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On-demand Webinar
On-demand Webinar

From AI Output to Engineering Outcomes | Harness Resource

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
  • Faster cycles and more output don’t automatically mean better performance.
  • Leaders need new ways to interpret what “good” looks like in AI-driven workflows.
  • Performance visibility must evolve with AI workflows
  • Traditional metrics alone can miss the full picture.
  • A more accurate view connects day-to-day development work with delivery health and team effectiveness.
  • Engineering metrics should tie back to business outcomes
  • The goal isn’t just measuring activity. It’s understanding how engineering efforts drive broader business impact and strategic goals.
Published
June 2, 2026

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What you'll learn

Key Takeaways

Identify power users and eliminate ghost seats

Between 30 and 50 percent of enterprise AI coding licenses remain unused, generating costs without value. Meanwhile, the top 10 percent of developers produce the vast majority of shipped AI code.

Most AI tokens never ship actual code

Token waste is outpacing value due to abandoned code generation, context bloat, and routing simple tasks to expensive models. Nearly half of tokens are spent on code that is ultimately abandoned or substantially rewritten.

AI accelerates both code and technical debt

While AI helps developers move faster, it also accelerates the introduction of bugs, security vulnerabilities, and architecture mistakes. If defect rates climb alongside commit rates, true productivity has not improved.

Focus on business outcomes over vanity metrics

Metrics like lines of code generated or pull requests opened do not reflect true business value. Organizations must measure features shipped to production, change failure rates, and cycle time to prove return on investment.

API limitations require deeper measurement approaches

Relying solely on vendor APIs provides a limited view of how AI coding assistants are used. Correlating IDE data with local agents offers a much deeper understanding of actual developer behavior and code generation.