The world is becoming more dynamic and making use of real-time data is becoming increasingly important. We see a growing number of services becoming ‘on-demand’, including food, transportation, delivery etc. This trend will only continue to grow. The current state of the art in machine learning cannot perform in this environment, since it just relies on past patterns and correlations to make predictions of the future. In order to make consistently accurate predictions and to achieve true artificial intelligence, the development of new science that enables machines to understand cause and effect is required. Understanding true causal drivers enables causal AI to navigate complex and dynamic systems, being able to perform as its environment changes. In addition, causal AI is capable of ‘imagining’ scenarios it has not encountered in the past, allowing it to simulate counterfactual worlds to learn from, instead of relying solely on ‘training’ data. Perhaps most interestingly, understanding causality gives an AI the ability to interact with humans more deeply, being able to explain its ‘thought process’ and integrate human knowledge.