Explainability has been a core research topic in AI for decades and therefore it is surprising that the current concept of Explain- able AI (XAI) seems to have been launched as late as 2016. This is a problem with current XAI research because it tends to ignore existing knowledge and wisdom gathered over decades or even centuries by other relevant domains. This paper presents the notion of Contextual Impor- tance and Utility (CIU), which is based on known notions and methods of Decision Theory. CIU extends the notions of importance and utility for the non-linear models of AI systems and notably those produced by Machine Learning methods. CIU provides a universal and model-agnostic foundation for XAI.