Picture a busy support queue: an AI system reads a ticket, finds the relevant policy, and drafts a reply. That can save an employee a search and a first draft. Letting the same system issue a refund or alter an account is a different level of responsibility.
Drafting is not taking action
Many emerging uses are most practical when the system prepares work for a person rather than completing every step on its own. A suggested response can be checked against the customer’s record and current policy before it leaves the queue. The distinction matters because a fluent answer is not, by itself, proof that the right action has been taken.
Put review at the right point
A useful workflow makes the handoff visible: show the source information, the proposed action, and the point where a person must approve or edit it. Low-risk tasks, such as sorting incoming requests, may need less friction than changing billing details or making an exception to policy. The review step should reflect the consequence of an error, not just the speed of the demo.
Watch the whole workflow
When a company announces an AI agent, look for what it can actually do inside the tools people already use, and which actions still require approval. Also ask how teams handle wrong answers and keep a record of decisions. Those operational details are a better guide to real-world usefulness than the agent label alone.