This paper introduces a novel measurement framework to evaluate human-AI decision-making, shifting focus from mere model accuracy to the readiness of human-AI teams for safe and effective collaboration. It proposes a taxonomy of metrics and connects them to the Understand–Control–Improve lifecycle to assess calibration, error recovery, and governance in real-world deployments.