As someone who somewhat works with AI it's good if your data is good and terrible if it's not, my job is literally to clean up AI failures, and due to bad data

As someone who somewhat works with AI it's good if your data is good and terrible if it's not, my job is literally to clean up AI failures, and due to bad data or dealing with interpretation a lot can be done with data good or bad. My work helps retrain the model, but sometimes because of bad management we have unclear answers on how to interpret the data and this leads to some coworkers training it wrong and you'll see this linger between projects. Now if the people were better trained the data would be too but oh well. Someone wants to cut costs. I think this kind of issue will always exist no matter the model, you can't really make up data you don't have (I mean you certainly can but it all has to be figured out how to fix it when it does just start guessing poorly) and sometimes there are no answers but bad answers in some edge cases.