This chapter explores the broader applicability and future potential of the artificial intelligence methodologies developed in this study beyond the authentication of William Billingsley’s rose painting. It outlines pathways for extendingAutoencoder autoencoder-based anomaly detection to other decorative motifs, including alternative floral species, botanical illustration, landscapes, gilt decoration, and figure painting, highlighting the feature adaptations required for each genre. The chapter further considers the integration of two-dimensional decorative analysis with three-dimensional form analysis through photogrammetry and morphometric techniques, enabling holistic authentication frameworks that encompass shape, relief, and surface decoration. Opportunities for application to other ceramic artists and manufactories are discussed, alongside prospects for multimodal and probabilistic fusion of diverse analytical outputs. By framing these developments within museum practice, market authentication, and scholarly research, the chapter positions artificial intelligence as a scalable, extensible, and interdisciplinary tool for the future of ceramic art analysis.