Shadow mode, where the AI runs alongside the existing process without affecting decisions, makes sense when the cost of a wrong recommendation is high and you need evidence of how the AI performs before relying on it. It lets you compare the AI’s output with what people actually decided, on real work, without any risk to the job.
It should have a defined end, with clear criteria for moving out of shadow mode, otherwise it can drag on indefinitely. Once the results show the AI is reliable for a specific use, it can move to a supporting role where people still make the decision with the AI’s input in front of them.