Pick a task you already understand
Do not begin with the most complicated process in the organization. Choose a repeated task with a clear input and a result you know how to judge. Summarizing meeting notes, categorizing inquiries, preparing an outline, or comparing documents can be useful starting points.
Avoid high-stakes decisions and irreversible actions in the first experiment. The goal is to learn what the tool can do, not to prove that AI belongs everywhere.
Record the current baseline
Before changing the process, record what happens today. A useful baseline can be simple.
- How many times does the task occur in a week?
- How many minutes does one example take?
- Which mistakes or delays happen most often?
- Who checks the result?
- What does the current software or labor cost?
Test representative examples
Collect 15 to 20 examples that reflect normal work, edge cases, incomplete information, and a few situations where the system should stop. Define what a good result must contain before you run them.
Keep a person approving every output. Track corrections, failures, time, and tool cost. Save both the successful and unsuccessful examples so the test does not become a highlight reel.
Decide after 30 days
At the end, compare the same measures. Stop if the correction burden, risk, or cost outweighs the benefit. Revise if a narrow change could solve the problem. Expand only when the result is repeatable and the people doing the work understand the new process.
This is a practical framework, not a promise of savings. A useful pilot produces evidence, including evidence that a particular idea is not worth pursuing.
Write a one-sentence test: “For 30 days, we will use [tool] to help with [task], while [person] approves every result. We will continue only if [measure] improves without [unacceptable failure].”
Sources and further reading
Product capabilities and policies change. These are the primary sources used for this guide.