We might be headed for the trough of disillusionment, but in terms of technology and industry applications, we are still in the early phase of modern AI (big data + ML).
I downvoted one of your comments that was essentially a cut-and-paste of this comment here; I have noticed that you have done this more than once here within the comments.
Please refrain from doing this; it seems to add nothing but noise to the conversation. If you believe your commentary has merit within the context of the thread at hand, do your best to restate that opinion in an original manner, not by simply copying and repeating the exact same statement.
I hope this explanation clarifies why I downvoted you; I believe your comment does have merit to the discussion as a whole, but posting it once should be enough to reach the audience with it's intended message.
It is however an uphill battle when these 80% systems offer such immediate gains. We really need governments to be informed and active.
An evaluation process, it's very common here in Sweden. I mean how much money can you really loose in six months? You can kick that shithole straight out day one if that's your perogative. It's a good deal for both parties.
You see people trying to shoehorn ML into many such system though, and there is money in it, which is why people are chasing it to have it on their CVs.
Like the noSQL hype of a some years ago it'll settle down and people will gravitate back more towards the right tool for the job (which will sometimes be ML based, but often not, just as "noSQL" is sometimes the right tool or right enough). ML will survive where it is the best tool for the job, or at least where it can be genuinely useful and not significantly sub-optimal.
> our problems are CRUD at modest scale.
I see some of our client base looking into ML and "Big Data", and I despair a little because they often fail badly at getting "little data" correct. It is actually part of the sales pitch for ML: let the AI filter out the crap in your inputs and give you something approximating a decent answer as output. They'd be much better served working on fixing the data sources or using more traditional cleansing methods, but that seems like harder work compared to the new magic some consultant is extolling. ML isn't a magic bullet, but it is currently being sold as one.