Choose for the job,
not for the hype.

1. Define the task, not the technology

Replace “I need AI” with an observable outcome: summarize a consented meeting, draft three poster directions, review a pull request, or find sources for a literature review. State what a successful result would contain and what you must still check yourself.

2. Run a small, comparable test

Use two or three anonymized examples you already understand. Give competing tools the same instructions and compare accuracy, editability, time to a useful result, and failure modes. A convincing demo is not the same as a repeatable workflow.

3. Check the data boundary first

Before uploading a contract, customer record, unpublished idea, or source code, review the vendor’s privacy terms, retention options, training settings, and team access controls. If you cannot share the data, test with a synthetic example instead.

4. Verify consequential output

For research, follow citations to original papers and read the methods. For code, run tests and inspect dependencies. For medical, legal, or financial questions, do not treat generated text as professional advice. Always retain a human review step.

5. Confirm commercial-use rights

Generated images, music, voices, and fonts can have different rights under different plans. Check the current terms and any third-party material or likeness in the output before publication. Never assume “generated” means cleared for commercial use.

6. Budget for the whole workflow

Compare usage limits, required subscriptions, the cost of revisions, export options, and whether the tool fits your existing software. Choose the least complicated option that passes your real test—not the product with the longest feature list.