# Team AI Onboarding Practical playbook for team leads introducing AI to their team. Not enterprise governance. Hands-on team adoption. ## Step 1: Assess Team AI Literacy Who's using what already? Most teams have [[Shadow AI]] happening whether you know it or not. Survey the team: What tools are people using? For what tasks? What data are they putting in? This is your baseline. See [[AI Literacy]]. ## Step 2: Define Team AI Policy What tools are approved. What data can be shared. What requires review before shipping. This doesn't need to be a 50-page document. One page covering approved tools, data boundaries, and review requirements. See [[AI Usage Policy]]. ## Step 3: Create Shared Context Team CLAUDE.md (or equivalent) with coding conventions, architectural decisions, processes, priorities. This is the single highest-leverage action. Without shared context, every team member reinvents the wheel. See [[Team Context Management (TCM)]] and [[Context File Hierarchy]]. ## Step 4: Start With One Shared Skill A team code review skill. A PR template skill. A deployment checklist skill. Pick one workflow the whole team does, codify it, and share it. This demonstrates the value of [[AI Skill Distribution]] in concrete terms. ## Step 5: Establish Review Practices Never ship unreviewed AI output. Code review still applies. Content review still applies. AI is a draft generator, not a final answer. See [[Human-in-the-Loop]]. ## Step 6: Measure and Iterate What improved? What broke? What do people need? Run a retrospective after 2-4 weeks. Adjust the policy, update the context, refine the skills. ## Common Failure Modes - **Forcing adoption.** Let early adopters lead. Mandating AI use creates resentment and low-quality adoption. The skeptics come around when they see results. - **No shared context.** Everyone reinvents. Five people on the same team writing five different system prompts for the same codebase. Waste of time. - **No quality gate.** AI output ships unchecked. This erodes trust fast. One bad AI-generated bug in production sets adoption back months. - **Tool proliferation.** Standardize on 1-2 tools. Every new tool is a new context silo, a new security surface, a new thing to learn. See [[AI Agent Permissions]]. ## References ## Related - [[Team Context Management (TCM)]] - [[AI Usage Policy]] - [[Shadow AI]] - [[AI Skill Distribution]] - [[AI Agent Permissions]] - [[Human-in-the-Loop]] - [[Context File Hierarchy]] - [[AI Literacy]]