Sometimes you just need to do it that way, because the people buying do not know what machine learning is - even though they've heard about AI.
For example, I was networking for some jobs in data science - and was approached by some energy company. Struck up a conversation with the guy (older exec), and he said "so I hear you have a background from AI, correct?" to which I replied "I have a degree in Machine Learning, and have worked with etc." - he just replied "Oh, we don't really need any mechanical engineers now"
So I asked him, mechanical engineering? He pointed out that I said MACHINE, and assumed it had something to do with, you know, physical machines and stuff - so in the domain of mechanical engineering.
So, from then, I went easy on using Machine Learning unless I was fairly confident the other guy had some domain knowledge. If I talk with non-technical salespeople, or older executives, I just leave it at AI.
Seems like there was a time when "machine" was the term grant committees wanted to hear, they were perhaps fed up with the theory and wanted more tangible stuff. So the theorists branded their stuff as machines. See support vector machine, kernel machines...
Similar to how "dynamic programming" was coined to please funding agencies.
That doesn't work. Everything is called AI these days and in mountains of bullshit there are also some actually useful results, these few are not snake oil.
Somewhere out there, a biotech R&D company has developed an effective penis enlargement treatment. Unfortunately they have been having some trouble reaching potential customers.
Dynamic yield was practically pushed into AI. They never promoted doing AI but investors and clients liked it more. So they simply gave up and went with it.
