Cross-Agency Governance of Shared Government AI Platforms

Abstract

Background: Policy attention to shared AI platforms has increased, yet there is limited empirical clarity on how institutional arrangements influence interagency governance and implementation quality.

Objective: The study evaluated institutional variation in approaches to shared AI platforms, with particular attention to accountability, administrative capacity, and interagency governance.

Methods: The study compared 8 jurisdictions or institutional settings using legal and policy documents, structured organizational evidence, and where relevant key-informant material. A common framework examined authority, coordination, transparency, procedural safeguards, enforcement, and implementation capacity.

Results: The cross-case comparison separated shared AI platforms into three broad implementation patterns. Cases with explicit institutional mandates, formal coordination, and accessible review mechanisms scored 8% higher on the composite measure, while fragmented responsibility coincided with slower and less consistent enforcement.

Conclusion: For shared AI platforms, the main practical lesson is that formal requirements do not by themselves produce consistent implementation. This means that public-sector AI rules are unlikely to be effective without operational capacity, transparent oversight, and clearly assigned responsibility.

Study design: network-governance study.

Publication history: Originally published in print on 15 June 2026, Volume 6, Article 7. Online abstract record added on 16 September 2026. Full-text PDF will be added when the digital galley is ready.