Engineering teams have rapidly adopted AI coding tools, but organizations still struggle to understand their impact. Existing dashboards focus on activity, such as daily active users, acceptance rates, or lines of generated code, but these metrics don’t answer a more important question: Are teams actually shipping more, faster, and with fewer issues?
Datadog AI Impact connects AI usage data with your software delivery and DORA metrics so that you can evaluate how AI affects both velocity and stability. By correlating AI-assisted code with outcomes such as lead time and change failure rate, you can move beyond adoption metrics and understand how AI tools influence real engineering performance.
In this post, we’ll show you how to:
- Measure whether AI is helping your teams ship faster
- Evaluate the stability of AI-assisted code in production
