AI-enabled knowledge sharing and learning: redesigning roles and processes

Abstract

Purpose

This paper aims to investigate the role of AI in facilitating knowledge sharing and learning in organizations and the redesign of AI-enabled knowledge workers’ roles and processes.

Design/methodology/approach

This paper develops a framework for analyzing AI’s role in different knowledge management activities, explores the impact of AI in transforming knowledge workers’ roles and processes in knowledge sharing and learning and presents recommendations for tailored AI-enabled knowledge management systems for modern knowledge worker environments.

Findings

The authors synthesize the elements from different parts of the relevant literature and develop a unified framework consisting of three dimensions of AI systems, three knowledge management (KM) activities and two types of AI–human interactions. Based on this framework, the authors summarize the primary use cases supported by AI-enabled knowledge management systems (KMS) and compare them with the traditional KMS use cases. The authors find that a single type of AI system is insufficient to support the increasingly complex nature of knowledge workers’ activities, manifested in three dimensions – process, engagement and content; a tailored AI system should be developed to support knowledge workers in their unique roles and processes.

Originality/value

With the growing interest in AI and its applications to KM, this research provides managerial insights for practitioners to effectively adopt AI in managing knowledge assets in organizations.

Citation

Sundaresan, S. and Zhang, Z. (2022), "AI-enabled knowledge sharing and learning: redesigning roles and processes", International Journal of Organizational Analysis, Vol. 30 No. 4, pp. 983-999. https://doi.org/10.1108/IJOA-12-2020-2558