This is a great insight. My company recently started working with agricultural analysts, and your example about moving averages is spot on. Initially, they also wanted the “magic” of NNs, and fortunately, after several prototypes, they understood that what they need is much simpler. As a result, after a couple of years, we’re starting to actually apply those NNs productively, having solved the simpler problems.
Basically by convincing them that to make "the real AI" work, they would need a lot of high-quality data that they wouldn't be able to produce. After several iterations with less data-hungry statistical methods they finally realized that they need a real product real soon (they were some kind of innovation department within a large corporation) and realized that the thing they need most is just a centralized GIS with basic computational capabilities.
Having successfully built that, those capabilities could be applied at scale and then we started experiments with more advanced analyses, this time more successful since both we had much more data and the customer became familiar with the data-intensive development.
