Every dataset has errors and inconsistencies. It is true that some have more than others, but what really matters is how that affects the end goal. Sometimes, the level of inconsistencies doesn't impact model performance as much as one would expect. In other cases, it is the main cause of a poor model performance, at least in one area (for instance, for a specific set of classes). I totally agree with you that companies that succeed in putting and maintaining AI models in production pay particular attention to the quality of the datasets that are created for training and testing purposes.