Start an AI pilot with one useful workflow.

Choose a task you can describe

Pick a bounded workflow with a clear input, expected output and person responsible for the result. Avoid starting with a promise to automate an entire department.

An example might be turning an approved internal procedure into a draft response for a person to check. Another might be preparing a first pass at a routine report from approved source data. These are possible pilot shapes, not claims of results for a particular client.

Write down how the task is done today, where people spend time and which mistakes matter. If the current process is unclear, clarify it before automating it.

Decide what information it can use

List the sources the workflow needs. Check who owns the information, who may access it and where it may be processed or retained. Give the pilot only the access it needs.

A small-business workflow and a credit-union workflow may have very different data restrictions. Do not copy confidential information into a new tool just because it is convenient. Use approved test material while the controls and terms are being reviewed.

Separate advice from action

Generating a draft and changing a live system are different responsibilities. Decide which outputs require review and which actions, if any, the workflow may perform.

For an initial pilot, it can be useful to produce a recommendation for a person to apply. If the workflow will take actions, define the allowed systems, exact task boundaries, logging and stop conditions before connecting it.

An assistant's confident answer is not evidence that an action happened. Check the actual result in the system involved.

Test the normal work and the awkward cases

Keep a small set of representative examples with expected outcomes. Include incomplete input, conflicting information and situations where the right answer is to ask a person.

• Does the result use the right source information?
• Does it stay within the task and access limits?
• Can a person understand and correct the result?
• What happens when a tool fails or a source is unavailable?
• Can the workflow stop without leaving half-finished work?

Record the observed results. Keep the failures alongside the successes so the rollout decision is based on the whole test.

Measure usefulness before expanding

Compare the pilot with the existing process. Track the time a person spends, the amount of correction needed and whether the output is usable. Include review effort in the comparison.

Agree on the conditions for continuing, revising or stopping. Keep a way to return to the existing process. Expansion should follow a useful result, not just a polished demonstration.

I provide AI implementation support from Detroit as part of clearly scoped technology work. The goal is a workflow your team can understand, operate and check.