Back when O'Reilly was still hosting events (sigh), at one of their AI conferences, someone from Google gave a talk about differences between research/academic AI and applied AI. I think she had a PhD in the field herself but basically she made the argument that someone who is just looking to more or less apply existing tools to business or other problems mostly doesn't need a lot of the math-heavy theory you'll get in a PhD program. You do need to understand limitations etc. of tools and techniques. But that's different from the kind of novel investigation that's needed to get a PhD.
Lol.
With the exception of niche groups in compressed sensing, math doesn't get too hard. Furthermore, ML isn't math driven in the sense people are trying things and somebody tries to come up with the explanation after the fact.
