The seamless integration of polymer informatics and AI/ML models with automated experimental platforms in day-to-day research activities could radically reduce the time for materials discovery and development. Continuous flow polymerization offers unique advantages for the synthesis of new polymeric materials and to serve as platform for automated experimentation. Automated continuous flow can greatly amplify the quantity of experiments and the breadth of polymer composition/architecture space that may be reliably accessed, providing rich datasets which can deliver deep insight into polymerization processes and catalysis. Additionally, these datasets can form the basis of new predictive AI models for catalyst design, materials synthesis, and materials property prediction. To take full advantage of the capabilities of automated continuous flow polymerization, however, a fully integrated lab infrastructure is needed. Specifically, this entails the ability to both process, manage and store experimental and characterization data for consumption within AI/ML pipelines as well as the ability of AI/ML models to deliver actionable hypotheses to researchers. In this talk, we will discuss our approach towards development of continuous flow platforms for ring-opening polymerization, how these systems may be readily automated to enhance experimental capability, and how the experimental data obtained from continuous-flow platforms can be utilized for AI/ML model creation. Challenges associated with the data modeling experimental data from polymerization experiments and new approaches towards representation of stochastic materials within computational systems will also be discussed.