The best AI model still doesn't know how your business actually runs. Here's how we're changing that.

The renewed debate about the pace of AI and related safety risks is extremely important, and I am glad it is happening between the labs that train the most capable models. UiPath does not train frontier models, so I will not put a number on the risk and I don't think anyone can. What UiPath does have is more than a decade of experience deploying automations in the most regulated environments there are: banks, insurers, hospitals, government agencies, and more. We have one job: making sure those automations do exactly what they are supposed to do, and nothing else. This experience is relevant to this debate. My advice? Slow down on what you cannot govern, and speed up on what you can. Most of your mission-critical business processes are in the second bucket. I wrote more on Substack (substack.com/home/post/p-216…) and at length in The Work That Remains (uipath.com/resources/automat…). Let's keep building, carefully as always.

Years ago, it became clear to us that our customers' hardest problem would be coordinating work from beginning to end, and we started building for it. We bet on orchestration because that is where the challenge sits. A process has to exist in a form that agents and people can both act on, held and governed in one place, and coordinated with the systems doing the rest of the work. That layer is orchestration, and it is what we chose to build the @UiPath Platform around. Today, UiPath has been named a Leader in the @Gartner_inc Magic Quadrant™ for Business Orchestration and Automation Technologies. This one means a great deal to me. Execution is whether what we build holds up in production. Vision is whether we are building the right thing for what comes next. Being recognized in both says the direction was right, and that the market now sees it the same way. Thank you to the UiPath team for the years of work behind this, and to our customers for trusting us. You can read the full report here: uipath.com/resources/automat…

My book is out today: The Work That Remains — Human Judgment, AI, and the Architecture of the Next Enterprise. I wrote it because most of what executives hear about AI comes from one of two rooms — one in panic, one in euphoria. Neither helps you decide anything. What enterprises need is a pragmatic account of where AI is genuinely strong, where it is structurally limited, and a deployment model that follows from both. That is what this book tries to be. My bias, declared up front: I run an automation company, and the conclusion is convenient for companies like mine. Do not take the argument on my authority — and do not dismiss it for the same reason. If you leave with one argument, take this one. AI genuinely embodies characteristics of human intelligence — it reasons, plans, drafts, explains. But it lacks the one thing every employee you ever hired does naturally: it does not learn on the job. A new hire arrives knowing nothing about your company and becomes valuable by absorbing everything nobody ever wrote down. The model arrives brilliant — and shows up exactly as new every morning. The entire method in this book is built around that limitation. I wrote it with Claude and ChatGPT, and the last chapter explains how. I learned more about AI writing this book than in any year of running an AI company. I think every reader will leave with something that changes how they deploy it. Read it here: uipath.com/the-work-that-rem…

AI can absorb knowledge, reason within well-defined domains, and take on more of the work we once assumed only people could do. But knowledge is not identity. Memory is not experience. And capability is not motivation. In Episode 2 of The Path Forward, I sat down with @AndradaMorar to discuss the human side of the AI transformation and what people and companies need to preserve, develop, and rethink as the nature of work changes. Leaders need to look beyond the visible output of a role. Every organization has two ledgers. One records measurable outcomes: the contract reviewed, the case resolved, the deal closed. The other captures value that is harder to measure but just as important: the trust someone builds with a customer, the judgment developed through experience, the mentorship they provide, and the culture they carry. When companies treat AI only as a reason to compress jobs, they risk losing what does not appear in the first ledger. You can find Episode 2 on: Youtube: piped.video/watch?v=bb-Lsf1T… Spotify: open.spotify.com/episode/3Yo… Apple Podcasts: podcasts.apple.com/us/podcas…

In the first episode of The Path Forward, I sat down with Michael Atalla to talk about a question I keep hearing from customers: what comes after using AI as a productivity tool? Work does not usually begin with someone opening a chatbot. An invoice arrives. A customer request comes in. A case moves from one stage to another. A process has to continue, with the right context, controls, approvals, exceptions, and record of what happened. Michael made the point that no large enterprise runs on one stack. Work crosses clouds, applications, legacy systems, partners, and teams. That is why orchestration matters. It acts as the practical layer that connects people, agents, actions, workflows, systems, and governance into a business outcome. You can find episode 1 on: YouTube: piped.video/watch?v=YkZm-0kg… Spotify: open.spotify.com/episode/08i… Apple Podcasts: podcasts.apple.com/us/podcas…

A friend of mine, a lawyer, told me she finally has the associate she always wanted. It drafts in seconds. It never gets tired. It never pushes back. Then she said the part that I always remember. The machine did not need her to have become anyone first. It arrived already good. She was not celebrating. The years of being wrong in front of a partner, of slowly becoming a lawyer, are the years the machine skips. And she wondered who, in her firm, will ever become her now. I have spent years building the layer that turns AI into work that gets done. I was convinced that intelligence plus action was the whole system. I was one layer too shallow. I am starting to write about what I think is actually missing, and why your AI strategy and your workforce are not two decisions but one. Part I is up. It is the start of a series I am calling Between Panic and Euphoria: ddines.substack.com/p/the-se…

Today we announced our first quarter results, with ARR growing 12 percent year-over-year to $1.901 billion. One year into general availability, our agentic products are moving from pilots into real deployments, with customers standardizing on UiPath as the orchestration and automation execution layer for their enterprise AI transformation. The next step in that transformation is reducing the distance between an idea for automation and a governed system running it in production. With the launch of UiPath for Coding Agents, we bring coding agents into the full automation lifecycle. This changes the economics of implementation. It reduces the friction between what a customer wants to build and what they can deploy, govern, and scale. I’m grateful to the UiPath team for the focus and discipline behind this quarter, and to our customers who continue to use our platform to drive real transformation.