it is classification all the way down
and now that models are great at training models themselves (especially smaller ones that can be trained off of larger backbones) the bottleneck is creating and cleaning synthetic data
The most obvious cases for a company to fine-tune a model today is anything that looks like "old ml": classification, search, routing, etc.
In the future, taste will be a big area too. Not just "did the model do the thing?", but "did them model do the thing the way I wanted it to?"