Local governments face stark disparities in AI adoption

Artificial intelligence is accelerating change in public administration, but its adoption remains patchy and uneven across local governments. Disparities in institutional capacity, digital infrastructure, and workforce skills are creating a widening gap between municipalities that can implement AI tools and those that cannot.

A new study, “Mapping Territorial Disparities in Artificial Intelligence Adoption Across Local Public Administrations: Multilevel Evidence from Germany”, published in Administrative Sciences (Vol. 15, Issue 7, 2025), highlights the drivers behind this imbalance. The research presents a multilevel analysis of how local and regional factors determine the pace and scope of AI integration in Germany's administrative systems.

What drives local governments to embrace AI?

Germany’s federal structure provides fertile ground for analyzing regional gaps in digitalization, and the study utilizes this to build a composite AI Adoption Index across 347 local governments. The research combines five indicators, including chatbot implementation, digitized services, internal automation, IT investments, and the presence of AI-focused strategies. The researchers conclude that AI adoption is not driven by technology alone but also by the broader institutional environment and structural readiness of local administrations.

Key local-level factors influencing AI integration include the share of IT personnel, average income, and education levels. A higher percentage of IT staff within an administration significantly raises the probability of AI adoption. The study shows that each additional percentage point in IT staff increases the likelihood of implementation by more than 5%. This highlights the importance of human capital and organizational capacity in driving digital transformation.

Similarly, wealthier municipalities, with higher average per capita income, are more likely to invest in AI tools. Financial resources not only enable procurement of technology but also help sustain IT teams and innovation ecosystems. Education levels among administrative employees also play a role: better-educated workforces are more open to technological experimentation and capable of adapting to digital workflows.

The degree of urbanization adds another dimension. Urban municipalities, typically better connected and with higher service demand, are more inclined toward algorithmic solutions than rural areas. The presence of universities and digital ecosystems in these regions fosters an environment conducive to innovation and experimentation.

How do regional conditions shape AI integration?

While local institutional features strongly influence AI adoption, the study emphasizes that regional digital readiness exerts a significant contextual impact. Using the European Commission’s Digital Economy and Society Index (DESI), the research incorporates federal-level conditions to build a multilevel explanatory model. This two-tiered approach captures both micro-level municipal behavior and macro-level structural conditioning.

The findings reveal that regional DESI scores positively affect the likelihood of local AI adoption. Municipalities located in states with high DESI scores, such as Bavaria, Baden-Württemberg, and Hesse, demonstrate substantially greater integration of AI tools. These regions benefit from better broadband access, digital services, and support for innovation, which translates into more favorable conditions for local algorithmic governance.

Conversely, states in eastern Germany, such as Brandenburg, Saxony-Anhalt, and Mecklenburg-Vorpommern, suffer from lower DESI scores and face constraints related to digital infrastructure and human capital. In these areas, AI adoption is less likely, creating a clear internal digital divide across the country.

Approximately 10% of the variation in AI adoption can be attributed to differences between federal states, demonstrating that territorial affiliation itself is a significant predictor. This finding reinforces the importance of state-level investment in digitalization, policy coherence, and infrastructure development to support municipal modernization.

What policy strategies can bridge the digital divide?

The research not only diagnoses disparities but also proposes actionable strategies to address them. Through simulations based on structural profiles of different regions, the authors highlight the potential of targeted policy interventions. Municipalities were grouped into four typological categories: innovative western regions, urban emerging areas, eastern states in transition, and rural peripheral zones.

The "Innovative West" cluster, with abundant IT staff, high income, and advanced infrastructure, showed the highest probability of AI adoption. These municipalities require minimal intervention and can instead benefit from being AI testbeds or pilot sites for more complex digital tools. Governments in these areas should be encouraged to pursue ethical AI governance and citizen-centered service models.

Urban administrations in transition, while showing moderate levels of readiness, often lack IT specialization. Policymakers are urged to introduce continuous professional development programs and foster local innovation labs to bridge this skills gap.

The situation is more challenging in the "East in Transition" and "Rural Peripheral" profiles. These municipalities lack both technical infrastructure and skilled staff, making AI adoption highly improbable without external support. For these cases, the study recommends forming territorial consortia, investing in IT recruitment and training, and promoting shared-service models using open-source technologies. Inter-municipal cooperation and government-backed mentoring programs could enable smaller administrations to benefit from collective expertise.

The authors also advocate for interoperability and open standards across administrative units to ensure that AI technologies can be scaled and adapted according to local needs. Additionally, the paper emphasizes the need for robust monitoring and evaluation frameworks that measure not only adoption rates but also service efficiency, ethical safeguards, and public satisfaction.

A new role for local governments in the algorithmic age

The study rejects the notion of AI as a neutral or purely technical upgrade and instead presents it as an institutional reform process, deeply influenced by geography, capacity, and policy context. German local governments are no longer passive recipients of federal technology mandates; they are becoming autonomous agents shaping the algorithmic future of governance.

By positioning local administrations as strategic actors in digital modernization, the study broadens the policy discourse around AI. It underlines the necessity for differentiated policy responses, rooted in evidence and tailored to regional and municipal realities. As AI continues to reshape public services, ensuring equitable access and inclusive innovation will be vital to maintaining democratic legitimacy and trust in government institutions.

The policy implications extend beyond technical solutions and call for investments in people, institutions, and territorial cohesion.