AI has sped up work, but few companies can point to organization-wide return on investment (ROI). Everyone is calling this a paradox. This three-part series names the three things buying the tools never fixes on its own, the people, the organization, and the foundation, and what to do about each. Read all three to find where your own gap is hiding.
Part 1: The People | Part 2: The Organization | Part 3: The Foundation
Somewhere in your company right now, a handful of people have become several times more productive with AI. Not because of the training program everyone had to take or the Copilot licenses that were purchased. On their own, on real work, because it made their week better.
If you asked them, they would tell you it feels like work that used to take them days, now completes in minutes. They are in every organization.
But your company’s productivity numbers didn’t move. That gap is the paradox in its most personal form.
Atlassian’s 2026 State of Teams research puts a number on it: 89% of executives say AI has increased the speed of work, but only 6% can point to organization-wide ROI. Individual speed is everywhere. Organizational results are rare. The interesting question isn’t whether AI works; your people have already answered that. It’s why individual 10x doesn’t add up to organizational 10x.
Where the gains go
The gains are real. They’re just trapped. Three things trap them, and none of them is technical.
First, the gains live in individuals. The prompts, the workflows, the personal knowledge base, the judgment about when to trust the AI output and when not to, all of it sits in someone’s chat history and someone’s head. When that person is on vacation, the gain is on vacation. When they leave, it leaves with them.
Second, nobody can see the gains from the outside. The report still lands on Friday. The analysis still arrives before the meeting. The work looks identical to what it looked like a year ago. People are using the time they saved to do more of the old job, or to go home at a reasonable hour. Neither shows up in a productivity dashboard.
Third, people have a reason to keep quiet. The honest reaction to suddenly being several times faster is not pride. It’s something closer to this feels like we shouldn’t be doing this. If the work that used to take a week now takes an hour, what does that say about the week? About the role? About the headcount? Your most proficient people may be under-reporting, not over-claiming. The paradox has a visibility problem before it has anything else.
What we learned running it on ourselves
This summer we ran an experiment at Robots & Pencils called Robocon. Four weeks, company-wide, across seven cross-functional pods we were given challenges to build and publish working AI skills to a shared internal library, then show them off at a live event. No course. No training. Not just engineers, but everyone in the company. Build something real and put it in front of people.
Two lessons came out of it, and neither was the one we expected.
The first was that the barrier wasn’t skill. Teams lost hours, in some cases days, getting environments, repositories and file-share access to work before they could build anything. Once these hurdles were cleared, people began creating at an alarming rate. Everyone in the organization from marketing, sales, product, project managers, design, engineering, came out the other side with real AI proficiency. The expensive part of proficiency wasn’t teaching people. It was clearing the path so they could do the work.
The second lesson landed harder. The library filled up fast, and plenty of good work shipped and then sat there, because nobody’s job was to turn what one person built into something the rest of the company used. Not everything built was worth keeping, and the pieces that were, had to be deliberately identified, hardened and surfaced to the rest of the company. Sorting them was a separate act, done by different people, with a different skill.
That’s the pattern, and we think it generalizes. The event produced proficiency in individuals. Amplifying it across the organization was a second job, and it didn’t happen on its own.
Roles aren’t redesigned. They are discovered.
Most advice about AI and the workforce runs top-down: redesign the roles, then deploy the tools. In our experience it works the other way around. People pick up the tools, use them to create value for themselves first, and the role starts to change underneath them. The job description is the last thing to move. AI is moving from individual productivity to multiplayer, collaborative productivity.
That’s the good news, because it means the change is already happening in your company. The challenge is that discovery doesn’t distribute itself. A role that has quietly changed in one person’s hands stays there unless someone does three things on purpose.
Find it. Who has already changed how they work? You won’t learn this from a survey; you learn it by asking a different question: not “are you using AI?” but “what part of your job have you automated?” People who have stopped doing something manually are the ones whose role has evolved.
Harvest it. Turn what one person does into something others can use. Not a training deck or show and tell, but the actual workflow, the actual prompts, data and tools they have connected, and the actual judgment calls about when the output can be trusted. Then be honest about what holds up. Our own library taught us that a lot of good individual work is not, in fact, reusable. The discipline is in keeping what is.
Make it official. Redefine the role around what changed and retire the old work. Skip this and people end up twice as fast in a job still defined as if they weren’t. The time saved gets refilled with more of the old job, and the productivity number never moves.
We did this to ourselves. A client team that used to be seven to ten people is now three to five. AI didn’t take the seats — the people kept them and got more done. Each person now works with AI the way they’d work with a strong analyst: it drafts, researches, checks and runs the routine parts, and they spend their time on the judgment calls. Once that was true in practice, we rewrote how we define what a team is on paper: who’s on it, what each role owns, what nobody does by hand anymore.
The job nobody has hired for
Boris Cherny, who leads the Claude Code team at Anthropic, has described how engineering and product roles on his team have melted into six archetypes: the Prototyper who generates ideas most of which don’t ship; the Builder who turns a validated idea into production; the Sweeper who simplifies and removes; the Grower who iterates for scale; the Maintainer who owns the mature system; and the Orchestrator, who knows which of the other five a team should be in, and when to move between them. He notes the Orchestrator is rarer than the other five combined, and that it has no established job title. Most people are doing 2 or 3 of these archetypes today.
We think that last role is the one the productivity paradox is missing. The Orchestrator is the person who does the finding, harvesting and socializing to teams. They notice that a role has changed before the org chart does.
Nobody at this table has hired one. Most organizations don’t know it’s a job. But somebody in your company is already doing a version of it informally — the person other people go to when they want to know how so-and-so got that done so quickly. That person is your first Orchestrator. They just don’t have the title yet.
Questions to take back
Your people are already changing their jobs. The organizations that get results from AI aren’t the ones that designed the change from the top. They’re the ones that noticed it from the bottom and made it official before it evaporated.
Two questions for your leadership team this week:
- Who in your company has already changed their job without telling you?
- And whose job is it to notice?
Up next in this series, Part 2: Organization
Individual proficiency is the easy part. The harder part is turning it into something the organization can run on without you in the room. In Part 2, we break down the three structural mistakes that stall AI pilots before they reach production, and what to build instead. Read Part 2: Common mistakes in AI org design (and how to fix them).
Sources referenced: Atlassian, State of Teams 2026 (89% / 6% figures). Boris Cherny, public posts on role archetypes (X.com).
About the Author
Brendan Flynn is SVP, Strategist at Robots & Pencils where he heads industry strategy within the Generative & Agentic AI Studio.
Key Takeaways
- Individual AI proficiency and organization-wide ROI are not the same thing. Atlassian’s 2026 State of Teams research found 89% of executives report AI has increased the speed of work, while only 6% can point to organization-wide ROI.
- Productivity gains stay trapped in three places. They live inside individuals who never document how they work, they stay invisible to standard reporting because the output looks the same as before, and they stay inside people who have a reason to stay quiet about how much time they have saved.
- Robots & Pencils ran a four-week, company-wide AI build event called Robocon. The barrier to proficiency was never skill. It was clearing access to the environments and tools people needed to start building.
- Roles change from the bottom up. People adopt AI, get faster, and the job description catches up later, if anyone is watching for it.
- Turning individual proficiency into organizational capability takes three deliberate steps: find who has already changed how they work, harvest what they are doing into something reusable, and make the new role official on paper.
- Anthropic’s Boris Cherny describes a role missing from most org charts, the Orchestrator, the person who notices when a role has changed and helps it spread. Most companies already have one working informally and have not named the job.
FAQs
What is the AI productivity paradox?
It is the gap between how fast people say AI has made their work and how rarely that speed shows up in company-wide results. Atlassian’s 2026 State of Teams research found 89% of executives report faster work from AI, while only 6% can point to organization-wide ROI.
Why don’t individual AI productivity gains show up in company-wide numbers?
The gains stay trapped in three places. They live in one person’s prompts and judgment instead of a shared process, they are invisible to standard reporting because the output looks the same as before, and people often stay quiet about how much faster they have become.
What did Robots & Pencils learn from running Robocon, its internal AI build event?
The barrier to AI proficiency was never skill. Once people had access to the environments and tools they needed, proficiency spread quickly across the company. The harder problem was turning one person’s work into something the rest of the organization could use.
What three steps turn individual AI proficiency into organizational capability?
Find who has already changed how they work, harvest what they are doing into a reusable workflow, and make the change official by redefining the role and retiring the old work it replaced.
What is an AI Orchestrator?
A term from Anthropic’s Boris Cherny for the person who notices when a role has changed because of AI and helps spread that change across a team. Most organizations already have someone doing this informally, without the title.
