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Daniel Dines shared thisMy 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: https://lnkd.in/g3PwkM4q
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Daniel Dines shared thisEvery serious AI strategy eventually arrives at the same problem: the company was designed for people who could fill in the gaps. AI is forcing companies to expose the operating knowledge people have always supplied without being asked: what a customer promise actually commits the company to, which exception changes the path, who owns a decision, and what can safely happen next. Much of this never had to be formalized because people carried it through judgment, memory, and relationships. I keep hearing some version of the same hope: the next model release will automate the business. Better models will matter enormously, but intelligence is only one layer. The company still has to make the work legible by defining its objects, states, rules, permissions, gates, owners, and execution rails. That is what allows AI to contribute inside a system where people retain ownership of judgment and consequence. This changes where AI transformation should begin. Start with one meaningful workflow. Map the work, assign the rights, define the boundary between proposal, decision, and execution, and preserve the human knowledge the workflow has quietly depended on. Part IV of Between Panic and Euphoria, The Operating Model, is now live on my Substack. For leaders, it offers a sequence for designing the agentic enterprise. For employees, it offers a map of where durable work is moving. You can read it here: https://lnkd.in/g9Berau4
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Daniel Dines shared thisAI 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 Andrada Morar 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: https://lnkd.in/g_F-jCJw Spotify: https://lnkd.in/gC9vbfCq Apple Podcasts: https://lnkd.in/gzUvWwCi
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Daniel Dines shared thisIn 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: https://lnkd.in/dg4UPnwd Spotify: https://lnkd.in/d8ghsRn9 Apple Podcasts: https://lnkd.in/dre3sjp9
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Daniel Dines shared thisA 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 live. It is the start of a series I am calling Between Panic and Euphoria: https://lnkd.in/dYU9UJwc
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Daniel Dines shared thisA smarter model does not automatically become a trusted participant in the business. It does not know by default where the boundaries are, what the consequences of an action might be, who is allowed to approve something, when to escalate, or what needs to be recorded for audit and security. The progress in AI has been extraordinary. Models reason better, agents write code, and systems generate and analyze increasingly complex work. So it is natural that leaders are now asking a more urgent question. How do we bring this intelligence closer to the real work of the enterprise? To make AI successful, the environment around it has to be engineered too. The business entities, the actions, the permissions, the controls, and the implications need to be clearly defined. That is why we started The Path Forward, a new UiPath podcast. I see it as a place to think out loud with people who are close to this work, including our own teams, customers, partners, and external voices who are working through this shift in real organizations. I want the conversations to stay close to the work itself, exploring where AI helps, where it breaks, what people still need to decide, and what companies have to build before they can trust it in production. You can find The Path Forward on: Spotify: https://lnkd.in/dzPaeDfn Apple Podcasts: https://lnkd.in/d97JzjMJ YouTube: https://lnkd.in/dZ5SSDar
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Daniel Dines shared thisA model can understand. A robot can execute. A person can apply judgment. On their own, none of them produces a business outcome. Enterprise work is connected even when enterprise technology is not, and the value only shows up when all three work together across systems and teams, with the right context and controls. One New Zealand started where most enterprises do: a business that already works, but works through many systems, teams, and handoffs. They didn't stop to rebuild from zero. They made what they already had more intelligent, more coordinated, and faster. And their results speak for themselves. A process that used to take 10 days now takes 5 to 10 minutes. Multi-year operational outcomes delivered in weeks. Thank you Jason Paris, Summer Collins, Cy Wright, Ram Chandra Pal and the entire One New Zealand team for the trust and partnership. We're proud to be part of this, and we're watching what you do next.Daniel Dines shared thisOne New Zealand believes the future of telecommunications is AI first, but human where it matters. Discover how UiPath Maestro is orchestrating AI, #automation, legacy systems, and people into a single connected operation. 🤝 https://ow.ly/R2y750Z6mHz
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Daniel Dines shared thisToday 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.Daniel Dines shared thisOur quarterly business update: 1Q ‘2027 revenue of $418M, up 17% year-over-year, with $1.901B in ARR, which is growing 12% year-over-year. Read more in our full earnings release: https://ow.ly/SL9850Z5mB1
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Daniel Dines shared thisAs AI models become more capable, the question of what humans contribute uniquely becomes more pressing - and more interesting. I felt this in Bengaluru at Devcon, surrounded by a community of builders that has shaped UiPath from the beginning. Builders that take technology seriously enough to push it beyond the obvious use cases. While I was there, I had the great pleasure of welcoming Romal Shetty for a fireside chat on what this next era asks of people and organizations. One idea stayed with me: regardless of how much AI models evolve, there are certain human-skills that remain essential. Humans bring the initiative to decide what should be built, the judgment to know whether it works, and the ability to connect domain understanding, process knowledge, and technology. We also discussed why this makes the development of junior talent so important. Judgment is not formed in theory. It comes from mentorship, responsibility, and being close enough to the work to understand the consequences of decisions. The people who will guide AI-powered organizations tomorrow need the opportunity to build, question, and learn today. Romal, thank you for joining us. It was a great honor to have you at Devcon.
Daniel Dines commented on a post
4mo
All the best Dylan, excited for your chapter as an operator, this is the best time to be a builder!
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Daniel Dines liked thisDaniel Dines liked thisIntelligent Automation Ten days ago, at One NZ, we demonstrated one of our most complex intelligent automations live to our Board using UiPath’s Maestro platform. For the first time, AI wasn’t described, it was observed. Transactions moved autonomously across provisioning and fulfilment systems. Manual coordination disappeared; exceptions were detected then routed to human-in-the-loop and performance metrics were updated in real time. Prior to Christmas, we deliberately set out to test intelligent automation across multiple legacy platforms operating without integration. These systems had become operational silos connected by people, not architecture. Humans were the integration layer, navigating between systems, validating data, reconciling discrepancies, and managing exceptions. Our approach was simple: if it couldn’t work in a complex, fragmented environment, we couldn’t scale it. Working with the SMEs, we built a BPMN blueprint and layered agentic orchestration on top of RPA. When we soft launched with real orders, we discovered how the process truly behaved. Variations surfaced. Hidden loops appeared. What we thought was 95% complete required over 100 configuration changes to capture the nuance that previously lived only in people’s heads. This was not a failure, our human-in-the-loop controls facilitated discovery and refinement. The outcome still makes me smile. A year ago, I never expected to watch an order being processed, let alone see it orchestrated by AI across our legacy platforms in less than 10 minutes. More importantly, intelligent automation changes how transformation happens. It does not rely on traditional IT delivery models or structural platform changes. SMEs shape the agents directly, and issues are resolved quickly as the process evolves in real time. It enables organisations to rewire themselves around the processes that matter most, delivering not just incremental efficiency, but meaningful change without the normal structural disruption. I once believed you should never automate a bad process. My experience has shifted that view. In complex environments, intelligent automation exposes how work truly happens, surfacing divergence, workarounds, and hidden dependencies far faster and more deeply than documentation or workshops ever could. With full process transparency, there is nowhere to hide. It becomes a catalyst for improvement rather than a reinforcement of inefficiency. None of this would have been possible without the incredible work & support by the team. Thank you to UiPath’s Pankaj Sharma, Naveen Muralidharan, Alina Capota, Jesse Fallowfield, Hayden Sherriff and Peter Graves; Ashling Partners’ Ryan Hayes and Ben Lingard; the One NZ Provisioning & Fulfilment team Sue Rutter, Lucy Krone, Kylie Hancock and Sammie Gurnsey; and the One NZ delivery team led by Ram Chandra Pal and Sanjeet Churi. Now we scale. #IntelligentAutomation #AI #RPA #EnterpriseAutomation #DigitalTransformation
Experience & Education
Patents
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Method and system for batch task creation and execution
Issued US 20060075253
See patentA method of granting permission to use computer software resources when the user may be offline is accomplished through the use of proxy accounts. Each proxy account encapsulates a single set of user credentials. A set of user credentials contains at least a user name and a secret identifier, such as a password. These credentials are used by a scheduler function as an agent for the user to utilize computer resources to run jobs or tasks on behalf of the user. An embodiment of the invention…
A method of granting permission to use computer software resources when the user may be offline is accomplished through the use of proxy accounts. Each proxy account encapsulates a single set of user credentials. A set of user credentials contains at least a user name and a secret identifier, such as a password. These credentials are used by a scheduler function as an agent for the user to utilize computer resources to run jobs or tasks on behalf of the user. An embodiment of the invention allows for many different proxy account objects each having one set of credentials. The credentials are used at runtime to impersonate the user and allow a job to run. The job may involve multiple software subsystems. The architecture allows multiple proxy accounts to be created which allows system administrators flexibility in assigning different permissions to different users across multiple software environments.
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