I want to highlight #1 and #2 especially. Machine learning is not necessarily the correct tool to use to solve a problem. You won't even understand whether it might be unless you've done at least some of the work for Rule #2. If the data you've collected and the metrics you think you can nudge with a machine learning product support it, make sure you have as complete a set of metrics as possible to measure whether your ML product is useful.
It's easy for data scientists and machine learning engineers see P/R, AUC, or whatever as the goal, especially if there isn't much support in the organization for measuring product performance. It's often not the end goal. Measurements of a model's performance in this context indicate some measure of statistical performance with respect to training and test data. Real, live measurements from "in the wild" application are the true fitness test.