This study AIms to systematically review the literature on generative artificial intelligence in customer information processing and management and to develop a conceptual framework explAIning its role in marketing. Given the rapid expansion of generative AI applications and the fragmented nature of the existing literature, a structured synthesis is needed to identify major applications, clarify value-creation mechanisms, and highlight governance considerations. This research adopts a systematic literature review approach based on the PRISMA 2020 guideline. Relevant studies published between 2018 and 2026 were retrieved from major academic databases and analyzed using comparative and thematic synthesis after applying inclusion and exclusion criteria. The findings show that applications of generative AI in customer information management can be grouped into six major clusters: information generation and enrichment, customer feedback analysis, personalization and recommendation, conversational interaction, marketing decision support, and information governance. The results further indicate that the value of this technology emerges through transforming structured and unstructured customer data into actionable knowledge outputs; however, this process depends on data quality, transparency, human oversight, and bias control. Accordingly, the study proposes a conceptual framework linking customer information inputs, generative processes, knowledge outputs, and marketing outcomes, moderated by governance-related factors. The article contributes theoretically by integrating the literature on generative AI and customer information management and contributes practically by offering guidance for the responsible deployment of this technology in marketing.