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Machine Learning (ML) systems are applied in organizations to substitute or complement human knowledge work. Although organizations invest heavily in ML, the resulting business benefits often remain unclear. To explain the impact of ML systems, it is necessary to understand how their application changes business processes and affects process performance. In our exploratory multiple case study, we analyze the application of multiple productive ML systems in one organization to (1.) describe how activity composition, allocation, and sequence change in ML-supported processes; (2.) distinguish how the applied ML system type and task characteristics influence process changes; and (3.) explain how process efficiency and quality are affected. As a result, we develop three preliminary change patterns: Lift & Shift, Divide & Conquer, and Expand & Intensify. Our research aims to contribute to the future of work and IS value literature by connecting the emerging knowledge on ML systems to their process-level implications.



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