Quantitative business methods have crossed a threshold in 2026: the tools are no longer primarily about measurement — they are about action. The statistical and econometric foundations that anchored quant practice for decades remain essential, but they now sit beneath successive layers of machine learning, causal inference, time-series forecasting, and AI-powered analytics that together enable organisations to move from describing what happened to recommending what to do next. Decision intelligence combines causal inference, probabilistic forecasting, and reinforcement learning to drive actions, not just insights. Self-service BI platforms democratise analysis for domain teams. MLOps pipelines ensure reproducibility and model governance at scale. And the rise of agentic AI — autonomous systems that pursue multi-step objectives — is raising the stakes for explainability, human-in-the-loop controls, and ethical guardrails. Taken together, these shifts make quantitative capability a strategic leadership priority, not a technical backroom function. This edition traces the full arc, from foundational concepts through applied methods and real-world deployment