your own systems of record, you can bring your own agents built outside of UiPath. It doesn't matter. The problem we saw this accreting these complex processes, planning systems, agents, build on multiple platforms. somewhat 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 [indiscernible]. 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 senses and they happen all the time. The orchestration layer needs to pick up exactly where it left us to ensure that we provide mission-critical reliability built on this engine we call the event [indiscernible]. That's really at the cut of this new wave of products that [indiscernible] building, Maestro, case management and of course, all of the vertical solutions as well. And then, obviously, business user stain control. Humans are in the loop, but there at moment for triaging escalations, approvals and so on. Every step of Maestro is audited and government end-to-end. So that -- as I mentioned earlier, there's concerns from the CIO is about governance, and this directly addresses that governance concerns that they raised. And last, but critically probably equally important to all the rest is Macro has or will soon have native integration for coding agents. So these orchestrations don't take months to it. Developers can use coding agents to also test and deploy them quickly closing the loop on time-to-value study that we just cover. 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. Our claim comes in, an AI agent runs evaluations and branches, maybe the confidence is low, it escalates to a human, the adjusted, correct the data. It looks back and there's probably some lead and compliance coverage or reviews that need to happen in parallel, maybe some parts of the process needs to be reevaluated and so on. Noticed the notes here, there are AI agents participating in this process. There are some deterministic automations, APIs and RPA automation on the mix, the nodes that are darker the red-colored nodes are the humans in the loop. A single process we through all these three components, agent 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 to bring down, they can handle the happy backlog. But the exceptions, the handoffs but in the eye in humans, the parallel branches, that's where you need a true orchestration platform, and that's exactly what we put. Now -- what you saw in the previous slide was the reality, right, the messy and tangle 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 genetic case management. At the core of this is our newest technological mode, what we call the case managing agent. This is a foundational investment that makes our push into these complex processes possible. What does it do? It maintains and manages state in 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 has broken up into these three stages, the intake step, where the claim comes in, an agent processor, an AI agent extracts the document, the case manager agent decides the path dynamically and not using a fixed flow chart. Did 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 [indiscernible] there. And remember, even this is [indiscernible] and power, like I mentioned earlier. Moving on to -- and this is a bit glance at how this manifests in our product, we'll see a demo [indiscernible]. Now moving further along the value chain to providing solutions, as I mentioned earlier. This matters because many enterprises do not buy in the abstract, they don't 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. And 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, same governance layers, the same AI trust layers, the same data and integration layers run on death. 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 [indiscernible] what makes us uniquely biased to succeed with this. And our answer is a simpler core thesis is actually pretty simple. AI, as you can all -- as you all know, we'll reengineered every major business process. And we believe we are uniquely positioned to lead because we combine deterministic and [indiscernible] in one credible platform with strong governance support [indiscernible] regulated areas and systems. And what we're building are solutions, not point products. The architecture, as you can see in this picture includes what businesses care about. Domain expertise is built in specialized agents that understand bespoke industry-specific logic and bespoke business-specific logic, workflows preconfigured for use cases and ROI dashboards that speak the businesses as language correctly. And we're also disciplined about how the scale. Instead of attempting massive transformation, we start with high-value subprocesses where we can prove impact quickly with trust and then broaden from there. And then here, we are populating the previous picture with a few vertical solutions that we are investing in. On the left, you see by industry where you'll see our investments in financial services, health care and life sciences. These are our customers that we have -- these are industries where we have a proven strong customer base, and we understand these industries deeply. On the right, you will see the departmental level use cases, QA testing account accounting and procurement Test Cloud is our beachhead into the QA department already a leader in the Gartner in Forester Quadrant, 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 to it's that compounding one that makes the solutions and the platform stronger over time. Now I'd like to make a lot of this reality. I'd like to invite Mark Rubinstein, Director of Product Management, who is walking through a real example of this vertical solution that he's happening. Mark, why don't you explain to us the product that you built your demo and then explain to our folks here. So take it away.

Okay, great. share. All right. My name is Mark Rubinstein. I'm helping lead our vertical solutions team on financial services. And 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. And 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. And on the back end of the process after underwriting, QA analysts work through hundreds of business rule checks. They're manually leaping through dozens of documents to check and catch compliance or data entry issues before closing. And 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 cost spike every time volume surges and risk keeps accumulating with every file that relies on humans to catch it. And similar to what Ray 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 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 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. And 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-reference. I can just field their pain looking at this slide. And this is the environment that our solution must work within. And these aren't just operational headaches. They show up directly in the numbers, 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, and 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. So this is where our UiPath solution for loan origination comes in. It has two purpose-built modules. We have loans set up 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. And then 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. And 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. And together, they're designed to cut setup time and half cut QA/QC review from hours down to minutes, so that they can lead to faster time to close, lower overhead loan and fewer defects. And all of this is coordinated using UiPath Maestro, automations that pulling loan data and documents from their systems of record. We have agents that extract relevant fields and execute hundreds of checks. And 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 can iterate quickly. But we're seeing the same challenges at significantly larger global as and we're working to onboard more of these customers and expand. So without further ado, let me switch over to our QA/QC demo, so I can show you a little bit about how this works. So in this case, I'm 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 air and staying in regulatory compliance, all while reducing our overhead. And 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. And most tactically up here, I can see all of 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 an individual QA analyst would be doing my work. This solution, again, it aggregates all of 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. And on top, I can see a summary of the loan. So if I come right in, I can see where the loan is at. I can see exactly what my QA agents rather have already done on my behalf. And below are a series of checklists that I have to work in. And before these were all reviewed manually, they were tracked and Excel spreadsheets with dozens of documents and windows open on multiple monitors to triage hundreds of different business rules per loan. And 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 as needed. So instead of needing to manually steer 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. So 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. So clearly, someone entered the wrong date on the credit approval memo. So I, again, as a human can mark this as a no, not matching. But let's just say that I disagree with the agents finding. Maybe you got something wrong, I can easily override that and include a note to indicate why the agent was wrong. And 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 2 actions that I can take now as a QA analyst. So one, very often when this is done, I need to escalate back to the processing team so that they can remediate the issues. And before that required me to collate notes on another screen, write an e-mail and send it to them. But 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 e-mail right here. The other action that I typically do is that I generate a report that can be used post closing for auditing purposes. And before this is all done manually typed in award document, for example. But given, again, the solution has all the necessary context, I can automatically generate this report, which before I again, had to do manually. So everything you saw here was something that used to take me hours. We can now be done in minutes. And 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 bank's existing stacks. Thank you. Raghu, I'll pass it back over to you.

Yes. Mark, [indiscernible] I'm not as sure as what this overall platform and product look in practice. Now let's bring it home, like why you Pat. We opened the session with the reality that CIOs face at about 50% of them struggling with integrations and data quality issues all kinds of governance challenges and shadow, AI. These days 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 proven at scale and in the most complex and regulated industries. We're adding core engagement support [indiscernible] and barrel all throughout our stack to take developers from natural language to production-ready workflows. Obviously, we're building HTCs orchestration and case management with the case manager agent that I talked to you about earlier, the 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. And then all of this is built on our enterprise grid 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 outcomes, not just tools. And that's why we believe we are uniquely potion to drive a lot of value to our customers upcoming. Now I want to switch to helping bring this to life for the 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 is driving a pretty significant agenda to leverage AI as the petite 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. And UiPath is at the core of their transformation strategy. I think it took 5 weeks to bring their auto to cash process and to production. It reduce their cycle time, the processing times from multiple days, 4, 5 days to 5, 10 minutes. And they're not 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 bidding on for their long-term transformation with orchestration at the center. Let's hear directly from Jason. Jake, do you want to take it away? Sorry, Jake and Allise can you take it away, please? Jake, shared you're taking the time to share your story. Can you walk us through your transformation goals and how you see one New Zealand evolves as AI transforms our industry?

A 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. And so 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 we're at better. So 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. And the way that we can do that is by using a partnership with you to automate at scale. And so there's pretty much not a single part of our organization currently, which is not being process met, rewired, automated and having Agenetic tools laid on top of that.

Yes. It's amazing that you've been able to take your employee base along JP as you've incorporated AI into your technology stack to the way work gets done in on 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 you have at specifically at [indiscernible] fit into that strategy. And I'd be curious also to learn a little bit about specific impact or ROI that you achieved with the platform.

Yes, that's a great question because I think everyone is deploying artificial intelligence very few being able to bank cash. That's not the case with our partnership, which is why we are scaling our partnership with UiPath. So as I mentioned before, there's not a part of our organization that we are not trying to process map, automate and rewire using advanced artificial intelligence tools. And that -- an important part of that ecosystem is a partnership with UiPath, you're most until we see as an orchestrator over the time 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, mobile billing platforms. And so what we haven't needed to do is a major replatform or replacement to partner with UiPath. And so 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. So customer -- business customer wants to replace the handset either because it's broken or they need to refresh it. That's a path that goes across model parts of our businesses using multiple technologies, multiple processes, including external technology and external support. Currently, we are used to be about 4 to 5 days to make that process happen end to end. And then it's not acceptable when your mobile phone is your life promote. If you want to refresh it or you need to get it replaced. You need it replaced within a day, not within days. And so what we've been able to do with UiPath is exactly what I've just said before, proceeds [indiscernible] have an orchestration layer over our existing processes, no change to existing processes, and we've changed that 4 to 5 days to 5 to 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 5 days to 5 to 10 minutes. So the ROR and that, of course, is extremely strong, and that's why we're scaling this across the organization.

Yes. JP, I mean, your commentary here really resonates. I think the most complex enterprises, such as yourselves, is a combination of modern and legacy technology stack 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. So you don't have to rip out what is working for you. You don't have to forcibly modernize what is there for you and so on. So it's great that our actuation platform has worked for you in the way in the way that it has. I also learned, JP, that you went from a proof of concept to production grade deployment. It's just a handful of fix, like 4 or 5 weeks. Can you talk a little bit about what enabled that level of speed for you?

Yes. 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 of market. And to be fair, we did have or 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 cash advance 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 PA plus orchestration. And again, because that proof of concept work well, that meant that we wanted to scale quickly. So I think we built our very first Gentech agent within about 12 hours, and then it took us a few weeks to deploy it because we declared the data at, make sure that it was operating appropriately. And so -- that's why depends so much confidence to scale so quickly. I would say that's not just the mix of the technology, though, Raghu. It's also the subject met experts, the capability that we've had sitting side by side. We have a kind of in a box model. I think as you'll be aware, we've got our own iteratives its working on the issue. So it's something that we've really benefited from and getting UOP expertise to upskill and reskill our own people within the organization at the same time. And then, of course, you want to make sure that you test it end-to-end for scalability. And so again, the proof of concept did that well. 5 weeks, we can see the value, and now it's being scaled across the organization.

Yes. The partnership has been incredible across [indiscernible] for sure. Now as you -- JP, as you scale this technology across your organization, where do you see the biggest opportunities for you next? And how central is you add 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. And so your biggest decision is where do you prioritize first. And so we're prioritizing we're really the biggest layers of volume and complexity and cost set. So areas like provisioning, finance, us, fraud and also even really big complex programs and IT like SAP upgrade. So -- but 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.

Yes. And we're looking forward to supporting and partnering with you through this transformation that I know Loktak and barrel you're going through at an New Zealand. That's fascinating. Now you've -- I know you've evaluated a number of leading AI and automation platforms at One New Zealand Newer what ultimately led you to choose ads? And then what gives you confidence that UiPath can support that mission-critical execution of your most complex mission-critical automation is not just experimentation.

Yes. Well, I think the first part of it is like when you're looking and you're looking at what the technology is available to you, you want to make sure the technology partner is noted. And so you need to make sure that it would work across a legacy lease environment with a lot of complex technology. So that was the first tick at the UiPath received. Then also, you want to make sure that it avoids kind of multi-tool complexity. So again, it's an integrated tool that works in combination, not just to orchestration layer, but across AI and RPA, which makes sense. And then 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 take -- and then when we did the proof of concept, we could see that not only did it work, but it could scale and you can scale it quickly and you can. As we talked about before, get a cash return on it. So a pretty simple checklist that anyone should be going through as 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. And then can at scale across the organization and deliver the money step, right, cash that you can either bank or reinvest in other parts of our businesses. So -- all of those have been taxed for us, and that's why we chose you and we're delighted that we did.

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 to win in this space as it becomes more crowded?

Yes. 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 pursue some kind of purely agent-to-agent orchestration, like having a swap of agents, we give them a goal. And the agents will communicate to each other great planning will split the task something maybe more akin to like open grow is happening. When we speak about orchestration, we speak about process orchestration. So it means that in order to achieve an enterprise goal that is being compliant with all the regulation of the regulations in place and understanding the complexity and the many actors involved. You need a bit of a different approach. And typical way to an enterprise solve process orchestration and to have like a process view process description. Many people would use something like this business process modeling notation for showing the beating, the process, the workflows involved with the caveat there can be, as I said in the beginning, hundreds of sub workflows there. And each workflow can have many steps. Some steps can be purely deterministic and they can be sold by RPA or API, automation, some steps will be agenetic 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 talked 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 the specific stage in the process. So yes, I would say that the advent of agent makes even more compelling for enterprises to have platform that offers a building process orchestration. It's much more -- it's much easier and more compelling proposition to have in the context of the same platform, the nondeterministic agent court, the [indiscernible] and the humans. And -- and enterprise workflows that organize that basically manage all the interaction between these actors. We can apply the same on same governance, the same one security model, you will have the same audit trails, same observability, more the 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 go across of an end-to-end process.

Great. Thanks, Daniel. Unfortunately, that brings us to the bottom of the hour. So 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 gives 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. And I want to finish saying somehow under the hood, we built this amazing the [indiscernible] platform in the market. We are the only platform that is built right now on the top of a new model workflow engine that is really very good for -- to be used by coding agents. And on the top of this engine, we built business friendly way to describe a process using BPM. And then, we have our proven scalable engine that was capable of delivering for many years, automation escape. And we are talking about hundreds and thousands of automations that run in parallel, concurrent rate big scale, you need to orchestrate them to manage them to have to feed them with data to understand analytics. That's not something that you can build over a light in -- and it requires a lot of deep engineering architecture of thoughts. And then the third important pillar, we have the task automation capabilities. Basically, we have the capability to integrate with every system out there. The legacy system and model system will continue to coexist for the foreseeable future. And it's so powerful to have all of these components and a platform that offer integrated security and government. So 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 as you possible. Thank you.