AI-Enabled Knowledge Management Systems for Organizational Intelligence

  • Authors

    • Dr. Zainab Yusuf Cybersecurity & Digital Forensics, San Joaquin Delta College, Canada. Author
    • M. Vinoj Cybersecurity & Digital Forensics, San Joaquin Delta College, Canada. Author

    DOI:

    https://doi.org/10.67228/30713315/IJAIDT-2024PI5G2N7

    Published 03-05-2024

  • Artificial Intelligence, Knowledge Management Systems, Organizational Intelligence, Machine Learning, Natural Language Processing, Data Mining, Semantic Search, Decision Support Systems

    Issue

    Vol. 7 No. 1 (2024)

    Section

    Articles

    How to Cite

    [1]
    Z. Yusuf and M. Vinoj, “AI-Enabled Knowledge Management Systems for Organizational Intelligence”, IJAIDT, vol. 7, no. 1, pp. 01–15, Mar. 2024, doi: 10.67228/30713315/IJAIDT-2024PI5G2N7.

  • Abstract

    The rapid development of Artificial Intelligence (AI) has significantly transformed organizational operations, particularly in Knowledge Management Systems (KMS). In today’s dynamic and data-driven environment, competitive advantage depends on effectively capturing, storing, retrieving, and utilizing knowledge. AI-based KMS (AI-KMS) represent an evolution from traditional systems, shifting from rule-based repositories to intelligent, adaptive, and context-aware platforms that enhance organizational intelligence. Traditional KMS faced limitations in scalability and efficiency due to reliance on structured data and manual input. In contrast, AI-enabled systems leverage machine learning, natural language processing, and data analytics to process unstructured data such as documents, emails, and multimedia, enabling semantic search, personalized recommendations, and predictive insights. These systems also support continuous learning by automatically updating knowledge bases through user interactions. This paper examines the architecture of AI-KMS, focusing on components like knowledge acquisition modules, inference engines, and user interfaces, along with the integration of deep learning and ontologies for improved knowledge representation. It also addresses key challenges including data quality, privacy, scalability, and ethical concerns. A detailed literature review traces the evolution of KMS and AI integration prior to 2018. The proposed methodology uses a hybrid model combining supervised and unsupervised learning for knowledge extraction and classification. Experimental results show improved accuracy in knowledge retrieval and decision-making efficiency compared to traditional systems, supported by quantitative analysis. Overall, AI-KMS enhance organizational intelligence by accelerating decisions, fostering collaboration, and driving innovation. The paper concludes by recommending future research in explainable AI, governance frameworks, and integration with emerging technologies like IoT and blockchain.

  • References

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