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<p>This research aims to develop a structured approach for implementing Artificial Intelligence (AI) in municipal governance. The study addresses three key questions: (1) What principles can be derived from existing AI implementation frameworks? (2) How should an approach for municipal AI projects be designed? (3) What are the main risks at each implementation stage? The research methodology combined three components: (1) a literature review of AI and software implementation approaches and municipal challenges, (2) analysis of findings from long-term collaborations with German municipalities and two specific AI implementation projects, and (3) low-threshold validation through two webinars with municipal representatives. The study produced an eight-phase implementation framework emphasizing iterative experimentation and risk awareness, while highlighting the distinct challenges of AI compared to traditional software implementation. Key phases include task identification, AI suitability assessment, data evaluation, solution development/procurement, MVP creation, testing, operational transition, and continuous monitoring. Each phase incorporates AI-specific steps and risk factors tailored to municipal contexts. While the framework provides practical guidance for municipal AI implementation, positioning cities for the gradual transition toward post-smart cities with AI-enabled governance, its current foundation primarily reflects German municipal experiences. Further research and case studies are needed to validate and adapt the framework for diverse global contexts.</p>

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Municipal AI integration: a structured approach

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年5月6日

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  • Lutz Eichholz

摘要

This research aims to develop a structured approach for implementing Artificial Intelligence (AI) in municipal governance. The study addresses three key questions: (1) What principles can be derived from existing AI implementation frameworks? (2) How should an approach for municipal AI projects be designed? (3) What are the main risks at each implementation stage? The research methodology combined three components: (1) a literature review of AI and software implementation approaches and municipal challenges, (2) analysis of findings from long-term collaborations with German municipalities and two specific AI implementation projects, and (3) low-threshold validation through two webinars with municipal representatives. The study produced an eight-phase implementation framework emphasizing iterative experimentation and risk awareness, while highlighting the distinct challenges of AI compared to traditional software implementation. Key phases include task identification, AI suitability assessment, data evaluation, solution development/procurement, MVP creation, testing, operational transition, and continuous monitoring. Each phase incorporates AI-specific steps and risk factors tailored to municipal contexts. While the framework provides practical guidance for municipal AI implementation, positioning cities for the gradual transition toward post-smart cities with AI-enabled governance, its current foundation primarily reflects German municipal experiences. Further research and case studies are needed to validate and adapt the framework for diverse global contexts.