Take this use case: A business analyst is trying to answer why sales dropped for a product last quarter. In the past, a human would explore the data, come up with possible reasons, test them, and suggest a plan. Now, an AI co-pilot is being trained to do most of that work. It pulls structured data, breaks it into groups, tests different ideas, and surfaces the insights. This kind of system is still in testing but shows what agents might be able to do soon.
A better approach
Even with these early wins, most companies are still trying to add agents to old workflows, which limits their impact. To really get the benefits, businesses will need to redesign the way work is done. The agent should be placed at the center of the task, with people stepping in only when human judgment is required.
There is also the issue of trust. If the agent is only giving suggestions, a person can check the results. But when the agent acts directly, the risks are higher. This is where safety rules, testing systems, and clear records become important. Right now, these systems are still being built.
