We provide an overview of the many ways in which biodiversity scientists are applying AI-related technologies to solve research problems in ecology, global change biology, and conservation. Currently, much of this work focuses on the challenges of large-scale data acquisition, analysis, and integration, especially with large-volume digital data sources such as images and audio. Scientific uses cases are broad, including biodiversity monitoring and discovery, understanding the effects of environmental change, and untangling ecosystem dynamics. We provide three case studies from our own work. The first focuses on how AI can be used to derive information from plant photographs about key phenological traits such as flowering duration and time of leaf budburst. This case study also demonstrates an end to end system that relies as well on data integration tools that have their basis in symbolic AI approaches. The second case study focuses on AI applied to acoustic recording from the environment via new, low cost passive sensors. We zoom back out at the end to discuss the challenges with data quality and biases, inherent in all AI approaches, and describe a longer-term view of research agendas in this wide-open research space.