For life science companies, healthcare providers, patients and consumers, AI offers great potential to streamline processes and achieve better treatment results. On the one hand, the findings from data generated in the real world setting could take personalized medicine to a new level by individually tailoring diagnosis and treatment in terms of effectiveness and safety to the patient. On the other hand, the linking of clinical study data and real world data with the enormous advances in biology and medicine is a prerequisite for more targeted research and more efficient development processes. In her talk Dr. Anna Bauer-Mehren describes the role of real world data, data science or data analysis in pharmaceutical research and the resulting new opportunities for personalized medicine. In particular, she addresses the importance of high quality data and Roche’s efforts to make data FAIR. In their view, this is essential for the success of AI methods in R&D. Using several examples, she shows in which areas of pharmaceutical research AI is already being used successfully, but also discusses which areas still have great challenges. Her examples include the use of deep learning (AI) for process optimization in the development of therapeutic antibodies and for the automatic annotation of tumor biopsy images in digital pathology. She also discusses how AI is used to develop new digital or image-based biomarkers to differentiate between different tumor immunophenotypes.