Leveraging Artificial Intelligence and Machine Learning for Decision-Making in Business Management: A Comprehensive Analysis

Abstract

This paper focuses on how AI and ML have changed decisions in retailing, healthcare, financing, and manufacturing careers. They demonstrate how AI is used in supply chain management to support the decision-making process by making forecasts, processing data, and optimizing operations, leading to higher efficiency, decreased costs, and increased customer satisfaction. Thus, the research incorporates quantitative and qualitative approaches, such as surveys and interviews with key stakeholders, and employs statistical and content analysis methods. Significant outcomes include a 20% enhancement of forecasting precision reduction while the operational cost decreases by 21 percent. Nonetheless, the research also discovers an essential issue that employs complex challenges that embrace high-cost implementation, resistance from the workforce, allowance of data privacy, and bias besides algorithms. Some are ethical concerns, and the importance of their regulation is noted. While adopting the decision theory and systems thinking perspectives, this research paper highlights the necessity of effectively and adequately implementing AI into an organization permanently to achieve more benefits. The following are realizable out-of-the-box solutions that the study suggests, including audiences for employees, data protection for compliance, and conscientization of fairness in AI algorithms. Future directions include situations where these applications are to be broadened to weigh on ethical issues and to encourage optimal technological fairness that will, in turn, ensure sustainable business improvement and innovation.