Swift ✉️ Memos

Good morning, AI enthusiasts. AI has changed the world that we live in. Every CEO is talking about AI, and most of their employees are wondering what it means for their paycheck.

At the center of that shift sits UiPath, which just marked its five-year IPO anniversary — evolving from a company that once automated tasks to one that now orchestrates how AI agents, automation, and people work together.

We sat down with the company's CMO, Michael Atalla, to understand what's really happening inside organizations: why AI promises often fall short, who's winning, and what it all means for everyone whose job is changing because of this technology.

In today’s AI rundown:

Five years in: what's changed, what hasn't
The wall most AI projects never get past
AI's job anxiety is real, but so is the nuance
Where AI takes over — and where it doesn't
Quick hits with Michael

LATEST DEVELOPMENTS

LESSON FOR AI LEADERS

📅 Five years in: what's changed, what hasn't

The Rundown: UiPath’s pitch five years ago was simple: automate the task. Today it's something more ambitious — orchestrate how every AI agent, robot, and human in a workflow works together.

Cheung: Five years ago this week, UiPath rang the IPO bell as the category leader in robotic process automation. Today you're pitching “agentic business orchestration.” What's the single biggest thing that has changed about the bet — and what has stayed exactly the same?

Atalla: Five years ago, the promise was simple: automate the task, free the person. It worked. It still works. But walk into most enterprises today and you'll find dozens of automations running in parallel with no real way to connect them to each other, or to what the business is actually trying to accomplish.

The question customers used to ask us was “can we automate this?” The question now is “how do we get all of these things working together?” Orchestration is the answer. AI agents, automation, people, and systems running end-to-end, with visibility across the whole thing.

Atalla added: The bet that hasn't changed: technology should take friction out of people's work, not add new kinds of it.

Cheung: You spent 15 years at Microsoft leading the marketing of Office through its shift from on-premise to cloud-based Office 365. What did that teach you about moving enterprises through a paradigm shift that most AI leaders today are missing?

Atalla: In 2011, I was demoing Exchange features like "conversation view" and the "Do Not Reply All" button to customers who were skeptical about moving their email off a server they could physically touch. That was my Office 365 education.

You can have the right product and still lose the customer if you can't help them rethink how work gets done. We weren't selling cloud software. We were asking people to change how they collaborated, where they stored information, and whether they trusted a system they couldn't see.

AI conversations today fixate on the model. What it can do in theory. Enterprises don't care about theory. They care whether it works reliably under real conditions, inside real workflows, with real accountability. The companies that got stuck in the cloud transition weren't short on ambition. They just lifted and shifted without redesigning anything. That same pattern is playing out with AI right now.

Why it matters: If your team is evaluating AI tools right now, the question to bring to the next vendor meeting needs reframing from “what can this model do?” to “what does our workflow need to look like for this to even work?” Get that wrong, and you might end up as the company in the 70–80% who never make it out of AI pilots.

AI BARRIER

‼️ The wall most AI projects never get past

The Rundown: Atalla says the core issue behind lagging AI initiatives is a lack of coordination. Whether in pilot or deployment, tools running in isolation, disconnected from each other and from business goals, is where costs accumulate & ROI disappears.

Cheung: 70-80% of agentic AI initiatives never even make it out of the pilot stage. What are the honest reasons most companies are failing?

Atalla: AI pilots almost always run in isolation. One agent in one corner of the business. One automation in another. No visibility between them. The pilot succeeds, leadership asks what's next, and nobody has a real answer. Costs accumulate. Results are hard to measure. Eventually, somebody decides it wasn't worth it.

The organizations getting past that stage aren't doing anything radical. They stopped treating AI agents as tools to deploy. They started treating them as components of a larger, governed workflow. That's the whole game.

Cheung: A survey found nearly half of organizations call AI a "massive disappointment" despite heavy investment. What is going wrong post-deployment?

Atalla: Nobody sets out to fail at this. The ambition is there all the way down. From the CEO to the person whose Tuesday is supposed to get easier. So when you see numbers like those, it's almost never a motivation problem.

What I hear from customers is a coordination problem. They've automated tasks. They've got AI tools running. But they're definitely not connected to what the business is trying to accomplish. ROI disappears in that gap.

The customers who break through start with a different question. Not "which AI tool should we buy?" but "where does work begin, where does it get handed off, where are the decisions getting made?" Start there, and the tech choices get much clearer.

Why it matters: Coordination is a critical piece of the AI adoption puzzle. Redesigning workflows for AI is important, but the next step is making sure the tools running inside those workflows remain aligned with business goals. Once you crack that, value compounds — every tool becomes more useful as it's working as part of a system.

AI IMPACT ON JOBS

😰 AI's job anxiety is real, but so is the nuance

The Rundown: With the advances in AI, Atalla acknowledged that the anxiety in the job market is real. However, he pushed back on the idea that human involvement is becoming optional, saying roles are changing shape, not disappearing.

Cheung: Three-quarters of AI experts are optimistic about AI's impact on jobs, but only 23% of the public agrees. Who is closer to reality, and why such a disconnect?

Atalla: Honestly? Both groups are seeing something real. They're just looking at different parts of the picture. The experts see what the technology is capable of. The end user sees what's being handed to the technology and wonders what that leaves for them. It's a reasonable read of the signals.

What I'd push back on is the idea that human involvement becomes optional as AI gets smarter. An LLM cannot ask "should we?" It has no motivation, no taste, no instinct for risk. Every system we deploy at UiPath still needs humans to oversee it, make judgment calls, and apply it in ways that add value. The role evolves. The need does not go away.

Cheung: Entry-level dev jobs dropped nearly 20% since 2024 as senior roles grew. UiPath's CEO himself said the goal is to "grow without growing headcount." Is the anxiety in the job market justified, or are people worried about the wrong thing?

Atalla: The anxiety is real, and it deserves to be taken seriously. A meaningful number of entry-level roles are being reshaped right now. That's not nothing, especially for people who built their career expectations around a different set of conditions.

The redistribution is more nuanced than the headlines suggest. Routine, structured work is getting absorbed. But the work itself doesn't disappear. It changes shape. New roles are emerging around workflow design, AI governance, and end-to-end process ownership. The demand is there. The skills being demanded are different.

My daughter is 13. When she applies to colleges in five years, the jobs she'll be competing for probably haven't been named yet. That's cold comfort if you're 24 right now. But it's also not the same thing as replacement.

Why it matters: Every worker watching AI transform their industry is asking — is my job next? The answer is both yes and no. AI will absorb the routine, structured parts of the work. What it won’t replace is judgment, taste, and instinct, or the parts that require a human to ask "should we?" Now, it’s all about upskilling for the work that's left.

UIPATH AI PLAYBOOK

🤖 Where AI takes over — and where it doesn't

The Rundown: Atalla says UiPath deploys agents for tasks that involve ambiguity — like interpreting an invoice that doesn't fit a standard template — while keeping humans on anything that carries real accountability.

Cheung: What does AI and automation working with people actually look like inside UiPath for someone in finance, or HR, or ops?

Atalla: Picture a finance team reconciling data across five systems and chasing approvals through email. The automation handles the structured, repeatable parts —pulling data, matching records, and routing requests. An agent steps in where there's ambiguity — flagging an anomaly, interpreting an invoice that doesn't fit the standard template. The person in that role stops doing the reconciliation and starts reviewing the exceptions, making the calls that actually require judgment.

The person's time shifts toward the things only they can do. That changes how the job feels day to day, which is a bigger deal than it sounds.

Cheung: With autonomous agent tools gaining serious traction, what kinds of tasks do you think AI agents will actually handle on their own in the next year or two?

Atalla: The "full autonomy" conversation runs way ahead of what's actually happening. What I see at UiPath is more specific and, honestly, more interesting.

Agents are very good at handling unstructured data, making context-aware decisions inside a defined process, and managing exceptions. Think document understanding, fraud detection, and customer service triage. The kind of work where the input isn't clean, and a rules-based system either fails or needs constant babysitting.

Deterministic, rules-based work still runs better on traditional automation. And the decisions that carry real accountability, approvals, escalations, anything with consequences, those stay with people.

The near-term model is agents operating inside orchestrated workflows. More cognitive responsibility. Still governed. Still observable.

Why it matters: While frontier AI giants continue to talk up full autonomy, UiPath's approach is more grounded — deploy agents only where they genuinely do well, and humans on higher-value tasks. It's less exciting, but it keeps the machinery moving with outcomes and is the version of AI adoption that actually holds up.

LIGHTNING ROUND

⚡️ Quick hits with Michael

The one thing enterprises get wrong about AI more than anything else?

Atalla: Expecting it to fix a broken process. AI makes good workflows faster and bad ones more expensive.

What are companies getting wrong in how they introduce AI to their teams?

Atalla: Framing it as something happening to people rather than something they'll build with. Anxiety starts in that framing, and it's usually avoidable.

If you weren't at UiPath, what AI problem would you want to be working on?

Atalla: The gap between what organizations believe AI will do for them and what it's actually set up to do. That's a clarity problem, not a technology one. I find it genuinely fascinating.