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AI tools are launching faster than anyone can test them. Many are genuinely useful, but plenty are thin wrappers with weak security and vague pricing. Before connecting anything to your finances, customers, or content, I run through these seven questions.

1. Where does my data go?

Check whether your inputs are stored, shared, or used to train models. Look for a clear data policy, not a vague privacy page. If you can’t find a straight answer in two minutes, treat that as an answer.

2. Can I opt out of training on my data?

Reputable tools offer this, often on paid plans. If you handle client or financial information, it’s non-negotiable.

3. What happens when it gets something wrong?

Every AI tool makes mistakes. The useful question is whether it shows its sources, flags uncertainty, or lets you review outputs before they go live.

4. How does pricing scale?

Free tiers are for testing. Check usage caps, per-seat fees, and which integrations sit behind paywalls before you build a workflow around it.

5. Can I export everything?

Early-stage products pivot or shut down. If you can’t export your data and settings, you’re locked in.

6. Who is behind it?

Look for a named team, a changelog with recent updates, and responsive support. Anonymous products with no update history are a risk.

7. What do independent users say?

Skip the landing page testimonials. Read community threads and reviews where people describe real problems, not just wins.

A quick scoring rule

Give each question a pass or fail. Five or more passes is worth a trial. Fewer than four means keep looking.

Your turn

Which of these has caught you out before, and what would you add to the list? I’m especially curious how people handle data privacy when testing new tools.

I write more breakdowns like this on business, tech, AI, and finance at Journ Magazine.