I'm coming to suspect that even our data isn't enough for useful AI. Imagine you had a truly general sci-fi AI at your office. It still couldn't just look at your database and answer a simple question like "What was the difference in client churn rates between mobile and desktop last month?" or "What was the effect of experiment 1234 on per-client revenue?" Hell, a human couldn't do it. As far as the human or AI would know, you just presented it with a bunch of random tables. This matters because it's incredibly helpful to know which pieces are randomized. Which rows are repeated measurements as opposed to independent measurements. Which pieces are upstream of which others. There's so much domain knowledge baked into data, while we just expect an algorithm to learn from a simple table of floats.
The human state of the art solution seems to be going on slack and asking questions about the data provenance, which will decidedly not work for an automated approach.
A primary reason I can do better a better job than a generic algorithm is because you told me where the data came from (or I designed the schema and ETL myself), while the algo can't make any useful assumptions because all that info is hidden.