TL;DR. Half your AI seats are idle six months in. The reflex is to fund a training program. That is the wrong move. The 6x gap between power users and everyone else is a disposition gap, a workflow gap, and a manager-signaling gap. Curriculum addresses none of the three. The fix is to kill the bad approved tool, fund the champions you already have (and hire one for every function that lacks one), redesign three workflows around them, set the manager signal from the top, and stop measuring attendance as if it were adoption.
You ran the audit from Evaluating Spend. The shape of your usage report was uncomfortable. Six months later you ran it again. Same shape. Maybe ten percent better at the top. The bottom thirty percent is still bottom thirty percent. The new seats you handed out after the last all-hands have already gone quiet.
Your head of HR has a proposal on your desk. It is a thirty thousand dollar AI enablement program. Six modules. Mandatory completion. A vendor with a deck full of “AI-fluent workforce” pull quotes. A certificate at the end. There is a similar memo from a department head asking for an “AI champions network.” There is a third asking to stand up an internal Center of Excellence.
None of this is going to move the number you actually care about.
Why training doesn’t move the number
The default leadership move when usage stays flat is to schedule learning. It feels safest in front of a board. It is also the most reliably wrong response to an adoption gap in enterprise software I have watched executives reach for.
The reasoning behind the training reflex goes like this. Usage is low. People don’t use what they don’t know how to use. Therefore teach them. Therefore the gap closes. Each step in that chain is intuitive. The problem is the second one. The premise that low usage is caused by low knowledge is empirically wrong for AI tools, and has been from the beginning.
The 6x engagement gap that OpenAI publishes is not a knowledge gap. The Federal Reserve number on average productivity gain (5.4% of work hours, roughly two hours a week on a forty-hour schedule) is the average of a population in which a quarter of users save nine or more hours a week and the rest save almost nothing. BCG’s 2025 AI Radar surveyed 1,800-plus executives and found that fewer than one-third of companies have managed to upskill even a quarter of their workforce on AI. Their headline finding was not “train more.” It was the opposite: their leading firms run on a “10-20-70” principle in which 10% of effort goes to algorithms, 20% to data and technology, and 70% to people, processes, and cultural transformation. The training inside that 70% is a small piece. The bulk is workflow redesign and operating-model change.
The pattern across every credible 2025 and 2026 dataset is the same. The companies extracting value are not the ones with the most enablement hours delivered. They are the ones that picked a small number of workflows, redesigned them, and assigned the work to people who were going to do it well no matter what.
Curriculum is downstream of the gap, not the cause of it.
What’s causing the gap
Three things, in order of how much budget should go to fixing each.
Disposition. Roughly the top decile of any role is willing to delegate work to a model, iterate on a wrong answer, and treat the chat tab as a workspace rather than a search engine. That posture isn’t taught in a session. It is a temperament. The people who have it figured the tool out in their first week. The people who don’t have it never get there, regardless of the curriculum. Recognizing Leverage covers the shape of this distribution and Selecting Talent covers how to identify the people who sit at the head of it. The shorthand for that person is champion. They are the load-bearing variable in any adoption program. The disposition gap is real, it is durable, and it is dominant.
Workflow fit. Most of the broad middle of your org is not refusing to use AI. They are dutifully opening it once a week, asking it to summarize a document, copying the output into an email, and closing the tab. They get marginal value because their workflow has not changed. They are using a 2026 tool to do their 2022 job. The model is doing the part of the work it can see, which is a tiny part. The other ninety percent of the work is in steps the user never thought to surface to the model because the workflow was not designed to surface anything. The workflows your champions did compress all had a cheap way to check whether the output was correct, which is the underlying property that decides which work yields to AI. The leftover workflows the broad middle is staring at usually don’t, and asking those people to use AI harder without redesigning the work is asking them to swing at air. This is not a training problem. It is a process-design problem.
Manager signaling. Your director-level managers are not visibly using AI. They are not pasting drafts into their team channel and saying “here is the first cut Claude gave me, fix it.” They are not asking in their one-on-ones what their report’s prompt setup looks like. They are not modeling the behavior. In the absence of that signal, the team correctly reads the situation as “the official line is to use AI, but the actual review and promotion incentives haven’t changed.” So nothing changes. Adoption of any new working practice in a knowledge-work org tracks the practices of the manager two layers up, not the official policy. AI is no exception.
The three failure modes interact. A leader without disposition can’t sense the workflow that needs redesign. A redesigned workflow that the manager doesn’t visibly use doesn’t stick. A manager who signals usage in a workflow that was never redesigned looks like theater within two weeks. You have to fix all three. Training is somewhere on the list, but not in the top three.
The three populations of non-user
There is one more frame that helps before the prescription. Your idle seats are not a single population. They are three.
The won’t. People with the wrong disposition for this tool, in this role, at this stage of their career. They are doing fine work. They are not going to be your AI leverage story. Stop trying. Take their seat back, pocket the money, redirect it. Selecting Talent calls this group out at length, because pretending it doesn’t exist is the leadership move that costs the most.
The can’t. People in roles where current AI tools genuinely don’t have product-market fit. The relational sales lead in a six-quarter cycle. The senior litigator. The field service tech. The line worker. Recognizing Leverage names these. They are not the gap. They are the floor.
The won’t-bother. People with the disposition, in a role with fit, who have looked at the approved tool, found it inferior to what they used at home, and decided it isn’t worth the effort to swim against the procurement current. This is the only one of the three groups that responds to anything you do this quarter. They are also the largest of the three. They are your reallocation pool, and they will follow your champion.
If you can’t tell which of your idle seats falls into which bucket, you don’t have an adoption problem yet. You have a visibility problem, and the audit in Evaluating Spend is how you fix it.
What moves the number
Five moves. Order matters.
Kill the bad approved tool
If the audit shows that your headline AI tool has 4% paid penetration, a negative satisfaction NPS for nine straight months, and your power users are paying out of pocket for something else, the tool is not the floor of your AI program. It is the cause of your adoption problem.
Every additional month it sits in front of your broad middle as the “official AI” is a month they are forming the conclusion that AI does not work. They are not wrong. The tool they were given does not work for the job they have. They are correctly inferring its quality from the experience. They will then resist the next tool you put in front of them, because they have learned, accurately, that your IT-procured “official” anything is downstream of a vendor relationship and not of their actual workflow.
The fix is not a memo. The fix is to switch the default. Make the tool your power users have already chosen the approved tool. Move the seats. Eat the procurement awkwardness. The shadow AI line in your expense report is the user research. Act on it.
Fund the champions
Take the dollars freed up from the seat audit and concentrate them on the people in your org who already have leverage. The shorthand is champion, and the long version is in Selecting Talent: the person on each team who has already built something nobody asked for, who keeps the chat tab open while they work, who has an opinion about the next tool and the next workflow change. Give them the higher tier. Give them the API budget. Give them a small operating budget for tools, prompt libraries, and time on the calendar. Tell them, in writing, that the expectation is they produce reusable artifacts (prompts, workflows, scripts, internal tools) that the rest of their team can run.
This is the inverse of the usual move. The usual move spreads the budget evenly across a broad cohort and produces a flat usage curve. The right move concentrates it on the steep part of the curve, because the steep part of the curve is where the leverage propagates from. Your champions become the channel through which good practice reaches the broad middle. They become it because they are doing the work and the work is now visible. No curriculum will produce that effect. A few people with budget and a mandate will.
If a function doesn’t have a champion you can name, that’s a different problem and a hiring brief, not an adoption tactic. Selecting Talent has the interview questions and the assessment exercise. Run that loop for the function. Don’t try to solve a missing-champion problem with a training program. It won’t work.
Redesign three workflows, not thirty
Pick three workflows. Three. Not an enterprise-wide process re-engineering effort. Three specific recurring workflows in three different functions, each consuming meaningful weekly time, each currently bottlenecked at a step a model can plausibly do.
Examples that work. Monthly close reconciliation in finance. Account research and call prep in sales. First-draft contract redline in legal. Internal report writing in operations. Customer ticket triage and response drafting in support. Pick yours.
For each, assign the champion on that team to redesign the workflow with AI in the loop, on a thirty-day clock, with a specific output: a written description of the new process, the prompt and tool stack used, and the new cycle time compared to the old. This is not a training deliverable. It is a process-engineering deliverable. The training falls out of it as a side effect, because the team running the workflow learns the new shape by doing the new shape.
BCG’s data on this is direct. Companies that focus on 3.5 use cases see 2.1 times the ROI of companies spreading themselves across 6.1 use cases. The discipline is depth, not breadth. Three workflows redesigned to completion will move your usage numbers more than thirty workflows lightly touched.
Make the manager signal louder than the policy
Your directors and VPs need to be visibly, repeatedly using AI in front of their teams. Not “I support this initiative” emails. Actual artifacts. The first draft of the strategy memo, with a note that it is a first draft from Claude. The market analysis with the prompt attached. The “here is what I asked the model and here is where it got it wrong” post in the team channel.
This sounds soft. It is the most load-bearing move on the list. Adoption of any new working practice in a knowledge-work organization is set by the manager two layers above the IC. If your director uses AI visibly, the team uses AI. If your director does not, the team does not, and no amount of policy will overcome that signal because the policy is an abstraction and the manager’s behavior is a concrete prediction of what gets rewarded at review time.
The implementation move is small and uncomfortable. Sit down with your top two layers of management. Tell them, by name, that visible AI use in their own workflow is now part of how you evaluate their leadership over the next two quarters. Not their team’s usage stats. Their own. Then check.
Run the quarterly audit, and act on it
The seat-level audit in Evaluating Spend is not a one-time exercise. It is the operating cadence of an AI program that works. Every quarter, you pull the report. You sort by activity. You kill the bottom third of seats. You redirect the savings. You ask your champions what they need. You act on the answer within thirty days.
This sounds like an obvious operations habit. It is. Almost no one does it. The reason is that the first time you do it, you have to take a tool away from someone who said in a survey that they “find AI useful.” The audit shows they have not opened it in sixty days. The survey was polite. The audit is the truth. Choose the audit. The second quarter is much easier than the first.
The five moves
If you screenshot one thing from this guide.
- Kill the bad approved tool. The “official” AI that your broad middle uses once and gives up on is teaching them that AI doesn’t work. Replace it with the one your power users already chose.
- Fund the champions, not the average. Concentrate budget on the people who already have leverage. Give them tools, API, and a mandate to produce reusable artifacts. They are your distribution channel. Where you don’t have a champion in a function, that’s a hiring brief, not a training brief. See Selecting Talent.
- Redesign three workflows. Not thirty. Three specific recurring workflows, one per function, thirty-day clock, written outputs, owned by the function’s champion. Depth beats breadth at 2.1 to 1.
- Set the manager signal from the top. Directors and VPs must be visibly using AI on their own work. Make this part of how you evaluate them. Their behavior is the policy.
- Audit and act, every quarter. Kill the idle seats, fund the active ones, ask your champions what they need, deliver in thirty days.
Notice what isn’t on the list. Mandatory training. AI champions networks (the corporate kind, with a logo and no authority, which is not the same thing as the actual champions named above). Centers of Excellence. Enablement modules. AI-fluency certifications. None of these have moved a usage curve. They produce attendance and the appearance of action. They don’t produce adoption.
This doesn’t mean training has zero role. Once the first three workflows are redesigned and the manager signal is set, a tight, role-specific, hands-on session for the team running that new workflow is useful. Forty-five minutes, not six hours. Run by the person who redesigned the workflow, not by an external vendor. Anchored to the artifact, not to the abstract concept of “prompt engineering.” That is training that works. It’s also a tenth the cost of the program your HR head proposed.
Something to carry
Pick the function you trust most. Find your champion there (the person who has already built something nobody asked for, who keeps the chat open while they work, whose teammates name unprompted as the person to ask). If you can’t name them, Selecting Talent is the next read, and the answer for that function is a hire, not an enablement program.
Where you can name a champion, find the single recurring workflow on that team that consumes the most weekly time and is currently bottlenecked at a step a model can plausibly do. Hand the workflow to the champion, with a thirty-day clock, the budget for whatever tool they need, and a clear deliverable: a written description of the redesigned process, the prompt and tool stack, and the new cycle time.
Tell them you’ll personally walk through their result with them on day thirty. Then do it.
You’ll get one of two outcomes. Either the workflow is meaningfully faster, in which case you have your first internal case study, your first piece of reusable IP, and your first concrete data point for the rest of the org. Or it isn’t, and you’ve learned something specific about where the current generation of tools doesn’t yet fit your business. Both are more valuable than another quarter of training attendance reports.
The point of an adoption program is not to spread AI across every desk. It is to find the workflows where AI changes the work, give them to the champion, and let the manager signal carry the practice across the rest of the org.