That is why ML as an hands-free service, just like what a database is, doesn't work. To my surprise, I would say, currently ML/AI is a quite manual thing to get right, and it requires constant attention, not just one time effort, since the data is ever changing.
AutoML might be a solution to this, with the help of a working HPO solution, but both are not really public accessible at this point, requires long time and big computation resource.
CV has been common in SMD pick-and-place machines for 15 (probably 20, 25) years. For industrial applications, hardware size and price essentially do not matter, so the new AI approaches do not bring anything fundamentally new to the table (industrial solution vendors will eventually integrate smaller and cheaper solutions, but price is just not a huge discriminator here). What's interesting is the scaling down that is happening and making AI viable for consumer applications where budgets and device size are restricted.
[edit] Or kind of like cryptography. Thanks god we don't need to understand the underlying algorithms to apply them to real world applications. Just having a high level understanding of what's going on inside is enough.