Author
Listed:
- Farhana Akter
(Master of Business Administration, St Francisco College, Brooklyn, New York-11021, USA)
- Hamina Talukder Ria
(Economics Discipline, Social Science School, Khulna University, Khulna-9208, Bangladesh)
- Uthej Thunga
(Masters in Business Analytics, International American University, Los Angeles, CA 90010, USA)
Abstract
Artificial Intelligence (AI) is significantly reshaping the landscape of business decision-making by offering enhanced speed, precision, and data-driven capabilities. As organizations confront increasingly complex and competitive environments, the adoption of AI has emerged as a strategic imperative for gaining insights, minimizing uncertainty, and improving operational efficiency. This study explores the transformative role of AI in modern decision-making processes across diverse sectors such as finance, healthcare, and retail. Employing a mixed-methods approach, the research integrates qualitative case studies and expert interviews with quantitative survey data to present a comprehensive analysis. Key findings highlight the benefits of AI, including improved predictive analytics, streamlined workflows, and automated operations. Analytical tools like decision tree algorithms and sentiment analysis provide deeper insights into the effectiveness and perception of AI-driven decisions. Despite its advantages, the study also identifies significant challenges to widespread adoption, such as high implementation costs, ethical considerations, and resistance to change. The results underscore the importance of understanding AI’s evolving impact and call for ethically sound strategies to leverage its potential in shaping future-ready business models.
Suggested Citation
Farhana Akter & Hamina Talukder Ria & Uthej Thunga, 2024.
"Exploring the Impact of Artificial Intelligence on Business Decision-Making Processes,"
Journal of Business and Management Studies, Al-Kindi Center for Research and Development, vol. 6(6), pages 357-366, November.
Handle:
RePEc:alk:jbms00:v:6:y:2024:i:6:id:11179
DOI: 10.32996/jbms.2024.6.6.19
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