Digital technologies are used in various local government activities. Adopting suitable digital technology strategies could enhance service efficiency, effectiveness, and accountability. The challenges of technology adoption among local governments, however, are also evident. One of the major challenges is capacity, including the lack of knowledge or awareness of how to balance the local government’s resources and the strategies that need to be implemented. This challenge also forms a research gap. The study aims to consolidate the understanding of local government digital technology adoption strategies via the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). It analyses the adoption opportunities, challenges, and strategies through the lens of people, processes, and technology frameworks. The results show that: (a) Strategies concerning the people aspects include building a platform for public participation, employees’ skills, and decision-makers' positive mindset development. (b) Strategies concerning the process aspects include recognizing the players’ roles, having a clear aim and procedure, proper regulation, and receiving user input. (c) Strategies considering the technology aspects include understanding the effect of the technology, technological preparedness, and convenience adoption. The findings inform local government policymakers in digital technology adoption and transformation endeavors.

In an era marked by rapid technological progress, the pivotal role of Artificial Intelligence (AI) is increasingly evident across various sectors, including local governments. These governmental bodies are progressively leveraging AI technologies to enhance service delivery to their communities, ranging from simple task automation to more complex engineering endeavours. As more local governments adopt AI, it is imperative to understand the functions, implications, and consequences of these advanced technologies. Despite the growing importance of this domain, a significant gap persists within the scholarly discourse. This study aims to bridge this void by exploring the applications of AI technologies within the context of local government service provision. Through this inquiry, it seeks to generate best practice lessons for local government and smart city initiatives. By conducting a comprehensive review of grey literature, we analysed 262 real-world AI implementations across 170 local governments worldwide. The findings underscore several key points: (a) there has been a consistent upward trajectory in the adoption of AI by local governments over the last decade; (b) local governments from China, the US, and the UK are at the forefront of AI adoption; (c) among local government AI technologies, natural language processing and robotic process automation emerge as the most prevalent ones; (d) local governments primarily deploy AI across 28 distinct services; and (e) information management, back-office work, and transportation and traffic management are leading domains in terms of AI adoption. This study enriches the existing body of knowledge by providing an overview of current AI applications within the sphere of local governance. It offers valuable insights for local government and smart city policymakers and decision-makers considering the adoption, expansion, or refinement of AI technologies in urban service provision. Additionally, it highlights the importance of using these insights to guide the successful integration and optimisation of AI in future local government and smart city projects, ensuring they meet the evolving needs of communities.

Artificial intelligence (AI) is increasingly embedded in how cities are governed, shaping decisions on mobility, land use, public services, and environmental management. Yet urban AI is predominantly governed through fragmented frameworks designed at national or corporate scales, offering limited guidance for municipal decision-making and overlooking place-specific social and ecological consequences. As the level of government closest to everyday urban life, cities are uniquely positioned to steer AI toward public value, but face persistent tensions between efficiency, equity, accountability, and sustainability. This paper argues that responsible urban AI cannot be governed through top-down or one-size-fits-all approaches. To address this, the study aims to conceptualise and advance a ground-up model of responsible urban AI governance that places cities and local governments at the centre of decision-making. It addresses the following research question: How can municipal authorities translate high-level ethical principles into practical, context-sensitive governance arrangements that respond to local capacities, risks, and public values? Drawing on global governance principles and illustrative city experiences, we propose a locally grounded, stage-based framework for municipal AI governance. The framework addresses institutional capacity gaps, fragmented responsibilities, and algorithmic externalities, advancing a participatory, place-sensitive, and adaptive model that aligns urban AI innovation with democratic legitimacy, social justice, and sustainable urban futures.

In an era marked by swift technological progress, the pivotal role of Artificial Intelligence (AI) is increasingly evident across various sectors, including local governments. These governmental bodies are progressively leveraging AI technologies to enhance service delivery to their communities, ranging from simple task automation to more complicated engineering endeavours. While more and more local governments are adopting AI, it is imperative to understand the functions, implications, and consequences of AI. Despite the growing importance of this domain, a significant gap persists within the scholarly discourse. This study strives to bridge this void by exploring the applications of AI technologies within the context of local government service provision and using this inquiry to generate lessons and best practices for similar smart city initiatives. Through a comprehensive grey literature review, we analysed 262 real-world AI implementations across 170 local governments worldwide. The findings underscore several key points: (a) There has been a consistent upward trajectory in the adoption of AI by local governments over the last decade; (b) Local governments from China, the US, and the UK are at the forefront of AI adoption; (c) Among local government AI technologies, Natural Language Processing and Robotic Process Automation emerge as the most prevalent ones; (d) Local governments primarily deploy AI across 28 distinct services; (e) Information management, back-office work, and transportation and traffic management are leading domains in terms of AI adoption. This study enriches the extant body of knowledge by providing an overview of existing AI applications within the sphere of local governance. It offers insights for smart city policymakers and decision-makers considering the adoption, expansion, or refinement of AI technologies in urban service provision. Additionally, it underscores the importance of using these insights to guide the successful integration and optimisation of AI in future smart city projects, ensuring they meet the evolving needs of communities.

Artificial intelligence (AI) is increasingly transforming local government by enhancing data-driven decision-making, service delivery, and administrative efficiency. However, its growing adoption also raises significant governance challenges relating to transparency, accountability, institutional capability, data governance, and public trust. This report examines the emerging role of AI in local government and identifies the governance mechanisms required to support its responsible implementation. The report combines conceptual analysis, policy review, and empirical evidence to examine AI adoption from both institutional and societal perspectives. It reviews the evolution of AI research and policy within local government and maps current and emerging AI applications across key service domains, including urban planning, transportation, environmental management, healthcare, public safety, and citizen engagement. The report further examines the policy and governance landscape supporting AI adoption and develops a responsible AI framework centred on four core principles: transparency, accountability, institutional capability, and citizen engagement. Empirical findings, drawing on cross-national public surveys alongside surveys and interviews with local government practitioners, reveal both shared and divergent perceptions regarding AI adoption. While respondents recognise AI’s potential to improve efficiency, service quality, and evidence-based decision-making, concerns remain regarding fairness, explainability, accountability, regulatory readiness, and institutional capacity. Synthesising these findings, the report identifies six interrelated categories of governance risks encompassing regulatory, institutional, operational, accountability, democratic, and workforce dimensions. The report concludes that realising the benefits of AI in local government requires governance approaches that extend beyond technological implementation. Responsible AI adoption depends on robust policy frameworks, organisational capability, effective data governance, transparent oversight, meaningful public engagement, and workforce preparedness. By integrating these governance principles, local governments can harness AI to improve public services while safeguarding democratic values, institutional integrity, and public trust.

AI is becoming increasingly embedded in municipal operations, creating new opportunities for urban innovation while raising challenges concerning accountability, transparency, public trust, organisational capacity, and responsible technology use. Although existing research has examined AI governance frameworks and policy design, less is known about how municipal AI policies are interpreted and enacted in everyday practice. This study investigates how local government practitioners understand, implement, and adapt AI policies across diverse governance contexts. Drawing on in-depth interviews with 14 practitioners and government experts from Australia, the United States, Spain, Saudi Arabia, and Hong Kong, the study applies a grounded theory approach to develop an empirically grounded model of municipal AI policy enactment. The analysis identifies five interrelated dimensions: policy governance, policy objectives, policy implementation, policy challenges, and policy requirements. The findings show that AI policy enactment is not a linear process of translating formal rules into administrative action. Instead, it is a cyclical and adaptive process in which governance arrangements shape policy objectives and implementation, while implementation challenges generate requirements that feed back into subsequent governance practice. Policy governance emerges as a structural anchor, while communication, transparency, compliance, training, and adaptability support operationalisation. Resource limitations, administrative complexity, digital disparities, stakeholder knowledge gaps, and innovation trade-offs constrain implementation. The study contributes a grounded conceptualisation of adaptive municipal AI governance and highlights the importance of context-sensitive institutional capacity, accountability, and policy learning for responsible AI adoption in smart and sustainable cities.