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Cited by 104Big Data and Business IntelligenceImpact of AI and Big Data on Business and SocietyArtificial Intelligence Applications

AI-driven business analytics and decision making

Open access
Oluwaseun Badmus, Shahab Anas Rajput, John Babatope Arogundade, Mosope Williams·2024·World Journal of Advanced Research and Reviews
DOI 10.30574/wjarr.2024.24.1.3093
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Abstract

The rapid advancement of Artificial Intelligence (AI) and Machine Language (ML) has revolutionized business analytics, transforming the way organizations make decisions. This paper explores the integration of AI-driven technologies into business analytics to enhance decision-making across various industries. By leveraging predictive and prescriptive analytics, AI enables organizations to not only analyse historical data but also forecast future trends, allowing for more informed, proactive strategies. Machine learning plays a pivotal role in automating data-driven decisions, offering real-time insights that help businesses respond quickly to changing market dynamics. This automation significantly reduces manual intervention, increases efficiency, and enhances the accuracy of predictions. The paper further discusses the integration of AI with Business Intelligence (BI) tools to deliver deeper insights from complex datasets in real time. These insights empower companies to optimize enterprise resources, improve supply chain management, and drive operational excellence. Case studies from AI-driven analytics within Systems, Applications, and Products in Data Processing (SAP) environments highlight the practical applications of AI in real-world business contexts, demonstrating its impact on decision-making and overall performance. The paper concludes with best practices for implementing AI in business analytics, focusing on data quality, system integration, and workforce readiness to embrace AI-enabled decision-making frameworks. The findings underscore the potential of AI as a game-changer in modern business landscapes, fostering smarter, faster, and more effective decision-making processes.

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  • The individual may feel they are acting on their own desires, but those desires have been carefully stage-managed.

    “The individual may feel they are acting on their own desires, but those desires have been carefully stage-managed (Adeniran et al., 2024;Al-kfairy et al., 2024;Badmus et al., 2024).”

    From Surveillance Capitalism, Consumer Subjectivity and Marketing in the Age of Artificial Intelligence (2025) · cites this paper as Badmus et al., 2024

    Section 5.1, “Marketing and Customer Insights” (page unavailable), describes tailoring recommendations and promotions to consumers’ existing preferences. It does not address whether people mistakenly experience stage-managed desires as their own; searches for manipulation, autonomy, and influenced desires found no passage making that point. (LLM, conf=0.77)

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Research works printed but not linked: 26

  1. Agrawal, R., & Srikant, R. (1994). Fast algorithms for mining association rules. In Proceedings of the 20th International Conference on Very Large Data Bases (Vol. 1215, pp. 487-499).
  2. Agarwal, S., Gans, J. S., & Goldfarb, A. (2019). The role of data in the age of AI: Reassessing the contribution of information to financial forecasting. Journal of Financial Data Science, 1(1), 14-28. https://doi.org/10.3905/jfds.2019.1.014 · DOI
  3. Bholat, D., Choudhry, M., & Svirydzenka, K. (2018). Machine learning in finance: A review of the literature. Bank of England Staff Working Paper, No. 746.
  4. Büyüköztürk, S. (2020). Streaming analytics for real-time big data processing. IEEE Transactions on Knowledge and Data Engineering, 32(3), 547-559. https://doi.org/10.1109/TKDE.2019.2904660 · DOI
  5. Cohen, M. A., & Lee, H. L. (2021). Operations and supply chain management in the era of COVID-19: Challenges and opportunities. International Journal of Production Economics, 231, 107859. https://doi.org/10.1016/j.ijpe.2020.107859 · DOI
  6. Cohen, S., Fink, D., & Ranjan, J. (2020). Artificial intelligence in the financial services industry: Opportunities and challenges. The Journal of Financial Transformation, 52, 73-83.
  7. Davenport, T. H., & Harris, J. G. (2017). Competing on analytics: The new science of winning. Harvard Business Review Press.
  8. Duan, Y., Edwards, A., & Dwivedi, Y. K. (2019). Artificial intelligence in supply chain management: A review of the literature and future research directions. International Journal of Production Research, 57(11), 3347-3366. https://doi.org/10.1080/00207543.2019.1565925 · DOI
  9. Fildes, R., Goodwin, P., & Kourentzes, N. (2009). Forecasting with predictors: A review of the literature. International Journal of Forecasting, 25(4), 681-702. https://doi.org/10.1016/j.ijforecast.2009.01.002 · DOI
  10. Goertzel, T., & Pennachin, C. (2007). Artificial general intelligence. Springer.
  11. Gonzalez, R. C., Woods, R. E., & Eddins, S. L. (2020). Digital image processing using MATLAB. Pearson.
  12. Han, J., Kamber, M., & Pei, J. (2011). Data mining: Concepts and techniques (3rd ed.). Morgan Kaufmann.
  13. Hastie, T., Tibshirani, R., & Friedman, J. (2009). The elements of statistical learning: Data mining, inference, and prediction. Springer.
  14. Hyndman, R. J., & Athanasopoulos, G. (2018). Forecasting: principles and practice (2nd ed.). OTexts.
  15. Kahn, M. G., et al. (2020). A harmonized data quality framework for electronic health records. Journal of Biomedical Informatics, 104, 103-140. https://doi.org/10.1016/j.jbi.2020.103140 · DOI
  16. Koehn, P. (2017). Neural Machine Translation. Cambridge University Press.
  17. Kumar, V., Rajan, B., & Moschis, G. P. (2013). Marketing strategy and firm performance: A customer-based approach. Journal of Marketing, 77(1), 1-20. https://doi.org/10.1509/jm.11.0457 · DOI
  18. Lacity, M. C., et al. (2015). A new approach to automating business processes. MIT Sloan Management Review, 56(4), 63-70.
  19. Lipton, Z. C. (2016). The mythos of model interpretability. Communications of the ACM, 61(3), 36-43. https://doi.org/10.1145/3287560.3287598 · DOI · Paper page
  20. P Oladokun, A Yetunde, T Osinaike, I Obika, Leveraging AI Algorithms to Combat Financial Fraud in the United States Healthcare Sector, September 2024. DOI: https://doi.org/10.38124/ijisrt/IJISRT24SEP1089 · DOI
  21. Parikh, R. B., et al. (2019). Addressing biases in artificial intelligence in health care. Health Affairs, 38(10), 1818- 1822. https://doi.org/10.1377/hlthaff.2019.00092 · DOI
  22. Pasquale, F. (2015). Black box society: The secret algorithms that control money and information. Harvard University Press.
  23. Pera, R., & Sweeney, J. C. (2019). A data-driven approach to enhance service quality. Journal of Service Management, 30(2), 215-239. https://doi.org/10.1108/JOSM-04-2018-0094 · DOI
  24. Pentland, A. (2014). Social physics: How good ideas spread-the lessons from a new science. The Penguin Press.
  25. Sweeney, J. C., & Soutar, G. N. (2020). Understanding the impact of service quality on customer loyalty. Journal of Service Management, 31(1), 56-70. https://doi.org/10.1108/JOSM-03-2019-0141 · DOI
  26. Yang, K. C., et al. (2018). Artificial intelligence in healthcare: Anticipating challenges to ethics, privacy, and data security. Journal of Healthcare Management, 63(4), 255-265. https://doi.org/10.1097/JHM-D-18-00040 · DOI

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Authors
Badmus, O., Rajput, S. A., Arogundade, J. B., & Williams, M.
Affiliations
  • Illinois State University (US)
  • University of Bradford (GB)
Corresponding author
Oluwaseun Badmus
Venue
World Journal of Advanced Research and Reviews
Publisher
GSC Online Press
Published
2024-10-30
Volume
24
Issue
1
Pages
616-633
ISSN
2581-9615 (ISSN-L)
DOI
10.30574/wjarr.2024.24.1.3093
OpenAlex
W4403255812
Cited by
103
References
0
Citations by year
2026: 34 · 2025: 65 · 2024: 4
FWCI
48.51
Type
journal article
Language
en
Open access
Licence
cc-by-nc-sa
Locations
World Journal of Advanced Research and Reviews (publishedVersion, cc-by-nc-sa)
Topics
  • Social Sciences › Business, Management and Accounting › Management Information Systems › Big Data and Business Intelligence
  • Social Sciences › Decision Sciences › Management Science and Operations Research › Impact of AI and Big Data on Business and Society
  • Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
Keywords
management decision-making, predictive analytics, prescriptive analytics, machine learning, business intelligence tools, supply chain management, SAP, data quality, career readiness

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