How to Choose an AI Tool
Choose from the job backwards, not from the tool forwards. Name the task, decide whether you need a general assistant or a specialist, test the candidates on one real piece of work, then check the data terms and the exit. Most bad AI purchases come from skipping the testing step, not from picking the wrong category.
Step 1: name the job
Write down what you are actually trying to produce, and how often. "Learn about AI" is not a job. "Turn a two-hour interview recording into a publishable article every week" is.
Everything after this step depends on it. A weekly publishing task justifies a paid subscription; occasional curiosity does not.
Step 2: decide whether you need a generalist or a specialist
General assistants handle language, reasoning and drafting across many tasks. Specialists do one thing with more control — image generation, transcription, code editing.
The common mistake is buying a specialist for a job a general assistant already covers, then paying for two tools to do one job. The opposite mistake is expecting a general assistant to do something that needs precise control, such as producing a brand-compliant design.
Step 3: test on real work
Run a task you already know the answer to, and one you do not. The first tells you whether the tool is accurate; the second tells you whether it is useful.
Give each candidate the same task, and judge the output the way you would judge a colleague's draft. An hour of this is worth more than any feature comparison, because the differences that matter in practice are not the ones on the pricing page.
Step 4: check the terms and the exit
Two questions, both easy to skip:
- What happens to your data? Look for whether your input is used for training, how long it is retained, and whether you can turn that off.
- What happens if you leave? Can you export what you have created? If the tool holds your content in a proprietary format, that is a cost you pay later.
A worked example
Suppose you run a small marketing team and publishing has stalled.
The job is "produce four posts a month from existing customer conversations." A general assistant covers drafting. A transcription tool covers turning calls into text. Nothing here needs image generation, so that category is out of scope.
Test the general assistant on one real call transcript, and check whether your plan allows commercial use of the output. Then check the export: if the drafts are trapped inside the tool, you have created a dependency rather than a workflow.
That is the whole framework — job, generalist or specialist, real test, terms and exit — and it takes an afternoon rather than a procurement cycle.
Frequently asked questions
How do I know if an AI tool is worth paying for?
Test it on real work first. If it saves time on a task you do weekly, the subscription is easy to justify. If not, the free tier is the answer.
Should I use one AI tool or several?
One general assistant as a default, plus specialists only for jobs it genuinely cannot do. More tools means more context switching.
What should I check before using AI output commercially?
The tool's terms on commercial use and whether your input is used for training. Both vary between providers and between free and paid plans.
How do I avoid lock-in?
Prefer tools that let you export your content in a standard format, and avoid storing work you would struggle to recreate.