These days everyone is "doing AIML" and not nearly doing enough commonsense. Having been in this business for a long time, the desire to seize this shiny object is hardly new. There must be an use case out there that AIML can blow out of the water - is the general thinking of the financial buyer of such solutions specially when they are data literate.
Reality often fails to match up to the hype unless the problem being solved is has a long established track record. Spam classification and fraud detection for example are going to yield results but these are not novel or "innovative" problems to solve. People have been there done that so you will not excite the exec who is looking to make her mark in her new role. AIML is an overused and abused sales tool from what I have seen and the results can be quite questionable at times. In the words of one PhD student :
Kapoor says that many researchers are rushing to use machine learning without a comprehensive understanding of its techniques and their limitations. Dabbling with the technology has become much easier, in part because the tech industry has rushed to offer AI tools and tutorials designed to lure newcomers, often with the goal of promoting cloud platforms and services. “The idea that you can take a four-hour online course and then use machine learning in your scientific research has become so overblown,” Kapoor says. “People have not stopped to think about where things can potentially go wrong.”
These folks with the four hours of online training are being promised that they will becomes masters of the universe never mind that they have high school level mathematics and no research background whatsoever. That would all fall into the category of "undifferentiated heavy lifting" that the technology will do for you.
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