A systematic review of theoretical perspectives frameworks and research gaps in AI localization for customer service

Integration of Artificial Intelligence (AI) into the operations of customer service has become an important strategic task for organizations aiming to improve their service quality, increase efficiency, and raise the level of customer satisfaction. However, the effective localization of AI technologies within organizational contexts continues to be a challenge especially in Arab countries due to certain contextual challenges such as organizational culture, infrastructure, and human capital. This paper provides a systematic review of the literature on AI localization in customer service by synthesizing the findings of 150 peer-reviewed articles published between 2015 and 2025 in Scopus, Web of Science, and IEEE Xplore. The review uses the PRISMA framework to ensure methodological rigor and reveals four key theoretical perspectives that underlie AI localization: Resource Dependence Theory (RDT), Resource-Based View (RBV), Continuous Improvement (CI), and Technology Acceptance Model (TAM). The synthesis reveals three categories of localization frameworks – governance-focused, collaborative adoption, and service improvement – as well as the key challenges across the dimensions of technical, organizational, cultural, and ethical. Three important research gaps emerge: (1) integration gap – the absence of the comprehensive frameworks addressing technological localization and its relation to service quality outcome, (2) mediation gap – the limited insight into the mechanisms of translation of AI into quality improvements, and (3) contextual gap – the lack of the research on AI localization in the Arab organizational context. The paper concludes with the research agenda for future studies in this field.