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On-demand Webinar
On-demand Webinar

Measuring the Impact of AI in Software Development: A Data-Driven Approach | Harness Resource | Harness

Are you truly measuring the impact of your AI investments in software development?

While AI coding assistants are a game-changer for productivity and code quality, many organizations struggle to quantify their true value. Without a clear measurement framework, you can't optimize your AI strategy or justify the return on investment.

Join Nathen and PB for a practical, data-driven webinar on how to measure and maximize the benefits of AI in your development pipeline. Drawing on real-world insights, you'll learn how to go beyond basic adoption metrics and measure what truly matters.

In this webinar, you will discover how to:

  • Establish a baseline: Understand your team's current performance before you implement AI.
  • Track what matters: Identify and measure key metrics like development velocity, code quality, and developer satisfaction.
  • Compare and analyze: Use a proven cohort-based methodology to compare AI-assisted teams with traditional workflows.
  • Get a holistic view: Balance quantitative data with qualitative insights to build a complete picture of AI's impact.

Learn how Harness Software Engineering Insights—built in collaboration with Google Cloud—provides the comprehensive framework you need to make informed, data-driven decisions and drive lasting improvements in your software delivery.

Published
November 6, 2025

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What you'll learn

Key Takeaways

AI Adoption Drives Developer Productivity

Approximately 90% of developers now use AI tools, averaging two hours of usage per working day. Over 80% of these users report significant enhancements to their individual productivity.

The AI Trust Paradox Requires Healthy Skepticism

While most developers observe improved code quality with AI, about 30% still lack trust in its outputs. Maintaining validation mechanisms for AI-generated code remains an essential practice.

Measure Performance with DORA Metrics

Software delivery performance is evaluated using five key metrics balanced across throughput and stability. Tracking these indicators helps predict organizational success and overall team well-being.

Balance Velocity Gains with Code Quality

When measuring AI-driven productivity increases like higher commit rates, organizations must ensure velocity does not compromise output. Tracking quality metrics acts as a necessary counterweight to development speed.

Metrics Should Empower Development Teams

Performance data must be used to empower practitioners rather than just reporting to executives. Teams can use these insights to identify friction points and guide their own continuous improvements.