AI-assisted development: how to roll out and measure impact

AI-assisted development can support tasks such as code understanding, test writing, refactoring, and autonomous pull-request creation, but organizations should evaluate it through specific workflows rather than broad adoption or generated-code volume. Effective rollouts begin with a bounded, recurring engineering problem, a clear decision owner, defined acceptance criteria, and a baseline that measures usage, spending, accepted work, review effort, delivery, reliability, and relevant user outcomes. Interactive assistants and autonomous agents should be assessed separately when their costs, supervision needs, and risks differ, while financial records and AI-attribution evidence should remain transparent about uncertainty. Before expanding a pilot, teams should compare similar work over consistent periods, account for factors such as task complexity, staffing, release practices, and learning effects, and investigate whether apparent gains merely shift bottlenecks into review, testing, or deployment. The recommended approach treats before-and-after results as observations rather than proof of causation, expands workflows only when quality and cost remain acceptable, and maintains repeatable decision records as tools, models, pricing, and repositories change. The text also presents Weave as a platform for connecting AI usage and cost data with engineering output, reviews, quality, and delivery metrics, while emphasizing that teams must still set standards and determine whether users benefited.

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