The integration of Artificial Intelligence (AI) into Internet of Things (IoT) systems has garneredconsiderable attention for its ability to enhance efficiency, functionality, and decision making.To drive further research and practical applications, it is essential to gain a deeper understandingof the different roles of AI in IoT systems. In this systematic literature review, we analyze103 articles describing Artificial Intelligence of Things (AIoT) systems found in three databases,i.e. Scopus, IEEE Xplore, and Web of Science. For each article, we examined the tasks for whichAI was used, the input and output data, the application domain, the maturity level of the system,the AI methods used, and where the AI components were deployed. As a result, we identified sixgeneral tasks of AI in IoT systems, and thirteen subtasks, the most frequent being prediction,object and event recognition, and operational decision-making. Moreover, we conclude thatmost AI components in IoT systems process numeric data as input and that healthcare isthe most common application domain followed by farming and transportation. Our analysisfurther revealed that most AIoT systems are in early development stages not validated in realenvironments. We also identified that Convolutional Neural Networks is the most frequentlyemployed AI method, with supervised learning being the dominant approach. Additionally, wefound that both AI deployment, either in the cloud or at the edge, are frequent, but that hybriddeployment is not that common. Finally, we identified key gaps in current AIoT research andbased on this, we suggest directions for future research.