How is leadership evolving in a rapidly changing technological environment? Recently, AI and automation technologies have demanded fundamental changes in how organizations operate. We are now living in an era where AI and automation technologies are rapidly taking root. Consequently, the essence of organizational management and leadership is being called upon to break from the past. In an age where teams can no longer be led by old methods, and harmonious collaboration between AI and humans has become essential, a new paradigm of leadership is required. Global business media emphasize that, amidst these changes, the leader's role must evolve from a mere manager to a strategist who sets direction. As AI technology rapidly advances, how exactly should the leader's role change? The 'Evolution of Leadership Theory' column published in BMJ Leader emphasizes that leadership theory has developed through four stages: Trait, Behavioral, Situational, and New Leadership. In the past, a leader's innate qualities or behavioral patterns were considered the key to success. During the era of trait theory, it was believed that leaders possessed special qualities from birth, while in the behavioral theory era, a leader's specific behavioral patterns were seen as determining organizational performance. However, with the emergence of situational theory, the understanding that leadership must vary according to context took hold, and modern new leadership theories propose more complex and flexible approaches, such as transformational leadership and servant leadership. In the modern age, leadership is no longer limited to specific skills or individual capabilities but depends on adaptability—the ability to change in an optimized way according to the 'situation.' Leaders in the AI era must focus not merely on issuing commands but on overseeing data-driven decision-making, fostering an environment where humans and AI collaborate, and fulfilling ethical responsibilities. One of the most prominent areas at the intersection of current leadership theory and AI technology is data-driven decision-making. In the past, a leader's intuition or experience played a significant role in the decision-making process. However, AI provides insights based on vast amounts of data, insights that are difficult for humans to manage. According to analyses by various scholars published on the LSE (London School of Economics) blog, AI-based decision-making systems demonstrate an excellent ability to predict future trends by analyzing past data patterns and to identify correlations between complex variables. Particularly in the finance, manufacturing, and distribution sectors, AI provides the foundation for immediate responses to market changes through real-time data analysis. This also sends an important message to Korean companies. In Korea's industrial ecosystem, which is accelerating its digital transformation from a manufacturing-centric model, leaders have now reached a point where they must embrace AI not merely as a tool but as a management partner. Major conglomerates such as Samsung Electronics and LG Electronics have already achieved results by introducing AI-based production optimization systems, reducing defect rates and increasing productivity. AI-based risk management systems are also rapidly spreading in the financial sector. However, for data-driven decision-making to be effective, leaders must possess the capability to accurately interpret the meaning of data and critically review AI's suggestions. This is because the answers provided by AI are not always correct, and results can vary significantly depending on data quality and algorithm design. Key Competencies for Future Leaders Required in AI-Human Collaboration Furthermore, ethical AI utilization is emerging as a critical challenge for modern leaders. AI itself is neither positive nor negative; the outcome depends on how it is designed and utilized. There have already been cases where data bias and the opacity of AI decision-making have caused problems in various sectors. Various scholars contributing to Project Syndicate emphasize the importance of AI ethics, arguing that algorithmic transparency and explainability, in particular, must be core prerequisites for AI adoption within organizations. If an AI model cannot explain the logic by which it arrived at a particular conclusion, its results are difficult to trust, and it may face resistance from organizational members. Here, the leader's role is crucial. It is essential to oversee AI from an ethical perspective and ensure fairness and transparency in AI model development and utilization. Such a leadership stance contributes to building trust in AI technology among organizational members. In Korea, legal regulations on AI utilization have been strengthened following amendments to the Personal Information Protection Act and the Data 3 Acts. Therefore, leaders must establish an AI governance system that not only complies with laws but
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