A new AI productivity tool can look convincing before it has done anything useful. The landing page promises saved hours, the demonstration is perfectly rehearsed and the first output appears in seconds.
Before connecting your calendar, uploading personal notes or paying for a year, run a five-minute test.
Minute one: define one real job
Choose a task you already understand. Avoid a vague goal such as “make me more productive.” Use something observable:
- turn these meeting notes into five accurate actions;
- organise this list without changing the deadlines;
- summarise this document and identify uncertain claims;
- draft a weekly plan using the hours I actually have.
A tool cannot be evaluated against a goal that has no clear finish line.
Minute two: remove sensitive information
Use a safe sample rather than private material. Replace names, addresses, account numbers and health or employment details. Check whether the provider explains how prompts are stored and whether they may be used to improve models.
Convenience is not a reason to disclose information you would not put in an ordinary online form.
Minute three: run the same task twice
Repeatability matters. If the same input produces radically different actions, dates or claims, the tool may be unsuitable for a dependable workflow.
Variation is not always a failure—creative work benefits from alternatives—but factual and administrative tasks need consistency. Record both outputs rather than judging only the better one.
Minute four: verify the difficult parts
Check names, numbers, dates, links and quoted text against the source. Look for confident additions that were never present in the material.
Also notice what happens when the tool is uncertain. Does it flag the gap, ask a question or quietly invent a plausible answer?
Minute five: measure the whole task
Include setup, correction and checking time. A tool that generates a draft in ten seconds but needs twenty minutes of repair has not saved twenty minutes.
Write down:
- total time;
- errors found;
- corrections required;
- information you had to provide;
- whether the final result was genuinely better.
For a more rigorous version, the transparent AI-tool review methodology published by AI News & Updates provides a reusable framework covering repeat runs, evidence labels, failure logging and disclosure.
The decision
Keep the tool only if it performs a specific job better than your current method and the benefit survives verification. Delay the annual subscription until you have tested it on several real examples.
The goal is not to find the most impressive AI. It is to find a tool whose limits you understand—and to walk away quickly when the evidence is weak.
Editorial disclosure: AI News & Updates is a companion publication. Its methodology is linked because it expands the evaluation process used in this article.
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