TL;DR. A small number of people in your org are already doing the work of two, five, sometimes ten of their old selves. You probably can’t name them. Your seat report can’t see them either, because a seat count measures badges issued. Your consumption report can. Sort usage by person, descending, and the top of that list is where your leverage is sitting. This guide is a lens for reading that list, and a way to start the conversation that gets you the rest of the picture.

There are people on your team right now who are quietly doing the work of an extra hire you didn’t make. They aren’t on a special program. You didn’t pick them. You probably haven’t noticed yet, because the thing they’re doing is invisible from where you sit. They are not producing more reports. They are producing the kind of work that used to require somebody else.

That’s the most useful lens I can give you for AI in your organization. Leverage is an extra colleague, sitting at someone’s desk, that you didn’t approve a requisition for. Your job is to figure out who they’re sitting next to.

The unit is a person, not a seat

Most executive reporting on AI is built around the wrong unit. Seats licensed. Seats active. Hours of training delivered. A vendor’s adoption curve. Survey results on whether people feel more productive.

Each of these is a proxy for something a vendor wanted to sell you. None of them measures the thing you actually want to know: who in your organization just got dramatically better at their job.

One number now comes close, and you started getting it by accident. Enterprise AI pricing moved to a meter in 2026, so your bill is now consumption, not badges.¹ Finance reads that as a forecasting problem. Read it the other way. A per-user consumption report, sorted descending, is the first instrument any of us has had that points at individual people rather than at license counts. Seat counts are blind by construction: everyone who has a badge looks identical. Consumption is not blind. It has a top and it has a bottom, and the people at the top are running work through the tool at a volume nobody instructed them to.

That list is a shortlist, not an answer. Consumption is input, not output, and somebody burning tokens on a task they don’t understand looks the same on the meter as somebody clearing a backlog. The unit is still a person, not a seat, and the last step is still a conversation. But you no longer have to guess who to talk to. Pull the usage report by user, take the top of it, and start there.

The shape you’ll find is consistent wherever anyone measures carefully. Start with the average. A St. Louis Fed paper on the November 2024 survey wave found that workers who had used generative AI in the previous week reported saving 5.4% of their work hours, roughly 2.2 hours a week.² Self-reported, and only among the people already using it. That is the polite number that lands on your slide. Now look underneath it. Enterprise telemetry from 2026 found half of AI users had twelve conversations or fewer while the top 5% had at least 144, and that only 18% of users touched AI in a given week at all.³ OpenAI’s enterprise data shows the same lopsidedness inside a single company: the 95th percentile of users sends about six times the messages of the median employee, and about seventeen times for coding.⁴ A small head, a long tail, and a published average that describes neither population.

The most detailed look inside a company that has gone all the way is Anthropic’s study of its own staff: 132 engineers and researchers, self-reported Claude use in 59% of their work, an average self-reported productivity gain around 50%, and 27% of the assisted work described as work that would not have been done at all otherwise.⁵ Anthropic says plainly that its engineers get early access to frontier models and that the findings don’t generalize, so read it as a ceiling rather than a forecast. The last number is the one worth carrying. Leverage doesn’t only move the existing queue faster. It adds work that was never going to happen.

Once you’ve seen the shape, the seat report becomes useful for what it actually is: a count of badges issued. It stops being a measure of the program. The program is in the usage data and in the work.

What the extra hire looks like

Look for what you’d notice about a strong new employee in their first quarter. Work that used to require somebody else is suddenly getting done. Backlogs that have been “next quarter” for two years are quietly clearing. The “we’ll get back to you” answer disappears from a part of the business where it used to be standard. A team that was supposed to grow doesn’t ask to.

This shows up most legibly in engineering, because the work is countable and the tools are mature. The leveraged engineer is shipping pull requests at a volume that used to belong to a small team. Old internal tools are getting rewritten on weekends nobody asked for. The backlog is moving in a direction it has not moved in years. The person is not working longer hours. They picked up a coding agent and it is doing the part of their job that used to require a meeting.

The same shape appears outside engineering, but the surface looks different, and since the spring of 2026 the clearest non-engineering example stopped being a person typing at all. A finance lead who used to spend the first week of every month on the reconciliation now finds it waiting, already done, every Monday morning, because she set an agent to produce it on a schedule and reused the reclaimed week to build the analysis her CFO has been asking for since last year. A marketer ships a campaign end to end without briefing the agency. An operations manager closes a vendor support ticket by writing the script herself instead of waiting for IT. A general counsel notices that outside-counsel spend on routine work is dropping and can’t quite explain why. The reconciliation case is the one to watch, because it’s the form of leverage that finally reached non-coders: recurring work with a checkable output, produced on a cadence by something somebody set up once.

What ties them together is the relationship to the work. These people aren’t using AI as a search engine. They aren’t using it once a week to summarize a document. They have it open as a collaborator, and they’ve changed how they work to keep it open. They delegate. They iterate. They treat a wrong answer as a draft, not as evidence the tool is broken. None of that is in your training curriculum. It’s a temperament. The people who have it pick it up in their first week with a real tool. The people who don’t, don’t, regardless of how many hours of enablement you fund. This is the disposition gap, and no training budget on record has closed it. What changed in 2026 is that it stopped being the only gap that matters: a second kind of leverage arrived, one a single well-designed workflow can hand to people who never developed the temperament at all.

The second shape of leverage: designed, not personal

Everything to this point describes leverage as a person: a temperament, a desk, a colleague you didn’t requisition. That’s the first shape, and until 2026 it was the only one that mattered. A second shape arrived in the middle of that year, and it doesn’t run on temperament at all.

The mechanism is the scheduled task. Someone writes a prompt once, picks a cadence, and an agent runs the work on its own: a Monday status brief assembled from five systems, a reconciliation that flags what didn’t tie out, a competitive scan sitting in the inbox before anyone arrives. No code, no IDE, no daily habit to change. This is the proactive agent pattern, and it’s what the Q3 2026 briefing is about: leverage reaching non-engineers through standing, checkable work.

What this does to your hunt is pull the leverage and the beneficiary apart. In the first shape they’re the same person; the engineer shipping the pull requests is the one who picked up the tool. In the second shape they’re different people. The analyst who reads a finished reconciliation every morning may not use AI at all, may not even know an agent produced it. The leverage isn’t hers. It belongs to whoever designed the workflow and set it running once.

So the question you’re asking grows a second half. You’re no longer only looking for the person who got dramatically better at their own job. You’re also looking for the person who made a process better for people who changed nothing. That person is leverage for the entire team, and they’re harder to see than the power user, because their fingerprint is on a workflow rather than on a visible body of output. They are worth the effort to find, because one person who can spot a recurring, checkable task and schedule it lifts everyone downstream, which is exactly why the disposition gap matters less than it did: you no longer need a teamful of naturals, you need one designer.

Where you won’t find it yet

A note on where the unhired colleague isn’t showing up.

The work that’s most physical, most relational, and most adversarially correct is the work where current tools have the least to offer. Field service, line manufacturing, hands-on healthcare. Late-stage M&A, executive coaching, complex relationship sales. Final-pass legal work, audit signoffs, anything where the value is in being right rather than plausible.

This isn’t a permanent statement about those roles. It’s a current one. If you go hunting for the engineering-style step change in your senior litigators, you won’t find it, and you’ll conclude that AI is overhyped. The right conclusion is that the leverage is somewhere else and you were looking in the wrong place. The deeper reason these roles resist compression is that the work has no cheap way to check whether a given output is correct, which is the bottleneck that decides which workflows yield to AI and which stay stuck.

The signals

If you remember nothing else from this guide, remember the signals. These are observable, in your own organization, over the next month. A person is operating at AI leverage if you can see at least one of them. The first one you can pull this afternoon. The rest require you to look at the work.

Their consumption is several multiples of their team’s median. Sort the usage report by person, descending. In the telemetry, the top 5% of users run more than ten times the volume of the median user, and the median user is barely there at all. Whoever sits at the top of your own list is running work through the tool that nobody assigned them. That is not proof of leverage, because spend is input and a person can burn tokens going in circles. It is the shortlist, and it costs you one export to get.
They are doing work that used to require another person. A marketer who no longer briefs an agency. An engineer who no longer needs the data team to pull a report. A finance lead who no longer needs the BI queue.
They are doing work that used to be deferred indefinitely. Backlog items getting closed. “We should clean that up someday” things suddenly cleaned up. Reports nobody had time to build appearing in chat.
Their cycle time on a recurring task has dropped by more than half. Not 10% faster. Half or better. If it’s only marginally faster, it’s a small efficiency gain, not leverage.
Their output has shifted in a way peers in the same role haven’t matched. Same level, same tenure, dramatically more shipping. Not longer hours. Not corner-cutting. Just more.
Other people are starting to depend on something they built with AI. A script. A prompt template the team uses. An internal tool that was supposed to be a one-off. This is the strongest signal, because it means the leverage is starting to spread.
They have stopped asking for headcount in places they used to ask for it. Quietly, often without being asked. The hiring request that didn’t come in. This is usually the cleanest financial signal in the entire program, and it appears nowhere on a dashboard.
A recurring task that used to need a person now arrives on a schedule, and you’re not sure who set it up. The Monday report that’s simply there. The reconciliation that’s already done. This signal is different in kind from the seven above, because the person benefiting from it may not be an AI user at all and may not know an agent is involved. The leverage isn’t theirs. Trace the standing job back to whoever designed it and pointed it at a cadence: that person is operating at leverage for the whole team, and they’re the hardest of all to spot, because the only mark they leave is work that quietly keeps happening.

A usage report sorted by person gets you the first one and narrows the search for the rest. An ROI chart gets you none of them. If you start looking for these in your weekly business reviews, you will find them within a week.

Something to carry

Here is the move I would make if this were my organization, and the one I would suggest you make in yours.

Write down three names. Your honest guess at the three people on your team most likely to be operating at AI leverage right now. Not the loudest about it. Not the most senior. The three you suspect are quietly outproducing what you used to expect from their role. Then add a fourth, of a different kind: the person most likely to have built a standing workflow that lifts other people without anyone noticing. The first three are doers. The fourth is a designer, and the designer is the name least likely to show up in any report you already run.

Then pull the consumption report, sorted by person, and read it against your four names. Where the list agrees with you, you were right and you now have the evidence. Where it surfaces someone you didn’t write down, that’s the useful part: it’s a person doing something with the tool that nobody asked them to do, and you didn’t know.

Then book thirty minutes with each of them, separately, with no agenda except this conversation. Three questions are usually enough.

What are you using, and how do you have it set up?

What work used to take you a day or a week that now takes you an hour?

What would you be able to do if I gave you a budget for tools, time, or people to support this?

What you’ll get from those three conversations is the shape of the program your organization actually has, not the shape on the slide. The gap between them is your real roadmap.

Enterprise AI pricing moved to a metered model across the serious vendors in 2026: a small seat fee plus consumption billed against a committed annual spend or token volume. Admin consoles expose per-user and per-workspace consumption and spend limits, which is what makes a per-person usage sort possible at all. See The State of AI: Q3 2026. Terms current as of mid-2026 and change frequently. ↩
Alexander Bick, Adam Blandin and David Deming, “The Impact of Generative AI on Work Productivity,” Federal Reserve Bank of St. Louis, February 27, 2025, on the November 2024 wave of the Real-Time Population Survey. Workers who used generative AI in the prior week reported saving 5.4% of their work hours, about 2.2 hours per week. Self-reported, and the whole-workforce equivalent is closer to 1.1-1.4%. Last verified August 2026. ↩
LayerX, “State of AI Usage Report 2026,” May 28, 2026, based on enterprise browser telemetry rather than survey response. 18% of enterprise users used AI in a given week; half of users had 12 conversations or fewer while the top 5% had at least 144, at an average of 18 prompts per conversation against an overall average of 2. Last verified August 2026. ↩
OpenAI, “State of Enterprise AI,” December 2025, across more than a million business customers. Workers at the 95th percentile of usage send roughly 6x the messages of the median employee at the same company, 17x for coding messages. This is message volume, not output. OpenAI’s own 2026 follow-up reframes the gap at firm level (frontier firms use about 3.5x as much intelligence per worker) and finds that message volume explains only 36% of the difference. Last verified August 2026. ↩
Anthropic, “How AI Is Transforming Work at Anthropic,” December 2, 2025: 132 engineers and researchers, 53 interviews, 200,000 internal Claude Code transcripts. Self-reported Claude use in 59% of work, average self-reported productivity gain around 50%, and 27% of Claude-assisted work reported as work that would not have been done otherwise. Anthropic notes its staff have early frontier-model access and that the findings do not generalize. Last verified August 2026. ↩

Recognizing Leverage