Leadership risks intensify as SMEs hand decision-making to AI

Artificial intelligence (AI) has transformed the global business landscape, offering unprecedented speed, accuracy, and data-driven insights. However, a fresh enquiry into the intersection of AI and organizational ethics reveals that while AI may enhance operational efficiency in small and medium-sized enterprises (SMEs), it risks undermining ethical leadership and human judgment. 

Published in Administrative Sciences, the article titled “Ethical Leadership and Management of Small- and Medium-Sized Enterprises: The Role of AI in Decision Making” examines how AI is transforming managerial decision-making structures and the implications this has on value-based governance.

AI’s rising influence in SME decision-making

The study explores how AI tools are increasingly integrated into routine, analytical, and even strategic decision-making layers within SMEs. Štrukelj and Dankova argue that the growing reliance on machine-led processes is redefining how businesses interpret data, solve problems, and allocate resources. Through models like the MER framework, the paper shows how AI supports enterprises in operational functions such as sales forecasting, customer segmentation, and inventory management.

But while AI thrives in structured, data-heavy environments, the authors caution that it begins to falter when confronted with nuanced ethical judgments. Within the layered decision-making framework, comprising strategic, normative, and tactical levels, AI’s dominance is most beneficial in routine tasks. However, its application at strategic and normative levels, which involve value systems, long-term thinking, and stakeholder consideration, may compromise ethical clarity.

This signals a turning point for SMEs, where AI is no longer a passive tool but an active participant in shaping organizational behavior. As a result, business leaders are faced with a new responsibility: ensuring that AI-driven processes do not override moral accountability or dilute ethical reasoning.

Why ethical decision-making remains a human responsibility

The paper draws attention to the irreplaceable role of human intuition, emotional intelligence, and cultural awareness in ethical decision-making. Leadership, especially within SMEs, often unfolds in socially embedded contexts where trust, empathy, and long-term relationships play decisive roles. These are dimensions that algorithms, no matter how advanced, cannot adequately replicate.

Štrukelj and Dankova argue that while AI may enhance the analytical backbone of decision-making, it lacks the moral compass required to weigh the socio-ethical consequences of business choices. For instance, decisions involving layoffs, sustainability trade-offs, or community partnerships demand more than just quantitative optimization, they require judgment rooted in human values.

Moreover, the researchers emphasize that ethical leadership is not reducible to logic trees or predictive models. The deployment of AI in normative decisions without careful oversight risks creating ethical blind spots. These blind spots may go unnoticed in data-rich outputs but have far-reaching consequences for brand trust, employee morale, and social legitimacy.

The concern is not merely theoretical. As AI tools become more autonomous, their capacity to act on behalf of leadership increases. Without well-defined ethical guardrails, this could shift accountability away from people and toward machines, undermining transparency and diminishing moral responsibility at the executive level.

Charting a responsible path forward for AI in business

The study offers a roadmap for integrating AI into SME management while preserving ethical integrity. Rather than treating AI as a replacement for human leadership, the authors advocate for a complementary model. AI should support evidence-based decision-making in areas where data volume and speed are essential, but ultimate control should remain with leaders trained in both ethics and technology.

One of the key recommendations is the development of ethical AI governance frameworks at the enterprise level. These frameworks must define where AI can operate autonomously, where it must defer to human judgment, and how outcomes should be audited for fairness and impact. Training programs for SME leaders should also be reimagined to include not just digital skills but ethical reasoning competencies.

The paper also points to the importance of regulatory awareness. As global AI accountability standards evolve, SME leaders must stay informed and engaged in shaping policies that reflect ethical business priorities. A passive approach to AI governance could expose enterprises to legal and reputational risks, especially in stakeholder-sensitive markets.

Transparency, inclusivity, and fairness must be embedded into algorithmic workflows. Leaders should ask critical questions: Who programs the AI? What biases might it inherit? How are stakeholders affected by machine-made decisions? These questions must inform not only the deployment of AI but also the values that guide its integration.