Make AI part of a workflow.

AI becomes useful when it supports a real task with a clear owner and a way to check the result. A fluent answer is not evidence that it is correct. Begin with a repeated task where errors can be noticed and handled. Avoid starting with an open-ended goal such as “use AI across the business.”

Write down the workflow in ordinary language: what information goes in, what output is needed, who reviews it, and what happens when the result is uncertain. A first experiment might draft a response from approved guidance, summarize a known document set, or sort requests into categories for a person to confirm. The examples should reflect routine and difficult cases.

Choose an evaluation method before comparing tools. Check factual accuracy, missing information, consistency, tone, and the amount of correction required. Keep a small set of representative inputs and note where the system fails. If an incorrect result could affect someone’s money, rights, safety, or access to service, add stronger review and escalation steps or choose a different approach.

Consider information handling early. Decide what content may be sent to a provider, whether it contains confidential or personal data, who can access the workflow, and how long records should be retained. Review provider terms and costs rather than assuming a free tool is appropriate for organizational use.

Measure the whole task, including review and rework. A faster first draft may still increase total effort if it needs extensive correction. Expand only when the workflow is useful across representative cases and the people responsible understand its limits. Sometimes the best result is a simpler non-AI process.