AI-Based Decision Intelligence Platforms for Digital Business Transformation
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DOI:
https://doi.org/10.67228/30713315/IJAIDT-2025PII9N3MPublished 10-03-2025
Artificial Intelligence, Decision Intelligence, Digital Transformation, Business Analytics, Machine Learning, Predictive Analytics, Intelligent Decision Support Systems, Enterprise Intelligence Issue
Section
ArticlesHow to Cite
[1]V. Glushkov and V. Glushkov, “AI-Based Decision Intelligence Platforms for Digital Business Transformation”, IJAIDT, vol. 8, no. 2, pp. 01–16, Oct. 2025, doi: 10.67228/30713315/IJAIDT-2025PII9N3M.Abstract
AI-Based Decision Intelligence (DI) Platforms are transforming digital business operations by combining artificial intelligence, machine learning, predictive analytics, natural language processing, and business analytics to support intelligent decision-making. Unlike traditional Business Intelligence systems that mainly provide historical insights, DI platforms deliver predictive and prescriptive recommendations that improve decision accuracy, operational efficiency, business agility, and customer satisfaction. The proposed framework integrates data collection, intelligent analytics, predictive modeling, decision optimization, and continuous learning within a scalable cloud-based ecosystem. The study demonstrates that AI-driven decision intelligence significantly enhances organizational performance and supports successful digital transformation across various industries. Furthermore, emerging technologies such as Explainable AI, federated learning, autonomous decision systems, and ethical AI governance will further strengthen enterprise decision ecosystems. Overall, AI-Based Decision Intelligence Platforms serve as a critical enabler for sustainable growth, competitive advantage, and intelligent enterprise transformation in the digital era.
References
[1] S. Russell and P. Norvig, Artificial Intelligence: A Modern Approach, 4th ed. Hoboken, NJ, USA: Pearson, 2021.
[2] T. H. Davenport and R. Ronanki, “Artificial Intelligence for the Real World,” Harvard Business Review, vol. 96, no. 1, pp. 108–116, 2018.
[3] 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.
[4] D. Kahneman, O. Sibony, and C. R. Sunstein, Noise: A Flaw in Human Judgment. New York, NY, USA: Little, Brown Spark, 2021.
[5] Gajula, S. (2024). Cybersecurity risk prediction using graph neural networks. Journal of Information Systems Engineering and Management.
[6] M. T. Ribeiro, S. Singh, and C. Guestrin, “Why Should I Trust You? Explaining the Predictions of Any Classifier,” in Proc. ACM SIGKDD Int. Conf. Knowledge Discovery and Data Mining (KDD), San Francisco, CA, USA, 2016, pp. 1135–1144.
[7] A. Esteva et al., “A Guide to Deep Learning in Healthcare,” Nature Medicine, vol. 25, no. 1, pp. 24–29, 2019.
[8] T. M. Mitchell, Machine Learning. New York, NY, USA: McGraw-Hill, 1997.
[9] Y. LeCun, Y. Bengio, and G. Hinton, “Deep Learning,” Nature, vol. 521, no. 7553, pp. 436–444, 2015.
[10] I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning. Cambridge, MA, USA: MIT Press, 2016.
[11] 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
[12] T. H. Davenport and R. Bean, “Big Data and AI Executive Survey 2023,” MIT Sloan Management Review, pp. 1–18, 2023.
[13] J. Pearl, The Book of Why: The New Science of Cause and Effect. New York, NY, USA: Basic Books, 2018.
[14] M. Janssen, H. van der Voort, and A. Wahyudi, “Factors Influencing Big Data Decision-Making Quality,” Journal of Business Research, vol. 70, pp. 338–345, 2017.
[15] K. Schwab, The Fourth Industrial Revolution. Geneva, Switzerland: World Economic Forum, 2016.
[16] B. Marr, Artificial Intelligence in Practice: How 50 Successful Companies Used AI and Machine Learning to Solve Problems. Hoboken, NJ, USA: Wiley, 2019.
[17] 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
[18] A. Rai, “Explainable AI: From Black Box to Glass Box,” Journal of the Academy of Marketing Science, vol. 48, no. 1, pp. 137–141, 2020.
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How to Cite
[1]V. Glushkov and V. Glushkov, “AI-Based Decision Intelligence Platforms for Digital Business Transformation”, IJAIDT, vol. 8, no. 2, pp. 01–16, Oct. 2025, doi: 10.67228/30713315/IJAIDT-2025PII9N3M.