Introduction
Artificial Intelligence is reshaping how public services are designed, delivered, and evaluated across the United States
The use of AI in the public sector brings immense opportunities—but also immense risks. That’s why we’ve created this Government AI Landscape Assessment to evaluate the readiness of U.S. state governments in responsibly adopting AI.
Code for America is dedicated to advancing the use of human-centered AI in government. We hope this Landscape Assessment provides the civic-tech community with a clear, actionable picture of how AI is transforming public service delivery. The rapid evolution of AI means that states are at varying stages of AI readiness.
The Landscape provides a comprehensive snapshot across key dimensions such as Leadership & Governance, AI Capacity Building, and Technical Infrastructure & Capabilities. Most states are navigating early or developing phases in these categories, building foundational capabilities while defining governance structures and strategic direction. But a few have emerged as national leaders—setting up dedicated AI offices, launching sophisticated pilot programs, training their staff, and building out infrastructure.
Reflecting a moment in time
This is a rapidly changing landscape. Our assessment was conducted in spring 2025 and published in July. States are making changes, implementing new programs, and piloting new tools that may not be reflected in this assessment.
Responsible AI in government
Like any technology, AI can do harm if it isn't stewarded responsibly. Read more about Code for America's approach to responsible AI in government.
Assessment dimensions
Leadership & Governance
Strong leadership and governance are foundational elements to effectively integrate AI across state agencies. This category evaluates the organizational structure and leadership dedicated to AI initiatives. It assesses whether there is executive-level ownership through a Chief AI Officer or equivalent position, the existence of a cross-agency AI advisory group, and an overall governance approach to AI implementation.
AI Capacity Building
All organizations are in learning mode as rapid technological evolution requires upskilling and education. This category evaluates the state’s investments in developing AI literacy, skills, and expertise across its workforce. It examines formal training programs, partnerships with educational institutions and industry for knowledge transfer, and structured upskilling pathways for employees.
Technical Infrastructure & Capabilities
Robust technical infrastructure is essential for successful AI adoption. This category evaluates the technical foundation necessary to support quality data and advanced AI implementations. It examines data infrastructure and accessibility, computing resources and platforms, and partnerships with technical vendors and service providers.
Readiness levels
Early
Initial steps in AI adoption with foundational elements beginning to emerge
Developing
Core components in place with growing capabilities and emerging formalization
Established
Mature implementation with systematic approaches and demonstrated effectiveness
Advanced
Sophisticated capabilities with comprehensive frameworks and innovative approaches
States may find themselves at different maturity levels across different categories, reflecting their unique strengths and focus areas. This variation is expected and can help identify where to concentrate resources for advancement.
Trends & highlights
Our analysis reveals that most states find themselves in the “Developing” stage across all major categories, signifying that core components for AI adoption are beginning to emerge but require further formalization and enhancement. But in each dimension we mapped, there are some standouts—states that are ahead of the curve in preparing themselves for AI.
Leadership & Governance
Early
7 states
Developing
16 states
Established
25 states
Advanced
3 states
Most states have taken some steps to organize AI-related leadership, often through task forces or assigning AI responsibilities to Chief Data Officers or CIOs. However, relatively few have advanced to a state where a Chief AI Officer with executive authority is in place.
Spotlight
Utah
Utah established the Office of Artificial Intelligence Policy and appointed a Chief Data & AI Officer, supported by a cross-agency AI Council. This structural investment underscores the state’s executive-level leadership on AI.
Spotlight
North Carolina
North Carolina appointed a statewide AI Governance and Policy Executive, supported by an AI Task Force and extensive analytics capabilities under the Government Data Analytics Center.
AI Capacity Building
Early
14 states
Developing
23 states
Established
10 states
Advanced
4 states
Capacity building remains one of the most immature areas nationwide. The majority of states are still in the early stages, with only a few offering structured AI training or engaging in workforce development at scale.
Spotlight
New Jersey
New Jersey trained over 9,000 state employees through a partnership with InnovateUS and launched the NJ AI Assistant to enable practical skill-building in generative AI.
Spotlight
Pennsylvania
Pennsylvania partnered with Carnegie Mellon University and OpenAI for pilot programs and training, providing AI upskilling across multiple agencies and functions.
Technical Infrastructure & Capabilities
Early
9 states
Developing
23 states
Established
16 states
Advanced
3 states
More states have made progress in building data infrastructure and adopting cloud technologies, but very few have the scalable, specialized infrastructure required to fully support advanced AI systems across agencies.
Spotlight
Colorado
Colorado developed a statewide data inventory and implemented strong governance protocols under its AI legislation, supporting ethical and scalable AI deployment.
Spotlight
Minnesota
Minnesota utilizes enterprise data platforms, consolidated data centers, and partnerships with the University of Minnesota’s supercomputing institute to power robust AI experimentation and services.
The Road Ahead
The AI Road Ahead for State Government
State governments are poised for significant advancements in AI readiness over the next year, influenced by both industry developments and internal policy shifts. Here are some of the driving trends in the three categories.
Leadership & Governance
- Federal initiatives: New executive orders on AI and further encouragement for efficiency gains may encourage more states to appoint Chief AI Officers and establish dedicated AI task forces.
- Legislative action: States are enacting laws to regulate AI use in government, promoting transparency and ethical standards. For instance, New York's legislation mandates monitoring AI applications within state agencies.
- Public-private partnerships: Collaborations between state governments and tech companies are fostering the development of AI governance frameworks.
AI Capacity Building
- Educational initiatives: There’s a growing emphasis on integrating AI and computer science education in schools, with over 200 CEOs advocating for mandatory AI courses.
- Workforce development: States are investing in training programs for public sector employees to enhance AI literacy and skills.
- AI innovation and learning hubs: Many states are setting up AI innovation and learning hubs within and between agencies. These hubs will increasingly be responsible for training staff as they experiment and test new AI technologies and use cases for government.
Technical Infrastructure & Capabilities
- Sandbox and testing environments: Many state governments are establishing infrastructure for testing and vetting AI models in contained environments.
- LLM vendor adoption: New products like ChatGPT Gov are designed for secure government use.
- Cybersecurity focus: With the rise of AI applications, there is a heightened emphasis on securing technical infrastructure against potential threats.
Partner with us
Let’s build your state’s AI readiness together
We've partnered with states across the country to implement AI in safe and responsible ways, including improving chat functionality to serve more customers, and leveraging data matching strategies to clear records and speed up benefits delivery.
Methodology
To develop a comprehensive view of state-level AI readiness and use, we conducted extensive desk research by reviewing publicly available materials. This included:
- Executive orders: Gubernatorial executive actions that established task forces, governance frameworks, or AI strategies.
- Legislation and policies: Laws and bills related to artificial intelligence.
- Agency guidance and reports: Strategic plans, policy documents, and technical guidance issued by state agencies, particularly IT departments.
- Media and trade articles: Local and national news coverage, civic tech blogs, and industry reporting.
- Direct state input: Opportunity for direct feedback and correction from states upon reviewing draft analysis.
Acknowledgements
The Government AI Landscape Assessment was made possible by the work of many people. We are especially grateful to Kyle Doherty for his data visualization design support, and to Stephen Rockwell for leading our research and analysis efforts.
The Assessment was supported by Code for America’s generous partners and funders, including Google.org. The findings and conclusions contained within the Government AI Landscape Assessment are those of the authors and do not necessarily reflect positions or policies of the funders.