Every AI system will be pushed too far. Plan the recovery.
Every useful AI system will eventually be given more than it can handle. Not through carelessness: when something works, people delegate more to it, and they find the limit only by crossing it. The question is not whether that happens, but where, and what it costs when it does.
The limit is found by going past it
No amount of planning tells you exactly how much a system can be trusted with. Tests show what it does on the cases you thought of. Real work brings the ones you did not. The honest way to learn the limit is to push, see it fail, recover, and set the boundary just inside where it failed.
That recovery is the actual learning process. Which makes the real question: where can you afford to fail?
Not on your most important customers
Do the pushing where a failure is contained: a separate team, a subset of work, a process running alongside the old one. Not on the accounts that pay the bills, not on processes where a mistake reaches every customer at once.
Undo before you push
Before any system is given more authority, there must be a way to undo what it did: return to last week's instructions, last month's model, last quarter's rules, for that one system, without stopping everything else. And for actions that cannot be undone, such as payments, deletions or messages to customers, a delay or a person's approval before they take effect.
With undo in place, a failure is a lesson that costs an afternoon. Without it, the same failure can cost a customer, a month of data or the company's reputation.
Expect the failure. Choose where it happens. Make sure you can undo it.