Cross-Context Comparison of AI-Enabled CRM: Mechanisms, Challenges and Future Directions in B2B and B2C Settings

Abstract

Artificial intelligence is reshaping customer relationship management (CRM) in both business-tobusiness (B2B) and business-to-consumer (B2C) contexts, yet the two settings differ markedly in relationship structure, decision-making processes, data characteristics and customer expectations. The core argument of this review is that AI-CRM exhibits significant contextual differences between B2B and B2C, with variations in its capability dimensions, action pathways, performance outcomes, and facilitating and hindering factors. Accordingly, firms cannot directly transfer AI-CRM strategies from one context to another; instead, they should adapt strategies based on differences in relationship structures, decision-making processes and customer expectations. By integrating existing literature, this paper systematically compares the mechanisms, performance outcomes and challenges of AICRM across B2B and B2C scenarios. The study finds that AI-CRM in the B2B context places greater emphasis on organizational capabilities, institutional support and process integration, while the B2C context focuses more on personalized experiences, emotional interaction and consumer trust. Both contexts face common challenges such as ethics, privacy and data bias, but differ in focus and response approaches. Existing research mostly examines B2B or B2C in isolation, lacking direct comparison. Future research should strengthen cross-context theoretical construction and empirical testing. Finally, this paper outlines the future research directions derived from the review.