From AI Output to Engineering Outcomes

AI is changing how work gets done across engineering teams, but it’s also making performance harder to interpret. More activity, faster cycles, and new workflows don’t always translate into clear insights for leaders.

In this session, we’ll break down how to build a more accurate view of engineering performance in the age of AI, focusing on how to connect day-to-day development work to delivery health, team effectiveness, and broader business goals.

Key Discussion Topics:

• AI increases activity but not necessarily clarity
• Performance visibility must evolve with AI workflows
• Engineering metrics should tie back to business outcomes

For more data on how AI is changing engineering measurement, check out Harness’s State of Engineering Excellence report: https://www.harness.io/state-of-engineering-excellence

To learn more about Harness AI DLC Insights check out this blog: https://www.harness.io/blog/introducing-ai-dlc-insights-to-prove-the-roi-of-your-ai-engineering-investment

To see it in action: https://www.harness.io/demo/ai-dlc-insights