AI coding tools like Claude Code, GitHub Copilot, Cursor, and Codex are now table stakes. Most engineering orgs have licenses. Some have training. Almost everyone has a slide somewhere with a plan to achieve "X% productivity gains."
Yet everyone is wrestling with one simple question:
How do we measure success?
At DevClarity, we work with some of the largest PE-backed software companies in the world. Across dozens of teams, we've found that you need an opinionated metric stack that provides clarity in two core areas:
Everything else is noise.
Adoption metrics are the leading indicators that we can control. Impact metrics are the lagging indicators that ultimately matter. Having a clear view on both is critical to driving progress.
Reach out for our complete framework on engineering metrics in the AI age.
Get access to the complete DevClarity AI-SDLC Metrics Framework
Schedule a call with DevClarity if you need a structured approach to AI adoption and enablement across your engineering organization.
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