Governing at the Speed of Change: An AI-Enabled Adaptive Framework for Complex Challenges

State and local governments face increasing—and increasingly complex—economic and social problems that require more agile decisionmaking with less certainty than ever before, as RAND describes in its Social and Economic Policy Rethink Initiative. However, there may be new solutions that can support government officials as they face the future.

AI technology is rapidly advancing, offering new capabilities for processing vast amounts of information and relief from administrative tasks burdening a shrinking public sector workforce. State and local policymakers lack systematic approaches to government AI integration. Efforts remain fragmented, leaving practitioners struggling with AI's evolving governance applications. In 2024, 150 state bills on the government use of AI were considered, 10 governors issued AI study orders, yet only 10 legislatures required comprehensive AI inventories. AI adoption carries risks requiring careful management and adaptive governance frameworks for these dynamic technologies.

Our recent article in Governing described the interested parties, approaches, government functions and potential benefits of AI-enabled governance (Figure 1). The purpose of this article is to describe what AI-enabled governance could look like in practice, particularly for dynamic and connected social and economic policy problems. We outline how these approaches can be implemented in a way that fairly maximizes participation and augments rather than replaces human judgment, keeping policymakers and the public in charge of final policy decisions. In short, this article begins to lay the foundation for a practical AI-enabled adaptive governance framework. We step through the complex case study of health, housing and the environment. These are areas that are being rapidly affected by AI usage in service delivery, but how should government use AI to keep pace on the policy response?

The first two columns match interested parties with the approaches they need:

Parties Interested in AI	Approaches Needed to Scale AI
Funders	Funding strategies
Procurement Specialists Vendors and Suppliers	Smart procurement
Chief Information Officers State and Local Government Personnel	Workforce development (e.g., upskilling)
AI experts	Training and technical assistance
The Public	Participatory governance

To the right of the second column is a curly brace that indicates that all of the content in the first two columns is a group related to the third and fourth columns, which match government functions with potential benefits:

Government Functions Using AI at Scale	Potential Benefits of AI at Scale
Improve administrative efficiency Answer questions from the public, vendors, and suppliers Automate the processing of documents or components of key processes (e.g., matching vendors/suppliers with business needs)	Efficient Government
Monitor policy and program implementation Automate reporting and process documentation Continuously monitor implementation and suggest corrections when these metrics get off track	Transparent Government
Synthesize policy options and solutions Synthesize and compare policy solutions: research and available evidence to efficiently identify best practices and evidence-based approaches Synthesize and compare policy options	Cutting-Edge Government
Forecast policy and program needs and impacts Predict policy options most likely to succeed based on context, historical performance, etc. Forecast procurement needs and budgets Recommend tailored changes to policies to improve their outcomes and success	Anticipatory Government
Improve public access and deliberation d Scale publication consultation Improve accessibility [translation, voice-to-text) Promote deliberative conversations	Inclusive Government

Source: Acosta and Hughes How to Scale Up AI in Government, 2025

Using AI to Support Government Functions at the Nexus of Housing, Health, and a Changing Environment

Severe housing shortages, rising greenhouse gas emissions, and growing health disparities linked with air quality and urban design are three interconnected crises that have been addressed separately, creating inefficiencies and missed opportunities. But let's consider how addressing these through an AI-enabled adaptive governance framework could help to maximize government benefits, using the framework domains above.

• Improve administrative efficiency: AI could be used to synthesize, compare, and reconcile information from environmental, housing, and health plans, reports, and impact assessments to track housing development patterns, environmental changes, and health simultaneously. This could promote streamlined planning, and identify areas for collaboration.
• Monitor policy and program implementation: AI could monitor whether integrated housing, health, and climate change policies are being enforced and their impacts on community economic health and wellbeing. Policymakers could also ask AI to alert them when integrated policies aren't reaching the intended communities or are creating unintended consequences.
• Synthesize policy options and solutions: AI could be used to identify “hot spots” for affordable housing development that would simultaneously maximize transit use, minimize emissions, and promote health. These hot spots take into consideration how affordable housing decisions cascade across systems and link affordable housing placement to transit infrastructure and climate goals.
• Forecast policy and program needs and impacts: AI could forecast future climate risks, population growth, and housing development, and recommend policy options that would be the most beneficial to maintaining affordable housing and a healthy environment. It also could suggest budget reallocations between housing vouchers, transit improvements, and air quality interventions to maximize overall system efficiency.
• Improve public access and deliberation: AI could be used to allow residents to provide comments on draft plans and policies, as well as implementation decisions related to those plans and policies. It could also synthesize or translate predecisional drafts for the public and compile the comments into a summary for policymakers.

These are just a few examples of AI-enabled government functions that demonstrate the potential of AI to handle the complexity of implementing government initiatives at scale.

Using AI to Support Adaptive Governance at the Nexus of Housing, Health, and a Changing Climate

Now let's consider an AI-enabled adaptive government that performs all these functions simultaneously and where these functions interact with one another to create seamless real-time decisionmaking support for policymakers (Figure 2). For example, say a policymaker is considering a proposal to develop low-income housing units on the east side of a county and is trying to determine if these units are needed and if the proposed location is the right fit based on environmental risks and available services.

To help determine the suitability of the proposed low-income housing units, AI synthesizes local and state governments' housing, health, and environmental plans, budgets, and data on community conditions, and then matches community needs, local and state government services and programs, and housing, health, and climate policies to identify mismatches between housing needs, anticipated risks, and associated government efforts. This analysis suggests that low-income housing is needed in the county, but given the challenges facing communities on the east side, transitional housing is also needed to provide low-income families a path to more stable housing. AI also identifies the need for supportive services (employment, emotional well-being) and flood mitigation efforts to ensure these individuals can take advantage of the new low-income housing units. Based on the synthesis of community needs, AI also recommends additional “hot spots” in the county for placing new low-income housing units and transitional housing—both in colocated and separated locations that would simultaneously maximize transit use, minimize emissions, and promote health. These recommendations are ranked by AI based on their alignment with forecasted future climate risks, population growth, economic development plans, and housing development.

AI then synthesizes these rankings for two audiences—policymakers and the public—and delivers the public synthesis to the local participatory governance platform. The public uses this platform to provide input, noting that the estimated housing need seems low and overlooks the large number of domestic workers in the community. AI uses this information to identify gaps in government plans and services and finds that local economic development plans don't account for these domestic workers and that school budgets don't account for the children of these domestic workers. AI then adds recommended “hot spots” that account for the public feedback and synthesizes this new information for the policymaker and the public. AI also recommends budget reallocations to support the services needed for transitional housing. The policymaker is able to quickly make a decision about the need and location for the proposed low-income housing units that is based on multiple data sources and public input. In short, AI allows the policymaker to make a decision that is informed by the potential cascading set of needs and conditions and best aligned with the goal of improving community conditions to optimize economic health and well-being (orange box under Community Conditions).

Each section of the flow chart has a list of items that mostly go in order, with arrows pointing from one to the next. Some items have arrows pointing to other items within the same section, as well as items in other sections.

Government Processes

1. AI automates document processing or components of key processes (e.g., Matching vendors/suppliers with business needs)
2. Then AI extracts information from these processes and from public feedback and feeds it directly into reporting and process documentation
3. Then AI uses this information to create implementation measures and analyze these measures and suggest corrections when they get off track
4. AI forecasts procurement needs and budgets based on current conditions and imminent policy changes
5. Government personnel improve responsiveness of processes and services

Public Participation

1. Public is provided real-time updates on processing of their information or service status and provides feedback on quality and timeliness to inform quality improvement
2. Public is provided regular updates on changing community conditions
3. AI provides the public information about policy options and solutions in an accessible format
4. Public engages in deliberative conversations facilitated by AI about policy options and solutions
5. Public provides feedback on policy options and solutions to changing community conditions

Community Conditions

• AI uses community data to alert the public and policymakers to changes in key conditions and needs
• Community conditions improve to optimize economic health and well-being based on regular public feedback, Informed policymaker decisions, and responsive government services and processes

State and Local Policy

1. Policymakers receive real-time information about the status of policy implementation and government operations
2. Policymakers receive regular updates on emerging community needs
3. AI synthesizes and compares policy options and solutions to address emerging community needs, including public feedback
4. AI recommends policy options most likely to succeed based on context, historical performance, public feedback, etc. and any tailored changes needed to maximize their success

As state and local governments take up AI tools and technologies, there is no universal governance model, and there will be no one-size-fits-all adaptive governance model. Rather, AI governance must be tailored to state and local contexts and AI capacity (skilled workforce, funding, etc.) and include critical reflection on the outputs generated by AI tools. Additionally, there must be attention to the implementation of AI to ensure it is unbiased and accurate, and that sensitive and confidential information is protected, even as risks evolve. State and local governments may want to consider creating an Office of Algorithmic Accountability to ensure AI systems are responsible for their outputs or an AI Ethics and Oversight Bureau to ensure that the ethical and moral dimensions, as well as the practical monitoring of AI functions, include fact-checking of any AI-produced information. Additionally, policymakers may want to require an AI impact assessment to assess the potential human and financial damage of planned AI before its implementation. Transparency and trust in these processes will be essential to effective AI-enabled adaptive governance.

AI-enabled adaptive governance stands at a pivotal moment: it promises to revolutionize policymaking through seamless, real-time decisionmaking that responds quickly to emerging challenges, yet its transformative power makes thoughtful implementation not just important, but essential to determining whether this technology becomes democracy's greatest tool or its greatest vulnerability.