Giving developers access to an AI coding tool is easy. Getting them to build it into their regular workflow is harder.
The 2025 Stack Overflow Developer Survey found that 84% of respondents were using or planning to use AI tools in development, while 51% of professional developers reported using them daily. Those figures come from different respondent groups, but they point to the same rollout problem: broad interest and access don’t automatically become daily use.
The familiar enterprise rollout sequence is straightforward: procure licenses, assign seats, announce the program, provide training, and monitor activation. That can tell you whether developers had access and whether they tried the tool. It doesn’t tell you whether they found enough value to keep using it.
Microsoft’s 2026 field study makes that distinction explicit. The researchers separated initial use from retention because the factors associated with each were different. Among engineers eligible for Copilot CLI, coworker use was one of the strongest signals associated with trying the tool. Retention, defined in the study as use on at least five of the first 14 days, was more closely associated with an engineer’s existing coding activity.
That matters for rollout design. A launch can produce a large burst of first-time users without producing sustained workflow change. The goal is to give developers reasons and opportunities to return to the tool after the novelty wears off.
Start with engineers and teams that the tool can help and that have enough coding activity to evaluate it meaningfully. The Microsoft study found that engineers who were already creating more pull requests were more likely both to try Copilot CLI and to retain it. Engineers creating two or more PRs per week before rollout had 31% higher odds of retention than engineers creating none.
The point is to create credible examples of the tool being used in actual engineering work before asking the broader organization to change its habits.
The Microsoft findings give rollout leaders a strong reason to make successful use visible. Reviewer-peer use, skip-level-peer use, and direct-manager use were all associated with higher odds that an engineer would try Copilot CLI. The strongest association appeared among skip-level peers.
That suggests a practical rollout approach: let engineers see how coworkers use the tool for specific tasks. Short demos, team examples, code-review discussions, and reusable prompting or workflow patterns make the behavior visible without turning the rollout into another generic training program.
Trying an AI coding tool once is not the same as knowing where it fits. Developers need to learn which tasks are a good match, how to give the tool useful context, and when the verification effort outweighs the benefit.
Larridin’s AI Fluency measurement separates sporadic users from regular adopters and daily power users. It also identifies teams that have moved into daily AI integration so their practices can be used to mentor other teams.
The rollout isn’t done when everyone has a license or when activation reaches a target percentage. Track whether developers continue to use the tool, how frequently they use it, and whether it becomes part of meaningful engineering workflows.
Larridin’s AI Adoption dashboard tracks adoption trends across teams, while AI Fluency distinguishes stages from observers and experimenters through adopters and power users. Together, those views help show where usage is deepening and where access has not yet turned into a habit.
There’s no research-backed four- or six-week threshold. Use behavioral signals instead of a fixed calendar. The seed group should have enough repeat usage to identify useful workflows, common failure points, and examples worth sharing. A broader rollout makes more sense once there’s something credible for peers to observe and reuse.
Don’t assume that seniority predicts resistance. In the Microsoft study, senior individual contributors were somewhat more likely than mid-level individual contributors to try Copilot CLI, while tenure generally had little relationship with either initial use or retention. Ask what’s getting in the way: output quality, verification effort, task fit, workflow friction, or something else.
Treat non-retention as a diagnostic signal, not automatically as resistance. Some developers may not have a strong use case. Others may need better workflow examples, more relevant enablement, or a different tool. If a licensed user repeatedly has access but little meaningful usage, that can eventually become a license-allocation question as well.
There’s no universal percentage at which a social network becomes “dense enough” for broad rollout. Look for repeat use among the initial group, visible examples that other teams can apply, and demand from developers who have seen the tool used successfully. Those signals are more defensible than an arbitrary adoption threshold.
AI coding adoption isn’t one event. First use, retention, and deeper workflow integration are separate stages, and the same rollout tactics don’t necessarily work for all of them.
Larridin’s AI Adoption and AI Fluency capabilities help leaders see where teams sit across those stages, where usage has stalled, and which teams have developed practices that can help adoption spread.
Book a discovery call to see where your AI rollout is creating sustained use and where it is stopping at access.