My own experience has shown that dirty data impacts advanced AI just as much as it impacts far more basic ML techniques.
Even for the most advanced AI we work on, we spend just as much time worrying about clean data as we do anything else.
In general: duplicate data, missing fields, different formats for different parts of the data, inconsistent naming schemes
For text: character encodings, special symbols, escape characters, punctuation, extra or missing spaces and newlines, capitalization
For images: different sizes, rotations, crops, blurry images
For numbers: inconsistent decimal point/comma, outliers with obviously nonsense values or zeros, values in different units of measurement etc.
And then there's bugs in your data pipeline: browser (particularly IE) bugs, logging bugs, didn't understand your distributed databases's conflict resolution policy bugs, failed attempts at cleaning all the previous categories, incorrect assumptions about the "shape" of your data, self-DOS attacks (no joke - Google almost brought down itself by having an img with an empty src tag, which forces the browser to make a duplicate request on every page) which result in extra duplicate requests, incorrectly filtering requests so you count /favicon.ico as a pageview, etc.