This is tough. I use to work for a large AutoML company that worked with oil and gas companies. It's difficult and often frustrating for non ML people to use AutoML tools. To use ML you need to know how to setup your problem - what is the target, partitioning data, . . It takes an understanding of ML to do this. Otherwise you will end up with people with 20 rows of data wanting to make a prediction or trying to use ML for something a simple rule would do or building a multilabel model where a binary model would have been better.

My suggestion is to keep them in the descriptive world, and if they want to move to ML, someone needs to introduce ML concepts to them before they start using the tools.