Limitations of Maximizing Shareholder Value in the Era of Artificial Intelligence and Machine Learning

The increasing adoption of artificial intelligence (AI) and machine learning (ML) in business has introduced profound opportunities for improving decision-making, efficiency, and profitability. Traditionally, the purpose of business has often been framed through the lens of maximizing shareholder value (MSV), a concept popularized by Milton Friedman and further developed by Jensen and Meckling. While AI and ML can enhance shareholder returns by enabling precise forecasting, automated decision-making, and optimization of operations, reliance on MSV as the sole objective introduces significant ethical, strategic, and societal risks. This paper examines the limitations of pursuing MSV in AI-driven business contexts, focusing on short-termism, stakeholder neglect, reputational risks, and ethical dilemmas. Through practical scenarios drawn from financial services, retail, and human resource management, the analysis highlights how AI/ML, if unmoderated, can amplify the inherent shortcomings of MSV. Additionally, the paper identifies gaps in current research, noting that existing studies have rarely integrated discussions of AI ethics with the classical MSV debate. By providing a conceptual framework that links AI-driven analytics to stakeholder-inclusive approaches, this study contributes to a more responsible understanding of value creation in contemporary business environments. Recommendations are offered for integrating ethical AI practices, multi-metric performance evaluation, and long-term strategic planning to balance shareholder and stakeholder interests.