Enterprises operate in environments marked by volatility, uncertainty, complexity, and ambiguity (VUCA), where decision latency and organizational inertia can erode competitive advantage. This paper examines how contemporary artificial intelligence (AI)—spanning predictive and prescriptive analytics, large language models (LLMs), agentic systems, and decision‐intelligence platforms—can systematically reduce decision cycle times, increase decision quality, and enhance strategic agility at scale. We develop an integrative perspective linking AI capabilities (data sensing, inference, simulation, and orchestration) to dynamic capabilities (sensing, seizing, and reconfiguring), and we position governance, risk, and compliance (GRC) as the enabling scaffold that converts AI potential into reliable, auditable enterprise outcomes. The discussion synthesizes recent empirical and design‐science evidence on AI’s impact across functions (strategy, operations, finance, supply chain, and customer experience), outlines architectural patterns (LLMOps, retrieval‐augmented generation, and agentic workflows) for production-grade deployment, and surfaces boundary conditions including data quality, bias, model brittleness, and socio-technical adoption. We conclude with a research and practice agenda that prioritizes measurability (decision KPIs), robustness (controls and monitoring), and adaptability (closed-loop learning) to translate AI investments into durable strategic efficiency and organizational agility.,.