AI-Driven Proactive Customer Success in B2B SaaS: A Systematic Review and Conceptual Framework for Retention and Scalability

Customer Success has become strategically important in B2B SaaS because subscription-based revenue models depend on ongoing value realization, retention, and expansion rather than one-time transactions. At the same time, recent scholarship has begun to formalize Customer Success Management (CSM) as a proactive post-sale business function, while related research streams have explored customer health, B2B customer experience, digital customer journeys, AI-enabled customer care, and churn prediction. Yet academic research on AI-driven proactive Customer Success remains fragmented rather than consolidated. This article presents a systematic literature review and conceptual synthesis of research published primarily between 2020 and 2025. Rather than assuming a fully established "AI in Customer Success" literature, the review integrates related but directly relevant streams: Customer Success Management, customer health, B2B customer experience, customer journey digitalization, AI-supported customer care and service recovery, SaaS churn prediction, and subscription business logic. The article focuses on two outcome domains that are strategically central to B2B SaaS firms: retention and scalability. Based on the synthesis, the article proposes a conceptual framework in which AI-driven proactive Customer Success operates through five interdependent mechanisms: data integration, customer health monitoring, risk detection, intervention orchestration, and organizational learning. The article contributes by consolidating a fragmented evidence base, clarifying the boundary between AI augmentation and human-led Customer Success work, and identifying a focused research agenda for future empirical testing in B2B SaaS.