Engineering is the function that has most adopted AI (Cursor, Claude Code, Copilot) and most needs to govern it. As CTO, you balance enabling speed with managing risks. Here is the practical playbook for B2B engineering orgs.
1. Code generation and editing. Cursor, Claude Code, Copilot are now standard. AI does 30-60% of code production in well-run engineering orgs.
2. Code review. AI as the first reviewer; senior engineer as the second.
3. Documentation. API docs, README files, ADRs, runbooks.
4. Test case generation. AI generates from requirements; engineer refines.
5. Incident response and post-mortems. Faster triage, better writeups.
1. AI tool approval list. Which AI coding tools are approved for use with company code.
2. Code/data isolation policy. Customer data, prop code — where can it go.
3. Open-source compatibility. AI-generated code mixed with OSS has licensing implications.
4. Security review for AI-generated code. AI code can contain vulnerabilities. Security review process.
5. Code review standards. Whether AI-authored code requires same or different review rigor.
Buy off-the-shelf coding AI. Cursor, Claude Code, Copilot. Do not build your own coding assistant.
Build internal AI products only when you have proprietary data that creates moat. Most of the time, off-the-shelf with proper integration wins.
Consider MCP servers for your internal systems. Standardize how AI tools access your internal data via the Model Context Protocol.
— Bill Colbert, Treetop Growth Strategy