Process automation: where does AI help?

Define the input and the result.

A request arrives, data is checked, a tool is updated and a person is informed. Write down steps and exceptions before choosing technology. When rules are stable and explicit, conventional automation may be enough and easier to control.

Use AI for a specific purpose.

Classifying text, drafting a summary or searching documents are possible uses. Define the expected result, permitted sources and situations requiring a person’s decision. A model can produce incorrect answers: important decisions still need review.

• Documents: usage rights, versions, formats and user permissions.
• Answers: accessible sources, test examples and acceptance criteria.
• Exceptions: no reliable answer, missing information or an action requiring approval.

Work out the cost of an execution.

Development is a launch expense. Tool subscriptions, API calls, document volume and model usage can create recurring costs. Estimate frequency, volumes and exceptional cases. Expected time savings only become a business result after comparison with a baseline.

Test routine and difficult cases.

Prepare representative examples and expected results. Add duplicates, incomplete documents and service interruptions. Define who receives alerts and how an operation can be resumed. Starting with a bounded process makes these checks easier than automating an entire business at once.