I'm running automatic machine learning SaaS for 2 years, and after this time, I can tell you that it is huge problem that data scientist are living in their own world (including me! and including data science tools).

I had such situation: my user created 50 ML models (xgboost, lightgbm, NN, rf) and ensemble of them. Let's say that single best model was 5% better than single best model with default hyper-params and ensemble was 2% better than tuned best single model. For me it was a huge success, but the user didn't care about model performance. He wants to have insights about data, not tuned blackbox.