In this chapter we describe several works that explore the relation between causal discovery and artificial intelligence. In one case study, a causal Bayesian network is used to reduce the problem of bias when training a machine learning system. A second example, shows how to understand causal relations between events in textual descriptions using large language models. Then we present the incorporation of causal models in generative adversarial networks, providing a finer control of the images generated. The next work, describes how the causal relations between objects in an image can be estimated from the causal direction between pairs of image features based on a neural network classifier. Finally, another case study, shows the use of causal models for accelerating learning in drone navigation, including learning the causal model at the same time that the drone is learning a policy using deep reinforcement learning.