Trust comes from the workflow
Operators trust AI systems when they can see where inputs come from, understand why a recommendation was produced, and know when human review is expected.
Interface clarity matters because adoption depends on repeated use under real operating pressure, not only model quality.
Design for control and improvement
Applied AI workflows should make exceptions visible and give teams a practical way to refine prompts, sources, and escalation rules over time.
This keeps AI agents useful as the business process changes.