This in‑depth e‑book draws on more than a decade of hands‑on experience transforming internal audit functions into truly data‑driven, AI‑augmented assurance capabilities. It goes far beyond theory—walking readers through the full analytics and AI lifecycle in audit, from data sourcing and feature engineering to model deployment, monitoring, and continuous improvement. Using concrete examples of algorithms and analytics techniques—such as anomaly detection for transaction testing, outlier analysis for issue identification, process mining for end‑to‑end control assurance, sentiment analysis for audit culture assessment, and predictive models to anticipate emerging risks—the book demonstrates how audit can evolve from retrospective checking to forward‑looking, real‑time risk intelligence. At its core, the e‑book explores the human side of transformation: how auditors' roles, skills, judgement, and credibility evolve when working alongside AI. It shows how internal audit can shift from manual assurance activities to continuous, insight‑driven assurance, enhancing relevance, influence, and strategic value across the organisation—while remaining grounded in governance, ethics, and professional standards. Throughout the book, conceptual frameworks are tightly connected to real‑world application. Readers learn not only what AI‑augmented auditing looks like, but how to implement it in practice—across the audit lifecycle, from risk assessment and planning to fieldwork, reporting, and follow‑up. The result is a practical, experience‑based guide for audit leaders, professionals, and transformation teams who want to future‑proof internal audit, proactively anticipate risk, build analytics capability, and position audit as a trusted, forward‑looking partner in an increasingly complex and data‑intensive business landscape.