Choose a problem people can describe
An AI initiative becomes easier to evaluate when it starts with a specific piece of work. “Help our team prepare a submission summary” is a more useful brief than “bring AI into the business.”
Ask the people doing the work where they spend time finding information, re-entering it or preparing a first draft. Look for a repeatable task with a recognisable input, a useful output and someone who can judge whether the result is good.
The first workflow should be small enough to understand end to end, but meaningful enough that improving it would matter.
Define the result before the interface
Write down what a good output looks like. A useful summary might need to distinguish supplied facts from unanswered questions, preserve important qualifications and point a reviewer back to the source material.
Also define what the system should do when it cannot produce a reliable result. Asking for clarification, leaving a field blank or handing the task to a person can be valid outcomes.
These expectations help the team evaluate the work consistently. They also prevent a polished interface from becoming the main evidence that the solution is ready.
Keep the first boundary clear
Decide what the workflow may read, what it may propose and what it may change. Generating a draft for review is a different responsibility from sending that draft to a customer or updating an operational record.
For an initial implementation, a narrow boundary can make feedback easier to interpret. The team can inspect the output, identify errors and understand which part of the process needs improvement.
Access to information should follow the needs of the task. There is no advantage in giving a workflow more data or more authority than it needs to deliver its intended result.
Evaluate ordinary work and awkward examples
A demonstration often uses a clean example. Everyday work also includes incomplete documents, contradictory details and requests that do not fit the expected pattern.
Build a small, representative set of examples with appropriate permission to use the information. Include cases where the correct response is to stop and ask for help.
Review the outputs with the people responsible for the work. Record the changes they make and the reasons behind them. Their feedback can reveal whether the issue is missing context, unclear instructions, unsuitable source material or a workflow that needs redesign.
Expand when the evidence supports it
Agree on what would justify the next step. That might include acceptable correction effort, clearer handovers and a dependable way to deal with exceptions. Compare the new workflow with the existing process rather than judging it only in isolation.
If the result is useful, expand deliberately: another document type, another team or another step in the process. Each change introduces new assumptions to examine.
A focused first workflow gives an AI initiative a practical foundation. It creates something people can use, question and improve—and a clearer basis for deciding what to build next.
