Developing industrial analytics solutions usually requires specific know-how in the data science domain. In the engineering domain, experience in data science is sparse, which prevents unleashing the power of artificial intelligence and machine learning on the factory shop floor. This talk highlights challenges and our experience in implementing AI analytics models for real-world machinery applications, by addressing why a pure data driven approach does not lead to satisfying models. Markus Köster will demonstrate hands-on examples on automated machine learning to create industrial analytics models without data science know-how. To summarize, he names five factors for successfully adopting AI and ML in the engineering domain.