This presentation introduces Variety Dynamics and demonstrates its practical application to highly complex problems, revealing both its effectiveness and efficiency in situations where conventional systems thinking approaches typically prove inadequate or fail entirely. The axioms of Variety Dynamics indicate that most difficult, interesting or important real-world situations do not conform to the foundational assumptions on which Systems Science methods are based. Yet practitioners routinely apply these methods regardless, leading to demonstrable failure. This challenges the field's foundational belief that systems approaches can address any situation regardless of complexity. Variety Dynamics is epistemologically more valid and much faster than causal systems approaches for complex and hyper-complex systems, providing more insightful, rapid guidance for professional decision-making in situations involving power asymmetries: disaster management, diplomacy, user interface design, epidemics, climate change, transport management, urban planning. Variety Dynamics identifies leverage points through variety distributions, revealing solutions invisible to causal methods. Easy for managers, decision-makers and designers to understand and use, the fundamental shift to variety-based thinking marks a useful change from causal systems methods. Four case studies demonstrate rapid, effective application of Variety Dynamics across diverse domains, achieving in minutes what conventional approaches require weeks or months—when they succeed at all. In each case, Variety Dynamics provides rapid insights for situations that would be intractable and slow using conventional causal Systems methods. Variety Dynamics works without requiring massive technical resources or specialist expertise that other Systems methods demand. It requires thinking in options (variety) rather than causes. It integrates well with AI: gathering information, applying Variety Dynamics axioms, and analysing situations faster than humans while being easily questioned. Practical experience with Claude AI demonstrates this is valuable and effective for real-world complexity. The presentation concludes with implications for systems science. If highly complex problems can be addressed more effectively without causal analysis, what does this suggest about our methodological toolkit? How should practitioners be trained? What research becomes possible beyond the causal paradigm? This presentation challenges fundamental assumptions of Systems Science and offers Variety Dynamics as a practical, demonstrable alternative for addressing the majority of real-world complex challenges which are in reality beyond the reach of causally-based Systems Science methods.