Why metrics for top engineering teams look different

Engineering teams are experiencing increased speed in software delivery due to AI-assisted coding and improved CI/CD pipelines, but they face challenges in managing code in production, leading to frequent rollbacks and firefighting. According to the LaunchDarkly AI Control Gap Report, while 94% of teams report faster shipping, 91% exercise caution with production releases, and 70% still perform weekly rollbacks or hotfixes. Despite having tools like feature flags and monitoring, many teams lack runtime control, resulting in a disconnect between speed metrics and release success. High-performing teams distinguish themselves by focusing on operational discipline, treating releases like critical infrastructure and separating deployment from release with runtime control. They track metrics beyond speed, such as manual intervention, mean time to detect incidents, and deployment frequency alongside change failure rates. These teams prioritize release health by ensuring that features can be controlled in real time, allowing for immediate rollbacks if issues arise, and striving for zero-touch releases that require no manual follow-up. By measuring operational effort, risk signals, and team experience post-deployment, they maintain visibility and control over their production environment, ultimately demonstrating effectiveness beyond velocity.

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