The biggest reason behind that seems to be the fact that the machine learning is not really about the underlying algorithm, but it is about the data in a form that can be used.
For any business to use the data that they are collecting for their ML solutions, they first need to define what inferences they want to draw. Once they know what inferences they want to draw, they need to look at the data and decide whether those inferences can be drawn from the data they collected. If the answer to that question is yes, an effort needs to be started to accurately tag the data and define the training set for their data.
All this is a lot of work and even with this the end result may not be worth the investment. Organizations need to understand, ML and AI is more about the data that they collect and less about that fancy algorithm that you can cook up.