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

A research paper proposes adaptive risk management frameworks for governing frontier AI in public sectors through 2030, arguing that static compliance models are insufficient given rapid capability advancement and incomplete knowledge of AI harms. The work emphasizes that effective governance requires organizational redesign, stronger policy capacity, and scenario-aware regulation rather than purely technical solutions.

This academic paper addresses a critical gap in AI governance: the institutional and policy infrastructure needed to manage frontier AI deployment in government operations. Rather than focusing on technical AI safety alone, the authors recognize that public-sector AI adoption is fundamentally a problem of organizational design, data governance, and institutional accountability. This represents a maturation in AI policy thinking—moving beyond model performance metrics to systemic implementation challenges.

The paper's core insight—that governments face an 'evidence dilemma' where they must decide under uncertainty while AI capabilities advance unevenly—reflects real-world policy gridlock. Regulators lack complete information about both AI risks and effective interventions, yet cannot postpone decisions indefinitely. This uncertainty is compounded by the fact that AI outcomes in government depend heavily on how organizations restructure workflows, manage data, and establish accountability mechanisms.

For the AI industry and policymakers, this framework has significant implications. It suggests that future regulation will prioritize adaptive governance mechanisms—continuous monitoring, risk tiering, and conditional controls—rather than prescriptive rules. This approach could reduce regulatory burden on responsible developers while maintaining safeguards. However, it also signals that governments will increasingly demand transparency into organizational AI adoption processes, not just model specifications.

Investors and developers should monitor how public sectors implement these recommendations, as government procurement patterns and compliance requirements often cascade into private-sector standards. The emphasis on 'policy capacity' strengthens the case for governance-focused AI companies and consultancies.

• →Frontier AI governance must shift from static compliance to adaptive risk management frameworks that remain robust across uncertain technological futures.
• →Government AI adoption depends primarily on organizational redesign and institutional capacity, not technical performance alone.
• →An 'evidence dilemma' exists where policymakers must decide on AI regulation with incomplete knowledge of both harms and effective safeguards.
• →Risk-tiering and scenario-aware regulation will likely become standard in public-sector AI governance by 2030.
• →Stronger policy capacity and clearer responsibility allocation are prerequisites for effective frontier AI governance.