The Problem with AI Adoption
Most teams adopt AI tools the same way: someone installs Copilot, a few people try Cursor, nobody shares what works, and six months later you have a fragmented mess with zero compounding benefits. I've seen this pattern at every company I've worked with.
The fix isn't a maturity model or a steering committee. It's three things:
Pick tools deliberately. Don't let everyone freelance. Standardize on 2–3 tools that complement each other. I cover exactly what to buy in the tools guide.
Write a CLAUDE.md. This is the single highest-leverage thing you can do. One file at your repo root that tells every AI tool about your project's architecture, conventions, and commands. Without it, every AI interaction starts from zero. The developer guide has real examples.
Share what works. The productivity gap between someone who's learned to use AI tools well and someone using them casually is enormous and growing. If your team isn't actively sharing patterns and configurations, you're leaving most of the value on the table.
Not Sure Where to Start?
Worried AI will hurt code quality? Start with zero-risk adoption — begin with review tools that don't write code, then layer on completions and agents as you build confidence.
The Three Roles
AI tools aren't just for developers. I break adoption into three roles because each has fundamentally different workflows:
Developers — Multi-agent setups, CLAUDE.md patterns, real IDE configs, and when to stop trusting the output.
Designers — Lovable, V0, and Cursor for people who want to ship, not learn React.
Support — Claude via Slack for tracing issues and understanding systems without paging engineering.
Start with the guide for your role, then read What to Actually Buy before spending money.