TRANSFORMATION OF MANAGERIAL ROLES UNDER THE INTEGRATION OF ARTIFICIAL INTELLIGENCE TECHNOLOGIES INTO MANAGERIAL ACTIVITY
TL;DR
The article addresses the conceptual problem of how managerial work should be understood when analytical and supervisory activities are partly transferred to intelligent systems and considers the competencies required for interpreting algorithmic outputs, coordinating human and digital actors, managing organizational change, and preventing the formal displacement of accountability.
Abstract
The accelerating integration of artificial intelligence into business organizations makes the transformation of managerial roles an economically significant research issue. AI-based systems increasingly participate in information processing, forecasting, resource allocation, employee monitoring, and decision support, thereby changing the conventional distribution of tasks and authority within management structures. These developments affect organizational productivity, transaction and coordination costs, the quality and speed of managerial decisions, workforce autonomy, and the allocation of responsibility for economically consequential outcomes. The article addresses the conceptual problem of how managerial work should be understood when analytical and supervisory activities are partly transferred to intelligent systems. Attention is devoted to the changing content of informational, interpersonal, and decision-making roles, as well as to the relationship between technological automation and the augmentation of human judgement. The discussion covers the boundaries of human–AI delegation, the organizational design of hybrid work, and the conditions under which managerial intervention must remain decisive. Particular emphasis is placed on differences among strategic, middle, and operational management, since the economic value and risks of AI adoption depend on the level at which decision authority is exercised. The article also considers the competencies required for interpreting algorithmic outputs, coordinating human and digital actors, managing organizational change, and preventing the formal displacement of accountability. Ethical and governance issues are examined in connection with transparency, explainability, fairness, data confidentiality, employee interests, and opportunities to review or contest automated recommendations. The research perspective treats AI integration as a contingent organizational process shaped by task characteristics, technological properties, decision risk, reversibility, and the potential impact on stakeholders.