The way I see it, only those companies that had already been using a data oriented approach to business can really reap the benefits of ML. From a company's point of view, ML/AI should be a natural evolution of an existing tool set to better solve problems they have been trying to solve in the past using deterministic methods and then statistical methods, etc. Any other project that is diving right into ML is likely to fail because

1. There's no clear problem statement. They have never formulated one and now trying to bolt ML on to their decision making.

2. They don't have well catalogued data for engineers/scientists to work with because they never tried to do rigorous analysis of data before ML became a thing.

3. Managers have no idea how to deal with data driven insights. What if the results are completely unintuitive to them? Are they going to change their processes abruptly? What if the results are aligned with what they have been always doing? Is it worth paying for something that they have been doing intuitively for decades?

I'm not a data scientist. But the biggest complaint I hear from my colleagues is that they lack data to train models.