Building an AI roadmap
An AI roadmap is a sequenced, costed plan for adopting AI — which use cases to pursue, in what order, with what data, governance and resources, and how success is measured. You build one by anchoring to business goals, prioritising use cases on value, effort and risk, and sequencing quick wins alongside foundational work.
What is an AI roadmap?
An AI roadmap turns an AI strategy into an executable plan. It lays out the use cases you’ll pursue, their sequence, the data and governance foundations they depend on, who owns them, and how you’ll measure success. It’s the difference between “we should use AI” and a plan leadership can fund and track. See What is an AI strategy?
How to build an AI roadmap
- Anchor to goals. Start from the business outcomes you care about.
- Discover use cases across functions.
- Prioritise each on value, effort and risk.
- Map dependencies — the data, tooling and governance each needs.
- Sequence — quick wins first, foundations in parallel, bigger bets staged.
- Assign owners and metrics for each phase.
- Review and adapt as you learn.
An example roadmap structure
- Phase 1 (0–3 months): readiness assessment, one or two quick-win automations, data gap fixes.
- Phase 2 (3–9 months): pilots on higher-value use cases, governance framework, early integrations.
- Phase 3 (9–18 months): production builds, scaling proven use cases, ongoing optimisation.
Get a roadmap you can deliver
Alugence builds costed AI roadmaps and helps deliver them. Explore AI strategy or book a call.