AI-Enabled Organizational Learning Systems for Continuous Innovation

  • Authors

    • Narendra Karmarkar Mathematician and Computer Scientist, Tata Institute of Fundamental Research, India. Author

    DOI:

    https://doi.org/10.67228/30713315/IJAIDT-2025PII2D5Y

    Published 08-04-2025

  • Artificial Intelligence, Organizational Learning Systems, Continuous Innovation, Knowledge Management, Machine Learning, Cognitive Computing, Reinforcement Learning, Knowledge Graphs, Intelligent Enterprises, Predictive Analytics

    Issue

    Vol. 8 No. 2 (2025)

    Section

    Articles

    How to Cite

    [1]
    N. Karmarkar, “AI-Enabled Organizational Learning Systems for Continuous Innovation”, IJAIDT, vol. 8, no. 2, pp. 01–18, Aug. 2025, doi: 10.67228/30713315/IJAIDT-2025PII2D5Y.

  • Abstract

    This study presents an AI-based Organizational Learning System (AI-OLS) that enhances continuous learning, knowledge management, and innovation within enterprises. By integrating machine learning, natural language processing, predictive analytics, and adaptive recommendation mechanisms, the framework enables intelligent knowledge discovery, personalized learning, and data-driven decision-making. The proposed system improves knowledge sharing, employee engagement, organizational agility, and innovation performance while reducing learning and innovation cycle times. Experimental results demonstrate superior effectiveness compared to traditional learning management systems, highlighting AI’s potential to transform organizations into adaptive, knowledge-driven, and innovation-focused enterprises. The framework offers practical guidance for implementing intelligent learning ecosystems that support long-term competitive advantage in the digital era.

  • References

    [1] T. H. Davenport and R. Ronanki, “Artificial Intelligence for the Real World,” Harvard Business Review, vol. 96, no. 1, pp. 108–116, 2018.

    [2] S. J. Russell and P. Norvig, Artificial Intelligence: A Modern Approach, 4th ed. Hoboken, NJ, USA: Pearson, 2021.

    [3] D. L. Argote and P. Ingram, “Knowledge Transfer: A Basis for Competitive Advantage in Firms,” Organizational Behavior and Human Decision Processes, vol. 82, no. 1, pp. 150–169, 2000.

    [4] I. Nonaka and H. Takeuchi, The Knowledge-Creating Company: How Japanese Companies Create the Dynamics of Innovation. New York, NY, USA: Oxford University Press, 1995.

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

    [6] Gajula, S. (2024). Cybersecurity risk prediction using graph neural networks. Journal of Information Systems Engineering and Management.

    [7] D. E. O’Leary, “Enterprise Knowledge Management,” Computer, vol. 31, no. 3, pp. 54–61, 1998.

    [8] D. J. Teece, G. Pisano, and A. Shuen, “Dynamic Capabilities and Strategic Management,” Strategic Management Journal, vol. 18, no. 7, pp. 509–533, 1997.

    [9] IBM Corporation, “Cognitive Computing and the Future of Knowledge Work,” IBM Journal of Research and Development, vol. 60, no. 2–3, pp. 1–12, 2016.

    [10] T. Gruber, “Knowledge Graphs and Semantic Technologies in Artificial Intelligence,” AI Magazine, vol. 41, no. 3, pp. 26–32, 2020.

    [11] M. A. Wiering and M. Van Otterlo, Reinforcement Learning: State-of-the-Art. Berlin, Germany: Springer, 2012.

    [12] E. Brynjolfsson and A. McAfee, The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies. New York, NY, USA: W. W. Norton & Company, 2014.

    [13] Gajula, S. (2024). Adaptive zero trust architecture for securing financial microservices. Computer Fraud & Security, 2024(12), 643–655. https://doi.org/10.52710/cfs.845

    [14] K. M. Eisenhardt and J. A. Martin, “Dynamic Capabilities: What Are They?,” Strategic Management Journal, vol. 21, no. 10–11, pp. 1105–1121, 2000.

    [15] 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

    [16] M. M. Crossan, H. W. Lane, and R. E. White, “An Organizational Learning Framework: From Intuition to Institution,” Academy of Management Review, vol. 24, no. 3, pp. 522–537, 1999.

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

    [18] G. Paré, M. Trudel, M. Jaana, and S. Kitsiou, “Synthesizing Information Systems Knowledge: A Typology of Literature Reviews,” Information & Management, vol. 52, no. 2, pp. 183–199, 2015.

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