Artificial Intelligence (AI) in making Knowledge Management (KM) more effective in Quality Management Systems.

Abstract

Artificial Intelligence (AI) is the new paradigm to Knowledge Management (KM) in Quality Management Systems (QMS). This paper explores the significance of leveraging AI technologies for improving the effectiveness of KM in ISO 9001 standard quality management systems, which is a critical area of understanding for the systematic integration of AI into existing quality frameworks. Based on Nonaka and Takeuchi's SECI model, the study examines the implementation of AI technologies: Natural Language Processing (NLP), machine learning, knowledge graphs, and predictive analytics in each of the four modes of knowledge conversion: socialization, externalization, combination, and internalization. The study concludes that AI-based KM significantly outperforms traditional knowledge management by analyzing the systematic literature review in accordance with PRISMA guidelines and comparative effectiveness assessment in the six dimensions of KM, which resulted in an improvement of 117% in knowledge measurement and 97% in knowledge sharing. The results confirm that the semantic capabilities of AI are well aligned with the needs of KM processes and that knowledge graphs play a crucial role in integrating knowledge across siloed quality areas, while predictive analytics shift from quality management to proactive intelligence. Despite this, there are significant hurdles, such as the restrictions in data quality, organizational resistance, governance issues, and the "black box" effect of the machine learning algorithms, which may not align with the principles of evidence-based decision-making in the QMS. The proposed AI-SECI-QMS framework offers an integrative model for organizations to use AI to support continuous quality improvement. This research makes a theoretical contribution by building on the SECI model to include the transformative role of AI and provides practical insights for quality managers implementing AI. The results are relevant for practitioners, standard-setting organizations, and researchers, given the increase in the global AI market for quality management and the recently released standards such as ISO 22301:2022, which include AI in its new requirements for digital transformation.