Our team is considering a broader rollout of GitHub Copilot, moving from a few individual licenses to an organization-wide plan for about 50 engineers. Before we commit, we want to run a structured, measurable pilot. I'm naturally thinking about this from a security and audit perspective, but also from a productivity and adoption angle.
I'd like to propose a framework for the trial and am curious how others have approached this. My initial thoughts on key evaluation areas:
**Primary Metrics to Track:**
* **Acceptance Rate:** Percentage of suggestions shown that are actually accepted by developers.
* **Retention & Usage:** Daily/Weekly active users among the pilot group. A drop-off might indicate lack of perceived value.
* **Qualitative Feedback:** Structured surveys focusing on flow, code quality perceptions, and friction points.
**Security & Compliance Considerations:**
* Validate that our existing code scanning (e.g., for secrets, vulnerable patterns) still catches issues in AI-suggested code.
* Review any telemetry/data sharing implications for our compliance frameworks (SOC 2, ISO 27001).
* Assess if Copilot suggestions align with our internal secure coding standards.
**Trial Structure Suggestions:**
* Select a pilot group of 10-15 engineers from different teams (backend, frontend, infra).
* Run for a minimum of 4 weeks to overcome the novelty effect.
* Establish a clear "opt-out" process and a dedicated channel for feedback and support.
What has worked for your teams? Were there any unexpected pitfalls in measuring ROI or in the security review? I'm particularly interested in how you balanced quantitative metrics with qualitative developer experience feedback.
- Jane
Jane