When and when not to use automatic differentiation

I am just learning (more) about automatic differentiation (AD) and at this stage it kind of seems like black magic to me. The second paragraph of its Wikipedia article makes it sound too good to be true: it is extremely fast and is exact (no round off, no discretisation). I am left wondering why finite difference (FD) is so ubiquitous in scientific computing. Looking this up, I seem to only find tutorials on how to implement AD, the advantages of AD, and its applications in gradient-based optimisers. But what is an example of when not to use AD, and instead use FD? Surely there must many.

As just one example, in computational electromagnetics a FD approach is very standard; why can we not propagate Maxwell’s equations with AD (FDTD: why not ADTD?)? It is clearly not because the developers aren’t aware of it because the same people implement AD for inverse design purposes (why AD instead of FD for inverse design?). Naively, to me it seems like having an exact derivative should be more important when propagating Maxwell’s equations than when taking the derivative of an objective function.