Vol: 14, Issue: 1 2024

Page: 94-100

AI Driven Decision Support Systems for Business Operations

Parth Gupta

Received Date: 2024-01-08

Accepted Date: 2024-02-28

Published Date: 2024-03-21

http://doi.org/10.37648/ijtbm.v14i01.012

In the era of digital transformation, businesses are increasingly relying on intelligent systems to enhance operational efficiency and strategic decision-making. Artificial Intelligence-driven Decision Support Systems (AI DSS) have emerged as a pivotal innovation, offering advanced capabilities such as predictive analytics, real-time optimization, and adaptive learning. This paper presents a comprehensive study on the development, implementation, and impact of AI DSS across various business functions. It explores the integration of machine learning (ML), deep learning (DL), natural language processing (NLP), and explainable AI (XAI) in decision support environments, emphasizing how these technologies enable data-driven and agile decision-making.

References

• Arrieta, A. B., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., ... & Herrera, F. (2020). Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. Information Fusion, 58, 82–115. https://doi.org/10.1016/j.inffus.2019.12.012
• Business Insider. (2025). How AI & robotics help prevent factory breakdowns. https://www.businessinsider.com
• Choi, T. M., Wallace, S. W., & Wang, Y. (2018). Big data analytics in operations management. Production and Operations Management, 27(10), 1868–1889. https://doi.org/10.1111/poms.12838
• Cummins, L. et al. (2024). Explainable Predictive Maintenance: A Survey… arXiv. https://arxiv.org
• Davenport, T. H., & Ronanki, R. (2018). Artificial intelligence for the real world. Harvard Business Review, 96(1), 108–116.
• Duan, Y., Edwards, J. S., & Dwivedi, Y. K. (2019). Artificial intelligence for decision making in the era of Big Data – evolution, challenges and research agenda. International Journal of Information Management, 48, 63– 71. https://doi.org/10.1016/j.ijinfomgt.2019.01.021
• Frontiers. (2024). Examining integration of AI in SCM. https://doi.org/10.3389/frai.2024.1477044
• Ghosh, R., & Ghosh, A. (2021). Machine learning in credit risk modeling. Decision Support Systems, 142, 113465. https://doi.org/10.1016/j.dss.2021.113465
• Hassoun, F. et al. (2024). AI Based Decision Support Systems in Industry 4.0: A Review. ScienceDirect, S2949948824000374.
• MDPI. (2022). Explainable AI based Decision Support Systems. Electronics, 13(14), 2842. https://www.mdpi.com
• RePEc. (2022). AI contributions in operations research via DSS. Annals of Operations Research, 308. https://doi.org/10.1007/s10479-020-03856-6
• Ryll, L., Seidenschwarz, F., & Dauth, T. (2022). Artificial intelligence in financial decision making: The impact of ethical aspects on consumer adoption. Journal of Business Ethics, 180, 119– 139. https://doi.org/10.1007/s10551-021-04891-5
• Seddon, P. B., Constantinidis, D., Tamm, T., & Dod, H. (2017). How does business analytics contribute to business value? Information Systems Journal, 27(3), 237–269. https://doi.org/10.1111/isj.12101
• Shin, D. (2021). The effects of explainability and causability on human–AI interaction: Towards trustable AI. Journal of Behavioral and Experimental Finance, 29, 100454. https://doi.org/10.1016/j.jbef.2020.100454
• Shollo, A., & Galliers, R. D. (2016). Towards an understanding of the role of business intelligence systems in organisational knowing. Information Systems Journal, 26(4), 339–367. https://doi.org/10.1111/isj.12071
• Teixeira, A. R., Ferreira, J. V., & Ramos, A. L. (2025). Intelligent Supply Chain Management: A Systematic Literature Review on AI Contributions. Information, 16(5), 399. https://doi.org/10.3390/info16050399
• Ucar, A., Karakose, M., & Kırımça, N. (2024). Artificial Intelligence for Predictive Maintenance Applications. Applied Sciences, 14(2), 898. https://doi.org/10.3390/app14020898
• Wamba, S. F., Gunasekaran, A., Akter, S., Ren, S. J.-F., Dubey, R., & Childe, S. J. (2017). Big data analytics and firm performance: Effects of dynamic capabilities. Journal of Business Research, 70, 356– 365. https://doi.org/10.1016/j.jbusres.2016.08.009
• Wikipedia. (n.d.). Decision support system, algorithm aversion, automated decision making. https://en.wikipedia.org
• Additional studies: XPM frameworks, ABPMS, etc. arXiv. https://arxiv.org