Implementation Roadmap Template
The phased delivery framework we use for AI implementation projects. Use this to understand how we structure production builds.
Last updated: 2026-04-07
Phase 1: Foundation
Set up the environment, establish data access, and prove the integration works end-to-end.
- •Development environment setup
- •CI/CD pipeline configuration
- •Data source access confirmed
- •Core data pipeline (ingestion → processing → output)
- •Integration tests passing
- •Milestone: data flowing end-to-end
Phase 2: Core Build
Implement the AI logic, test accuracy, and iterate on quality until benchmarks are met.
- •AI technique implementation (model / prompt / pipeline)
- •Accuracy testing against sample data
- •Quality benchmarks established
- •Iteration based on test results
- •Edge case identification and handling
- •Milestone: AI accuracy meets agreed threshold
Phase 3: Production Hardening
Add the reliability layer that makes the difference between a demo and a production system.
- •Error handling and retry logic
- •Fallback mechanisms for graceful degradation
- •Input validation and output verification
- •Monitoring and alerting setup
- •Logging for debugging and audit trail
- •Performance testing under load
- •Milestone: system handles failures gracefully
Phase 4: Deployment & UAT
Client review in staging, production deployment, and close monitoring during the critical first days.
- •Staging deployment for client review
- •Client UAT walkthrough
- •Feedback collection and fixes
- •Production deployment
- •72-hour close monitoring period
- •Milestone: system stable in production
Phase 5: Handover
Knowledge transfer, documentation, and support transition so the client team can operate independently.
- •System documentation (architecture, data flow, configuration)
- •Operational runbook (troubleshooting, restart, scale, update)
- •Recorded handover session with client team
- •Support period agreement (typically 2–4 weeks)
- •Milestone: client team operating independently
Dependencies & Risk Register
Track what needs to happen when, and what could go wrong.
- •Phase dependencies (what blocks what)
- •External dependencies (client data access, approvals, infrastructure)
- •Technical risks with mitigation strategies
- •Timeline risks and contingency plans
- •Resource requirements per phase
- •Communication schedule
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