Integrating generative AI into knowledge management: A mixed-methods framework with machine learning–assisted conceptual coherence illustration

Abstract

Generative artificial intelligence (AI) is increasingly transforming knowledge management (KM) in knowledge-based organizations (KBOs). This study examines the integration of generative AI into KM within the United Arab Emirates (UAE), focusing on the technological, human, organizational, cultural, and regulatory factors influencing adoption. The study aims to identify the key enablers and barriers affecting generative AI adoption in KM and to examine how UAE-specific factors, including national AI initiatives, public–private partnerships, workforce diversity, and SME characteristics, shape adoption pathways. A mixed-methods approach combined thematic analysis of government strategies, organizational reports, industry publications, and peer-reviewed literature with ML-assisted conceptual coherence assessment. The procedure examined framework relationships without empirical validation, causal testing, or prediction. The framework was informed by Knowledge-Based View, Socio-Technical Systems, and Technology Acceptance Models. Effective AI-enabled KM depends on technological capabilities, human expertise, organizational culture, leadership, and regulatory conditions. UAE-specific factors, including the national AI agenda, institutional environment, workforce characteristics, and organizational differences, influence adoption, indicating that technological readiness alone is insufficient without effective socio-technical alignment. The study concludes that generative AI-enabled KM requires a balanced socio-technical approach integrating technology, human expertise, organizational practices, and governance. The proposed framework supports responsible AI adoption in UAE organizations and contributes to understanding AI-enabled KM in emerging economies.