I've been at a few startups and ML has typically meant shoddy rules-based logic or some out of the box model. Only one company applied it with rigor and even then it was a slog - lackluster results, tinkering with different models, poring through research papers to figure out where the cognitive gap came from. The rest of the company thought we were brilliant as did prospective clients. Funny thing is it's possible (though less common) to get paid just as much if not more doing data analysis / engineering than vaunted ML/AI work. Businesses are swimming in data but it's siloed or dirty. I don't really see that and the actual analysis being automated away - too much messiness (human error inputting data into systems like Salesforce, ETL breaks in prod leading to gaps, etc).