Artificial intelligence has recently experienced a remarkable increase in attention, following staggering achievements in applications such as image, text and speech recognition, self-driving cars or chess and Go tournaments. It is therefore not surprising that also the financial industry is ever more heavily trying to improve investment decisions by incorporating self-learning algorithms into the investment process. For that matter, the application of quantitative tools and algorithms in order to define systematic trading strategies has already a strong history in the hedge fund industry. Against this backdrop, quantitative hedge funds may provide a fertile soil for the application of new machine learning techniques. But do all sectors of the asset management industry exhibit characteristics that can be exploited by artificial intelligence tools to uncover new patterns? What could be the especially relevant fields? Are there limits beyond which additional computing power and greater data availability have only marginal benefits? This research note provides some initial answers. It shows that the adaptivity and self-learning capability of machine learning tools could add value along the entire value chain of an asset manager. However, the inherently flexible nature of machine learning methods is also the biggest challenge. These methods must be applied thoughtfully and in the right context. We start with a general overview of machine learning, then elaborate on specific applications in quantitative asset management, highlighting the limitations, challenges and possible remedies before reaching our conclusions.