Despite the great potential and clinical success of nanomedicines, their design and development remain far from trivial. At the core of this challenge is the approach to formulation. The initial stages of formulation development are traditionally reliant on trial-and-error, placing large demands on cost and time . Additionally, many of the nanomedicines reported in the literature are complicated, with the desire to create novel systems and publish in high-impact journals often trumping translational feasibility and commercial viability . This is fundamentally interlinked with the fact that experimental reproducibility is a growing concern, which is only hindered by more complex formulations . As a result, there is a pronounced gap between the number of formulations reported in the literature and candidate formulations that truly hold clinical promise. There is a sentiment among researchers that some form of standardization in reporting information would be beneficial for improving reproducibility and identifying clinical value, but the implementation of such standards is more contentious . A natural step is to draw inspiration from other scientific fields, including drug discovery or synthetic chemistry. These fields have made strides towards experimental automation, and the implementation of data-driven tools, such as artificial intelligence (AI) and machine learning (ML), to augment reproducibility and drive innovation. With an increasing number of such examples, from human-free synthesis of pharmaceutical compounds to the data-driven discovery of new antibiotics , there are lessons to be applied in pharmaceutical formulation development. In this article, we consider the potential role of automated pipelines integrated with AI/ML in nanomedicine formulation design and development (Figure ).