In an increasingly dynamic and complex environment, manufacturing systems must respond quickly to changes in order to remain productive. Hence, existing tasks and decisions in manufacturing have to be aligned in an ever more complex system of connected and dependent machines and devices. Various data-enabled assistance systems that help to coordinate tasks and support decisions are already existent. However, due to the volatile, uncertain, complex, and ambiguous (VUCA) environment, further demands for the assistance systems emerge. Increasing availability of data and decreased costs for computing such as storage and computing capacities for the use of machine learning (ML) indicate promising potential to address the ever-changing conditions securing productivity and thus remain competitive. But the promising potential widely remains untapped. The challenges faced by manufacturing companies especially lie in the identification of attractive application areas and the recognition of the associated learning tasks. Therefore, the aim of this paper is to derive and systematize challenges in the future VUCA-submissive manufacturing landscape to effectively design ML applications. The results provide a target system of objectives in which challenges can be positioned, such as an application navigator for archetypical challenges to be addressed by ML.