Despite the range of advanced methods available, many reactions, particularly in flow, are still optimized by changing one variable at a time (OVAT) with offline analysis of the results. Automation, data-rich experiments and real-time process analytics can drastically accelerate development and collect process-relevant data quickly and efficiently. Self-optimization and automated Design of Experiments (DoE) have been proven to be excellent methodologies for screening a broad process space and finding optimal process conditions. The material consumption for these optimization methodologies can be drastically reduced by the implementation of droplet or slug flow platforms. The aforementioned methods generally rely on single data points, which are measured once the reactor has reached steady state. Utilizing dynamic experiments can drastically accelerate the collection of dense datasets. In these experiments, ramps over time are executed to explore the design space. This dynamic change is followed with process analytical technology (PAT) and the collected data must be corrected to match the corresponding input setpoints. Several approaches have been described in the literature in recent years, including ramps for a single process variable (often residence time) or multiple process variables simultaneously. The implementation of PAT and data processing models is still a substantial hurdle for non-specialist chemists. This typically requires expert knowledge to develop and calibrate data processing (chemometric) models, in a timeand resource intensive procedure. Automating the workflow for collection, data processing and model generation from dynamic experiment data will drastically accelerate process development timeframes.