I develop energy-planning software whose forecasts, constraints and time resolution must support the decision being proposed, before a polished result becomes a customer promise.
My first models were satisfying because they made complicated relationships visible. Inputs went in, alternatives could be compared and a result emerged with enough precision to feel decisive. Product leadership taught me that the harder question was whether the model had been allowed to answer an easier problem than the customer faced. A forecast could know the future through its evaluation data. An optimisation could buy a better score by violating a supposed limit. An hourly average could remove the short peak that mattered to the decision.
A useful model should make the limits of its decision support harder to overlook than the attractiveness of its answer.