Today’s prediction engines struggle with changing environments. Predictions usually only generate value if they support some kind of intervention. (For example, a diagnosis that supports a treatment.) Interventions change the environment, which may undermine the initial prediction.

This is one reason why prediction engines have insatiable appetites for data. It also explains why the use cases and potential tasks for AI are exaggerated.