Artificial intelligence (AI) is poised to significantly transform all sectors of the economy and society in the United States, and in many cases is already doing so. Yet despite some promising early efforts, the programmatic infrastructure required for nationwide AI readiness remains underdeveloped. With support from the U.S. National Science Foundation (NSF) and the Alfred P. Sloan Foundation, SeedAI convened approximately 100 experts for the two-day AI-Ready America Workshop to examine how the United States can strengthen the institutional infrastructure needed for AI diffusion, access, and adoption. Sessions were organized around four interrelated areas: state and local coordination, nation-scale efforts, domain-specific strategies for AI readiness, and AI literacy and learning pathways.

Across sessions, participants consistently identified a set of interrelated challenges. The U.S. already possesses distributed institutional infrastructure capable of supporting AI diffusion at scale, including the Cooperative Extension System, Small Business Development Centers (SBDCs), libraries, universities and community colleges, and professional associations. But these networks lack the staffing, training, and sustained resources necessary to integrate AI expertise into their existing missions. The practitioners and intermediaries who staff these institutions, rather than end users, emerged as the highest-leverage investment target for national AI readiness, yet professional development for this workforce remains critically under-resourced. Effective programs exist across sectors and regions but remain largely isolated beyond their immediate networks, and the absence of coordination infrastructure, shared vocabulary, and common standards reinforces fragmentation.

Participants also converged on what works. AI adoption efforts that begin with defined community or institutional challenges consistently outperform those that start with the technology itself. Trust, earned through sustained local presence, demonstrated neutrality, and peer relationships, is a foundational condition for adoption and cannot be manufactured through communications campaigns or top-down mandates. And without intentional program design, the benefits of AI adoption will concentrate among those already best positioned to access them. Broad access must be an architectural choice, not an afterthought.

The findings and recommendations from each of the workshop's four sessions are summarized below:

Session I: State and Local Coordination

Findings

1. Existing trusted networks provide infrastructure for AI diffusion

2. Problem-first approaches drive more effective adoption

3. Trust is better earned through local legitimacy, not mandates

4. Professional development is critically under-resourced

5. Broad access requires intentional design and implementation

6. Cross-sector coordination is urgently needed

7. Workforce development should span all economic sectors

Recommendations

1. Fund capacity within existing trusted networks

2. Institutionalize problem-to-solution pathways and facilitator training

3. Build regional coordination capacity

4. Resource state and local readiness planning

Session II: Nation-Scale Efforts

Findings

1. Broad access should be treated as a structural prerequisite of any national AI strategy

2. Successful national technology adoption follows a proven three-part formula

3. Invest in "super nodes" – the intermediaries that multiply impact

4. National standards and shared vocabulary are valuable coordination opportunities

5. Public-private partnerships work when structured around use cases rather than tool adoption

Recommendations

1. Build and grow AI research and compute commons

2. Establish shared definitions and voluntary certification pathways

3. Implement national AI readiness indicators

Session III: Domain-Specific Strategies

Findings

1. AI readiness looks different in every domain – strategies must reflect that

2. Data infrastructure is a critical domain-specific bottleneck

3. Regulatory frameworks can create asymmetric barriers across sectors

4. Front-line practitioners are adopting ahead of their institutions

5. Domain-specific models reveal what works – and what doesn't

Recommendations

1. Establish coordinating infrastructure within and across sectors

2. Fund domain-specific data infrastructure and governance

3. Support institutional catch-up to front-line adoption

4. Equip existing diffusion infrastructure for domain-specific needs

5. Commission sector-specific regulatory reviews

Session IV: AI Literacy and Learning Pathways

Findings

1. Train-the-trainer models are needed at the national level

2. AI credentials proliferate without quality control or labor market alignment

3. Digital divide and trust barriers limit reach

4. Existing curricula are disconnected from practical utility

5. Traditional curriculum cycles cannot keep pace with AI's rate of change

Recommendations

1. Establish a national AI educator training consortium

2. Develop a framework for AI literacy credentials

3. Equip community colleges and the cooperative extension system with AI teaching capacity

4. Support trust-centered delivery for under-resourced communities

5. Build mechanisms for continuous AI curriculum renewal

Recommendations are directed to federal, state, and local government stakeholders; philanthropic foundations; civil society and community organizations; educational institutions; and industry partners.