UiPath, Inc. Investor Day 2026

Good morning, everyone. Welcome to UiPath Investor Day. This presentation will include forward-looking statements and non-GAAP financial measures. Please refer to our SEC filings for disclosures and reconciliations. We have a great lineup for you today. We will take you from the vision and the opportunity ahead, to the innovation in our product roadmap, to how we are going to market with our customers and driving value, and ultimately, the financial model. We will bring everyone back together at the end for Q&A, followed by an investor reception. We have a lot to share. With that, I will turn it over to Daniel for the company vision and the market opportunity ahead.

Thank you, Anith. Hello, everyone. It is really great to have all of you here in Vegas. I am incredibly excited about what we are going to show this week. I think this is going to be the most consequential event that we ever put here. You remember a couple of years ago, we were talking about the act two of this company, and now you are going to see this act two in motion, customer stories, huge innovation pipeline that we are going to be showing. That is going to reflect really the transformation of our company. First, a quick recap of what happened since our IPO. We have built a really solid business. Our revenue grew 3.5x since our IPO. We are crossing this year $2 billion in ARR. In the same time, we considerably increased our profitability and the cash generation.

I think I am right to call UiPath a scale company, and we have transformed our go-to market operating model. That gives us a solid foundation to build on our second act, to build on the future. I think it is very interesting to see how we got there and what is in front of us, and just to put things in perspective. If we think of the automation in general, automation of business processes, that was really a big theme from, I think, the introduction of computers. Maybe that was the biggest theme. What was very interesting that happened around 10, 12 years ago, this RPA was becoming an emerging trend. You know what was the cool thing about RPA? It could have connected disparate systems. In a way, many companies had in-apps automation stories.

RPA, I think, was one of the few successful technologies that can cross the boundaries of silos. I think the story of API automation and RPA grew kind of in the same time. Today, I think they still, both technologies, have a strong place in the automation toolkit. What is going on today? It is the advent of AI. I think it makes even much more clear to all the enterprises that they can reap enormous benefits by bringing AI and automation together. That raise really the question, how this is possible? What this presentation will show you, and what Raghu will go into much greater level of details. Orchestration is an essential feature foundation mechanism to deliver AI at scale into enterprise processes. I am not talking here about Copilot types that increase personal productivity.

I mean, how you can bring AI in the context of enterprise processes and really harvest the huge increase in productivity that is basically the promise of AI. What we are doing ourselves, because I want to make sure that I make clear, orchestration, it's a fundamental trend. That's not necessarily associated with us, UiPath. Orchestration, it's a big trend. I can make a statement that I'm staying behind it. You cannot really deliver AI in enterprise without orchestration and governance to basically offer the guardrails around AI. But how we are doing in this war, how do we capture this trend? You see, it was kind of a natural expansion to us. Even I said it multiple times from the beginning of the inception of our company. RPA was, for us, really a gate to get into the big enterprise game.

We got there via RPA, but then we extended it. Our goal was always to provide a full platform to offer end-to-end process automation. This is what we basically announced. If some of you have been here around 2019, when we showed our big platform, and we call it the Tapestry, that included not only RPA, but included APIs, included process and task mining, because discovery is a big thing, and included the Intelligent Document Processing. Then 2 years ago, we really announced our foray into orchestration. I think it's important that we make this distinction. Process orchestration, it's not the same thing as agentic orchestration. When people say agentic orchestration, they mean a swarm of agents that have the same goal, and they can act with a lot of agency to pursue that goal.

When we say process orchestration, we mean literally complex processes, and you have an orchestrator that has to coordinate between different type of actors, which are humans, different type of agents, different type of automations. You need to allocate machines to run these automations. You need to allocate credentials. You need to offer security guardrails. You need to offer very complete governance. This is basically an orchestration platform. We made a really bold move 2 years ago to build our orchestration on a new workflow engine. I think our approach to orchestration is really showing a huge technology leap. Because as we said a few times, the core engine that offers the durability of our orchestration is an open source technology called Temporal. That is here for a while. It's extremely resilient and powerful technology. It's a big shift in a way.

We said our orchestration is like generation 3 technology of orchestration that is out there. I think we are the only kind of scaled company that has built an orchestration engine that offer business process orchestration on this Temporal engine. So that gives us a really good technical advantage. You can see, nowadays we have, I think, one of the most complete orchestration and automation platform in the business. This platform, it's really required. Not necessarily our platform per se, but a platform that offers orchestration and automation is really the foundation into deliver enterprise AI. It's not only me that is saying this. I have Gartner that just released, I think a few days ago, their new Magic Quadrant in business orchestration and automation technologies. Guys, look here. To me, what makes this Magic Quadrant special, if you look at this, besides the usual suspects, you see IBM, Microsoft, SAP, AWS, Salesforce.

They participated in this. They competed for this Magic Quadrant, and you can see where they are positioned. Then look where we are positioned into this one. I think it is a huge achievement for us. Given that our orchestration is relatively new, it even more speaks to the level of sophistication that we brought into our platform. You will see going forward this level of sophistication. I think it is also very important to mention that we are not only a leader in this BOT, but we are a leader in RPA, we are a leader in IDP, and we are a leader in test automation. All of this contributes to our leadership in BOT.

This BOT is an overarching technology approach that really offers everything that is needed to do end-to-end process orchestration and automation. It is also interesting to I want to get more clarity. Why this orchestration and automation is It is so powerful, and it is fundamental in order to deliver AI. One of the first argument that comes to mind is if you look at the recent, I think, challenges around AI agents that got outside their boundaries and they attack different enterprise systems. I think at this point, it is becoming clear to everybody that this technology is not ready yet. I am just putting a swarm of agents, I will give them a goal like improve my invoice processing, and they will run the business. I do not think any sane customer is thinking in doing this. In the same time, the real question is, how do you benefit of the power?

This is also an extremely powerful technology, and nobody, I think, is denying that AI is a huge secular trend that will change industries and our way we work. To me, our platform facilitates this paradigm. That is really what resonates a lot with our customers, where we put AI in the context of the business. We give AI really the understanding of the business, but let them propose, let them draft, let them suggest how we should run, and put people in the seat where they decide. Then upon people's decision, our framework, our orchestration can run the automations in a very predictable way. Look, I think when I am speaking with customers, I bring to them a series of points that I think resonates with everybody.

One of the interesting point here is when you have work that can be described in rules, and this work should be extremely reliable and predictable, why you are not using software to run that work? I think it is kind of a no-brainer today, and everybody starts to understand. Whatever you can put in software, you put in software. It is 100% reliable. I use this word, exactness, which is so powerful. A payment, think about, you need to make your payment. Even if AI is going to be 99.9% accurate, it is still you need to be 100% accurate. It is not enough for regulatory perspective. The fact that the tool can do something, it does not mean that you must use that tool for something. I think right now it is also important to think of this. There is an asymmetry, very interestingly, into deploying AI in enterprise.

Deploying AI agents, it's actually not less complicated or faster than a year ago. But deploying determinism, exactness, it's way more faster because we are using coding agents in design time to print automations. And we get the best of both worlds. Automations work with exactness, and they are tokenless. It's impossible to run something that requires token as fast and as predictable with something that is software and runs purely deterministic. So that leads me to more like thinking, what's really the problem with the enterprise AI adoption? Because I know you'll tell me AI is vastly adopted. Everybody have CoWorks or Copilot. Again, this is personal productivity. I mean really AI that works autonomously in the context of enterprise processes. I think there are many pilots, and I think most of them kind of fail to graduate to production.

The real problem of AI in enterprise is not that AI is not reasoning well, because I have many reasons to believe that AI can reason better than most people. The main difference between an AI model and a human is that AI doesn't learn on the job. Now you tell me AI has memory. Yes, memory is not the same as learning. AI will need a manual. It's like every process, every business you have will have a full detailed manual of how your business operates. No company in the world has this manual, and AI needs this manual. This is really the biggest difference today, and this is the reason that AI can fail completely unpredictable.