Municipal AI integration: a structured approach
Abstract 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 proj...
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| Format: | Article |
| Language: | English |
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Springer
2025-05-01
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| Series: | Frontiers of Urban and Rural Planning |
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| Online Access: | https://doi.org/10.1007/s44243-025-00056-3 |
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| _version_ | 1849728998302547968 |
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| author | Lutz Eichholz |
| author_facet | Lutz Eichholz |
| author_sort | Lutz Eichholz |
| collection | DOAJ |
| description | Abstract 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. |
| format | Article |
| id | doaj-art-c7fe5ccf7ee6485a9cd24cfb7deebf9e |
| institution | DOAJ |
| issn | 2731-6661 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Springer |
| record_format | Article |
| series | Frontiers of Urban and Rural Planning |
| spelling | doaj-art-c7fe5ccf7ee6485a9cd24cfb7deebf9e2025-08-20T03:09:21ZengSpringerFrontiers of Urban and Rural Planning2731-66612025-05-013111110.1007/s44243-025-00056-3Municipal AI integration: a structured approachLutz Eichholz0Fraunhofer Institute for Experimental Software Engineering IESEAbstract 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.https://doi.org/10.1007/s44243-025-00056-3Artificial IntelligenceMunicipal GovernanceSmart CitiesPublic Sector InnovationImplementation Framework |
| spellingShingle | Lutz Eichholz Municipal AI integration: a structured approach Frontiers of Urban and Rural Planning Artificial Intelligence Municipal Governance Smart Cities Public Sector Innovation Implementation Framework |
| title | Municipal AI integration: a structured approach |
| title_full | Municipal AI integration: a structured approach |
| title_fullStr | Municipal AI integration: a structured approach |
| title_full_unstemmed | Municipal AI integration: a structured approach |
| title_short | Municipal AI integration: a structured approach |
| title_sort | municipal ai integration a structured approach |
| topic | Artificial Intelligence Municipal Governance Smart Cities Public Sector Innovation Implementation Framework |
| url | https://doi.org/10.1007/s44243-025-00056-3 |
| work_keys_str_mv | AT lutzeichholz municipalaiintegrationastructuredapproach |