Implementation Capacity for Risk-Based AI Regulation in National Regulators

Abstract

Background: Implementation Capacity for Risk-Based AI Regulation in National Regulators has become a practical governance concern because differences in institutional design and administrative capacity can shape how policy is applied in practice.

Objective: This study assessed the implementation of AI regulation, focusing on consistency, institutional conditions, and outcomes in relation to regulatory capacity.

Methods: The study surveyed 737 relevant officials, professionals, service users, or regulated actors. Questionnaire items covered implementation quality, capacity, procedural clarity, transparency, and perceived effectiveness; associations with implementation outcomes were assessed descriptively and with multivariable regression.

Results: For AI regulation, 724 complete responses were available for analysis. Consistent implementation was reported by 34%, whereas 18% identified material procedural or capacity barriers. After adjustment for organization size and jurisdiction, clearer roles, stronger staff capability, and access to formal guidance remained positively associated with implementation quality.

Conclusion: The evidence on AI regulation shows that policy design and administrative execution cannot be separated. Accordingly, public-sector AI rules are unlikely to be effective without operational capacity, transparent oversight, and clearly assigned responsibility.

Study design: regulator capacity survey.

Publication history: Originally published in print on 15 June 2025, Volume 5, Article 1. Online abstract record added on 16 September 2026. Full-text PDF will be added when the digital galley is ready.