[ethbr0]

ML needs data engineering like electrical devices need electrical infrastructure (i.e. generators, transmission lines, transformers, last mile lines).

It turns out companies have wildly different maturities and proficiencies with these precursors, in addition to simple company ages. Many from having under-invested in actual (not consulting BS) technology transformation and skilling for decades.

Consequently, ML is ridiculous to consider and doomed to failure for company A. While company B can toss a simple model at their well-architected data systems and get immediate ROI.

This is underappreciated, because VPs and consultants are not typically in the business of saying "Our systems are out of date and have poor hygiene, and we can't do this new thing because of that."