Artificial intelligence is reshaping quantitative methods in business, shifting the focus from static statistical analysis toward dynamic, predictive, and prescriptive decision-making. Techniques such as regression, optimisation, forecasting, and simulation are increasingly enhanced by machine learning, generative AI, and real-time analytics that process vast structured and unstructured data. AI improves the speed and accuracy of modelling and uncovers complex, non-linear relationships conventional methods often miss. Across finance, marketing, supply chains, and human resources, AI-powered quantitative models are sharpening demand forecasting, risk assessment, pricing, and resource optimisation, while automating routine analysis so managers can focus on strategy. None of this works, however, without data quality, transparency, and human oversight. This edition traces that arc — from method, to model, to deployment, to strategy.