When I was at MIT, the Op. Research Center was working on globally optimal decision trees. That is, a simple, single decision tree, that is devised by an optimi

When I was at MIT, the Op. Research Center was working on globally optimal decision trees. That is, a simple, single decision tree, that is devised by an optimization model such that it is going to be the best possible tree you could make with a given training set (vs. regular trees which are trained in a dumb greedy way). That always felt like the holy grail of actual useful business "ML" to me. But either way, you're often correct. For anything but the simplest decisions to be made, the bulk of the value will come from better decision making. ML can help refine the estimates of the value of each decision, but making the final choose is an easily overlooked and super important step.