Start with decision pressure
AI is most useful where people are already spending too much time interpreting patterns, triaging information, or moving data between systems.
Examples include support summarization, document extraction, scheduling suggestions, and anomaly detection in operations data.
Workflow fit beats novelty
The question is not whether AI can do something impressive. The question is whether it improves a real operational workflow without adding ambiguity or risk.
Successful AI feels like a sharper system, not a distracting demo.
Production readiness matters
Applied AI requires the same rigor as any other enterprise capability: observability, evaluation, permissions, fallback paths, and clear ownership.
When those foundations are missing, experimentation stays stuck in the lab.