AI integration: from prototype to production
An assistant can summarize a few documents well and still fail on an incomplete file. Before releasing an AI feature, establish where it works, how to detect errors and when a person needs to validate the result.
Separate suggestions from actions
An inaccurate draft and an incorrect change to an order have different consequences. Separate suggestions, decisions and actions before choosing a model. The more directly software acts on operations, the more explicit its permissions, validation steps and recovery options need to be.
Data, errors and permissions
- What output is expected, and what reference will it be compared against? Build representative examples, including difficult cases.
- What data reaches the model? Review quality, access, confidentiality and the provider’s processing terms.
- What actions are permitted? Limit permissions and require human approval for sensitive or difficult-to-reverse operations.
- How will drift be detected? Monitor errors, latency, costs and changes in models or input data.
Test against representative cases
Build a set of real examples, including incomplete documents and ambiguous requests. Compare expected and actual results, and set acceptance thresholds. Repeat the tests when models or data change. Before expanding use, plan error monitoring and a way to complete the task without AI when the system cannot respond correctly.