Recent advances and increased adoption of laboratory automation are accelerating the Pharmaceutical drug discovery and development process. These advancements have created a new set of unmet analytical challenges due in part to the variety and number of experimental samples being generated. This session uniquely combines both academic and industrial research to present state-of-the-art development of new approaches for these unmet challenges, including high throughput analytics, artificial intelligence modeling, and prediction techniques for chemical processes. The first portion of the session will focus on how machine learning can be utilized for high throughput analytical methods including chromatography and mass spectrometry. The seminar will then shift to using data generated in an automated environment to perform more accurate chemometric models and mechanistic models supported by artificial intelligence. The sessions provides a forum for engineers and scientists, from industry and academia, to share cutting-edge research and produce fruitful discussion on the use of robotics and machine learning in laboratory automation. This Pittcon symposium is a great opportunity to highlight the challenges for analytical scientists in the future of pharmaceutical R&D and the criticality of innovation in the field.