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Examples include cases where AI is used for assessing eligibility for social benefits, predictive policing models, automated grading, and diagnostics in healthcare. While these applications demonstrate potential benefits, they also introduce new challenges related to privacy, accountability, corporatization and alienation of street-level work, and client service experiences. This article is a critical reflection on the street-level potential of AI in providing public services. This study contributes to the ongoing debate on AI's impact in street-level work by emphasizing both the potential benefits and risks associated with AI integration in frontline service provision. While AI may mitigate some limitations of human decision-making (e.g., subjectivity, inconsistency, and bias), it can also introduce challenges that require careful consideration (e.g., lack of transparency, data-driven bias, and limited contextual adaptation). 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