Perhaps the biggest hurdle in this regard is the approach to machine learning. Nearly everything I have seen on machine learning is a primer on big data followed by a series of algorithms on making the best and smartest decision upon that mountain of data.
This is completely the wrong approach. Machine learning can be done on a dime, provided the proper nurturing and environment, but you have to be willing to make some concessions.
First and for most you have to be able to write a program that can make a decision. A simple "if" condition is sufficient.
Secondly, that decision is open to modification by asserting the evaluation (the "if" condition) against its result. In this regard the logic is fluid opposed to a series of static conditions written by humans hoping to devise organic decisions.
Finally, the decision is allowed to be completely wrong. Wrong decisions are better than either no decision or the same decision without deviation. This is how humans learn and it should be no surprise that computers would benefit from the same approach.
The key to getting this right is bounds checking and simplicity. A decision must find a terminal point in which to stop improving upon its outcome, and a narrow set of boundaries must be affirmed to prevent unnecessary deviation. It is perfectly acceptable if some grand master must occasionally prod the infantile program in the right direction. This is also something that people do to other people who are learning.
If you can do that you have machine learning. You don't need big data to get this. You certainly don't need complex transportation machines or voice activated software to validate it. AI on a dime. If you can do it on a dime you can certainly do it with a multi-billion dollar budget and thousands of developers.