Civic Intelligence: Why Municipalities Must Build Their Own AI Muscles

For AI to truly serve the public good, municipalities must invest in building their own capacity to understand, develop, and deploy artificial intelligence tools tailored to local needs. This means not only adopting off-the-shelf solutions, but also fostering local expertise, forming partnerships with academic institutions, and co-developing applications with community stakeholders. Cities such as Barcelona and Amsterdam have piloted AI-driven services while maintaining local control, including open registries of algorithms used in public decision-making and citizen oversight committees to monitor outcomes¹.

Municipal governments can begin by identifying priority areas where AI can deliver immediate value without compromising transparency, such as traffic optimization, public safety analytics, or predictive maintenance for infrastructure. These applications do not require large-scale generative models but can be built using smaller, interpretable systems that align with existing policy goals. Developing internal data governance frameworks and establishing AI ethics review boards at the local level can further ensure that technology implementation aligns with community values and legal standards².

Open Data as a Strategic Asset for Equitable AI

High-quality, well-governed open data is the foundation of equitable AI deployment. While large corporations often rely on vast proprietary datasets, cities and regions can balance this by investing in open, anonymized datasets from municipal services, community programs, and environmental monitoring. Ensuring datasets are diverse, representative, and free from systemic bias is critical to preventing discriminatory outcomes in AI-supported decision-making³.

Municipalities should implement open data standards that allow for interoperability and reusability by developers, nonprofits, and academic institutions. In practice, this means publishing datasets in machine-readable formats, documenting metadata, and implementing privacy safeguards. Programs such as the Open Data Charter and the European Data Portal provide frameworks and resources for cities looking to build responsible, transparent data ecosystems⁴. These data assets can then be used to train local AI models that reflect the realities of specific communities, rather than relying solely on generalized systems developed elsewhere.

Collaborative Procurement and Shared AI Infrastructure

Individual municipalities may lack the resources to compete with private sector AI investments, but through regional collaboration and shared infrastructure, they can collectively drive innovation. Joint procurement agreements, such as those used by the Nordic Smart City Network, allow cities to pool resources for shared AI platforms and negotiate better terms for ethical AI technologies⁵. This approach also promotes standardization and coordinated oversight across jurisdictions.

Creating shared digital infrastructure, including data lakes, cloud storage, and algorithmic auditing tools, reduces duplication of efforts and ensures consistent quality. These platforms can be governed through inter-municipal agreements with clear accountability mechanisms. By working together, local governments can increase bargaining power with vendors, enforce transparency requirements, and ensure that AI systems prioritize social outcomes over commercial interests.

Public Participation in AI Governance

Democratically governed AI requires meaningful public par