The test of a real AI system: people correct it less every month
Many AI systems in companies today are fixed automation with a chat window. They do what they did on the first day, no better. The test that separates them from a system that learns is simple: count how often people override it, and watch whether that number falls.
Every override is a lesson
When the system proposes and a person decides differently, something is learned: the customer was strategic, the policy has an exception, the stock figure was wrong, the legal risk was higher than it looked. If the reason is captured, it becomes the most valuable training data the company has. If it is not, the same mistake comes back next week.
So make recording the reason part of the override itself. One click and a short note is enough. Then review the reasons regularly and feed them back into the rules, the data or the instructions the system works from.
How a new system learns without your whole database
A new system starts with no experience of your business. Four sources close the gap without copying all your data into it. Past cases: a selection of earlier decisions for this one process, with what happened afterwards. Side-by-side running: while the old way continues, log every case where the system and the person disagreed. Overrides: the reasons, as above. And invented cases: realistic examples of rare situations, such as fraud or a supplier failure, so the system can practise on them without touching real records.
What to measure
Track the override rate per process, every month. If it falls, the system is learning and can be trusted with more. If it stays flat, it is not learning, whatever the vendor says, and it should not be given more authority.
A system that never needs correcting less is a tool. Treat it as one.