Sectoral AI Governance Act — What Proposed Federal AI Legislation Means for Innovation Policy

The Sectoral AI Governance Act of 2026 would authorize federal regulatory agencies to issue rules governing algorithmic decision-making systems within their existing enforcement domains. It applies to any organization deploying AI systems that could materially contribute to decisions affecting individuals in regulated sectors.

The sectoral approach — as opposed to a comprehensive federal AI law — has significant implications for how AI governance develops in the United States, and for the Innovation Policy questions that IRAIET’s research agenda addresses.

The sectoral model distributes regulatory authority across existing agencies — SEC, FDA, FTC, CFPB — rather than creating a single AI regulator. This approach has advantages: agencies bring domain expertise and existing enforcement infrastructure. It also has limitations: regulatory fragmentation creates compliance complexity for AI systems that operate across sectors, and coordination challenges between agencies may produce inconsistent standards.

The US does not have a federal AI law, but has a rapidly expanding patchwork of state legislation and sector-specific federal guidance. More than 25 states have introduced or enacted AI-related legislation. The practical effect is that most large US enterprises are already subject to multiple layers of AI-relevant regulation.

The Sectoral AI Governance Act would formalize this patchwork at the federal level — which may reduce uncertainty in some domains while increasing it in the gaps between sectors.

At IRAIET, our Innovation Policy research track examines exactly these governance architecture questions. The choice between comprehensive and sectoral regulatory approaches is one of the most consequential decisions in AI governance — and it requires independent research that examines both the efficiency and the effectiveness dimensions of each model.