The sixth mass extinction is well under way, with biodiversity disappearing at unprecedented rates in terms of species richness and biomass. At the same time, given the currentpace, we would need the next two centuries to complete the inventory of life on Earthand this is only one of the necessary steps toward monitoring and conservation of species. Clearly, there is an urgent need to accelerate the inventory and the taxonomic researchrequired to identify and describe the remaining species, a critical bottleneck. Arguably, leveraging recent technological innovations is our best chance to speed up taxonomic research. Given that taxonomy has been and still is notably visual, and the recent break-throughs in computer vision and machine learning, it seems that the time is ripe to exploreto what extent we can accelerate morphology-based taxonomy using these advances inartificial intelligence. Unfortunately, these so-called deep learning systems often requiresubstantial computational resources, large volumes of labeled training data and sophisticated technical support, which are rarely available to taxonomists. This thesis is devoted to addressing these challenges. In paper I and paper II, we focus on developing an easy-to-use (’off-the-shelf’) solution to automated image-based taxon identification, which is at the same time reliable, inexpensive, and generally applicable. This enables taxonomists to build their own automated identification systems without prohibitive investments in imaging and computation. Our proposed solution utilizes a technique called feature transfer, in which a pretrained convolutional neural network (CNN) is used to obtain image representations (”deep features”) for a taxonomic task of interest. Then, these features are used to train a simpler system, such as a linear support vector machine classifier. In paper I we optimized parameters for feature transfer on a range of challenging taxonomic tasks, from the identification of insects to higher groups --- even when they are likely to belong to subgroups that have not been seen previously --- to the identification of visually similar species that are difficult to separate for human experts. In paper II, we applied the optimal approach from paper I to a new set of tasks, including a task unsolvable by humans - separating specimens by sex from images of body parts that were not previously known to show any sexual dimorphism. Papers I and II demonstrate that off-the-shelf solutions often provide impressive identification performance while at the same time requiring minimal technical skills. In paper III, we show that phylogenetic information describing evolutionary relationships among organisms can be used to improve the performance of AI systems for taxon identification. Systems trained with phylogenetic information do as well as or better than standard systems in terms of common identification performance metrics. At the same time, the errors they make are less wrong in a biological sense, and thus more acceptable to humans. Finally, in paper IV we describe our experience from running a large-scale citizen science project organized in summer 2018, the Swedish Ladybird Project, to collect images for training automated identification systems for ladybird beetles. The project engaged more than 15,000 school children, who contributed over 5,000 images and over 15,000 hours of effort. The project demonstrates the potential of targeted citizen science efforts in collecting the required image sets for training automated taxonomic identification systems for new groups of organisms, while providing many positive educational and societal side effects.