Machine learning (ML) algorithms are rapidly advancing research across many fields of social science, including economics, marketing, and management information systems. Management and organization studies are yet to (fully) leverage these methods. The authors argue that ML algorithms can benefit both qualitative researchers engaged in a small number of cases and quantitative researchers faced with a large number of observations. Such benefits arise from the ability of MLtechniques to facilitate “algorithmic induction”—a form of inductive inference that yields identical (or highly similar) conclusions when applied by different observers to the same data. Algorithmic induction is valuable for researchers interested in theorizing through interpretative and comparative case analysis as well as generating hypotheses from large sets of quantitative data (followed by traditional testing approaches). The authors introduce variants of ML algorithms to management and organization researchers, develop the concept of algorithmic induction, and discuss its general potential for inductive theorizing in the field.