Managing the Risk of Organizational Knowledge Loss in the Context of Artificial Intelligence Use
Purpose: This study identifies the mechanisms through which artificial intelligence influences the risk of organizational knowledge loss and develops a conceptual model balancing AI-enabled organizational efficiency with long-term knowledge preservation. Design/Methodology/Approach: A qualitative multiple-case study based on secondary data was conducted. Five AI application contexts were analysed using cross-case analysis to identify recurring knowledge management mechanisms. Findings: AI improves knowledge accessibility and operational efficiency while transforming knowledge creation, transfer and employee competencies. Knowledge loss results primarily from excessive automation, weakened tacit knowledge transfer and insufficient competence development rather than AI itself. Practical Implications: The proposed model supports managers in combining AI implementation with governance, mentoring, competence development and knowledge retention practices. Originality/Value: The study integrates organizational efficiency, employee competencies and organizational knowledge potential into a single conceptual framework supporting organizational knowledge loss risk management.