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AI leadership and strategy
Leaders decide which AI work to fund, how to measure it and what to put around it. These articles cover measuring the impact of AI tools, governing AI in engineering teams and choosing what to scale or stop.
3 articles on this topic
Ordered from foundations to advanced, newest first within each level.
- AI Strategy for Engineering Leaders: Choose, Fund and StopHow engineering leaders decide which AI projects to back: a portfolio view, stage gates, stop criteria and evidence-based reporting, with reusable scorecards.Intermediate 9 min readLast reviewed 2026-10-11
- An AI Governance Framework for Engineering TeamsA lightweight AI governance framework for engineering teams: inventory, risk tiers, approvals, evaluation gates, incidents and a review cadence.Intermediate 11 min readLast reviewed 2026-10-11
- How to Measure the Impact of AI Coding Tools on Your TeamHow to tell whether AI coding tools are working: measure delivery, quality and developer experience against a baseline, and avoid activity metrics that mislead.Intermediate 10 min readLast reviewed 2026-10-11