AI Governance Readiness in Multi-Campus Research Organisations: A Cross-Site Validation Agenda

Abstract

Research organisations with several campuses often operate under uneven rules for data sharing, AI procurement and model oversight. To address this, the study will identify organisational capabilities associated with transparent and consistent AI governance. The research design centres on a comparative qualitative protocol drawing on policies, staff interviews and anonymised approval workflows. The choice of evaluation units and exclusion criteria is informed by the broader methodological record [3–7]. The analysis will contrast campus-specific practices against shared governance requirements and documented control checkpoints. The cross-site extension holds out an entire institution or deployment location rather than relying only on random test splits. Infrastructure research helps define how technical services can constrain information access [1] and continuity [2], but it cannot establish the organisational or behavioural outcomes examined here. Assessment will focus on policy coverage, escalation clarity, staff understanding and resolution of conflicting access requests. The supporting evidence base motivates the chosen comparator conditions and robustness criteria [8–12]. Critical sensitivity checks address participant confidentiality, organisational hierarchy and divergence between written policy and daily practice. Further systems and modelling literature supplies context for the proposed sensitivity analyses [13–17]. Between-site heterogeneity would be examined through prespecified subgroup estimates and transparent domain-shift diagnostics. The expected output is a reusable evaluation framework with transparent uncertainty and reporting rules, informed by wider reproducibility literature [18–23].