- Most AI roadmaps fail because they're built as a planning exercise instead of a scoring exercise.
- Rank every AI idea by leverage vs. effort before touching a model or vendor.
- A roadmap should end in a dated build plan, not a slide deck of possibilities.
- The fastest roadmaps take two weeks, not a quarter, because they cut scope early.
Why most AI roadmaps stall
The typical AI roadmap process turns into a series of workshops where every department pitches an idea and nothing gets ranked against anything else. Three months later there's a long list and no shipped feature. The fix isn't more meetings — it's a scoring pass on day one that kills 80% of the ideas before they consume a single engineering hour.
Score by leverage, not excitement
Every AI use-case should be scored on two axes: business leverage and implementation effort. The idea that gets everyone excited in a meeting is rarely the one with the best ratio. A boring internal tool that saves 200 hours a month usually beats a flashy customer-facing chatbot that saves 20.
Pick the model after the use-case, not before
Teams that start by picking a model — 'we're an OpenAI shop' — end up forcing every use-case through one tool. The right sequence is the reverse: rank the use-cases, then pick the model or approach that fits each one, since a support-ticket classifier and a document-drafting assistant rarely want the same model.
A roadmap needs an end date, not just a list
A roadmap that doesn't name a first build and a ship date is just a wish list. The output of a good AI consulting sprint is a ranked list, a chosen model per use-case, and a dated plan for the first one or two builds — not a 40-slide deck nobody reopens.
What a two-week sprint actually looks like
Week one: interviews, data audit, and a long-list of use-cases. Week two: scoring, model selection, and a build plan for the top pick. That's the whole roadmap — compressed because the goal is a decision, not a document.