# AI Implementation Roadmap A sequenced, phase-by-phase plan for organizations adopting AI. Not theory. Clear deliverables and success criteria at each stage. ## Phase 1: Foundation (Weeks 1-4) **Goal:** Establish baseline and make one thing work. - **Assess current state.** What tools are people already using? Where is [[Shadow AI]] happening? What data flows exist? - **Define [[AI Usage Policy]].** What's allowed, what's not, what data can touch AI systems. Get legal sign-off. - **Pick one use case.** Choose the highest-impact, lowest-risk workflow. Code review, documentation, customer support triage; something concrete. - **Select tools.** Standardize on 1-2 AI platforms. Avoid tool sprawl from day one. - **Set up approved accounts.** Enterprise licenses, SSO, audit logging. No personal accounts for work data. **Deliverables:** AI usage policy document, approved tool list, one pilot use case selected, accounts provisioned. **Success criteria:** Policy published, tools accessible, pilot team identified. ## Phase 2: Pilot (Months 2-3) **Goal:** Prove value with one team before scaling. - **Run a pilot with one team.** Small, motivated, technically capable. Let them break things. - **Establish team context.** Shared CLAUDE.md or equivalent with coding conventions, processes, priorities. See [[Team Context Management (TCM)]]. - **Measure results.** Time saved, quality changes, adoption rate, user satisfaction. Quantify everything. - **Iterate.** What worked? What didn't? What context was missing? Refine the approach before expanding. **Deliverables:** Pilot results report, refined context files, documented lessons learned. **Success criteria:** Measurable improvement in at least one metric, team willing to continue. ## Phase 3: Expansion (Months 4-6) **Goal:** Scale what works to additional teams. - **Expand to additional teams.** Use pilot learnings to onboard 2-3 more teams. - **Build shared skill library.** Codify repeatable AI workflows as reusable skills. See [[AI Agent Skills]]. - **Deploy team-level agents.** Each team gets agents configured for their domain, with appropriate context and permissions. - **Cross-team knowledge sharing.** What one team learns should benefit all teams. **Deliverables:** Skill library with 10+ reusable skills, 3+ teams actively using AI, cross-team sharing process. **Success criteria:** Consistent adoption across teams, skill reuse happening organically. ## Phase 4: Enterprise (Months 7-12) **Goal:** AI as organizational infrastructure. - **[[Enterprise Context Management (ECM)]].** Organization-wide context hierarchy: org level, division level, team level, individual level. - **Governance framework.** See [[AI Governance]]. Audit trails, compliance checks, regular policy reviews. - **Org-wide rollout.** All teams have access, training, and support. - **Continuous improvement.** Regular review cycles, skill library growth, context refinement. **Deliverables:** Enterprise context management system, governance framework, org-wide training program, quarterly review process. **Success criteria:** AI integrated into standard workflows across the organization, measurable ROI. ## Scaling Notes **Solopreneur:** Skip to Phase 1 essentials. You need a usage policy (even if it's just for yourself), one tool, and one use case. Context management is your competitive edge from day one. See [[Levels of AI use]]. **Small team (2-10):** Phases 1-2 compressed into 4-6 weeks. Phase 3 is your steady state. Phase 4 is optional. **Enterprise (50+):** Full four phases. Don't skip Phase 2. The pilot protects you from scaling mistakes. See [[Enterprise AI Deployment]] and [[AI Transformation Playbook]]. ## References ## Related - [[AI Usage Policy]] - [[Enterprise AI Deployment]] - [[AI Transformation Playbook]] - [[Levels of AI use]] - [[Team Context Management (TCM)]] - [[Enterprise Context Management (ECM)]] - [[AI Agent Skills]] - [[Shadow AI]]