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AI-driven business analytics and decision making
Open accessAbstract
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 (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
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Ranjankar, P. B. (2026) · International Conference on Sustainable Computing and Integrated Communication in Changing Landscape of AI (ICSCAI) · doi:10.1109/icscai68849.2026.11648791
Gogia, E. H., Shao, Z., Khan, M. A., Gao, T., & Vajna, I. (2026) · Work · doi:10.1177/10519815261493103
DATA ANALYTICS TRANSFORMS OPERATIONS INTO EFFICIENT MANAGERIAL DECISIONS
dos Reis, A. F., Pessoa, E. G., & Ferreira, F. H. (2026) · Boletim de Conjuntura (BOCA) · doi:10.56238/bocav25n79-041
Explainable AI Approaches for Enterprise Process Optimization
Fountis, A., Kohlmann, S., Singh, J., Gupta, V., & Akila, D. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-41311-6_2
Metadata
- 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)
- 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
- hybrid
- doi.org
- 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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