In terms of research, introducing some elements of automation and ML in the current approach to nanomedicine development is necessary to incite more widespread adoption of these tools. However, full automation or relying solely on ML-based predictions is not necessarily the end goal. Inevitably, certain processes will be too hard to automate, and may be more trouble than they are worth. As such, it will be necessary to evaluate the most salient and amenable processes for automation. For example, small-scale formulation screening is well suited for automation by increasing the throughput and improves long-term sustainability by reducing the amount of material otherwise needed if it was carried out by hand. Humans will continue to play a role and have the space to be creative, by spending more time in experimental design and planning. In any case, there are options to explore, such as the wide range of commercially available robots (e.g., liquid handlers, microfluidic systems) and expansive set of opensource ML frameworks (e.g., PyTorch, Tensorflow, etc.) which can be used to modulate the level of automation and ways that AI/ML is applied.