Transforming government through public service universities: an integrative framework for AI-enabled governance capacity

Purpose This study aims to examine when universities with an explicit public-service mission can contribute to artificial intelligence (AI) governance capacity in public organisations. Design/methodology/approach A problem-driven conceptual synthesis integrates public management reform, policy and dynamic capabilities, institutional logics, higher education, boundary work and training transfer. A targeted supplementary literature search supports comparison with the closest explanations. Findings The Intelligent Public Service University (IPSU) framework links mission framing, internal coordination arrangements and four established transfer functions: formation, circulation, translation and embedding. It specifies conditional relationships, not a universal sequence. Agency uptake depends on workplace authority, administrative context, information sensitivity and institutional independence. Proximal capacity is distinguished from public value and from legitimacy, which may also arise symbolically. Research limitations/implications The propositions require comparative and longitudinal testing. The synthesis establishes neither causal effects nor the superiority of any university type or transfer portfolio. Practical implications Universities and agencies can use the framework to diagnose disconnections between education, institutional governance and workplace use, selecting proportionate arrangements for their resources, mandates and risk context. Originality/value IPSU integrates existing mechanisms to explain a bounded problem: why similar university AI initiatives may yield different agency capacities when educational preparation, coordination and opportunities for use differ.