Governing frontier general-purpose AI in the public sector: adaptive risk management and policy capacity under uncertainty through 2030

TL;DR

This paper advocates for adaptive, scenario-aware governance strategies for frontier AI in the public sector, emphasizing institutional capacity, risk management, and sociotechnical adaptation under uncertainty through 2030.

Contribution

It introduces an adaptive governance framework for public institutions that combines capability monitoring, risk tiering, and institutional learning to manage AI risks amid uncertain technological trajectories.

Findings

AI capabilities are advancing rapidly and unevenly, creating uncertain policy challenges.

Effective governance depends on organizational redesign, data collaboration, and institutional capacity.

A proposed framework includes risk tiering, conditional controls, and standards-based interoperability.

Abstract

The governance of frontier general-purpose artificial intelligence has become a public-sector problem of institutional design, not merely a technical issue of model performance. Recent evidence indicates that AI capabilities are advancing rapidly, though unevenly, while knowledge about harms, safeguards, and effective interventions remains partial and lagged. This combination creates a difficult policy condition: governments must decide under uncertainty, across multiple plausible trajectories of progress through 2030, and in environments where adoption outcomes depend on organizational routines, data arrangements, accountability structures, and public values. This article argues that public governance for frontier AI should be based on adaptive risk management, scenario-aware regulation, and sociotechnical transformation rather than static compliance models. Drawing on the International AI Safety Report 2026, OECD foresight and policy documents, and recent scholarship in digital government, the article first reconstructs the conceptual foundations of the 'evidence dilemma', differentiated AI risk categories, and the limits of prediction. It then examines how AI adoption in government depends on organizational redesign, public-sector institutional dynamics, and data collaboration capacity. On that basis, it proposes an adaptive governance framework for public institutions that integrates capability monitoring, risk tiering, conditional controls, institutional learning, and standards-based interoperability. The article concludes that effective AI governance requires stronger policy capacity, clearer allocation of responsibility, and governance mechanisms that remain robust across divergent technological futures.