I've used random forests for a while, in both regression and classification applications, and usually found that prediction quality does not increase significantly above 250 trees at the most. According to what I've read on CrossValidated or elsewhere, this is in agreement with the theory.
However, I'm currently working with a new dataset -- I can't say much about it except that it has roughly 200 columns, a few million rows and consists of what I'd call "business data", a mix of numbers (largely currency values) and one-hot encoded categorical data. We first tried 100-250 trees and got okay-ish results when predicting on our validation data. Someone in the team tried a much higher number of trees (800), and the results improved dramatically.
What could be the cause of this? We've experimented a bit with other metaparameters, but for the sake of this question let's assume that the others are as default in sklearn's RandomForestClassifier.