AI-Driven Digital Assistants for Enterprise Knowledge Management

  • Authors

    • Dr. Rebecca Green Associate Professor, Arizona State University, USA. Author

    DOI:

    https://doi.org/10.67228/30713315/IJAIDT-2024PII1G4U

    Published 09-03-2024

  • Artificial Intelligence, Knowledge Management Systems, Digital Assistants, Natural Language Processing, Enterprise Systems, Knowledge Graphs, Machine Learning, Conversational AI

    Issue

    Vol. 7 No. 2 (2024)

    Section

    Articles

    How to Cite

    [1]
    R. Green, “AI-Driven Digital Assistants for Enterprise Knowledge Management”, IJAIDT, vol. 7, no. 2, pp. 01–16, Sep. 2024, doi: 10.67228/30713315/IJAIDT-2024PII1G4U.

  • Abstract

    Enterprise Knowledge Management (EKM) plays a vital role in leveraging organizational knowledge, improving decision-making, and maintaining competitive advantage. Traditional knowledge management systems struggle with scalability, real-time adaptability, and contextual understanding. AI-driven digital assistants address these limitations by using technologies like Natural Language Processing (NLP), deep learning, and knowledge graphs to enable intelligent automation and semantic understanding. These assistants support conversational interactions, making knowledge access and contribution more intuitive. The study proposes a modular architecture with components such as data ingestion, semantic processing, knowledge repositories, and user interfaces, enhanced by reinforcement learning for continuous improvement. Hybrid models combining rule-based and learning-based approaches improve reliability and interpretability. Findings show that AI-based assistants enhance knowledge retrieval accuracy, reduce response time, and increase user satisfaction. They are widely applicable in areas like customer support, internal knowledge discovery, and decision support. Overall, AI-powered digital assistants improve efficiency and promote knowledge democratization, with future research focusing on explainability, ethical AI use, and integration of multimodal data.

  • References

    [1] T. H. Davenport and L. Prusak, Working Knowledge: How Organizations Manage What They Know. Boston, MA, USA: Harvard Business School Press, 1998.

    [2] I. Nonaka and H. Takeuchi, The Knowledge-Creating Company. New York, NY, USA: Oxford University Press, 1995.

    [3] A. Tiwana, The Knowledge Management Toolkit: Practical Techniques for Building a Knowledge Management System. Upper Saddle River, NJ, USA: Prentice Hall, 2002.

    [4] M. Alavi and D. E. Leidner, “Review: Knowledge management and knowledge management systems: Conceptual foundations and research issues,” MIS Quarterly, vol. 25, no. 1, pp. 107–136, 2001.

    [5] R. Schank and R. Abelson, Scripts, Plans, Goals, and Understanding: An Inquiry into Human Knowledge Structures. Hillsdale, NJ, USA: Lawrence Erlbaum, 1977.

    [6] E. Feigenbaum, “Expert systems in the 1980s,” AI Magazine, vol. 1, no. 1, pp. 9–18, 1980.

    [7] T. Mitchell, Machine Learning. New York, NY, USA: McGraw-Hill, 1997.

    [8] C. D. Manning and H. Schütze, Foundations of Statistical Natural Language Processing. Cambridge, MA, USA: MIT Press, 1999.

    [9] Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature, vol. 521, no. 7553, pp. 436–444, 2015.

    [10] [10] D. Jurafsky and J. H. Martin, Speech and Language Processing, 3rd ed. Upper Saddle River, NJ, USA: Prentice Hall, 2023.

    [11] A. McTear, Z. Callejas, and D. Griol, The Conversational Interface: Talking to Smart Devices. Cham, Switzerland: Springer, 2016.

    [12] S. Young et al., “The hidden information state model: A practical framework for POMDP-based spoken dialogue management,” Computer Speech & Language, vol. 24, no. 2, pp. 150–174, 2010.

    [13] J. Hirschberg and C. D. Manning, “Advances in natural language processing,” Science, vol. 349, no. 6245, pp. 261–266, 2015.

    [14] G. Marcus, “The next decade in AI: Four steps towards robust artificial intelligence,” AI Magazine, vol. 41, no. 2, pp. 20–31, 2020.

    [15] B. Russell and P. Norvig, Artificial Intelligence: A Modern Approach, 4th ed. Upper Saddle River, NJ, USA: Pearson, 2021.

    [16] Gajula, S. (2023). A review of anomaly identification in finance frauds using machine learning system. International Journal of Current Engineering and Technology, 13(6), 568–575. https://ijcet.evegenis.org/index.php/ijcet/article/view/820

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