The increasing complexity and uncertainty of modern financial systems have intensified the need for advanced analytical tools capable of simulating crisis conditions and evaluating organizational resilience. Traditional scenario planning methods are often limited by static assumptions, expert bias, and inability to capture nonlinear interdependencies in financial environments. In this context, generative artificial intelligence (GenAI) has emerged as a transformative approach for designing dynamic, data-driven crisis scenarios and enhancing strategic decision-making processes. This study aims to investigate the application of generative AI models in constructing financial crisis scenarios and analyzing organizational resilience under uncertain and volatile economic conditions. The research adopts a conceptual-analytical methodology supported by simulation-based reasoning. Generative models such as large language models and diffusion-based architectures are utilized to generate plausible macro-financial shock scenarios, including liquidity crises, credit shocks, currency devaluation, and systemic risk propagation. Furthermore, organizational resilience is evaluated through adaptive response indicators such as absorptive capacity, recovery speed, and structural flexibility. The findings suggest that generative AI significantly enhances scenario diversity, reduces cognitive bias in strategic planning, and improves the robustness of resilience assessment frameworks. The study also highlights that organizations integrating AI-driven scenario generation into their risk management systems demonstrate higher adaptability and faster recovery in simulated crisis conditions. The results contribute to the literature on artificial intelligence in strategic management, financial risk analysis, and organizational theory by introducing a novel AI-assisted framework for crisis foresight and resilience evaluation.