Artificial Intelligence, Knowledge Sharing, and Organizational Performance: A Systematic Literature Review

Abstract

Aim/Purpose To systematically review the literature on artificial intelligence (AI) and knowledge sharing in order to identify AI that support organizational knowledge sharing, examine organizational factors that influence their effectiveness, and assess the resulting organizational performance outcomes. Background Organizations increasingly rely on AI to enhance knowledge sharing processes. While prior research has examined AI, knowledge sharing, and organizational performance as separate constructs, there is limited synthesis regarding how AI techniques, organizational conditions, and knowledge sharing practices collectively affect organizational performance. Methodology The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA 2020) guidelines were followed in this study. Articles published between 2015 and 2025 were retrieved from the Scopus and IEEE Xplore databases using predefined search criteria. After screening titles, abstracts, and full texts, 32 peer-reviewed studies that met the inclusion criteria were analyzed. Contribution This study integrates four dimensions of AI-enabled knowledge sharing into a single conceptual framework by synthesizing AI techniques, organizational moderating factors, AI–knowledge-sharing relationship types, and organizational performance outcomes. The study provides a comprehensive overview of current research while identifying important gaps for future investigation. Findings The findings indicate that Generative AI, machine learning, fuzzy logic, natural language processing, recommender systems, support vector machines, semantic AI, and random forests as the principal artificial intelligence techniques. Trust, leadership support, employee engagement, and role clarity are the most frequently reported organizational moderating factors. AI-enabled knowledge sharing is primarily associated with transformational organizational change and is linked to enhanced decision-making, innovation, and productivity. Recommendations for Practitioners This study offers specific and practical insights into the impact of AI-based knowledge sharing systems on business performance. Practitioners and organizational leaders are encouraged to evaluate the integration of AI into knowledge-sharing processes to enhance business outcomes. Recommendations for Researchers Subsequent research should validate and expand upon the relationships identified in this review by employing longitudinal and empirical research designs across a range of industries and organizational contexts. Impact on Society As organizations increasingly integrate AI into knowledge management, understanding how AI enhances knowledge sharing can improve organizational learning, innovation, and decision-making. Future Research Future research should investigate industry-specific AI-enabled knowledge-sharing practices, emerging generative AI applications, governance mechanisms, and longitudinal organizational outcomes associated with AI adoption.