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

 Corresponding author: Prince Peter Yalley. Copyright © 2025 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0. Limitations of Maximizing Shareholder Value in the Era of Artificial Intelligence and Machine Learning Prince Peter Yalley * Clarkson University. World Journal of Advanced Research and Reviews, 2025, 28(01), 1816-1825 Publication history: Received on 14 September 2025; revised on 19 October 2025; accepted on 22 October 2025 Article DOI: https://doi.org/10.30574/wjarr.2025.28.1.3600 Abstract 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 AIdriven 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. Keywords: Maximizing Shareholder Value (MSV); Artificial Intelligence; Machine Learning; Ethics; Stakeholder Theory; Business Analytics 1. Introduction The purpose of business has been a longstanding topic of both philosophical inquiry and practical debate. According to Otteson (2019), business exists primarily to create value for all parties involved in economic transactions, fostering conditions conducive to human flourishing. In contrast, the notion of maximizing shareholder value (MSV), widely popularized by Friedman (1970), posits that the central responsibility of business is to increase profits for shareholders. Jensen and Meckling (1976) formalized this concept within the principal-agent framework, arguing that aligning managerial incentives with shareholder interests can optimize firm performance. While MSV offers a clear, quantifiable objective, scholars have long criticized its narrow focus for potentially neglecting broader societal and ethical responsibilities (Stout, 2012; Porter & Kramer, 2011). In recent years, artificial intelligence (AI) and machine learning (ML) have transformed the operational landscape of businesses across industries. AI/ML technologies enable organizations to process vast datasets, predict consumer behavior, optimize supply chains, and automate complex decision-making processes (Brynjolfsson & McAfee, 2017). These capabilities can directly enhance shareholder returns by reducing costs, improving efficiency, and identifying new revenue opportunities. However, the integration of AI/ML into MSV-driven strategies presents unique challenges. World Journal of Advanced Research and Reviews, 2025, 28(01), 1816-1825 1817 By emphasizing short-term profit maximization, firms may inadvertently compromise ethical standards, marginalize stakeholders, or expose themselves to reputational and regulatory risks (Floridi et al., 2018; Davenport & Ronanki, 2018). Despite extensive research on AI adoption in business and the classical debate on shareholder value, there remains a significant gap in the literature linking the two areas. Most studies either focus on the technical benefits of AI/ML or critique MSV in abstract terms, without exploring the interplay between AI-driven decision-making and the ethical, strategic, and societal limitations of MSV. This paper aims to fill this gap by critically examining the constraints of MSV when amplified by AI/ML technologies, providing practical scenarios that illustrate these challenges, and suggesting pathways for integrating ethical, stakeholder-conscious approaches into AI-driven business strategies. The objectives of this paper are threefold: (1) to identify and analyze the limitations of MSV in AI-driven business contexts, (2) to illustrate these limitations through practical, real-world examples, and (3) to propose conceptual and operational frameworks for balancing shareholder interests with broader stakeholder considerations in AI-enhanced business environments. By doing so, the paper contributes both to scholarly discourse on business ethics and to practical guidance for firms seeking to leverage AI responsibly while creating sustainable long-term value as it identifies literature gaps regarding quantitative and qualitative assessments of AI-driven MSV outcomes and argues that incorporating a stakeholder-oriented AI governance framework can address these deficiencies. 2. Methodology This study adopts a conceptual and integrative methodology, drawing primarily secondary data and existing scholarly literature. The research synthesizes theories from corporate governance (Maximizing Shareholder Value), stakeholder theory, and AI ethics to build a comprehensive analytical framework. The approach is qualitative and interpretive, focusing on critical analysis and theoretical integration rather than empirical data collection. Peer-reviewed journal articles, books, and reports from credible academic and professional sources were reviewed to identify key patterns, contradictions, and emerging trends. The analysis emphasizes conceptual linkage examining how AI and ML technologies amplify the strategic and ethical limitations of the MSV paradigm and formulates actionable insights for firms and policymakers seeking to balance profitability with ethical responsibility. 3. Literature Review 3.1. The Maximizing Shareholder Value (MSV) Paradigm The concept of maximizing shareholder value has dominated business thought since the 1970s. Milton Friedman (1970) famously asserted that “there is one and only one social responsibility of business and that is to use its resources and engage in activities designed to increase its profits.” This principle formed the foundation of the MSV paradigm, which prioritizes shareholders above other stakeholders. Jensen and Meckling (1976) later formalized MSV within the agency theory framework, emphasizing that managerial incentives should align with shareholder interests to mitigate agency problems and optimize firm performance. Several scholars have noted the practical advantages of MSV. Stout (2012) argued that clear shareholder-oriented objectives provide measurable performance benchmarks, enabling boards and investors to evaluate managerial effectiveness efficiently. Similarly, Kaplan and Norton (2004) suggested that shareholder value creation, when combined with performance metrics, can streamline decision-making and improve accountability. However, critics have pointed out that MSV often encourages short-termism, incentivizing managers to prioritize quarterly earnings over sustainable growth, ethical conduct, or stakeholder welfare (Porter & Kramer, 2011; Piketty, 2014). Practical examples illustrate these limitations. In the financial sector, the 2008 global financial crisis exposed how banks, driven by shareholder returns, engaged in high-risk lending and securitization practices without adequately considering societal or client impacts (Acharya & Richardson, 2009). In retail, companies like Toys “R” Us prioritized shareholder dividends over investment in supply chain resilience, ultimately contributing to operational collapse (Hawkins, 2018). These cases demonstrate that while MSV can generate short-term profitability, it may also introduce systemic vulnerabilities when broader ethical, social, and strategic considerations are ignored. Despite its prominence, existing literature largely treats MSV as a static, abstract principle. Few studies have investigated its interaction with emerging technologies like AI and ML, which can significantly amplify both the benefits World Journal of Advanced Research and Reviews, 2025, 28(01), 1816-1825 1818 and risks of shareholder-focused strategies. This gap underscores the importance of examining MSV in the context of contemporary, data-driven business practices. 3.2. Artificial Intelligence and Machine Learning in Business Artificial intelligence and machine learning have rapidly transformed the business landscape, offering unprecedented analytical and operational capabilities. AI refers to the simulation of human intelligence processes by machines, while ML is a subset of AI focused on algorithms that learn from data to improve predictions or decision-making over time (Russell & Norvig, 2021). AI/ML applications in business range from predictive analytics in finance, personalized marketing in e-commerce, and demand forecasting in supply chains, to talent management and risk assessment in human resources (Brynjolfsson & McAfee, 2017; Davenport & Ronanki, 2018). These technologies provide significant advantages for MSV-driven strategies. By analyzing vast datasets, AI can identify cost-saving opportunities, optimize pricing strategies, and predict market trends with high accuracy (Agrawal et al., 2018). For example, hedge funds companies like Renaissance Technologies, Citadel, D. E. Shaw & Co., and Two Sigma employ algorithmic trading models that have realized consistent short-term profits by exploiting microsecond market inefficiencies, directly enhancing shareholder returns. Similarly, e-commerce firms like Amazon leverage AI-driven recommendation engines to maximize purchase likelihood, increasing revenue per customer. However, AI/ML also amplifies the limitations of MSV. What do I mean by this? Automation and predictive algorithms can inadvertently entrench biases, prioritize short-term gains over ethical considerations, or marginalize nonshareholder stakeholders. For instance, in recruitment analytics, ML models trained solely to maximize productivity, or profitability may systematically disadvantage minority applicants, creating ethical and legal risks (Bogen & Rieke, 2018). In financial services, AI-powered credit scoring systems designed exclusively for-profit maximization can exclude low-income borrowers or inflate systemic risk exposure (Hurley & Adebayo, 2016). These scenarios illustrate that while AI enhances operational efficiency and profit generation, its use within an MSV framework may exacerbate ethical, social, and reputational vulnerabilities. Despite the growing body of literature on AI ethics and corporate governance, few studies have explicitly linked AI/ML applications with the limitations of MSV. Existing research tends to focus on technical performance metrics or ethical guidelines in isolation, without integrating these concerns into shareholder-centered business models (Floridi et al., 2018; Mittelstadt et al., 2016). This presents a gap in understanding how AI-driven decisions may conflict with ethical imperatives, stakeholder needs, and long-term sustainability under an MSV paradigm. 3.3. Ethical and Strategic Limitations of MSV in AI-Driven Businesses The ethical and strategic limitations of MSV are well-documented, but AI/ML introduces new dimensions to these constraints. First, short-termism becomes more pronounced when algorithmic models prioritize immediate financial gains. For example, predictive maintenance models in manufacturing, if designed solely to minimize operational costs in the short term, may recommend deferring critical safety upgrades, exposing firms to long-term liability and reputational damage. Second, stakeholder neglect can be amplified when AI models optimize for revenue or shareholder returns without integrating employee, customer, or community considerations. In retail, automated pricing algorithms that maximize profit margins may inadvertently exploit vulnerable customers or manipulate market dynamics, creating ethical dilemmas and public backlash (Pasquale, 2015). Third, reputational risk is magnified by the opacity of AI algorithms. Black-box models, which produce decisions without transparent reasoning, can make it difficult for management to justify actions if stakeholders or regulators challenge them (Burrell, 2016). In finance, algorithmic trading errors have led to rapid market crashes, such as the 2010 “Flash Crash,” demonstrating how AI-powered pursuit of shareholder returns can generate systemic consequences beyond managerial control (Kirilenko et al., 2017). The literature has emphasized these challenges conceptually, but empirical research connecting AI applications with MSV limitations remains scarce. Most studies focus either on AI ethics, corporate social responsibility, or MSV individually, without an integrative perspective (Davenport et al., 2020; Floridi et al., 2018). Consequently, there is limited guidance for managers seeking to harness AI for profit while mitigating ethical, reputational, and strategic risks, highlighting a critical research gap. World Journal of Advanced Research and Reviews, 2025, 28(01), 1816-1825 1819 3.4. Research Gaps and Contributions A review of recent studies reveals several gaps: Lack of integrative studies: Research has largely treated MSV, AI/ML, and ethics as separate domains. Few studies analyze how AI amplifies the ethical and strategic limitations of shareholder-centric business models (Mittelstadt et al., 2016; Floridi et al., 2018). Limited practical guidance: There is a dearth of scenario-based analyses demonstrating how MSV-driven AI decisions impact real stakeholders in finance, retail, or human resource management. Insufficient focus on long-term outcomes: Existing literature tends to prioritize short-term performance or theoretical ethics, neglecting how AI-informed MSV strategies influence long-term sustainability and stakeholder relationships. Regional and industry-specific gaps: Most AI/ML research originates in industrialized economies and technologyintensive sectors, leaving gaps in understanding its impact in emerging markets or traditional industries. This paper addresses these gaps by: • Linking AI/ML deployment to the classical MSV debate, showing how algorithms can magnify existing limitations of shareholder-focused strategies. • Providing practical scenarios across multiple industries to illustrate ethical, strategic, and reputational risks. • Offering conceptual frameworks for balancing shareholder interests with stakeholder considerations in AIdriven contexts. • Expanding the geographic and industry scope to include emerging markets, such as Africa, where AI adoption is accelerating but ethical governance and stakeholder integration are nascent. 4. Limitations of MSV with AI/ML 4.1. Short-term Focus One of the most persistent critiques of MSV is its encouragement of short-termism, an emphasis on quarterly earnings and immediate stock performance at the expense of long-term innovation and resilience. AI and ML, with their ability to hyper-optimize for profit metrics, can inadvertently perpetuate this cycle. For example, an AI-driven recommendation system might prioritize products or content with the highest immediate conversion rate, boosting short-term sales while encouraging addictive or harmful patterns of consumption. Similarly, algorithmic supply chain optimizations may minimize costs today but increase vulnerability to geopolitical or environmental disruptions tomorrow. In these cases, the applied intelligence of algorithms does not equate to strategic foresight. On the contrary, it often accelerates the very shortsightedness for which MSV is already criticized. 4.2. Neglect of Other Stakeholders MSV’s exclusive focus on shareholders sidelines employees, customers, and broader society (Otteson, 2019). AI-driven businesses risk compounding this neglect because algorithms optimize for quantifiable metrics, often ignoring human elements that resist numerical capture. Consider AI-driven hiring algorithms. When designed primarily to minimize labor costs or maximize efficiency, they may systematically disadvantage candidates from underrepresented groups, reinforcing social inequalities under the guise of “neutral” data-driven decision-making. Similarly, predictive pricing models might maximize revenue by charging vulnerable populations higher rates, worsening inequity in access to essential goods or services. These outcomes highlight a deeper problem: stakeholder interests, such as dignity, fairness, and well-being exist outside profit optimization models. Firms that ignore these dimensions may not only cause harm but also face employee turnover, customer distrust, and regulatory backlash. In practice, businesses in emerging markets such as Ghana illustrate this vividly: firms that neglected employee welfare or customer trust eventually collapsed, even when their short-term earnings were promising. World Journal of Advanced Research and Reviews, 2025, 28(01), 1816-1825 1820 4.3. Reputational Risks and Ethical Blind Spots AI is not only a decision-making tool but also a reflection of the values embedded in its design. When MSV dominates, ethical and reputational risks multiply. Algorithms trained on incomplete or biased datasets may produce discriminatory outcomes, which, when exposed, can generate reputational crises. For instance, AI systems used in financial services have been found to approve loans at disproportionately lower rates for minority groups, even when controlling creditworthiness. Such practices may boost short-term profitability but erode public trust and invite extensive scrutiny from regulators. Similarly, algorithmic content curation can spread misinformation if engagement metrics are prioritized over truth and safety, as seen in social media platforms that have suffered severe public backlash. 4.4. Recruitment Analytics and Stakeholder Neglect AI and ML are increasingly used in HR to optimize recruitment and talent management. Algorithms can screen resumes, predict employee performance, and recommend compensation packages. While this enhances efficiency and supports MSV through cost optimization and productivity gains, unintended ethical consequences can arise. A multinational firm employs an AI model to hire candidates with the highest predicted performance-to-cost ratio. Historical data reveals systemic bias favoring candidates from specific universities or demographic backgrounds. Consequently, minority applicants are disadvantaged, reducing workforce diversity. Shareholders initially benefit from a perceived increase in productivity and reduced recruitment costs. However, the company faces legal challenges, reputational damage, and a decline in employee morale. This scenario illustrates the ethical tension between MSV-driven AI decisions and stakeholder fairness. While existing HR analytics literature explores algorithmic bias (Raghavan et al., 2020), few studies explicitly contextualize this within the MSV framework, demonstrating a critical gap. By integrating these perspectives, this paper contributes to understanding how AI can inadvertently exacerbate inequities in shareholder-focused strategies. 5. Conceptual Framework and Theoretical Integration Strict MSV orientation may lead firms to underinvest in compliance and ethical governance, leaving them exposed to regulatory penalties. In the European Union, the General Data Protection Regulation (GDPR) imposes stringent requirements on AI-driven data handling. Firms prioritizing shareholder profit over compliance face fines and operational disruption (Jobin et al., 2019). Otteson’s ethical framing provides a useful moral compass here: Would decision-makers accept the outcomes of an AI model if they were on the receiving end of its consequences? The question is particularly pressing because businesses often delegate ethical responsibility to machine outcomes, creating an accountability vacuum. 5.1. Introduction Understanding the implications of AI and ML in business requires a multi-theoretical lens. Maximizing shareholder value (MSV) has long guided corporate strategy, emphasizing profit generation and financial performance as primary objectives (Friedman, 1970). However, the increasing deployment of AI and ML introduces both efficiency gains and ethical challenges, amplifying limitations inherent in a purely shareholder-centric approach. According to Freeman (1984), stakeholder theory provides an alternative framework, emphasizing that firms have responsibilities not only to shareholders but also to employees, customers, suppliers, and the broader society. Integrating stakeholder considerations into AI-driven decision-making can mitigate ethical, social, and operational risks while aligning technology deployment with long-term sustainability. The conceptual framework developed in this section positions AI/ML deployment as a mediator between corporate objectives (MSV or stakeholder-oriented) and practical outcomes, including financial performance, ethical compliance, and social impact. World Journal of Advanced Research and Reviews, 2025, 28(01), 1816-1825 1821 5.2. Theoretical Foundations 5.2.1. Maximizing Shareholder Value (MSV) Theory MSV theory, popularized by Friedman (1970) and Jensen & Meckling (1976), asserts that the primary responsibility of corporate management is to enhance shareholder wealth. In AI/ML contexts, this manifests as algorithmic optimization of profits through predictive analytics, dynamic pricing, HFT, and operational efficiency (Brynjolfsson & McAfee, 2017). Practical illustration: A hedge fund uses ML models to identify micro-trends in financial markets, executing trades in milliseconds to capture arbitrage opportunities. While profits soar in the short term, reliance on AI-driven strategies can expose firms to systemic risk and market volatility, as observed during the 2010 Flash Crash (Kirilenko et al., 2017). Limitations identified in literature include but are not limited to: • Short-termism: AI models may prioritize immediate returns over long-term sustainability (Bebchuk & Fried, 2004). • Stakeholder neglect: MSV-driven AI often overlooks social, environmental, and ethical considerations (Gelles, 2021). • Opacity and accountability gaps: Complex ML algorithms create black-box decision-making, limiting managerial and regulatory oversight (Pasquale, 2015). Despite extensive literature on MSV, few studies systematically integrate AI/ML operationalization into shareholdercentric frameworks while considering ethical, societal, and long-term consequences, a gap addressed by this research. 5.2.2. Stakeholder Theory Freeman (1984) proposes that firms have moral and strategic obligations to a broad set of stakeholders, including employees, customers, communities, regulators, and shareholders. In the AI context, stakeholder theory advocates for inclusive analytics that balances profit objectives with ethical, social, and environmental considerations (Donaldson & Preston, 1995). For instance, a retail firm uses AI-driven dynamic pricing but incorporates equity constraints, ensuring vulnerable consumer segments are not disproportionately affected. While profits may be slightly lower than unconstrained optimization, the firm enhances reputation, customer trust, and long-term sustainability. Key contributions of stakeholder theory to AI/ML integration include: • Risk mitigation: Inclusive analytics reduce operational, reputational, and regulatory risks (Harrison & Wicks, 2013). • Long-term performance: Stakeholder-oriented strategies are correlated with sustainable financial outcomes (Edmans, 2012). • Ethical alignment: Incorporating stakeholder perspectives supports fairness, transparency, and accountability in AI systems (Floridi et al., 2018). However, the literature often treats AI deployment as a neutral tool, overlooking the influence of organizational priorities in shaping algorithmic outputs. Few empirical studies examine how stakeholder considerations concretely alter AI-driven decision-making, highlighting a gap this paper addresses. 5.2.3. AI Ethics Frameworks AI ethics frameworks emphasize principles such as fairness, transparency, accountability, and human-centricity in algorithmic decision-making (Jobin et al., 2019). These principles intersect with MSV and stakeholder theory in complex ways: • MSV alignment: AI ethics may conflict with profit-maximizing strategies if ethical constraints reduce short-term returns. • Stakeholder alignment: Ethics principles often align naturally with stakeholder theory, promoting socially responsible decision-making. World Journal of Advanced Research and Reviews, 2025, 28(01), 1816-1825 1822 For instance, a bank uses ML to approve loans. Ethical constraints such as avoiding discrimination based on race or gender may reduce immediate profit but enhance long-term stakeholder trust and regulatory compliance (Raghavan et al., 2020). 5.3. Conceptual Framework The conceptual framework for this study is designed to examine how companies use artificial intelligence and machine learning (AI/ML) depending on what their main goal is: maximizing shareholder value (MSV) or following a stakeholder-focused approach. It explores the tension between chasing quick profits and building long-term sustainability, especially since AI can boost profits but also create big ethical risks. 5.3.1. Key Parts of the Framework • Corporate Objective (The company’s main goal) o MSV Orientation: Based on Friedman (1970) and Jensen & Meckling (1976), this view says the company’s top priority is making money for shareholders. AI/ML is mainly used as a tool to maximize short-term profit. o Stakeholder-Oriented Orientation: Rooted in Freeman’s (1984) stakeholder theory, this view says companies should also care about employees, customers, communities, and the environment. Here, AI/ML is used in ways that build trust, fairness, and long-term value, not just profit. • AI/ML Deployment (How AI is used): AI/ML technologies enable predictive analytics, algorithmic optimization, dynamic pricing, workforce analytics, and operational efficiency. These functions, however, are not value-neutral: their outcomes are deeply influenced by the corporate objective guiding their use. • Outcomes (What happens as a result) o MSV Outcomes: AI/ML helps companies make quick money, but it can also lead to unfair practices, reputational damage, and regulatory trouble. o Stakeholder Outcomes: AI/ML builds trust, diversity, sustainability, and long-term resilience. The trade-off is that companies may earn less profit in the short run.. 5.3.2. Pathways The framework identifies four distinct pathways: World Journal of Advanced Research and Reviews, 2025, 28(01), 1816-1825 1823 Overall, the narrow view of “maximizing shareholder value” (MSV) is too limited in the AI era: it typically focuses only on financial returns. It ignores ethical concerns, reputational risk, and systemic risk. A stakeholder-oriented approach, by contrast, accepts that short-term profit may be reduced, but secures balanced outcomes: long-term stability, fairness, legitimacy, and trust. Embedding ethics into AI systems, evaluating performance with multiple metrics (not just profit), and having broad accountability helps overcome the blind spots of MSV and reimagine what value really means in a digital and AI-driven world. 5.4. Research Gaps Addressed Most existing studies focus either on AI technical performance or corporate governance in general, without integrating the two domains (Brynjolfsson & McAfee, 2017; Floridi et al., 2018). Few papers examine how MSV-centric AI decisions impact broader stakeholder groups, particularly in applied, cross-sectoral scenarios. Existing research also tends to emphasize developed economies, leaving gaps regarding emerging markets where regulatory enforcement may be weak. Operationalization of MSV in AI contexts: Existing literature often theorizes MSV without exploring real-world AI deployment and ethical consequences. Integration of Stakeholder Theory with AI: Few studies empirically connect stakeholder inclusion with algorithmic decision-making across multiple industries. Context-specific insights for emerging markets: Research rarely examines AI ethics and MSV trade-offs in institutional environments with weak enforcement, a focus of this paper. World Journal of Advanced Research and Reviews, 2025, 28(01), 1816-1825 1824 Bridging theory and practice: This framework links conceptual theories (MSV, stakeholder theory, AI ethics) with practical scenarios, enabling actionable insights for managers, policymakers, and AI practitioners. This study contributes by: • Integrating MSV theory, stakeholder theory, and AI ethics into a cohesive framework. • Providing practical examples across finance, retail, and healthcare that demonstrate real-world implications of MSV-focused AI deployment. • Highlighting the need for stakeholder-inclusive AI governance to mitigate ethical, reputational, and systemic risks. • Addressing a literature gap on the operational consequences of MSV-driven AI in emerging markets. By addressing these gaps, the paper not only critiques MSV-focused AI strategies but also demonstrates the value of integrating stakeholder perspectives and ethical principles in technology-enabled decision-making. 6. Conclusion In conclusion, the deployment of AI and ML in corporate settings highlights both the promises and limitations of a purely shareholder-centric approach. Maximizing shareholder value (MSV) remains a dominant framework for guiding corporate strategy, particularly in profit-driven industries such as finance, retail, and manufacturing. However, as the integration of AI/ML intensifies, the limitations of MSV become increasingly apparent. AI-enabled decision-making can accelerate profits, improve operational efficiency, and optimise predictive outcomes, yet it simultaneously amplifies ethical risks, reduces stakeholder consideration, and increases systemic exposure, as illustrated by financial market flash crashes and algorithmic bias in HR or lending practices. Theoretical grounding in stakeholder theory demonstrates that firms have responsibilities extending beyond shareholders to employees, customers, suppliers, regulators, and society at large. Incorporating stakeholder perspectives in AI/ML deployment allows firms to balance short-term financial gains with long-term sustainability, ethical integrity, and social trust. Practical scenarios across industries confirm that stakeholder-oriented AI strategies may incur marginal short-term trade-offs but generate enhanced reputational, regulatory, and strategic benefits. Furthermore, AI ethics frameworks provide operationalizable guidelines for fairness, transparency, and accountability, reinforcing the importance of aligning AI deployment with ethical standards and stakeholder expectations. By integrating MSV, stakeholder theory, and AI ethics into a unified conceptual framework, this study bridges critical gaps in existing literature. It addresses the scarcity of research on operational AI deployment through a multi-theoretical lens, particularly in contexts where institutional enforcement may be weak. Ultimately, this framework not only critiques the risks of an exclusive MSV focus in AI-enabled business but also provides actionable insights for managers and policymakers seeking to harness AI responsibly. By emphasizing stakeholder inclusion and ethical alignment, firms can leverage AI/ML technologies to achieve sustainable value creation that balances profit, societal impact, and long-term resilience. References [1] Bebchuk, L. A., & Fried, J. M. (2004). Pay without performance: The unfulfilled promise of executive compensation. Harvard University Press. [2] Brynjolfsson, E., & McAfee, A. (2017). Machine, platform, crowd: Harnessing our digital future. W. W. Norton & Company. [3] Donaldson, T., & Preston, L. E. (1995). The stakeholder theory of the corporation: Concepts, evidence, and implications. Academy of Management Review, 20(1), 65–91. https://doi.org/10.5465/amr.1995.9503271992 [4] Edmans, A. (2012). The link between job satisfaction and firm value, with implications for corporate social responsibility. Academy of Management Perspectives, 26(4), 1–19. https://doi.org/10.5465/amp.2012.0046 [5] Floridi, L., Cowls, J., Beltrametti, M., Chatila, R., Chazerand, P., Dignum, V., … & Vayena, E. (2018). AI4People—An ethical framework for a good AI society: Opportunities, risks, principles, and recommendations. Minds and Machines, 28(4), 689–707. https://doi.org/10.1007/s11023-018-9482-5 [6] Freeman, R. E. (1984). Strategic management: A stakeholder approach. Pitman.