AI agents are useful when they have a clear job, reliable context, guardrails and a measurable operational outcome.
Start with work, not novelty
AI agents become valuable when they support a defined task: classify a request, extract information from documents, draft a response, triage an exception or prepare a case summary.
They become risky when they are introduced as a general-purpose answer to unclear work. Ambiguity in the process becomes ambiguity in the output.
Design the human boundary
The important design question is often where the agent stops. Which decisions require review? What evidence is captured? How does a person override the recommendation? What happens when confidence is low?
Prove the operating benefit
A good AI use case should be attached to a metric: reduced handling time, faster first response, fewer errors, better case quality, improved compliance evidence or increased capacity without additional headcount.
Have a version of this problem?
The Operational Performance Sprint turns one painful workflow into a diagnosis, opportunity map, business case and implementation recommendation.