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 don't need a 30-metric dashboard.
You need a small, opinionated metric stack that gives clarity in two core areas:
Everything else is noise.
Download our complete framework to get the pareto approach to engineering metrics in the AI age.
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Schedule a call with DevClarity if you need a structured approach to AI adoption and enablement across your engineering organization.
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