The powerful new tools that drive Quality 4.0 lets us see and understand our products and processes with new clarity. This presentation illustrates AI for quality with real projects that used artificial intelligence and big data technologies to extract, summarize and prioritize customer feedback from over 1.4 million words contained in over 11,000 customer feedback and product return documents, too much to read and understand before Big Data. We used Natural Language Processing and other big data techniques to classify and rank all the text complaints into natural clusters and ranked those clusters by cost and quantity. We then used a Bayesian Multilevel Model to prioritize improvement efforts across numerous manufacturing sites. This combination of big data tools helps set our agenda so that our quality improvement efforts reap maximum benefit. This presentation focuses on the practical uses and benefits of big data tools for the Quality Professional rather than the technical details of the algorithms.