An AI workflow earns its place by making the next person's job easier. It can assemble records, draft a notice or bring exceptions into one view. The person using it should be able to see where the information came from, what needs checking and what remains unresolved.
“A person reviews it” is only useful if the workflow supports that review. A polished page with no sources and no visible gaps can take more effort to check than it saved in preparation. Design the output around the decision someone has to make.
Prepare the work for the person who receives it.
Start with the team's own way of doing the job. An SOP may already specify the output. A loose checklist or an expert walking through a past example can reveal the rest: which details matter, what gets questioned and when another person needs to be involved.
Build those distinctions into the workflow. A draft should identify missing inputs. A review should show the evidence for its findings. A handoff should separate completed information from questions waiting on someone else. That is useful preparation.
Make omissions part of the test.
A wrong item on a page draws attention. An item that never made it onto the page can be harder to notice. Test for both, using the source material and agreed requirements to check the output.
Illustrative example: a court-order notice.
An order contains a directive deep in a long paragraph, and one scanned page is unreadable. The workflow should bring the directive into the draft and identify the unreadable page. A coded check compares dates in the draft with dates in the order. Any deadline that would need calculating is listed for the attorney to confirm. The assistant reviews the notice; the attorney confirms its effect.
These are separate things to test. A workflow can copy dates correctly and still miss a directive. It can include all readable instructions and still fail to explain that a page was unreadable. Record each finding so a strong result on one check doesn't hide a miss on another.
Keep “nothing found” separate from “could not assess.”
A checking workflow should say which sources it read, what period they cover, how many records it expected and how many it assessed. Missing or excluded records belong in the output, alongside the findings.
Illustrative example: a property manager's certificate review.
The manager supplies the lease-requirements export and the certificates on file. The review identifies certificates expiring this month, tenants with nothing on file and requirements it could not read. Each finding points to the relevant lease or certificate, ready for the manager to confirm and follow up.
A useful review tells you what it found and what it could not establish.
Improve the instructions from the corrections.
When someone regularly corrects the same detail, investigate it. The source may be incomplete, the rule may be unclear or the workflow may need changing. The owner agrees the correction, then the revised workflow is tested against the affected cases.
That turns review into a way to improve shared practice. It also keeps the measure honest: count the time spent preparing, checking, correcting and acting. At Fully Powered AI, the first workflow prepares work in your own workspace from files and exports; your people take the next action. The goal is a useful result and a better-informed decision.