Knowledge Management in the Age of Machine Learning: A Review of Intelligent Approaches
Keywords:
Knowledge management, machine learning, artificial intelligence, digital transformation, organizational learning, data governanceAbstract
Objective: This study aimed to systematically review and analyze emerging intelligent approaches in knowledge management during the age of machine learning, identifying transformative models in knowledge creation, storage, and transfer within organizations.
Methods and Materials: This qualitative systematic review analyzed 12 peer-reviewed articles published between 2015 and 2025, retrieved from Scopus, Web of Science, and ScienceDirect databases. Data were analyzed using thematic content analysis with NVivo version 14 through open, axial, and selective coding until theoretical saturation was reached.
Findings: The analysis revealed three dominant themes in the literature: digital transformation in knowledge infrastructures, machine-based organizational learning, and foresight and policy development in intelligent knowledge management. Machine learning enables automation of knowledge processes through big data analytics, pattern recognition, and predictive modeling, enhancing organizational learning capabilities. However, issues such as data security, algorithmic bias, and limited data literacy remain key challenges in implementation.
Conclusion: Knowledge management in the age of machine learning is evolving toward intelligent, adaptive, and human–machine collaborative models. Achieving success in this transformation requires synergy among technology, organizational culture, data governance, and AI ethics. This review provides an integrated framework for understanding and guiding the strategic development of intelligent knowledge systems in future learning organizations.
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