Algorithmic Management and the Future of Work in Organisations: A Theoretical Account of Control, Autonomy and Skill Formation Under AI-Mediated Supervision
DOI:
https://doi.org/10.68854/4s419g75Keywords:
algorithmic management, future of work, human resource management, employee autonomy, organisational control, skill formation, cognitive offloading, platform workAbstract
Algorithmic management, the use of software and artificial intelligence (AI) to assign, monitor, evaluate and discipline work, has spread from platform labour into conventional organisations through productivity analytics, AI-assisted scheduling, automated performance scoring and generative AI assistants. Existing research treats algorithmic management largely as a single construct, concentrates on autonomy and well-being in the present, and pays little attention either to the long-run formation of expertise or to African labour markets. This study develops a theoretical account of what changes when supervision is mediated by algorithms. It adopts a conceptual, theory-building design based on an integrative review of thirty peer-reviewed journal articles published between 2023 and 2026, identified through keyword searches and citation chaining, screened against explicit inclusion criteria, verified against publisher records and checked for retractions and corrections. The articles were coded deductively against organisational control theory and inductively for outcomes and moderating conditions, drawing also on self-determination theory and theories of expertise and cognitive offloading. The analysis distinguishes three supervisory functions that algorithms take over (direction, evaluation and discipline) and shows that each affects employee autonomy, trust in managers and skill formation differently. It identifies a skill-formation paradox: the step-by-step algorithmic guidance that raises short-run productivity, particularly for novice workers, suppresses the effortful practice through which tacit expertise is built, so that organisations may trade future capability for present output. Eight testable propositions are derived, together with measures for each construct, and labour-market slack, informality and weak enforcement of data protection are specified as conditions that intensify these effects in African economies. The paper recommends that organisations decide separately whether to automate each function and avoid automating discipline by default, design guidance that fades as competence grows, explain performance scores to workers, and keep a named human responsible for sanctions with a route of appeal. Regulators are urged to translate data-protection provisions into enforceable workplace rights to explanation and human review.
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