AI enablement
The work of structuring and spreading AI practice across a team: versioned conventions, shared commands/skills, documented tool choices, onboarding.
Definition
AI enablement covers the concrete actions that turn AI usage from individual into collective: a versioned conventions file (`CLAUDE.md`, `AGENTS.md`) documenting how agents should treat the project, a library of commands or skills shared in Git rather than living in one person's head, a tool and model choice that's justified and written down, and an onboarding path that gets a new dev operational without rebuilding the experience from scratch.
postcursors perspective
This is real, measurable work — onboarding time, adoption rate, number of formalized workflows — not a job title. It almost always emerges from a practitioner dev, visible to peers on the topic, before it becomes a recognized function. Nobody gets appointed "AI lead" at the start: someone answers the questions because they've already dug into it.
In practice
Becoming an "AI ambassador" without an official appointment, simply because you're the only one using coding agents daily and it shows. Setting objective criteria together with the team, formalizing ~15 workflows as custom commands versioned in Git: a new dev becomes operational on the project's patterns in under an hour, versus several days of oral transmission before.
Common misconceptions
- ✗ Believing AI enablement needs a dedicated role or budget to start — a versioned CLAUDE.md and a first custom command are enough
- ✗ Confusing enablement with evangelism — without written, measurable conventions, it's internal marketing, not enablement