Automation, Machine Learning, and Artificial Intelligence are revolutionizing laboratory operations with key advancements in high throughput screening. The goal of automation remains the same; create a world where researchers are able to design, execute, analyze, model, and create a new set of experiments with support from advanced automated software and hardware packages. Exploring practical strategies to address real-world challenges is critical in implementing new technologies without interrupting the already operational DMTA cycle. In chemical research and development, identifying the correct instrumentation, software, and workflow aids in improving speed of deliverables. Alongside choosing the correct packages, outlining a clear strategy of implementation is essential in overall laboratory strategy. Examples of the planning through implementation process will be shared with honest hurdles experienced in the not-so-perfect world of automation.
