AI Coding Assistants: The Enterprise Reality

Three of my teams are now using AI coding assistants daily. GitHub Copilot has been embedded in workflows for months. Claude 3.5 Sonnet has become the go-to for complex code generation and review. The productivity impact is measurable and significant.

But as a PM responsible for delivery quality and client trust, I see both the promise and the problems.

What Is Actually Working

Boilerplate acceleration. Tasks that used to take two hours — writing API endpoint scaffolding, test cases for known patterns, data transformation utilities — now take thirty minutes. This is real, repeatable, and consistent across teams.

Code review support. Developers paste code into Claude 3.5 Sonnet and ask it to review for edge cases, security issues, or performance concerns. It catches things that human reviewers miss during high-volume sprint weeks.

Documentation generation. Nobody loves writing documentation. AI assistants generate solid first drafts of API docs, README files, and code comments. The developer refines rather than creates from scratch.

What Worries Me

Intellectual property questions. Clients are asking whether AI-generated code in their codebase creates IP risks. I do not have a great answer yet, and neither does our legal team. This is an industry-wide gap.

Code quality variance. Junior developers accept AI suggestions less critically than seniors. I have seen PRs where AI-generated code passed review because it looked correct but handled edge cases poorly. We need better review practices, not fewer reviews.

Security blind spots. AI assistants can generate code with subtle security issues — hardcoded values, overly permissive patterns, missing input validation. Our security scanning catches most of this, but "most" is not good enough.

My Current Stance

AI coding assistants are net positive for productivity. But enterprise adoption needs guardrails — clear usage policies, mandatory human review, security scanning in CI/CD, and honest conversations with clients about how these tools are being used.

We are early in this curve. The teams that establish good practices now will have a significant advantage.