Sweden's quiet revolution: rethinking ai training in business schools

Official URL: https://dx.doi.org/10.1016/j.caeo.2026.100363

This study develops an inventory-based diagnosis of how Swedish universities publicly stabilize staff-facing training in artificial intelligence and digitalization. Using an integrated framework of institutional logics, sociotechnical systems theory, and the capability approach, I map publicly visible provision and signposted enabling conditions across nine Swedish higher education institutions, anchored in AACSB-accredited business schools and complemented by digitally active additions (December 2024 to January 2025). Provision is coded through observable indicators that capture formalization and support, including named ownership, recurrence, stable access routes, governance signposting for integrity and data protection, and role-targeted pathways. The analysis identifies systematic cross-institutional variation in the density of institutionalization signals and in the visibility of conversion supports that could make participation and application feasible across staff roles. These differences are interpreted as variation in public formalization and capability-enabling conditions, not as evidence of uptake or effectiveness. The contribution is a replicable diagnostic for comparing staff-development provision without conflating availability with outcomes, and for informing institutional choices about governance, coordination, and inclusive capability-building under responsible AI expectations.