Your AI Dashboard Is Green. Your Delivery Isn't.

A recent article on Dev.to highlights the discrepancy between AI adoption metrics and actual software delivery performance. While teams using AI tools often report increased pull request counts, data suggests this does not always translate to improved delivery or quality. The author provides a diagnostic guide for engineering managers to evaluate AI impact, warning that activity metrics can be easily inflated. The guide includes a cheat sheet for interpreting common metrics like PR size, review time, and failure rates, urging leaders to look beyond vanity numbers. The article emphasizes that AI's effectiveness varies by codebase and team maturity, often leading to a 'faster but breaking' phase before any real gains are realized. Managers are advised to avoid ranking individuals, focus on team-specific trends over long periods, and prioritize quality guardrails to ensure that increased coding speed does not come at the expense of system stability.