// glossary
AI Roadmap plain-English.
Operator-grade definition. Plain words, plus where the term shows up in real work.
What is a AI Roadmap?
An AI roadmap is a sequenced plan of specific AI bets the company will run over the next quarter or two — each with a measurable bar, an honest cost estimate, and a kill condition. A roadmap is decisions, not slideware: one pilot, three follow-on bets, and a clear order.
The roadmap is the artifact a fractional AI CTO produces at the end of week one. It picks the first pilot, names the bar it has to clear, and sequences the three follow-on bets that depend on whether the first one lands. Each entry has a single owner, a single eval target, and a single decision moment.
The most common failure: roadmaps that read like wish lists. Twelve bets, no bar, no kill condition, no order. That is not a roadmap — that is a quarterly board appeasement document. A real roadmap can be defended at the next board meeting because each entry has measurable proof attached.
Good AI roadmaps share three properties: small and reversible (no irreversible commitments before the first pilot lands), evidence-anchored (each bet has a measurable bar), and operator-readable (the engineer who will ship it understands the bet within five minutes).
Related questions
How is an AI roadmap different from a product roadmap?
Same shape, different surface. AI roadmaps trade in models, evals, and inference costs; product roadmaps trade in features and shipping milestones. Healthy companies have both, linked.
How often should the AI roadmap change?
Re-scope at the end of each pilot. The whole point of small reversible bets is that the next quarter is informed by what just landed.
Who owns the AI roadmap?
Inside: an AI CTO or Head of AI, depending on org shape. Outside: a fractional AI CTO during the engagement, handed over to the permanent owner once it exists.
Related work
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