Many teams have an AI strategy deck but no production pathway. A reliable implementation roadmap should make ownership, evidence, and decision gates explicit.
Five stages that reduce execution risk
1. Discovery
Define the business outcome, affected workflows, and success metrics.
- Identify one high-priority workflow, not ten.
- Confirm baseline metrics before any build starts.
- Map system dependencies and non-functional constraints.
2. Feasibility
Validate whether the data, systems, and governance conditions are ready.
- Data completeness and freshness checks.
- Access and identity constraints across systems.
- Risk tier and minimum controls for the use case.
3. PoC
Build a narrow, measurable prototype.
- Use constrained scope and representative data.
- Define acceptance criteria for quality and latency.
- Capture evidence needed for governance review.
4. Scaling
Productionise the architecture with operations in mind.
- CI/CD for data and model changes.
- Runtime monitoring for quality and drift.
- Incident pathways and rollback plans.
5. Support
Run the system as an operational product.
- Monthly review of quality, cost, and adoption.
- Scheduled revalidation of prompts, models, and policies.
- Clear ownership for data, model, and workflow outcomes.
What should be true after 90 days
- One production workflow with measurable value.
- Governance and change control are active, not theoretical.
- Internal teams can operate the solution without vendor lock-in.