AI implementation can succeed when many different enabling factors align. These include the completeness of the AI model, the affordances provided by local technical infrastructure and the alignment of many other human factors, like clinical staff trust and acceptability, fruitful collaborations, robust governance and training, and the existence of local AI champions who ensure adequate leadership for coordination and change management. Adequate funding to cater for all associated costs of AI (purchase, deployment, post-market surveillance) is also essential. AI implementation in radiography practice builds on the evidence base, governance and training delivered in other fields as well, where there is already some sharing of context and expertise, such as radiology and oncology. It also creates its own evidence base and training and interprets governance relevant to the applications where AI seems to intersect radiography practice. However, the way AI is implemented in radiography and radiology may impact professionals in different ways. For example, while in some cases, it can create efficiencies and better work-life balance, for other contexts and settings, it may result in bottlenecks and potentially staff burn-out due to the new ways of working. Local context and policy will therefore impact AI deployment in many different ways. This chapter discusses the enablers and challenges of AI implementation considering the whole lifecycle of AI products; outlines related regulation and governance, the importance of multidisciplinary and multiagency collaboration and of the need for person-centred AI innovation; highlights theoretical and practical frameworks for AI evaluation; proposes ways by which one can draw a robust business plan; emphasises the need for more customised AI training, systems sustainability; and considers the multidimensional impact of AI. Finally, it briefly discusses why AI implementation may fail. It is important to read this chapter before attempting the chapters of individual modalities, because it provides vital background and context that can facilitate understanding of advanced AI techniques described in these chapters. This chapter has carefully and purposefully balanced theoretical frameworks from academic work and practical experience and advice from early adopters, to bring you the state of the art of AI implementation in radiography.