This chapter introduces agentic decision intelligence as a blueprint for AI-enabled enterprises. It begins by redefining decision-making through a multi-layered strategic intelligence stack that integrates decision theory, causal inference, reinforcement learning, generative models and agentic AI. Subsequent sections examine customer intelligence systems that transform raw behavioral signals into actionable priors and illustrate their impact; for example, one implementation increased cart creations while reducing abandonment (≈18%), and other companies attributed about one-third of purchases to recommender systems. The discussion then turns to operational excellence—encompassing movement intelligence, asset-health intelligence and resource-efficiency intelligence—and summarizes how AI-driven logistics and predictive maintenance yield double-digit reductions in route distances, fuel use and unplanned downtime (≈20–30%). A brief overview of emerging paradigms, such as retrieval-augmented generation and agentic architectures, highlights their transformative potential and the need for new governance and MLOps frameworks. The chapter concludes by emphasizing that AI agents woven into interlocking feedback loops not only drive efficiency and resilience but also generate compounding benefits over time.