Understanding Classification Thresholds Using Isocurves

You are in a conference room, presenting your work on a classification problem. You demonstrate all the magic you performed with feature engineering, predictor selection, model selection, hyperparameter tuning, and ensembling. You conclude your presentation with the predicted probabilities and the ROC curve, and a fantastic AUC. You sit down confident in a job well done. And the manager says, "What am I supposed to do with all this probability and ROC stuff? I just want to know if I should do x or y."