Agile strategic management has shifted from an operational preference to a survival imperative as organizations confront AI-accelerated disruption, data-driven competitive warfare, and compressed strategic decision cycles. Market volatility, digital-demand spikes, supply-chain oscillations, real-time consumer sentiment drift, AI-augmented competitors, and talent-skill entropy have replaced traditional industry stability assumptions. This study introduces an agile strategy framework that integrates AI decision support, predictive analytics, adaptive KPI design, competitive data intelligence, and automation-ready execution loops. Using a mixed quantitative design descriptive analytics, predictive modeling, decision-latency profiling, and strategic agility scoring the framework evaluates how AI reshapes managerial cognition, resource allocation, campaign orchestration, and competitive advantage sustainability. Results show that static strategic engines overestimate engagement proxies, misinterpret activity bursts, and delay risk surfacing by 3–6 weeks, while AI-enhanced strategic embeddings improve decision fidelity by 180–260% and reduce false strategic flags by 40–55%. Agile AI clusters identified execution fatigue and competitive contraction 2–3 weeks earlier than enterprise planning stacks. The study concludes that agile strategy must evolve from human-paced intuition to AI-paced evidence routing, where strategic resilience is measured by entropy-adjusted agility, not raw engagement. The contribution lies in reframing strategy as a dynamic competitive graph rather than a fixed plan,

enabling earlier intervention, stronger competitive attribution, and audit-grade strategic intelligence for decision-makers