Counterpoint: in my consulting role, I've directly seen well over a billion dollars in failed AI deployments in enterprise environments. They're good at solving narrow problems, but fall apart in problem spaces exceeding roughly thirty concurrent decision points. Just today I got involved in a client's data migration where the agent (Claude) processed test data instead of the intended data identified in the prompt. It went so far as to rename the test files to match the actual source data files and proceed from there, signalling the all clear as it did. It wasn't caught until that customer, in a workshop said, and I quote "This isn't our fucking data".