AI Value · Framework

How to Prioritize AI Use Cases: A Step-by-Step Framework for CIOs and Founders

Published 22 September 2026 · 8 min read · By the Intlectify team, Curiosbot Digital S.L.

Most teams don't need more AI ideas. They need a repeatable way to rank the ones they already have. This is the framework we built Intlectify AI Value's priority score around: three questions, asked the same way for every use case, so the answer doesn't depend on who's in the room.

Step 1 — Score value

Value is the size of the prize: hours saved per week, error rate reduced, revenue protected or released, risk avoided. Put a number on it, even a rough one, and note the assumption behind it. A range ("saves 4–8 hours a week per analyst") is more honest — and more useful later — than a single confident-sounding figure.

Step 2 — Score feasibility

Feasibility asks what it actually takes to ship: data availability and quality, integration complexity, whether the team has the skills in-house or needs to hire or contract them, and how mature the underlying AI capability is for this specific task. A use case with huge theoretical value but data that doesn't exist yet is not a near-term priority, however good it looks on a slide.

Step 3 — Score risk

Risk covers what goes wrong and how bad that is: regulatory exposure, the cost of a wrong answer reaching a customer, reputational downside, and how reversible the decision is if the pilot doesn't work out. A customer-facing use case in a regulated process carries different risk than an internal drafting tool, even if the value and feasibility scores look similar. Reviewing risk against a recognised framework — the NIST AI Risk Management Framework is a solid, vendor-neutral starting point — is worth the extra half hour for anything customer-facing.

Step 4 — Combine into one score, but keep the parts visible

Once value, feasibility and risk are scored, you can combine them into a single priority number to sort a backlog. The mistake to avoid is hiding the three inputs behind that single number. A stakeholder should be able to see why use case A outranked use case B — "higher value, similar feasibility, lower risk" — not just trust a score they can't unpack.

Step 5 — Re-score as you learn

Feasibility and risk especially change fast: a data source that didn't exist last quarter might now, and a regulatory question that was open might now be answered. Treat the ranking as living, not a one-time exercise done at the start of the year.