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How to choose an AI tool without wasting a month on trials

Most people pick an AI tool by scrolling a list of 200 options and choosing whichever one had the best homepage. Here's a five-step framework that gets you to a decision in an afternoon.

Start with the job, not the tool

The most common mistake is starting from the tool and working backwards to a use for it. You end up with a subscription you open twice and cancel in month three. Start instead with a sentence in this shape: "Every week I spend about X hours doing Y, and I wish it took less time."

That sentence does two useful things. It tells you which category you're actually shopping in, and it gives you a number to judge the subscription against. A tool that saves you four hours a month is easy to justify at £20. A tool that saves you twenty minutes is not, no matter how impressive the demo was.

  • Write down the three tasks that eat the most time in your week
  • Pick the one that is repetitive and text-, image- or code-shaped — those are where AI is strongest today
  • Ignore everything else until that one is solved

Set the budget before you start looking

Decide your monthly ceiling first, because every tool will feel worth it while you're inside its marketing. For most individuals, a realistic starting budget is one paid subscription — roughly £20 a month — plus free tiers around it. Small teams usually land between £50 and £150 across three or four tools.

Setting the number up front converts an open-ended browse into a comparison. You stop asking "is this good?" and start asking "is this the best use of my one paid slot?", which is a question you can actually answer.

Test on real work, never on the demo prompt

Every AI tool looks brilliant on the example the vendor chose. The only test that predicts anything is your own work: take a task you completed last week, run it through the trial, and compare the output to what you actually shipped.

Give each candidate the same three real tasks and time yourself. You are measuring two things — quality of output and how much editing it needed. A tool that produces 80% quality with a two-minute cleanup beats one that produces 90% and needs a rewrite.

  • Use three real tasks, not invented ones
  • Keep the prompts identical across tools
  • Count editing time, not just generation time
  • Run the test inside a single week so your standards don't drift

Check the exit before you check in

Before you subscribe, find out how you get your data out. Can you export your documents, prompt library, chat history and generated assets in a usable format? Is there a monthly plan or only annual? Who owns the output commercially?

This takes five minutes and saves the specific misery of having twelve months of work locked inside a product you've outgrown. Tools that make the exit hard to find are telling you something about how they expect to retain you.

Commit for ninety days, then review

Once you've chosen, stop shopping. The compounding value in AI tools comes from learning one deeply — building prompt templates, learning its failure modes, wiring it into your existing workflow. People who switch monthly stay permanently at beginner-level output.

Put a calendar reminder ninety days out with one question on it: did this save the hours I bought it for? If yes, keep it and consider adding a second tool. If no, cancel without sentiment and go back to step one with better information than you had before.

Tools mentioned

Frequently asked questions

How many AI tools does one person actually need?+

Usually two: a general assistant like ChatGPT or Claude, plus one specialist for whatever you do most (images, code, video, decks). A third rarely earns its keep until you're running a team.

Should I pay for AI tools or stay on free tiers?+

Stay free until you hit a limit that costs you time — usage caps, slower models, or missing exports. When you notice the limit weekly, upgrade. Before that, paying buys capability you aren't using.

How long should a trial last before I decide?+

One week of real use is enough. Longer trials mostly generate procrastination, because the deciding evidence — did it help on real tasks? — appears in the first few sessions.

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