In 2006, the British mathematician Clive Humby coined the famous sentence “Data is the new Oil” which marked the beginning of the big data era. This ever-growing amount of data we collect, and store everyday has also been instrumental in the development of modern Artificial Intelligence (AI). After decades of hesitation between the symbolic and the data-driven approaches, AI has embraced the power of data and definitively pierced the glass ceiling that was cornering it in academic labs. In particular, the development of deep learning has revolutionized various domains of application to the point that it is now the state-of-the-art solution for complex problems such as image or speech recognition, automatic decision making, natural language processing, chess or go playing, just to name a few. The successes of deep learning have been so various and impressive that one could have naively assumed that with enough curated data and CPUs, it should then be possible to solve any kind of problems. As of today, it turns out that this is not true, mainly because (i) curated data are often difficult, expensive, or simply impractical to obtain (ii) deep learning remains an extremely data inefficient technology and (iii) the opacity of the decision-making process of deep learning models slow down their adoption or make them just unsuitable for heavily regulated industries. To address these limitations, AI researchers are now focusing on the development of Knowledge-Aware AI systems (KA-AI). As reported in Deng et al, 2020, “integrating human knowledge into machine learning can significantly reduce data requirements, increase reliability and robustness of machine learning, and build explainable machine learning systems”. In this presentation we will review different ways to achieve KA-AI and present a concrete example from the airline travel industry.