What good looks like when companies roll out GitHub Copilot, Microsoft Copilot, and Claude Code — and where most teams actually land.
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These are the utilization rates we see across team deployments — measured as the percentage of licensed users actively using the tool in a meaningful way (at least 30 minutes/week). "Active" isn't "opened the tab." It's people using AI in their daily work.
These patterns hold across team sizes, industries, and tools. If you've rolled out an AI coding or productivity tool in the last 12 months, you've likely seen at least three of these.
Teams without structured training plateau at 20–35% utilization within 30 days of rollout. Organic adoption stalls after the initial curiosity wave. Without intervention, that number stays flat for months — sometimes permanently.
Most companies focus energy on senior devs. But senior devs figure it out themselves. The adoption gap is mid-level engineers and non-technical staff who don't know what "good" looks like for their specific role.
The single strongest predictor of team-wide adoption: whether the engineering manager uses the tool publicly in meetings and code reviews. If managers don't model the behavior, teams don't adopt.
"Watch this YouTube tutorial" and "here's the documentation" produce 3–5% behavior change. Live training with role-specific scenarios — where people practice on actual work they're doing this week — produces 40–60% behavior change within 30 days.
Only 18% of companies in our dataset had a utilization baseline before rollout. Without a baseline, you can't prove ROI — and L&D leaders end up unable to justify renewal or expansion. Measure before you train.
If a developer earns $120K/year and saves 1 productive hour/week from AI coding tools, that's ~$3,600/year in recovered value per seat. For a team of 20, that's $72,000/year — against a $5,000 training investment. The question isn't whether training pays. It's whether you can prove it to leadership. That's what a baseline measurement makes possible.
This page summarizes the top-level findings. The full PDF goes deeper on each tool, with role-by-role breakdowns and the specific interventions that move adoption numbers.
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