The study aimed to design a conceptual model for the adoption of generative artificial intelligence (AI) in the Iranian banking industry using thematic analysis and interpretive structural modeling (ISM). This applied research employed an exploratory mixed-method design. In the qualitative phase, data were collected through semi-structured interviews with 13 experts in management, IT, and banking, selected purposefully until theoretical saturation. Thematic analysis was used to extract core, organizing, and overarching themes. In the quantitative phase, the identified dimensions were structured and ranked through the ISM technique using Micmac software to determine the hierarchical relationships among the factors. The thematic analysis revealed 81 basic themes grouped into 20 organizing and 8 overarching themes: digital infrastructure maturity, organizational capabilities in managing technological change, governance, value creation, behavioral intention, perceived ease of use, perceived usefulness, and attitude. ISM analysis indicated that digital infrastructure maturity and organizational capability form the foundational layer, governance occupies the intermediate layer, and value creation represents the ultimate outcome of AI adoption. Behavioral intention emerged as a linking construct mediating between perception-based factors and organizational outcomes. Successful adoption of generative AI in Iranian banking requires a synergistic alignment of technological, organizational, and human dimensions. Enhanced digital infrastructure, effective governance, and positive user attitudes constitute the essential enablers for achieving operational efficiency, customer experience enhancement, and sustainable competitive advantage.