Artificial Intelligence (AI) has demonstrably improved banking’s financial performance, with Generative Artificial Intelligence (GenAI) predicted to add $200-340 billion through increased productivity. Despite growing organizational interest and investment in AI, GenAI implementation remains nascent, with no established frameworks and 95% of enterprise pilots failing to scale. The rise of GenAI has heightened strategic urgency for AI transformation. Value realization requires navigating complex strategic decisions. Digital transformation offers a useful analytical baseline for understanding AI strategy; however, AI exhibits distinctive characteristics including challenges to traditional resource classification, altered build vs. buy economics, and unprecedented development velocity that differentiate it from prior technology adoptions. This study examines how banking organizations and their leaders make strategic AI decisions through qualitative interviews of 10 leaders across diverse global institutions. We identify five strategic areas where banks face critical choices, shaped by five cross-cutting factors: leadership buy-in, regulatory context, organizational characteristics, legacy approaches, and perception of GenAI. Applying established strategic theories as interpretive lenses reveals that each offers partial insights, with AI’s distinctive characteristics challenging direct theoretical application. We contribute a comprehensive framework mapping strategic choices available to banking leaders and demonstrate that understanding strategic AI decision-making requires drawing on multiple theoretical lenses, with careful contextualization.