I think you nailed it. Often companies and exec want ML but don't have the basics: robust ETL pipeline, clean data, solid analytics foundation (dashboards, automated reporting, etc.). These appear boring to most but they will be the difference between a useless ML department who can't ship anything to production and a successful one that builds on top of the aforementioned foundations.

In addition I believe it's time we drop the data science term. It's an umbrella of different roles ranging from data engineer to DL researcher. Companies need to identify what they REALLY need and not go for the shiny PhD in ML.

The emergence of analytics engineering is the perfect example of this shift towards creating robust data pipeline first and enabling "data scientists" to do so.

I wrote a blog post about it yesterday, I don't want to post it here and self-promote too much, so check it in my profile if you want to.