<p>This study investigates the impacts of need-based factors on local governments’ adoption of AI for e-government in the United States. While several studies have been conducted on AI adoption in government, there has been limited investigation into the needs of the adopters. Our study fills this research gap by examining the determinants of AI adoption in e-government, with a special focus on the needs of the adopters, using the Technology–Organization–Environment framework. A survey was sent to 1000 IT professionals in municipal governments across the United States, and 141 completed the survey. The survey questionnaire asked about AI adoption, the types of AI technologies used for e-government, and the importance of factors such as accessibility and engagement, predictive analytics for data-driven decision-making, improved efficiency and automation, cybersecurity, and inadequate funding and a lack of AI expertise in adoption decisions. The data were analyzed using ordered logistic regression. Results reveal that the need for accessibility and engagement, predictive analytics for data-driven decision-making, and improved efficiency and automation drive local government adoption of AI for e-government. Conversely, inadequate funding and a lack of AI expertise serve as barriers to adoption. In addition, about twice as many rural municipalities, compared to their urban counterparts, do not use AI tools for e-government. This study has significant implications for policymakers and practitioners. From a policy perspective, priority should be given to addressing the barriers to AI expertise and funding. Initiatives such as public–private partnerships and grants from the federal government can help to bridge the expertise and funding gaps. From a practice perspective, federal and state initiatives should tailor support mechanisms according to contextual needs.</p>