AI-driven business analytics and decision making
Authors:
- Oluwaseun BadmusOBOluwaseun Badmus
- Shahab Anas RajputSRShahab Anas Rajput
- John Babatope ArogundadeJAJohn Babatope Arogundade
- Mosope WilliamsMWMosope Williams
Top Cited Papers
Open Access
- 30 October 2024
- journal article
- Published by GSC Online Press in World Journal of Advanced Research and Reviews
- Vol. 24 (1) , 616-633
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.Keywords
This publication has 25 references indexed in Scilit:
- ‘I digitize so I exist’. Searching for critical capabilities affecting firms’ digital innovationJournal of Business Research, 2021
- Addressing Bias in Artificial Intelligence in Health CareJAMA, 2019
- Cognitive and affective responses to political disinformation in FacebookComputers in Human Behavior, 2019
- Driving forces and barriers of Industry 4.0: Do multinational and small and medium-sized companies have equal opportunities?Technological Forecasting and Social Change, 2019
- Using multi‐criteria decision analysis for assessing sustainability of agricultural systemsSustainable Development, 2018
- Ashley R. McClellan, FACHE, President and CEO, The Woman's Hospital of TexasJournal of Healthcare Management, 2018
- Dermatologist-level classification of skin cancer with deep neural networksNature, 2017
- Predicting the “helpfulness” of online consumer reviewsJournal of Business Research, 2017
- Data quality for data science, predictive analytics, and big data in supply chain management: An introduction to the problem and suggestions for research and applicationsInternational Journal of Production Economics, 2014
- Non-linear predictability in stock and bond returns: When and where is it exploitable?International Journal of Forecasting, 2009