Machine learning-derived applications are impressive and give a good show until one winds up in a situation where they are expected to work reliably. Sure, it's nice that the insurance company's phone-based, voice-recognition-driven, registration/etc system can understand 99% of the choices people give them - except total failure in that 1% is actually going to leave a large population unserved and angry. Of course the company has keypad backup - except they don't 'cause that would cost the money they claimed voice recognition could save, etc.
Machine learning apps are great for situations where 1) You don't expect 100% reliably and the degree of non-reliably doesn't have to even be quantified. 2) Either you are accept that they'll degrade over time and have an army of tuners and massive data collect to keep that from happening OR you are dealing with an environment you completely control.
This is kind of the conditions for regular automation - except even more so.
There is a common expression that "AI is whatever computers can't do yet." At one time computers couldn't play board games or do vision, so those things were called AI.
This leads to both overuse and trivialization of the word, but also moving goal posts for the field. And actual progress isn't taken seriously, because nothing feels like intelligence when you understand it.
It's all a Mysterious Answer (http://lesswrong.com/lw/iu/mysterious_answers_to_mysterious_...), a codeword for magic (http://lesswrong.com/lw/ix/say_not_complexity/).
Applications involving computer vision and speech recognition are now buildable by small companies, which will hopefully yield a proliferation of really interesting novel applications.
Sure, we don't have terminator-style AI, but honestly people using the term AI need to shut up, a lot. There is no AI, these days, just massively creaky giant ML systems with a host of ph.d's being thrown onto the fire to keep them running. But the ML applications are super cool.