Interestingly enough, things like Machine Learning grew out of domain fields. A couple of decades ago, there were few - if any - dedicated programs for Machine

Interestingly enough, things like Machine Learning grew out of domain fields. A couple of decades ago, there were few - if any - dedicated programs for Machine Learning. The research and grads came mostly from domain-specific fields, like Computer Vision, Signal Processing, Computational Biology / Chemistry, Statistics, Applied Math, etc. Then when things got more cohesive, the most important parts formed into a more or less "pure" field of Machine Learning. Today, you can study Machine Learning without having to focus on any particular domain (well, other than stats and applied math, which lays the foundation for the theory). But, yes, it is tough and demanding to find people that have deep / expert knowledge in both their respective domain, AND machine learning / data science / AI. I think maybe one way to do it is to just look after domain experts, and learn them enough about ML and DS (if they lack the background) to work as generalists. Enough that they can read and discuss it. And then, you hire ML scientists and engineers to do the nitty-gritty work, with the input and feedback from the domain experts.