AI adoption
Also known as AI tool adoption, Coding assistant adoption
Definition
AI adoption is the sustained use of AI capabilities in real work, together with evidence that the use produces value for people or the organization. Installation, invitations, or isolated experiments are adoption signals, not proof of effective use.
Measure behavior and value
Useful adoption signals include weekly active users, assisted tasks, accepted suggestions, and repeat use across teams. Pair them with completion, quality, satisfaction, and cost. A high request count can mean the tool is useful, or it can mean users are retrying because results are poor.
Respect different workflows
Adoption varies by language, repository, role, and task type. Compare similar work and ask developers what makes the tool worth using. Do not treat one usage pattern as the definition of a productive engineer.
Learn from the path to production
Weave gives teams a way to follow AI-assisted work through review and delivery. That context supports rollout decisions based on useful outcomes, not pressure to maximize activity.
Adoption also includes enablement. Clear examples, safe defaults, and feedback channels help people use a tool well. Review adoption by team and task, then improve the workflow around the model when usage stalls.
How this relates to Weave
Weave connects AI usage with pull requests, code output estimates, review effort, and completed work. This helps teams distinguish active adoption from requests that were opened, abandoned, or heavily reworked.
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