In many cases, you may want to optimize one of the inputs to achieve the desired outcome. If you think of classic linear regression, you get a rough approximation of how each input affects the output. So, for example, if you built a linear regression model on house pricing, you could be relatively confident that an X increase in number of bedrooms, or proximity to public transport, or some other variable, has a direct affect on your output Y (home price). And in the case of linear regression, you get weights and power for each coefficient, so as a builder you can say "I'm going to build houses closer to the metro to fetch a higher price". Greatly simplified, but I think this is is what is missing from AI. If we're having AI classifying things, it's only one piece of the puzzle to actually do the work of classifying (and that's useful to a lot of people). But the other half of the equation is to explain why things are classified the way they are.