When to use AI in a business — and when not to

Start with uncertainty, not the model

Generative AI is useful when inputs are unstructured and outputs allow interpretation: classifying messages, summarizing documents, drafting responses or finding context across many sources. If a deterministic rule solves the problem, it will usually be cheaper, explainable and more stable.

Before integrating a model, define what happens when it is wrong. If there is no acceptable answer, the case likely needs human oversight or is not suitable for full automation.

Signals of a good use case

A strong candidate has enough volume, repetitive inputs and an output a person can validate quickly. The value comes from reducing reading or preparation, not from pretending the model is certain.

• The current process spends time interpreting text or documents.
• Real examples exist for evaluating quality before release.
• Responses can be constrained with sources, schemas or specific tools.
• Uncertain cases have a clear path to a person.
• Value exceeds model, maintenance and observability costs.

RAG does not repair poor information

Retrieval can improve an answer, but it cannot fix outdated documents, weak permissions or contradictory knowledge. System quality depends on how information is prepared, segmented, versioned and authorized.

The origin of context should remain visible. Citations and internal links make results reviewable and expose when an answer goes beyond its evidence.

Evaluate before scaling

A useful pilot is measured with real cases, including difficult ones. Accuracy, escalation rate, time saved and cost per run provide a much stronger basis than an isolated demo. AI stops being a promise when the system has limits, metrics and a clear owner.