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.
Osadchyi, V, Shantyr, A, Zinchenko, O, Bondarchuk, A, Lashchevska, N & Osadcha, K 2026, 'Educating Managers to Govern Artificial Intelligence', Sustainability (Switzerland), vol. 18, no. 11, 5590. https://doi.org/10.3390/su18115590
Osadchyi, V., Shantyr, A., Zinchenko, O., Bondarchuk, A., Lashchevska, N., & Osadcha, K. (2026). Educating Managers to Govern Artificial Intelligence. Sustainability (Switzerland), 18(11), Article 5590. https://doi.org/10.3390/su18115590
@article{2d051adf665d421aa3e3e0e112ecbf2d,
title = "Educating Managers to Govern Artificial Intelligence",
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.",
keywords = "artificial intelligence governance, algorithmic decision-making, AI risk management, socio-technical systems, AI accountability and oversight, technology-enhanced education, sustainable pedagogy, managerial education, organizational accountability, educational transformation",
author = "Viacheslav Osadchyi and Anton Shantyr and Olha Zinchenko and Andrii Bondarchuk and Nataliia Lashchevska and Kateryna Osadcha",
year = "2026",
month = jun,
doi = "10.3390/su18115590",
language = "English",
volume = "18",
journal = "Sustainability (Switzerland)",
issn = "2071-1050",
publisher = "MDPI AG",
number = "11",
}