Artificial intelligence (AI) has become increasingly prominent in consumer technology and is emerging as a core component of non-destructive testing and evaluation (NDT/NDE) 4.0. This chapter provides a comprehensive overview of AI fundamentals, common terms, techniques, and their applications in NDT/NDE. Despite its potential, integrating AI into safety-critical environments faces challenges, including data scarcity, lack of standards, and uncertainties within NDT/NDE organizations. The paper highlights a paradigm shift from standalone AI model development to holistic workflow integration, including the importance of user experience and lifecycle management consisting of dataset building, deployment, monitoring, and retraining of AI models as well as the implications on (digital) inspection infrastructure. While automated defect recognition (ADR) has been the center of many AI applications in NDT/NDE so far, new approaches like Critical Item Detection (CID) and Single Item Defect Analysis (SIDA) support an upcoming workflow orientation by combining (AI) algorithms in processing pipelines. While there is an absence of established standards for AI in NDT/NDE, ongoing initiatives, guidelines, and frameworks aim to address these gaps. As companies gain experience and better understand AI’s capabilities and limitations, standards will evolve, enabling more interconnected and intelligent solutions and advancing NDT/NDE 4.0.