This chapter explores how digital transformation, artificial intelligence (AI), and predictive analytics are reshaping the landscape of well integrity management. With the proliferation of real-time sensors, fiber-optic monitoring systems, and advanced logging tools, the oil and gas industry is generating vast volumes of data across all phases of the well lifecycle. Harnessing this “Big Data” presents both immense opportunities and complex challenges. When managed effectively, it enables a shift from reactive and scheduled integrity practices toward proactive, predictive strategies that can significantly improve safety, reduce costs, and extend well life. The chapter highlights the core applications of AI and machine learning in well integrity, including predictive maintenance, corrosion forecasting, early anomaly detection, dynamic risk assessment, and automated interpretation of complex logs. These techniques empower operators to anticipate failures and intervene before they escalate into costly or high-risk events. Central to this transformation is the deployment of Digital Twins—dynamic, virtual replicas of physical wells that integrate static and real-time data, physics-based models, and AI outputs into interactive dashboards. Digital twins facilitate enhanced diagnostics, scenario simulations, predictive modeling, and optimized operations, offering a holistic and adaptive framework for integrity management. To support these technologies, robust integrated data platforms are essential. The chapter underscores the importance of breaking down data silos, ensuring data quality, and using intuitive visualization tools to transform complex outputs into actionable insights for engineers and decision-makers. Moreover, the chapter introduces the concept of Smart Fields, where real-time control, automation (e.g., SCADA and PLCs), and AI-driven optimization converge to enable adaptive production and well integrity assurance. These smart systems can dynamically respond to integrity threats, such as SCP, annular pressure build-up, or thermal cycling, and even integrate geomechanical models to manage risks in high-pressure injection scenarios like CO₂ or H₂ storage. Through case studies, system architectures, and maturity models, this chapter presents a forward-looking vision where digitalization is not simply a technological trend but a strategic imperative for well integrity. By integrating AI, digital twins, and smart field technologies, operators can unlock new levels of operational intelligence, resilience, and sustainability in an increasingly complex energy environment.