Start with the task, not the technology
Before adding AI to a workflow, identify the work that needs improvement. Repeated tasks with clear inputs and outputs are useful starting points.
Choose a small scenario
Instead of defining the scope as automating everything, choose one repeatable task whose output can be assessed: classifying text, drafting a document summary or directing users to the right source.
Map the data flow
What information is sent to the model? Where is the output stored? Who can access it? Answer these questions before choosing a model. Confidential or personal data must also be considered under your organization's policies.
Where is human approval needed?
Turning AI output directly into an external action is not always appropriate. For customer-facing text or decisions affecting operations, design an intermediate review step.
- Limit and validate inputs.
- Request output in a defined structure.
- Handle incorrect or incomplete answers safely.
- Let users reject a suggestion.
Create a small evaluation set
Collect examples similar to real usage. Define expected answers and acceptance criteria. Reassess the same examples when the model, prompt or data source changes.
Failure is part of the design
API outages, delayed responses and incorrect model output are possible. Explain what happened, preserve existing data and provide a way to retry.
A reliable small workflow creates a foundation for larger automation.
