AI Use-Case Prioritization Matrix template.

What's inside — the exact file

This is the complete template, not a sample. The rows are worked examples — replace them with your data (and delete them before any platform import).

What this template is for

Every AI adoption conversation produces the same problem: fifteen candidate use cases, one budget, and no defensible way to pick. This worksheet scores each candidate on the three dimensions that actually predict success — monthly volume, risk if the AI gets it wrong, and data readiness — plus a human-review plan, so the roadmap is an argument you can defend rather than a vibe.

The six pre-filled examples span the spectrum deliberately: high-volume/low-risk winners (FAQ deflection, data entry), middle cases (draft-and-review workflows), and the high-risk candidates that score poorly on purpose (anything customer-facing with money or legal consequences and no review step). Score your own candidates against them and the sequence usually becomes obvious.

How to use it, step by step

1. Collect candidates from the people doing the work. Ask each team for their most repetitive, rule-describable tasks. Front-line staff know where the volume is; leadership brainstorms produce moonshots. You want the volume.
2. Score volume from real numbers. Monthly volume comes from ticket counts, email counts, record counts — not impressions. Low-volume tasks rarely repay automation effort no matter how annoying they are.
3. Score risk as the cost of a wrong answer. What happens when the AI is confidently wrong? Deflected FAQ: a follow-up question. Wrong refund decision: money and trust. High risk doesn't disqualify — it mandates the human-review column be filled in properly.
4. Score data readiness honestly. AI answers from your content and data. A use case backed by a current help center scores 5; one backed by tribal knowledge in a veteran's head scores 1 — and becomes a content project before it's an AI project.
5. Compute priority and sequence the roadmap. High volume × low risk × ready data goes first. Early wins build the organizational trust that harder use cases will spend.
6. Rescore quarterly. Shipped use cases free capacity; content projects raise readiness scores; new tools change feasibility. The sheet is a living roadmap, not a one-time workshop output.

What each column means

Common mistakes to avoid

• Starting with the most impressive use case instead of the most repayable one — failed moonshots poison AI appetite for a year.
• Scoring data readiness aspirationally ('the help center is mostly current') — the AI will find the gaps for you, in front of customers.
• Leaving human_review_plan blank on medium-risk cases because 'we'll figure it out' — review design is where AI projects succeed or quietly get unplugged.
• Treating the scoring as one workshop instead of a standing quarterly review — priorities move fast in this space.

Questions, answered

What makes a good first AI use case?

High volume, low risk, ready data, and a visible metric — support FAQ deflection and CRM data entry are the classic examples, which is why they lead our pre-filled rows. The goal of the first project is to be undeniably worth it; ambition comes second.

How should the priority score be calculated?

Simple and transparent beats clever: something like volume tier plus data readiness, minus a risk penalty. The exact formula matters less than everyone understanding it — this is a prioritization argument you need stakeholders to accept.

Do we need this if we're just deploying a support AI agent?

A one-use-case deployment can skip the scoring — but fill the risk and human-review columns anyway. Guardrails and escalation design (our AI agent guardrails checklist pairs with this sheet) are the difference between an AI agent that helps and one that makes headlines.

This is a Market Disrupt worksheet, not a vendor file. Import-format templates follow each platform's documented layout as of July 2026 — platforms evolve, so validate against current documentation before a large import.