Abstract: Many manufacturing companies are facing an acute shortage of qualified workers. Deploying robotic cells is a potential solution to address this challenge. Historically robots have been deployed only in mass production applications in manufacturing. A large fraction of manufacturing is classified as high-mix manufacturing where a large variety of products are produced. Manual programming of robots is not a viable solution in high-mix manufacturing applications. Robotic cells need to be powered by physical AI to make them useful in high-mix manufacturing applications. Physical AI in the context of manufacturing should be viewed as a complex system that involves interactions among multiple AI components. The system should use the right functional decomposition to ensure that it is able to achieve the desired trade-off in performance and modularity. This talk highlights key requirements for developing physical AI for powering robotic cells for high-mix manufacturing applications. It also makes the case for approaches that combine model-based and data-driven AI methods to meet the needs of manufacturing applications and describes the role of generative AI approaches in smart manufacturing applications. Finally, it describes how AI can be used to enhance digital twins and augment human-machine interfaces in manufacturing applications.