AI integration

Finding information, drafting a response or extracting details from a document may be useful starting points. We compare an AI approach with rules, search or an existing product before deciding whether a model earns its place.

Define what an acceptable result looks like and evaluate representative inputs, including incomplete and ambiguous ones. Accuracy, response time, operating cost and the effort of human review all affect whether the integration is useful.

Begin with a feasibility review and a limited evaluation. The output is a recommendation with the task boundary, evidence from test cases, data requirements, likely operating costs and the remaining risks. A prototype is not evidence of production reliability.

People should know where a result came from, what they need to check and what happens if the model or provider is unavailable. Permissions and data handling are part of the design. High-impact actions require appropriate human control.

If representative data cannot be used safely, mistakes cannot be detected, or the task requires a level of certainty the approach cannot provide, the next step may be a different system rather than an AI feature.