Automation, as the implementation of processes to perform activities towards a goal without human assistance, has been a key contributor to the evolution of humanity during the last 25 hundred years, saving us time, and resources, and opening playgrounds outside of human physical capabilities. Advances in operations research and symbolic reasoning powered by the proliferation of micro-electronics surpassed human decision making capabilities, replacing with ease human planners and schedulers. The last two decades also showed us how machine learning excels in the continuous problem spaces, providing predictive capabilities across enormous datasets. As intelligent automation systems grow and evolve to encompass combinations of discrete (symbolic) and continuous problems, their complexity increases exponentially, making building such systems difficult with a linear software engineering force. In this talk, we show how the Composite AI allows can address a wide range of real-world problems.