Artificial intelligence has been widely used in various scenarios due to its powerful learning and generalization ability. However, most of the existing AI techniques are facing three major challenges. First, existing AI techniques are hard to use for ordinary users, which depends on AI experts to select appropriate models, choose reasonable parameters and write programs, so it is difficult to be widely used in non-IT fields. Second, the training efficiency of existing AI algorithms is low, resulting in a lot of waste of computing resources, even delaying decision-making opportunities. Third, existing AI techniques are strongly dependent on high-quality data. If the data quality is low, it will make error decisions. The database technology can effectively solve these three problems, and AI-oriented data management has been widely studied. Firstly, this paper gives the overall framework of data management in AI. Then, it presents a detailed overview of AI-oriented declarative language model, AI-oriented optimization, AI-oriented execution engine, and AI-oriented data governance. Finally, the future research directions and challenges are provided.