Author
Listed:
- Viacheslav Osadchyi
(Faculty of Economics and Management, Borys Grinchenko Kyiv Metropolitan University, 04053 Kyiv, Ukraine)
- Anton Shantyr
(Leviathan Security Group, Tukwila, WA 98188, USA
Educational and Scientific Institute of Information Technologies, State University of Information and Communication Technologies, 03110 Kyiv, Ukraine)
- Olha Zinchenko
(Educational and Scientific Institute of Information Technologies, State University of Information and Communication Technologies, 03110 Kyiv, Ukraine)
- Andrii Bondarchuk
(Faculty of Economics and Management, Borys Grinchenko Kyiv Metropolitan University, 04053 Kyiv, Ukraine)
- Nataliia Lashchevska
(Educational and Scientific Institute of Information Technologies, State University of Information and Communication Technologies, 03110 Kyiv, Ukraine)
- Kateryna Osadcha
(Vrije Universiteit Brussel, 1050 Brussels, Belgium
Institute for Digitalisation of Education of the National Academy of Educational Sciences of Ukraine, 04060 Kyiv, Ukraine)
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.
Suggested Citation
Viacheslav Osadchyi & Anton Shantyr & Olha Zinchenko & Andrii Bondarchuk & Nataliia Lashchevska & Kateryna Osadcha, 2026.
"Educating Managers to Govern Artificial Intelligence,"
Sustainability, MDPI, vol. 18(11), pages 1-24, June.
Handle:
RePEc:gam:jsusta:v:18:y:2026:i:11:p:5590-:d:1957782
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