The U.S. AI Policy Framework: Convergence Without Uniformity

The recent release of a National Policy Framework for Artificial Intelligence by the White House Office of Science and Technology Policy marks a significant development in the evolution of AI governance in the United States.

While comparisons with the EU AI Act are inevitable, the two approaches reflect fundamentally different regulatory architectures.

The EU has adopted a centralized, risk-based model, characterized by defined classifications and prescriptive obligations. By contrast, the emerging U.S. approach remains decentralized, relying on a combination of sector-specific regulation, agency oversight, and policy-driven guidance.

This distinction has direct operational implications for organizations - including tech companies, online platforms, and digital services providers - deploying AI systems across jurisdictions, and will require companies to navigate two parallel regulatory logics:

• a rules-based framework in the EU;

• a principles-based, enforcement-led approach in the U.S.

Reconciling these approaches within a single product, governance model, or compliance framework presents a growing challenge, particularly for technology companies operating at scale, from large digital companies to early-stage startups seeking to scale across markets.

Looking ahead, any transition from a policy framework to binding U.S. legislation is likely to face several structural constraints. These include the definition and scope of "high-risk" AI systems, the allocation of competences between federal and state authorities, and interactions with existing legal regimes, notably in employment, discrimination, consumer protection, intellectual property, and cybersecurity.

At the same time, a degree of convergence is emerging. Both the EU and the U.S. frameworks place increasing emphasis on risk identification, governance structures, and organizational accountability as core elements of AI regulation.

In light of these developments, organizations operating across both regimes should avoid aligning strictly with one model.

A more effective approach is to build a cross-jurisdictional AI governance framework that serves as a baseline, structured around the highest common denominator of both systems.

For companies, in practice, this means:

• adopting a risk-based classification approach (aligned with the EU model), while retaining flexibility to address U.S. sector-specific requirements

• implementing documented risk assessments (including AI risk assessments and GDPR/data privacy-aligned DPIA-type analyses where relevant)

• ensuring clear internal accountability and corporate governance structures, rather than relying solely on external vendor assurances or contractor arrangements

• maintaining sufficient privacy documentation and traceability to respond to both prescriptive obligations (EU) and enforcement-based scrutiny (U.S.)

For many organizations, this will require moving beyond fragmented, use-case-specific compliance efforts toward a more integrated AI governance model embedded in product development and operational processes.

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