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In data-rich business environments, traditional strategic planning—built on long-term forecasts, annual budgets, and fixed resource allocation—has become increasingly ineffective. Digital markets reward speed, iteration, and real-time adaptation rather than prediction and control. This managerial perspective article argues that digital strategy must be reconceptualized as continuous experimentation: a strategic logic in which hypothesis generation, rapid testing, data-driven learning, and iterative decision-making replace static plans. Drawing on recent scholarship in digital transformation, agile strategy, and organizational learning, the article demonstrates how leading firms operationalize experimentation through A/B testing platforms, real-time analytics, and cross-functional feedback loops. A new strategic framework—the Continuous Experimentation Strategy Loop—is introduced to guide managers in embedding experimentation into core planning processes. The framework highlights six interlocking elements: hypothesis generation, rapid experimentation, data capture and analytics, learning and insight generation, decision and iteration, and scaling with feedback loops. Practical implementation challenges, including organizational structures, cultural barriers, and risks of over-testing, are examined. The article concludes that in volatile, data-abundant contexts, the ability to experiment continuously is not a tactical tool but the central mechanism of strategic renewal. Managers who treat strategy as perpetual experimentation gain superior adaptability, faster innovation cycles, and sustained competitive advantage.