AI coding tools are now embedded across engineering teams, but most organizations are still measuring adoption instead of impact. Token spend is rising, usage is growing, and leaders are being asked a harder question: what did that AI investment actually produce?

Join Harness for a conversation on how engineering leaders can move beyond seats, usage, and token consumption to understand which AI-assisted workflows are helping teams ship better software faster. We’ll introduce AI DLC Insights and share how teams can connect AI spend, generated code, and coding agent activity to the outcomes that matter: keep rate, ship rate, delivery velocity, quality, and cost to ship.

Learn how to identify waste, optimize model usage, and build a clearer line from AI activity to engineering impact.

Key Takeaways:

Measure AI impact, not just adoption - Move beyond tracking seats, usage, and token consumption to understand how AI investments influence software delivery outcomes and business results.

Connect AI activity to engineering performance - Learn how to correlate AI-generated code, coding agent activity, and AI spend with key metrics such as ship rate, delivery velocity, quality, keep rate, and cost to ship.

Optimize AI investments with data-driven insights - Identify waste, improve model utilization, and establish a clear framework for evaluating which AI-assisted workflows are delivering the greatest engineering value.