In most applications, an AI prediction remains an indicative value: useful for decisions but without binding effect. The step to commitment requires a model that is precise enough, and a business model that factors in the residual risk. In individual cases, the deviation between prediction and actual price can lead to a bad deal. At scale, however, such approaches are economically sound because overestimates and underestimates balance out statistically. The combination of a precise model and calculated risk turns predictive analytics into a binding pricing model.