For decades, business process optimization has been largely about art and craft (and sometimes wizardry). Apart from narrowly scoped approaches to optimize resource allocation (often assuming that workers behave like robots), a lot of business process optimization relies on high-level guidelines, with A/B testing for idea validation, which is hard to scale to complex processes. As a result, managers end up settling for a "good enough" process. Can we do more? In this talk, we review recent work on the use of high-fidelity simulation models discovered from execution data. The talk also explores the possibilities (and perils) that LLMs bring to the field of business process optimization. This talk was delivered at the Workshop on Data-Driven Business Process Optimization at the BPM'2023 conference.