AI Governance Readiness in Multi-Campus Research Organisations

Abstract

Research organisations with several campuses often operate under uneven rules for data sharing, AI procurement and model oversight. The objective is to identify organisational capabilities associated with transparent and consistent AI governance. Relevant methodological and data-quality literature informs the design of the sampling and validation strategy [3–7]. The proposed method is a comparative qualitative protocol drawing on policies, staff interviews and anonymised approval workflows. Comparators will include campus-specific practices against shared governance requirements and documented control checkpoints. 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. The design prioritises precise operational definitions and a straightforward baseline before introducing more complex alternatives. Statistical and computational sources help define the reproducibility checks and comparison procedures [8–12]. The primary outcomes are policy coverage, escalation clarity, staff understanding and resolution of conflicting access requests. Potential biases include participant confidentiality, organisational hierarchy and divergence between written policy and daily practice. Primary analyses would be reported by the main deployment or participant subgroup so that averaged results do not conceal systematic failures. Additional research informs the prespecified stress tests and limits on interpretation [13–17]. The intended contribution is an auditable research protocol rather than an unsupported claim of effectiveness; related work frames its transferability limits [18–23].