When I started working on The Work That Remains, I wanted to challenge the narrative that AI models will eventually become so advanced that they will take over most of the work people do. Exploring this led me to a framework built around the strengths and limitations that both humans and AI have: AI proposes, humans decide, automation executes. For the new season of The Path Forward, I was joined by Cassie Kozyrkov @decisionleader to discuss how we build technology that enhances our ability to take initiative, exercise judgment, and make decisions. In a world where reasoning is becoming increasingly accessible, it is important to distinguish between intelligence and will. The assumption that models can simply optimize toward a given goal overlooks how complex processes actually work. Within an organization, countless smaller decisions and initiatives shape the outcome of a process. We discussed why the ability to take initiative is one of our most durable human traits and why it will become increasingly valuable. If we look at the decision-making process at a high level, we start with information, apply logical reasoning, reach a conclusion, and evaluate the consequences. AI excels in certain reasoning tasks, but it cannot bear responsibility for the outcome. When decisions carry significant consequences for an organization, people need to remain involved, exercising judgment and taking responsibility for what follows. Cassie made an important point about how we think of humans in the loop. Too often, it means placing someone in a position to simply approve or reject an AI recommendation, without necessarily enabling them to exercise meaningful judgment. I strongly agree. This is one of the central challenges of deployment: finding the right balance between using the reasoning capabilities of AI models and giving people the ability to evaluate, question, or change what AI proposes. Cassie, thank you for joining us. You can listen to our conversation here: YouTube: youtube.com/watch?v=k8AkcH7q… Spotify: open.spotify.com/episode/5iX… Apple Podcasts: podcasts.apple.com/us/podcas…

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

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…

More details on our announcement here: uipath.com/blog/product-and-…

I’ve said it often because I believe it: coding agents will be as consequential for software as the cloud transition was. Despite the name, they don't just write code. They build. For the last few months, we've focused on a specific question: what happens after a coding agent finishes writing? How does that code become a working outcome inside an enterprise: secure, governed, running against real systems? Today we're announcing UiPath for Coding Agents. Builders can use the coding agent of their choice to create, test, and deploy automations across the full lifecycle on the UiPath platform.

The @UiPath Community has always been at the heart of everything we do. From the early days of UiPath to where we are now, some of the best ideas have come from the engineers, developers, and automation experts who explore our platform and push it in new directions. Every year at Fusion, it’s a tradition for me to meet some of our UiPath MVPs in person. It’s always one of my favorite moments: seeing familiar faces, welcoming new ones, and hearing firsthand how this community continues to drive innovation. Our MVPs are leaders who set the pace for what’s possible in automation and now in the growing space of agentic AI. Their creativity and dedication continue to shape how technology empowers people and organizations around the world. Applications to become a UiPath MVP are open through November 20. This year, we’re expanding the program to include new categories: AI & Agentic Workflows, Process Orchestration, and Solution Developers for those building with LLMs and coded agents beyond UiPath. Join us in shaping the future: community.uipath.com/mvp/

Scalability is what separates experimentation from real transformation in technology. The latest Everest Group report identifies scalability as one of the key drivers for intelligent process automation platforms adoption. Enterprises are shifting away from automating tasks in isolation to end-to-end, proactive solutions, demanding an architecture that can manage complexity, uphold governance, and evolve with AI. This shaped our pivot toward orchestration, which is why I am proud to share that UiPath has been named a Leader and Star Performer in the Everest Group Full Suite Intelligent Process Automation Platform PEAK Matrix® Assessment 2025. Orchestration applies as much to large, cross-department workflows as it does to smaller, team-level or individual processes. It creates the connective tissue that allows automation and AI to work together and provides the insights needed to understand how each element contributes to the whole. Gaining a 360-degree view of how work happens is essential to managing complexity and scaling automation responsibly. This principle continues to guide our product direction and the way we partner with customers on their transformation journeys. This recognition, along with our leadership position for Vision and Market Impact in the Intelligent Process Automation Platform (IPAP) PEAK Matrix® Assessment reflects our commitment to supporting enterprise-wide digital transformation.