Don't Train Tasks. Build Builders.

AI training shouldn’t measure completion, it should measure whether behavior actually changed.

Picture a typical enterprise AI rollout: three thousand licenses, a ninety-minute onboarding, a slide deck with screenshots. Six weeks later, leadership pulls up the usage dashboard and finds that no one has actually adopted the platform into their workflow. Or, on the opposite end of the spectrum, usage explodes, token costs spike, but nobody can explain what the company actually got for it. A lack of training is not the issue. New tools call for new training methodologies.

Teresa Marchek, our co-founder and Head of Enablement, has spent fifteen years building learning programs that change how people work. Her diagnosis: the playbook that worked for every enterprise tool before AI was optimized for a world with defined destinations: Here’s how you update a record in Salesforce. Here’s how a ticket moves through ServiceNow. You wrote the correct workflow down, taught it, and measured whether people followed it. Completion equaled deployment.

AI tools like Claude Code and Cowork don’t have a correct workflow. Their value comes from inventing them. Imagine a procurement manager who builds their own contract-review tool, an HR lead who automates the onboarding process, or a finance analyst who enlists a reporting assistant on a Tuesday afternoon. Those examples barely scratch the surface of what people can use and are using Code and Cowork to do, but that open-endedness also presents a problem: you can’t train toward a destination that doesn’t exist. That’s what most rollout playbooks are missing.

What the proven playbook gets right

These are a set of tested psychological principles that successful tech rollouts of the past have utilized to their advantage:

• People forget fast. We lose most of what we learn within days of a training event.¹ A single session, no matter how good, will inevitably fade. Spacing reinforcement over four to eight weeks triples retention.² On-the-job experience can’t bridge that gap on its own; it needs structure and managers who actively coach.

• Knowledge is rarely the real barrier. Lack of information doesn’t cause resistance. The real killer is lack of motivation. Mid-level managers carry more resistance than any other group, which is exactly why they need to be activated early, not treated as message-relayers.³

• Habits don’t form through willpower. A behavior requires three things to happen concurrently: motivation, ability, and a prompt.⁴ Motivation waxes and wanes. Ability and prompts you can engineer deliberately.

Microsoft’s Copilot enablement program is often held up as a standout. Their public adoption playbook combines executive sponsorship, celebrating Copilot “champions” (successful early adopters), a user community, phased rollout, and continuous usage measurement.⁵ The formal training is more of an afterthought. The reinforcement infrastructure, not the class, drives sustained usage.

The destination disappeared

With Salesforce, ServiceNow, and other collaborative tools, there was always a correct workflow waiting at the end of training. You could write it down, teach it, and know when someone had it.

Claude Code and Cowork don’t work that way. The value is in their open-endedness: users invent the workflows rather than follow preset ones. A program built to certify task completion can’t produce people who build their own automations. Teaching defined steps in an open-ended tool gets you shallow, literal usage, and leaders wondering why the licenses sit idle. Mastery of Code and Cowork means judgment: knowing what to automate, how to verify AI output, when to trust it, and how to spot a good opportunity in your own work. That skill can be built, but it takes a different approach than standard task training.

When it comes to AI platforms specifically, there’s an extra layer of skepticism employees often have that also needs to be dealt with. People are anxious about AI taking their jobs, its environmental impacts, or how their data is being used. That’s an issue with willingness to learn rather than capability. AI hesitation can’t be corrected by another training module, but rather by open discourse that addresses these beliefs directly. This is a topic that’s big enough for another article (which is in the works), but long story short, situations where emotions are running high can’t be formally trained into submission.

Diffuse the capability, don’t just drive adoption

Major tech transformations before this one concentrated new capability in small groups. Cloud computing went to platform teams; CRM went to the admins. A small group got the new power, and everyone else consumed the outputs.

Claude Code and Cowork do the opposite. They put building, automating, and agent-creation into the hands of people who were never builders, such as that procurement manager who builds a contract review workflow, or the HR team lead who builds an onboarding automation.

That means the enablement problem is not just about proficiency, but diffusion. The goal isn’t to certify everyone on a workflow, it’s to spread the confidence to experiment across the organization, then let social proof carry it. As shown by Microsoft’s Copilot rollout, one viable approach is to identify early adopters, make their wins visible, and let the majority follow their lead. The role of these champions isn’t to run the training. It’s to build something real and show people that it’s possible for them to do the same.

This also changes what managers are for. Managers can’t reinforce a workflow that doesn’t exist. Their job in this rollout is to create permission and time to experiment, and to surface what their people invent. That’s a different task than reminding their team to log in. This shift has to be addressed explicitly, because most managers will default to the behavior their last ten rollouts trained them for.

Closing the gap between AI access and usage

Microsoft Copilot is the enterprise AI tool that has the most history at this point, so it’s a good one to look at to understand what the difference between average and good adoption looks like. Roughly 36% of employees given access to AI tools actively use them. And only about 42% of provisioned Copilot seats are active within six months in large enterprises.⁶ Access alone isn’t sufficient for adoption.

Strategic enablement programs can close most of that gap. Microsoft’s own 62,000-person sales organization hit 60% usage of allotted Copilot seats daily and 98% monthly active use two years in.⁷ Champions programs like that one drive two to three times higher activation versus self-service alone.⁸

The gap between 36% and 80%+ is exactly the gap that the mechanics described above are documented to close. None of those outcomes require promising anything that can’t be measured. The metric to look out for is sustained usage on real work, which you can only claim if you can see it happening. Agree on the definition of “active” before the rollout launches, get admin dashboard access, and measure behavioral changes, not reactions or test scores.

What to actually do

Here are five design principles, re-imagined for open-ended tools:

1. Inspire people to find an easy and real first win. Not “I finished the training,” but “I built something that saved me an hour.”

2. Brief managers as permission-givers, not enforcers. Their job is to clear time for experimentation and surface what people invent, not chase dashboards.

3. Build the champions program like it’s a main driver. Because it is. Visible peer wins are the mechanism, and everything else is support.

4. Spread reinforcement over four to eight weeks. This is proven to lead to more retention over cramming a lot of information into a short amount of time.

5. Measure behavior, not completion. Agree on what “active” means before launch, get dashboard access from day one, and track sustained use on actual work.

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The organizations that win this rollout won't be the ones that trained the most people. They'll be the ones that built the most builders.

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Blank Metal works with enterprises and PE-backed companies on AI implementation—including the enablement programs that make rollouts stick. If this is the problem you're working on, we'd be glad to talk.

1. https://resources.indegene.com/indegene/pdf/articles/understanding-the-science-behind-learning-retention.pdf

2. https://www.worklearning.com/wp-content/uploads/2017/10/Spacing_Learning_Over_Time__March2009v1_.pdf

3. https://www.prosci.com/ai-change-management

4. https://www.thebehavioralscientist.com/articles/fogg-behavior-model

5. https://www.microsoft.com/en-us/microsoft-365-copilot/copilot-adoption-guide

6. https://www.worklytics.co/resources/2025-ai-adoption-benchmarks-employee-generative-ai-usage-statistics

7. https://www.stackmatix.com/blog/microsoft-copilot-adoption-statistics-2026

8. https://thinktechnologiesgroup.com/blog/8-next-step-ai-plays-turn-micro-wins-into-team-wide-momentum