Algorithmic management in a work context

TLDR

Machine‑learning advances have enabled automation of work processes, and algorithmic management—initially seen in gig platforms—is now spreading to conventional workplaces. The study examines algorithmic management as a sociotechnical construct to assess its impact on power relations and social structures within organizations. The authors identify three key issues: how algorithmic management reshapes power dynamics, creates new roles and competencies while provoking opposition, and alters knowledge and information flows through technical and organizational opacity. The paper concludes by framing these insights within broader debates on the future of work, accountability, and outlining directions for future research.

Abstract

The rapid development of machine-learning algorithms, which underpin contemporary artificial intelligence systems, has created new opportunities for the automation of work processes and management functions. While algorithmic management has been observed primarily within the platform-mediated gig economy, its transformative reach and consequences are also spreading to more standard work settings. Exploring algorithmic management as a sociotechnical concept, which reflects both technological infrastructures and organizational choices, we discuss how algorithmic management may influence existing power and social structures within organizations. We identify three key issues. First, we explore how algorithmic management shapes pre-existing power dynamics between workers and managers. Second, we discuss how algorithmic management demands new roles and competencies while also fostering oppositional attitudes toward algorithms. Third, we explain how algorithmic management impacts knowledge and information exchange within an organization, unpacking the concept of opacity on both a technical and organizational level. We conclude by situating this piece in broader discussions on the future of work, accountability, and identifying future research steps.