Choose the task before the AI tool

Start with the work as it happens today

Before choosing a model, follow one request from beginning to end. Note where it enters, who handles it, where it waits, and what gets redone. Pick one task with a clear input and a result you can inspect. If the expected result is still vague, you will not be able to tell whether AI helped.

Choose work that is easy to review

AI is most useful when checking the result takes less effort than producing it from scratch. It can search a set of documents for a fact, prepare the first draft of a support reply, or pull invoice fields into a table. The examples differ, but the condition is the same: someone can quickly tell whether the output is usable.

That review should happen before the result becomes a decision, reaches a client, or changes another system.

Leave judgment with the person who has the context

A client exception, a legal detail, or a sensitive support case depends on history that may never appear in the prompt. A model can collect the relevant material or prepare a draft. The final call still belongs to the person who understands the situation and carries the responsibility for it.

Prepare the handoff before adding AI

Decide when the task returns to a person, who receives it, and what they need to see. The person should be able to understand what happened and continue the work without reconstructing the whole case. If the manual path for unusual cases is unclear, adding AI usually hides that problem for a while rather than fixing it.

Measure the task on its own

Record the time and error rate before changing the task. After launch, track how much time it saves and how often people correct or reject the output. If most results need repair, narrow the task or remove AI from it. Return to a task with a result people can check without rebuilding the work by hand.