Educating Managers to Govern Artificial Intelligence
 
Educating Managers to Govern Artificial Intelligence 
 
Viacheslav Osadchyi, Viacheslav Osadchyi, Anton Shantyr, Anton Shantyr, Olha Zinchenko, Olha Zinchenko, Andrii Bondarchuk, Andrii Bondarchuk, Nataliia Lashchevska, Nataliia Lashchevska, Kateryna Osadcha, Kateryna Osadcha
 
Abstract 

Artificial intelligence (AI)-related harms are increasingly attributed to governance failures rather than to isolated technical malfunctions. This article reframes AI governance as a core managerial competence grounded in leadership authority, accountability design, and organizational communication. The study addresses a persistent gap in higher education and managerial training, namely the insufficient preparation of future leaders to govern AI-mediated decision systems responsibly. Using a structured conceptual synthesis grounded in socio-technical systems theory and the organizational governance literature, the paper identifies recurring governance failure modes, including authority drift from human decision-makers to automated systems, diffusion of accountability, governance debt accumulation, and reliance on average-case performance metrics that obscure worst-case risks. To illustrate early governance readiness, an exploratory survey of senior university students—representing early-stage managerial cohorts—was conducted, resulting in the AI Governance Readiness Composite Score (AGRCS). The findings illustrate preliminary patterns in self-assessed governance readiness among early-stage managerial cohorts, without implying statistical generalization or population-level conclusions. The study does not seek statistical generalization but uses empirical signals to support conceptual arguments. The main contribution lies in positioning leadership authority, intervention capacity, and governance-related communication as central pillars of sustainable AI governance. The article translates these governance principles into an educational agenda, proposing sustainable pedagogy practices such as authority mapping, escalation rehearsals, worst-case simulations, and governance-focused learning environments. By framing AI governance as a leadership and communication challenge rather than a narrow technical problem, the study contributes to sustainable organizational development, responsible decision-making, and long-term societal trust aligned with the United Nations Sustainable Development Goals.