Choose AI tools around the job, not the hype

New AI tools arrive constantly. A practical evaluation begins with the task, expected output, and acceptable cost of error.

1. Describe the job first

Write down the inputs, the output, the person responsible for approval, and what failure looks like.

2. Test with realistic examples

Compare tools using the same source material and acceptance criteria. Check accuracy, turnaround, usability, and the effort needed to correct the result.

3. Understand the data boundaries

Before using work materials, confirm where the data goes and what your organization permits.

4. Keep your stack intentionally small

Add another tool only when it solves a demonstrable limitation in your current process.

The best stack is the one you can operate, trust, and maintain.

A practical editorial framework, not a claim that a specific automation or commercial result has been verified.