From Knowledge Workers to Knowledge Agents: Redefining Organizational Learning

Abstract

The rise of artificial intelligence, automation, and digital collaboration tools is reshaping how organizations create, share, and apply knowledge. Traditional models of organizational learning have centered on “knowledge workers” as human actors who process information and generate expertise. However, emerging technologies are transforming these workers into “knowledge agents” — hybrid human–AI actors capable of autonomous learning, decision support, and continuous knowledge recombination. This paper reconceptualizes organizational learning by examining the shift from individual-based knowledge production to distributed, agent-based knowledge ecosystems. Drawing on contemporary literature in knowledge management, socio-technical systems, and AI-enabled collaboration, we propose a framework that integrates human cognition, algorithmic intelligence, and networked learning infrastructures. The study explores how knowledge agents enhance adaptive capacity, accelerate innovation cycles, and reshape leadership, governance, and ethical accountability. By redefining organizational learning as a dynamic interaction between human and artificial agents, this research contributes a forward-looking model for sustainable knowledge-driven organizations in the digital era.

Keywords: Organizational learning; Knowledge workers; Knowledge agents; Artificial intelligence; Digital transformation; Knowledge management; Socio-technical systems