Practical guide · Published
How to evaluate an AI tool before paying for it
A repeatable evaluation worksheet for usefulness, error rates, review effort, privacy and total cost, with a comparison against your existing workflow.
A convincing demo does not show whether an AI tool improves your own work. Start with one task and compare the result with the way you do it now. This is an evaluation method, not a hands-on review or recommendation of a particular product.
Define the job and the failure you cannot accept
Describe the input, required output and person who approves it. For an invoice extraction task, an incorrect amount or supplier is more serious than awkward wording. Decide which errors require human review before you choose a tool.
Build a small representative evaluation set
Use examples you have permission to process. Include typical inputs and difficult cases such as missing information, ambiguous wording and unfamiliar formats. Keep a separate set for the final comparison so tuning a prompt does not become the entire test.
Compare the complete workflow
Run the existing method and the proposed method on the same material. Record acceptable results, serious mistakes, time spent checking and fixing them, and whether another person can repeat the process. A faster first draft can still create more total work.
| Measure | Existing method | AI-assisted method |
|---|---|---|
| Inputs tested and accepted outputs | Record counts | Record counts |
| Serious errors and corrections | Record cases | Record cases |
| Total production plus review time | Record minutes | Record minutes |
| Subscriptions, usage charges and maintenance | Actual costs | Actual costs |
| Data handling and deletion requirements | Existing terms | Verify applicable terms |
Check data terms and the exit path
Before uploading private material, check retention, access, reuse and deletion terms for the actual plan you would buy. Check whether you can export the work and switch tools. Product names and plan terms change; verify them with the provider rather than assuming a consumer plan meets a business requirement.
Make a small, reversible decision
Proceed only if the observed benefit survives the review and maintenance costs. Keep a responsible person and a fallback workflow. Recheck the comparison when the model, task or input data changes.
This worksheet is our practical synthesis. NIST’s AI Risk Management Framework provides a broader voluntary framework for mapping, measuring and managing AI risks; it does not certify this checklist or any product.