This section investigates the ways in which algorithmic, machine-based, and generative technologies are fundamentally reshaping art historical research, catalyzing new paradigms in both methodology and collaboration. The chapter begins by outlining how the intersection of art history and computational systems has initiated a series of profound shifts, moving beyond conventional models of visual inquiry toward data-driven and automated approaches that uncover patterns and relationships invisible to the unaided eye. It critically assesses the use of generative tools for scholarly research, from large-scale data aggregation and multimodal interpretation to automated bibliometric mapping and the creation of synthetic datasets. Through detailed case studies, the discussion foregrounds emerging methodologies in computational art analysis, such as neural network-based attribution, stylistic clustering, and automated iconographic mapping, illustrating their strengths and limitations within academic practice. The chapter also explores how smart, collaborative platforms are breaking down disciplinary silos, fostering new alliances among art historians, computer scientists, data engineers, and cultural institutions.