> data scientists said they spent 65% of their time using relational datasets vs 18% for images

Part of the reason is that use cases are driven by limited definitions of ML or "AI" -- AutoML for example note they are "Making AI accessible to every business..." but they are just managing a small part of one type of ML (images with conv nets and res nets.) A senior exec who reads this might develop a narrow view of what AI is.

A general annoyance is how obvious techniques like regression, tree models, Bayesian models, etc on tabular data are so ignored while everyone gets hyper-obsessed over GANs or whatever. Almost 90% of low-hanging-fruit I see can be captured with simple classic ML applied to tabular data.