How to Build an Evidence-Based AI Tool Shortlist

# How to Build an Evidence-Based AI Tool Shortlist

A useful shortlist is not a collection of familiar names. It is a small set of candidates that meet a defined need and have enough evidence for a fair comparison.

Start with the workflow

Describe the task, the people involved, the input data, the expected output, and the decision that follows. Write down non-negotiable requirements before looking at products. This prevents a popular feature from becoming the hidden definition of success.

Use source classes

Prefer official product pages, documentation, pricing pages, privacy terms, and support material. Treat community commentary as context rather than proof of a product capability. Record the URL and the date checked for each important claim.

Normalize the candidates

Give every candidate the same fields: task fit, data boundary, workflow integration, review needs, ownership, pricing model, and exit path. If a field cannot be verified, mark it unknown instead of filling it with an assumption.

Decide what belongs on the shortlist

Set a threshold for the must-have requirements. A candidate that fails one of those requirements should not stay on the shortlist merely because it scores well elsewhere. Keep rejected candidates and reasons in a decision log so the same work is not repeated.

A shortlist is successful when another reviewer can understand why each candidate is present, what remains uncertain, and what the next test should prove.