SAAF Enablement Roadmap Template

Purpose: Guide enterprise teams through structured AI adoption—from discovery to governance—by outlining key activities, timelines, and roles needed to scale responsibly and sustainably.

Overall Duration: ~6 to 18 months

(Adjustable based on scope, size, and use case complexity)

Phase 1: Discovery & Alignment

Timeframe: 1–2 months

Goal: Understand current capabilities, align AI to strategy, and identify use cases.

Key Activities	Estimated Duration
AI Maturity Assessment (e.g., AI Maturity Index)	2–3 weeks
Identify & score use cases (Use Case Scoring Model)	2–3 weeks
Run discovery workshops with business units	2–4 weeks
Align with strategic goals & OKRs	1–2 weeks
Draft AI Adoption Charter	1 week

Phase 2: Governance & Investment Planning

Timeframe: 1–2 months

Goal: Establish governance, secure executive support, and plan for responsible AI adoption.

Key Activities	Estimated Duration
Form AI Governance Board	1 week
Conduct Ethical Risk Pre-Assessments on top use cases	2–3 weeks
Define success metrics and investment needs	2 weeks
Secure budget and leadership sponsorship	2–4 weeks
Finalize charter and risk mitigation plans	1–2 weeks

Phase 3: Planning & Enablement Design

Timeframe: 2–3 months

Goal: Create detailed initiative plans, training paths, and tech infrastructure.

Key Activities	Estimated Duration
Complete AI Initiative Blueprint(s)	3–4 weeks
Map Role-Based Enablement Plans	2–3 weeks
Define MLOps / integration workflows	3–4 weeks
Prepare prompt libraries / AI Agent plans	2–4 weeks
Build change enablement plan	2 weeks

Phase 4: Pilot Execution & Feedback

Timeframe: 2–3 months

Goal: Deploy low-risk pilots, evaluate real-world performance, and gather user insights.

Key Activities	Estimated Duration
Launch AI Agile Sprint(s) with pilot teams	2–4 weeks per sprint
Use Bias Audit Checklists and feedback loops	Ongoing during sprint
Run Ethical Go/No-Go reviews at sprint close	1 week per pilot
Measure results with Business Value Scorecard	1–2 weeks
Capture user feedback + lessons learned	Ongoing

Phase 5: Scale Decision & Change Activation

Timeframe: 1–2 months

Goal: Decide which pilots to scale, prepare teams, and communicate broadly.

Key Activities	Estimated Duration
Final Go/No-Go decision and readiness review	1 week
Execute change activation plan	2–4 weeks
Launch enablement materials and user playbooks	2–3 weeks
Prep support teams and issue resolution flows	2 weeks

Phase 6: Production Rollout & Monitoring

Timeframe: 2–4 months

Goal: Roll out AI capabilities to target groups, ensure support, and start post-launch monitoring.

Key Activities

Estimated Duration

Key Activities	Estimated Duration
Execute Production Readiness Checklist	1–2 weeks per team
Roll out to new departments or user segments	2–4 months (staggered)
Enable feedback + support loops (AI agents)	Ongoing
Monitor with Drift Dashboard + Performance Tracker	Weekly / Monthly

Phase 7: Optimization & Governance

Timeframe: Ongoing (start ~Month 6 onward)

Goal: Sustain, govern, and continuously improve AI systems and user adoption.

Key Activities	Estimated Duration
Log issues with Ethical Oversight Logs	Ongoing
Review Model Performance and Drift Monthly	1–2 hours / month
Host quarterly governance reviews	Quarterly
Refine prompts, retrain agents as needed	As triggered
Collect new use cases and evolve roadmap	Biannually

Suggested Add-ons & Visual Aids

• Confluence landing page with artifacts, phase status, and owner tracking

• AI Governance Calendar to align quarterly reviews, retraining, and audits