The massive data gathering part should only be part of the learning phase of the system imo, once it get a good model of reality it should infer useful knowledge information from few data, like an expert.
I guess the point of view is that if a department is well running, it means it is overressourced. So you reduce the ressources until it's breaking point, just enough for it to not fail. A jaded service manager told me it was part of its official training: if the clients was too satisfied that meant that human ressources were wasted on them, so he had to spin plates between clients. I guess it was economically optimal.
I think that engineering progress made while building those machines are maybe more relevant for practical technical development than the discovery they make.