Emerged from platform organizations, algorithmic management (AM) refers to a data-driven approach in which intelligent algorithms are employed to automate managerial functions. Given its organizational benefits (e.g., efficiency gains), AM is also increasingly used in other work contexts, including traditional organizations (with permanent employees). Against this backdrop, our study investigates what AM mechanisms are used in different organizational work contexts and to what extent, and why, these mechanisms translate to other contexts. We do so by systematically analyzing and synthesizing knowledge from 45 studies. Our results point to seven usage patterns regarding the contextual translatability of AM mechanisms. For example, while we find that some mechanisms are used across contexts but with differing intentions, we also identify several context-specific AM mechanisms that are not (easily) translatable. We conclude by discussing factors that help explain the identified usage patterns (e.g., worker status and skill level) and promising avenues for future research.