List your AI backlog. Prioritize scores each on value, feasibility and risk — plus reuse, strategic fit and dependencies — then tells you the right few to fund, in the right order. So you don't bet on the shiny one that's blocked.
Rigorous, multi-factor prioritisation — every idea scored consistently across your whole backlog, with the two things a simple matrix can't see: what's blocked, and what to sequence first.
Value · feasibility · risk · reuse/foundational · strategic fit · learning value — weighted, not a gut composite.
Flags what's blocked until a foundation exists, and sequences enablers first — so a high-value idea that can't run yet doesn't top the list.
Not just top-N by score — one quick win, one capability builder, one strategic bet, sequenced by horizon (Now / Next / Later).
Prioritisation is a scoring problem — and we tested whether AI actually beats a good rubric. It mostly doesn't.
Across a dozen backlogs, a transparent, dependency-aware formula gave the same top pick as the AI in ~83% of cases. So Prioritize ranks with that formula by default — value × feasibility × risk, plus strategic fit, team readiness, declared dependencies and a foundational boost — auditable, instant, and spending no AI tokens.
We even ran this workflow through our own Assess tool. It flagged it honestly: "the core job — apply the criteria, weight them, rank the results — is a deterministic scoring model. That doesn't need AI; build the rubric first." AI earns its place for exactly one thing: reading a dependency out of your description that you didn't tag. So it's here only as an optional cross-check, never the engine.
Knowing when not to spend on AI is the point.
Add the AI ideas on your plate. A name and a line is enough; the more context you give, the sharper the ranking. Add 2 or more, then rank.