Over the years we have learned to make consequential decisions from some of the world’s most complex computer simulations. The approaches are far from turn-key and bracketing confidence requires people to intervene and evaluate throughout. We have worked to co-develop these tools and methods together with the problems. That is, one cannot think of uncertainty quantification as an afterthought. In medically assisted decision making, as well as decision making in general from increasingly rich and complex data, there are yet no parallel methods that can provide quantifiable confidence in predictions. In this talk I will discuss how we can learn from where methods have worked to get us to a future where we can make decisions with an understanding of how certain we are.