Abstract The Swiss private banking sector is undergoing significant operational transformation driven by increasing automation, digitalization, and regulatory complexity. Despite these developments, many core operational workflows remain highly manual, fragmented, and exposed to operational risk, processing delays, and governance challenges. Strengthening operational resilience has therefore become a strategic priority for private banking institutions. This conceptual article introduces the GAB/BAG Integrated Framework, a dual-layer approach combining Generative AI for Banking (GAB) and Blockchain for Assurance & Governance (BAG). The framework is designed to enhance operational efficiency, decision support, traceability, and regulatory alignment while addressing the limitations of stand-alone artificial intelligence systems. Within the proposed architecture, the GAB layer focuses on intelligent interpretation, prediction, and workflow recommendation, whereas the BAG layer ensures validation, governance, and immutable auditability through distributed ledger mechanisms. Adopting a practice-based conceptual research methodology, this paper synthesizes existing academic literature on artificial intelligence, blockchain, and banking operations with observed practices in Swiss private banking. It establishes a structured theoretical foundation and formulates high-level research hypotheses to guide future empirical investigation. This working paper does not present empirical results. Instead, it provides the conceptual basis for subsequent doctoral research aimed at empirically validating the framework through qualitative and quantitative methods within private banking operational contexts.