Data-driven methods, machine learning and artificial intelligence methods are not yet exploited to their intended potential in solving the technical-technological challenges, especially in industrial applications, despite versatile development progress. This is mainly justified by the insufficient practicality of AI solutions. To exploit the potential of AI methods, technical practitioners often rely on interdisciplinary collaboration with data science specialists or consultants. In any development and application of AI methods, a plethora of methods must be mastered for solution-oriented acquisition, pre-processing and quality assurance of required data, as well as for the selection of suitable algorithms and their adaptation. Coping with this complexity usually requires a great deal of effort, both for the individual domain expert and for the data engineers and data analysts. Complexity and intransparency of AI methods therefore hinder the effectiveness and efficiency of AI deployment. Focusing on user-friendly delivery of AI-based applications, the paradigm of Usable AI (UAI) has been defined. This paper first summarizes the UAI paradigm. Finally, some application examples from the field of production engineering illustrate how UAI can improve the practical applicability of AI methods for domain experts.