A meta-analysis of the effects of AI agent implementation on customer-level outcomes

Firms increasingly use AI agents, such as recommendation algorithms and chatbots, to enhance customer value, yet research documents mixed effects on customer outcomes. To address and clarify these heterogeneous findings, we conduct a meta -analysis of 468 effect sizes reported in 95 articles with 82,751 participants examining AI agent implementation across both substitution contexts (AI replacing humans) and adoption contexts (AI introduced into previously non-AI processes). Results indicate that customers, on average, respond less favorably to AI agent implementation. Drawing on implicit prejudice theory, we attribute this aversion to a prejudicial bias that arises when AI systems intrude into domains traditionally managed by humans or manual systems, evoking a perception of threat and mistrust. However, the direction and magnitude of this effect depend on AI design, information, contextual, and cross-country factors. Together, these findings advance understanding of when and why customers resist or embrace AI agents, offering actionable guidance for managers.