For many years I worked on building data products at a start-up as the data guy (encompassing analytics, ML, data engineering). We started off pretty much all-in with ML, even implementing bleeding-edge models from scratch based on the newest research papers, and building crazy infrastructures around them (we had loads of fun tbh). In the end (after ~5 years), however, our product was a UI displaying a handful of "simple" stats, which was facilitated by a robust but relatively simple data ETL pipeline in the background.
Essentially, as we gained more experience and learned more about the domain and customers' needs, we found more value in "the basics" rather than fancy ML models.
That is not to say ML isn't a powerful tool in the right context, but I feel it is grossly over-hyped and over-used. And that seems to be a controversial stance. Even within start-up circles I've encountered push-back when suggesting going the simpler route and saving ML for much much later.
Is that generally a hype perpetuated by the ML people not wanting to lose on ML opportunities? (I guess ML sounds better, is more fun, and probably pays more than the "basic stuff")