To “In-House” or To Outsource? Artificial Intelligence in Canadian Local Governments

Sichen Wan and Renée Sieber

Journal of Urban Technology, 2025, vol. 32, issue 3, 71-91

Abstract: Artificial Intelligence (AI) promises significant benefits to municipalities, such as improved service delivery and operational efficiency. Given the resource-intensive nature of AI, municipalities must decide whether to develop AI in-house or outsource AI systems. Whereas outsourcing decisions have been widely studied in the private sector there is limited research to help us understand how municipalities navigate this choice despite their unique constraints, public accountability, and policy considerations. Our study addresses this gap by surveying representatives of 28 Canadian municipal AI projects to investigate the factors influencing their decisions. We found six factors that influence the decision of municipalities to “in-house” or outsource AI. We found that insufficient in-house AI expertise was the primary determinant of outsourcing. Funding and data sharing issues challenged both in-house development and outsourcing. Ensuring AI explainability and trust is perceived as more challenging when outsourcing. Contrary to common assumptions, AI maintenance is perceived as more difficult when outsourced. The lack of AI-specific regulations poses challenges for in-house development due to limited government guidance but also offers flexibility, while creating challenges in constructing AI outsourcing contracts. This paper is the first to compare in-house AI development and outsourcing within local governments. By capturing firsthand experiences of the participants directly involved in the AI projects, our research provides empirical insights into the trade-offs between these two approaches. Overall, these findings offer valuable guidance for municipalities seeking to make informed AI adoption decisions.

Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/10630732.2025.2477993 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:cjutxx:v:32:y:2025:i:3:p:71-91

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/cjut20

DOI: 10.1080/10630732.2025.2477993

Access Statistics for this article

Journal of Urban Technology is currently edited by Richard E. Hanley

More articles in Journal of Urban Technology from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().