Autonomous Enterprise Platforms Using Cognitive Computing Techniques

  • Authors

    • David Thompson Vice President – Operations, Global Solutions LLC, USA. Author
    • Jennifer Clark Senior Data Scientist, Innovate Analytics, USA. Author

    DOI:

    https://doi.org/10.67228/30713315/IJAIDT-2025PI5K2N

    Published 02-04-2025

  • Cognitive Computing, Autonomous Enterprise Platforms, Artificial Intelligence, Machine Learning, Enterprise Automation, Knowledge Systems, Intelligent Decision Making, Digital Transformation, Autonomous Agents, Business Intelligence

    Issue

    Section

    Articles

    How to Cite

    [1]
    D. Thompson and J. Clark, “Autonomous Enterprise Platforms Using Cognitive Computing Techniques”, IJAIDT, vol. 8, no. 1, pp. 01–17, Feb. 2025, doi: 10.67228/30713315/IJAIDT-2025PI5K2N.
  • Abstract

    Digital transformation has increased enterprise complexity, creating a need for intelligent systems that can autonomously learn, adapt, and make decisions. Cognitive computing integrates AI, machine learning, natural language processing, and reasoning techniques to develop Autonomous Enterprise Platforms (AEPs) that optimize business operations with minimal human intervention. The proposed framework combines data acquisition, cognitive analytics, machine learning, knowledge management, and autonomous execution layers, supported by reinforcement learning, deep learning, and NLP. Experimental results show improvements in decision accuracy, operational efficiency, automation, adaptability, and customer satisfaction compared to traditional systems. The study also addresses challenges such as data governance, security, scalability, and AI ethics, while highlighting future technologies including generative AI, explainable AI, federated learning, digital twins, and autonomous agents. The findings demonstrate that cognitive computing is a key enabler of intelligent, adaptive, and autonomous enterprise ecosystems.

  • References

    [1] J. E. Kelly III and S. Hamm, Smart Machines: IBM's Watson and the Era of Cognitive Computing. New York, NY, USA: Columbia University Press, 2013.

    [2] D. Ferrucci, “Introduction to ‘This is Watson’,” IBM Journal of Research and Development, vol. 56, no. 3.4, pp. 1–15, 2012.

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

    [4] 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

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

    [6] I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning. Cambridge, MA, USA: MIT Press, 2016.

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

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

    [9] J. Schmidhuber, “Deep Learning in Neural Networks: An Overview,” Neural Networks, vol. 61, pp. 85–117, 2015.

    [10] T. Gruber, “A Translation Approach to Portable Ontology Specifications,” Knowledge Acquisition, vol. 5, no. 2, pp. 199–220, 1993.

    [11] A. Hogan, E. Blomqvist, M. Cochez, C. D’Amato, G. D. Melo, C. Gutierrez, S. Kirrane, J. E. Labra Gayo, R. Navigli, S. Neumaier, A. Polleres, R. Rashid, A. Rula, and A. Zimmermann, “Knowledge Graphs,” ACM Computing Surveys, vol. 54, no. 4, pp. 1–37, 2021.

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

    [13] F. Baader, D. Calvanese, D. McGuinness, D. Nardi, and P. Patel-Schneider, The Description Logic Handbook: Theory, Implementation and Applications, 2nd ed. Cambridge, U.K.: Cambridge University Press, 2010.

    [14] D. Kahneman, O. Sibony, and C. Sunstein, Noise: A Flaw in Human Judgment. New York, NY, USA: Little, Brown Spark, 2021.

    [15] 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

    [16] D. Gunning and D. Aha, “DARPA’s Explainable Artificial Intelligence (XAI) Program,” AI Magazine, vol. 40, no. 2, pp. 44–58, 2019.

    [17] B. Marr, Artificial Intelligence in Practice: How 50 Successful Companies Used AI and Machine Learning to Solve Problems. Hoboken, NJ, USA: Wiley, 2019.

    [18] K. D. Sharma, A. K. Singh, and R. Kumar, “Cognitive Computing in Enterprise Systems: A Survey of Architectures, Applications, and Challenges,” IEEE Access, vol. 10, pp. 85432–85458, 2022.

  • Downloads