A practical framework for choosing AI tools
Start with the work, define the evidence, and resist buying a workflow you cannot explain.
A polished demonstration can make almost any AI product look inevitable. The harder question is whether it improves a real workflow after the novelty wears off.
A useful evaluation begins before the product trial. Write down the job, the current cost of doing it, the acceptable failure rate, and the person who will review the result. That short brief gives every tool the same test.
1. Name the decision or task
Avoid goals such as “use AI for marketing.” Name a repeatable action instead: turn one approved interview into three social drafts, classify support requests, or summarize a weekly research folder.
- How often does the task occur?
- What source material is allowed?
- Who checks the output?
- What happens when the tool is wrong or unavailable?
2. Test the failure path
The most revealing test is not the best-case prompt. Use incomplete inputs, conflicting instructions, and the ordinary messy material your team actually handles. A trustworthy workflow makes uncertainty visible and lets a person recover quickly.
A fast result is only useful when the team can recognize when it should not be trusted.
3. Measure the whole workflow
Count setup, review, correction, training, and handoff time—not only generation time. The winning tool is the one that makes the complete system simpler while preserving ownership and judgment.
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