Due to this wide variety of data-driven tools, process development is an interdisciplinary exercise, requiring expertise from synthetic chemists, analytical chemists, data scientists, chemical engineers and more (Figure , top). The synthetic route, process models, and control strategy are typically developed individually, resulting in overlapping tasks and a significant extent of repetition. Although continuous processing (flow chemistry) offers many benefits, such as enhanced product quality, increased efficiency, and cost savings, developing such processes requires even more of a multidisciplinary team of scientists and engineers from various backgrounds. By unifying and Figure Top: Example of a traditional optimization approach for process development. Typically, the reaction and the PAT models are developed separately by several team members with different background knowledge. Bottom: The dual modeling approach, utilizing a high degree of automation to combine PAT and reaction optimization, resulting in a full process model. automating the experimental and modeling steps in this workflow, we anticipate significant savings in time and materials, alongside enhanced control of the resulting processes.