This study aims to examine drivers and barriers to generative artificial intelligence (GenAI) adoption among managers and administrative staff at two Canadian public higher education institutions, addressing a gap in research that has focused primarily on students and pedagogy by exploring how institutional leaders navigate opportunities and constraints in regulated, complex environments.
A qualitative multi-case study design was used, guided by the technology–organization–environment (TOE) framework and organizational learning theory (OLT). Data were collected through participant observation, semistructured interviews and document analysis. An inductive approach combining open and axial coding was used to identify patterns across technological, organizational and environmental dimensions.
GenAI adoption remains fragmented, largely driven by individual initiatives rather than a coordinated institutional strategy. While efficiency gains are widely acknowledged, uneven skill levels, limited cross-unit coordination and concerns around regulation and data privacy continue to constrain broader use. Financial barriers appear minimal, highlighting a gap between the technology’s potential, perceived risks and insufficient organizational learning and knowledge sharing.
Institutions should move beyond fragmented experimentation by establishing clear policies on GenAI use, data protection and accountability. This should be reinforced through cross-functional governance structures that coordinate initiatives and institutionalize best practices. At the same time, targeted, role-specific training is essential to close skill gaps, build confidence and reduce uncertainty in adoption.
This study advances GenAI adoption research by shifting the focus to managerial and administrative actors within higher education. It demonstrates that in regulated public-sector contexts, environmental constraints can outweigh both technological readiness and organizational intent. By integrating insights from OLT with the TOE framework, this study offers a comprehensive lens for understanding the drivers and barriers shaping GenAI adoption.
