AI-Assisted Code Review: GitHub Copilot in Practice

GitHub Copilot has been running across our engineering teams for about six months now. Long enough to have real data and opinions, not just hype.

The productivity reality

Our developers report saving 30-45 minutes per day on boilerplate code, test scaffolding, and documentation comments. That's meaningful. But it's not the 10x productivity claim you see in marketing materials.

Where Copilot genuinely shines: writing unit tests, generating repetitive CRUD operations, and auto-completing familiar patterns. Where it struggles: complex business logic, security-sensitive code, and anything requiring deep domain context.

The governance questions

As a PM, the productivity gains are great. But I'm more concerned about the risks nobody's talking about in standup.

License compliance. Copilot was trained on open-source code. Are the suggestions it generates introducing license obligations we don't know about? We added a license scanning step to our CI pipeline specifically because of this concern.

Security patterns. Copilot sometimes suggests code with known vulnerability patterns. Junior engineers who accept suggestions without scrutiny are introducing risk. We made Copilot suggestions a mandatory topic in code review — reviewers must verify that AI-suggested code meets our security standards.

Dependency on the tool. I've noticed junior engineers struggling more when Copilot is down. There's a skill atrophy risk that concerns me. We now require that engineers can explain the logic of any code they commit, regardless of who — or what — wrote it.

My recommendation

Use Copilot. The productivity gain is real. But implement guardrails: license scanning, security review focus on AI-generated code, and clear expectations that engineers own every line they commit.

AI tools are force multipliers for good engineers. They're risk multipliers for careless ones. The PM's job is building the governance layer that makes the difference.