I beg to differ, at least as far as terms go now. Neural networks lived in the "field" of machine learning along with Kernel machines and miscellaneous prediction systems circa the early 2000s. Neural network today are known as AI because ... why? Basically, the histories I've read and remember say that the only difference is now neural networks are successful enough they don't have to hide behind a more "narrow" term - or alternately, the hype train now prefers a more ambitious term. I mean, the Machine Learning reddit is one go-to place for actual researchers to discussion neural nets. Everyone now talks about these as AI because the terms have essentially merged.
> A classic definition of intelligence (Legg&Hutter) is "Intelligence measures an agent’s ability to achieve goals in a wide range of environments".
Machine learning mostly became AI through neural nets looking really good - but none of that involve them become more oriented to goals, is anything, less so. It was far more - high dimensional curve can actually get you a whole lot and when you do well, you can call it AI.
Neural networks have always been part of AI, machine learning has always been a subfield of AI, all these things are terms within the field of AI since the day they were invented, there never was a single day in history when those things had not been part of AI field.
Neural networks were part of AI field also back when neural nets were not looking really good - e.g. the 1969 Minsky's book "Perceptrons", which was a description of neural networks of the time and a big critique about their limitations - that was an AI publication by an AI researcher on AI topics.
Your implication that an algorithm needs to do well so that "you can call it AI" is ridiculous and false. First, no algorithm should be called AI, AI is a term that refers to a scientific field of study, not particular instances of software or particular classes of algorithms. Second, the field of AI describes (and has invented) lots and lots of trivial algorithms that approximate some particular aspect of intelligent-like behavior.
Lots of things that now have branched into separate fields were developed during AI research in e.g. 1950s - e.g. all decision making studies (including things that are not ubiquitous such as minmax algorithm in game theory), planning and scheduling algorithms, etc all are subfields of AI. Study of knowledge representation is a subfield of AI; Probabilistic reasoning such as Kalman filters is part of AI; automated logic reasoning algorithms are one more narrow subfield of AI, etc.
There's lots in the field of AI that is not 'cognitive automation' - many currently popular things and use cases are, but that's not correcting a misnomer, that's a separate term for a separate (and more narrow) thing - even if that narrower thing constitutes the most relevant and most useful part of current AI research.
A classic definition of intelligence (Legg&Hutter) is "Intelligence measures an agent’s ability to achieve goals in a wide range of environments". That's a worthwhile goal to study even if (obviously) our artificial sytems are not yet even close to human level according to that criteria; and while it is roughly in the same direction as 'cognitive automation', it's less limited and not entirely the same.
For example, 'cognitive automation' pretty much assumes a fixed task to execute/automate, and excludes all the nuances of agentive behavior and motivation, but these are important subtopics in the field of AI.
But I am willing to concede that very many people are explicitly working only on the subfield of 'cognitive automation' and that it would be clearer if these people (but not all AI researchers) explicitly said so.