Put AI inside a workflow, not above it.

Choose a narrow decision surface.

“Use AI to run support” is too broad. “Classify this request, cite the evidence, propose a response, and route uncertain cases to a person” is a designable unit of work.

A narrow surface makes quality measurable. It also makes failure recoverable because the surrounding workflow still knows what happened and what should happen next.

Keep evidence attached to the output.

Model fluency can hide weak grounding. Production systems should carry source records, timestamps, policy versions, and transformation history alongside generated text or classifications.

That provenance gives reviewers something concrete to inspect. It also creates a better feedback loop: errors can be connected to missing context, ambiguous rules, or a model limitation instead of being filed as “the AI was wrong.”

• Identify the exact source material used.
• Separate extracted facts from generated language.
• Record confidence, exceptions, and reviewer changes.
• Preserve a non-AI path for critical work.

Design the human decision explicitly.

Human-in-the-loop is not a checkbox. The reviewer needs the right context, a clear action, an escalation path, and enough time to make a meaningful decision. If approval is buried in a noisy queue, the human becomes ceremonial.

Decide which outputs can publish automatically, which require approval, and which should never be delegated. Those thresholds should reflect business risk rather than model enthusiasm.

Measure the operating result.

Accuracy matters, but it is not the only outcome. Measure time saved, rework, exception rates, customer impact, policy violations, and the percentage of suggestions people actually accept.

The result may show that a smaller model task delivers more value than an ambitious autonomous system. That is not a compromise. It is product evidence.