AI roadmaps that survive contact with reality
How to build a useful 12-month AI roadmap: prioritise by value and feasibility, stage bets, define exit criteria and leave out what you cannot support.
Score use cases on value, readiness and risk
Long lists of ideas are easy. Rank them by the measurable value they could create, whether the data exists and can legally be used, and the cost of a wrong answer. High-value ideas with missing data become data projects first.
| Question | Strong signal | Warning sign |
|---|---|---|
| Is the value measurable? | A baseline metric exists today | Benefit described only as "efficiency". |
| Is the data ready? | Accessible, labelled, with clear usage rights | Data scattered or owned by no one. |
| What if it is wrong? | Errors are cheap and reviewable | Errors affect safety, money or rights unchecked. |
| Who owns it after launch? | A named team with budget | Ownership deferred to "later". |
Stage bets with exit criteria
Plan each use case as a sequence — discovery, a prototype on real data, a limited pilot, then production — with a decision point between stages. Agreeing in advance what result would stop the work keeps sunk-cost thinking out of the review.
- Keep the first test small enough to finish in weeks.
- Write the stop and scale criteria before starting.
- Review the roadmap quarterly; drop items that no longer fit.
What to leave off
Leave off ideas without an owner, ideas that depend on data you do not have, and broad platform builds with no first use case. A roadmap is as much about what you decline as what you commit to.
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