Before adding AI, understand the workflow.

Map the process before selecting a tool

Pick a specific workflow: handling an inquiry, preparing a report, or routing a support request. Record where the input arrives, what the team does next, and what a completed outcome looks like.

This often reveals simple integration work. Moving structured data between two systems may need a reliable rule and a good API rather than an AI model.

Separate rules from interpretation

A rule can route a form submission based on a selected department. AI may be useful when the department must be inferred from a free-text message. These are different kinds of decisions and need different checks.

Write down examples of acceptable and unacceptable outputs. Evaluate against representative inputs, including incomplete or ambiguous ones, before connecting the workflow to important business actions.

Keep people in control

Decide when the system can proceed, when it should ask for review, and what happens when a dependency fails. A useful automation needs a recovery path as well as a happy path.

For an initial release, generating a draft for a person to review can be more practical than sending messages or changing records automatically. The appropriate level of oversight depends on the task and its consequences.

Measure the work that matters

Observe whether the workflow reduces repetitive effort without creating a larger review burden. Track errors and exceptions, and revisit the process as the business changes. AI should earn its place through usefulness.