How to Evaluate AI Tools

Start with the problem, not the tool

The most common mistake is shopping for tools before defining the problem. Write down the workflow you want to improve and what a good outcome looks like first. That turns a vague comparison into a concrete checklist.

Criteria that actually matter

• Fit — does it solve your specific workflow, not a generic one?
• Reliability — how accurate and consistent is it on your real data?
• Data and privacy — where does your data go, and who can see it?
• Integration — will it connect to the tools you already use?
• Cost at scale — what does it cost once usage grows?
• Vendor stability — will the company and product still be here next year?
• Security — how does it handle access, secrets, and compliance?

Red flags to watch for

• Impressive demos with no way to test on your own data
• Vague answers about where your data is stored or used
• Pricing that becomes punishing as you scale
• Lock-in that makes switching later very costly
• Claims of transforming everything with no specifics

A simple evaluation process

1. Define the workflow and success criteria
2. Shortlist two or three tools that plausibly fit
3. Run a small test on your own data, not the vendor's demo
4. Score each against the criteria above
5. Decide — and document why — before committing budget