Role Remolding, Boundary Evolution, Behavioral Adaptation and Heterogeneous Judgment in Human-Machine Collaboration in the AI Era
Liyin Yang
(Business School, Macau University of Science and Technology, Macau, 999078)
Abstract ： The rapid development of artificial intelligence has profoundly reshaped human-machine collaboration within organizations. The traditional "substitution-augmentation" dual framework fails to explain a practical paradox: sophisticated creative work can be imitated by algorithms, while low-threshold emotional labor cannot be replaced by machines. This raises two research questions concerning the essence and dynamic evolution of human-machine collaboration boundaries. From the perspective of organizational behavior and integrating multidisciplinary theories, this paper constructs the theoretical framework of the algorithmizability boundary. The research indicates that the boundary is jointly determined by three dimensions, namely rule clarity, data completeness and value embeddedness, and evolves continuously within the closed loop of technology, task, subject and institution. Based on case analyses of four major industries, this paper verifies the effectiveness of the four-dimensional co-evolution model. The study finds that the core value of human beings in the intelligent era lies in non-standardized judgment integrating technical understanding, experience accumulation and empathy, whose essence is the creativity to produce original solutions in complex scenarios. This research provides comprehensive theoretical and practical guidance for organizational strategy, human resource management and leadership transformation, facilitating the shift of management paradigms from efficiency optimization to creativity stimulation.
Keywords: Human-AI Collaboration; organizational Behavior; Algorithmizability Boundary;Non-Standardized Judgment; Co-Evolution
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