This research explores the transition of artificial intelligence from a mere operational tool into a foundational source of algorithmic authority that dictates critical life outcomes. The research argues that ethical AI governance must move beyond simple compliance checklists to become a robust legitimacy infrastructure integrated into leadership strategy. This approach emphasizes procedural justice, ensuring that automated decisions are transparent, contestable, and subject to meaningful human intervention. By adopting a Sensing–Stabilizing–Legitimizing framework, organizations can manage the risks of systematic exclusion and reputational damage inherent in high-stakes automation. Ultimately, the research posits that maintaining social trust is a strategic necessity for sustainable innovation in volatile institutional environments. Successful leadership in the digital age requires institutionalizing accountability to prevent algorithmic power from becoming arbitrary or harmful.

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This research examines the shift of artificial intelligence from a mere tool to a primary decision-making infrastructure that profoundly impacts human lives. The research argues that traditional ethical frameworks often fail because they incorrectly assume social and technical stability. Instead, the research proposes a leadership-centered model focused on Sensing, Stabilizing, and Legitimizing to maintain authority when environments become volatile. By reframing AI governance as a strategic necessity rather than a technical checklist, the work highlights the importance of procedural justice and accountability. Ultimately, the researcg asserts that organizations must build legitimacy infrastructure to ensure their automated systems remain trustworthy and socially acceptable.

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This research explores the negative organizational impacts of treating artificial intelligence as a formal teammate or employee rather than a productivity tool. While giving AI agents names and positions on an organizational chart may seem like a helpful way to normalize technology, it often leads to diffused accountability and a significant decline in error detection. Managers working alongside "digital colleagues" frequently experience professional identity uncertainty and increased anxiety regarding their future job security. To mitigate these risks, the research suggests that leaders should move away from anthropomorphizing software and instead focus on rigorous human-in-the-loop protocols. By establishing clear oversight capabilities and explicit responsibility structures, organizations can harness the power of agentic AI without compromising quality standards or employee trust. The findings ultimately emphasize that maintaining a distinct boundary between human judgment and machine output is essential for sustainable value creation.

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