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The rapid integration of artificial intelligence and algorithmic systems into core organizational processes has transformed decision-making, yet it has simultaneously exposed critical deficiencies in traditional corporate governance mechanisms. Algorithmically mediated organizations now confront unique challenges in maintaining accountability for opaque automated decisions, ensuring transparency in high-stakes outcomes, and exercising strategic oversight amid rapid technological evolution. This conceptual manuscript synthesizes contemporary scholarship to map these tensions and introduces the SOAR Framework—Strategic Oversight for Algorithmic Responsibility—as a novel multi-layered governance architecture. Developed through a systematic review of peer-reviewed sources, the framework comprises six interdependent layers: the algorithmic decision core, transparency and explainability systems, accountability assignment protocols, strategic oversight bodies, risk and compliance shields, and adaptive feedback loops. By embedding human-AI hybrid controls and continuous audit mechanisms, SOAR enables organizations to align algorithmic mediation with ethical, legal, and strategic imperatives. The model addresses pressing gaps identified in existing literature, including the diffusion of responsibility in AI-driven environments and the insufficiency of conventional board-level oversight. Contributions to digital business and management studies include a practical blueprint for implementation and a conceptual foundation for future empirical testing. Ultimately, the SOAR Framework equips corporate leaders to govern algorithmic systems responsibly while preserving competitive advantage in digitally transformed enterprises.

Artificial intelligence is becoming increasingly embedded in business management, influencing decisions in strategy, operations, marketing, human resources, finance, and organizational control. Its managerial significance no longer lies only in its capacity to process information faster than humans, but in its growing ability to recommend, rank, predict, allocate, and sometimes decide. This shift raises important questions about how organizations should govern AI when it begins to affect managerial judgment itself. The central problem addressed in this review is that management research has often treated AI as a performance-enhancing tool while giving less sustained attention to its governance consequences. Three tensions remain particularly fragmented: the delegation of decision authority to algorithmic systems, the maintenance of organizational accountability in distributed human-machine arrangements, and the conditions under which managers trust or distrust AI-assisted decisions. These issues are analytically distinct but practically interdependent. The objective of this critical review is to synthesize literature on AI in business management through the integrated lenses of authority, accountability, and trust. Rather than presenting AI adoption as an inevitable route to efficiency, the review interrogates the organizational assumptions behind AI-enabled decision-making. It asks how AI changes managerial discretion, responsibility, oversight, and confidence in organizational decisions. The review concludes that AI governance in management must move beyond technical performance and address the institutional conditions under which AI-assisted decisions are authorized, explained, contested, and trusted. Authority, accountability, and trust should not be treated as separate implementation concerns but as a connected governance triad. Future management research should therefore conceptualize AI not merely as a tool, but as a socio-technical actor that reshapes managerial responsibility and organizational control.

Algorithmic management refers to the use of algorithmic systems to direct, monitor, evaluate, reward, and discipline workers. It has become especially visible in platform-mediated work, where task allocation, pricing, performance scoring, and access to work are increasingly organized through digital systems. Its influence, however, is no longer confined to gig work, because similar logics of monitoring, ranking, prediction, and automated evaluation are spreading into conventional organizations. The central problem addressed in this article is that management theory has not yet fully treated algorithmic management as a distinct managerial control logic. Much existing work explains algorithms as technological tools that intensify established forms of supervision, measurement, or coordination. This interpretation is useful but incomplete because it underestimates how algorithmic systems alter the basic architecture of control itself. The objective of this article is to develop a theory-driven account of algorithmic management as a new control logic. The article argues that algorithmic management reconfigures three core dimensions of work: employee autonomy, performance measurement, and accountability. It therefore requires a theoretical model that explains not only what algorithms do to workers, but how algorithmic systems reorganize the relations among workers, managers, data, and responsibility. The article distinguishes algorithmic management from traditional managerial control, explains the autonomy paradox created by algorithmic systems, analyses the density of digital performance measurement, and theorises the displacement of accountability in algorithmically managed work. It proposes a model linking algorithmic control intensity, autonomy suppression, performance measurement density, and accountability displacement. The central conclusion is that algorithmic management is not a neutral managerial technology but a distinctive control logic that demands new theoretical and practical responses.