One of the most fundamental lessons one has to learn with AI/ML, is that it will never be 100%. Of course, that is true with conventional programming as well,

One of the most fundamental lessons one has to learn with AI/ML, is that it will never be 100%. Of course, that is true with conventional programming as well, but when a failure is found that particular bug is fixed, and one has the illusion that the problem which was just fixed was the last one, and _now_ it is 100% reliable. But, with ML, it will never, ever be 100%. In some potential applications, this is ok, or in any case the alternatives are 100% _not_ working so it's the best there is. But in other potential applications, this is a deal-breaker, actually or politically. Part of finding the "plateau of productivity", is that organizations learn lessons like this, about where you can and cannot use it effectively.