This is just my general sense, as a very non-expert with more experience of doing than theory...but the benefit is someone knowing the theory AND being able to translate that into revenue.
I think most people view the hard part as doing the PHd, and so lots of people value that experience, and because they have that experience you have this endowment effect: wow, that PHd was hard, I must do very hard and complex things.
To give you an example: Man Group. They are a huge quant hedge fund, in fact they were one of the first big quant funds. Now, they even have their own program at Oxford University that they hire out of...have you heard of them? Most people haven't. Their performance is mostly terrible, and despite being decades ahead of everyone their returns were never very good (they did well at the start because they had a few exceptional employees, who then went elsewhere...David Harding was one). The issue isn't PHds, they have many of them, the issue is having that knowledge AND being able to convert it.
I think this is really hard to grasp because most people expect problems to yield instantly to ML but, in most cases, they don't and other people have done valuable work with non-ML stuff that should be built on but isn't because domain knowledge or common sense is often lacking.
A similar thing is people who come out of CS, and don't know how to program. They know a bit but they don't know how to use Git, they don't know how to write code others can read, etc.
