Literature chronicles how Artificial Intelligence (AI) and related technologies were initially prevalent in more process-oriented activities but have now progressed into the knowledge sector, creating a unique opportunity for professionals to rethink how they engage with their role in an organisational context (Chiu et al., 2016). While some suggest that a variety of accounting roles will be replaced by AI-related technologies (Frey & Osborne, 2017), there is a growing recognition that accounting can in fact harness AI’s potential to add value to organisations (Issa et al., 2016; Issa & Kogan, 2014).An exploration of the accounting literature revealed a dearth of research exploring accounting and AI. Both Gray et al. (2014) and Sutton et al. (2016) recognise the extent of literature on accounting-related topics in traditionally non-accounting publications, highlighting the need for multi-disciplinary research in this area. This study uses a form of topic modelling to analyse literature exploring AI and related techniques in an accounting context from 1990 to 2020. Latent Dirichlet Allocation (LDA) been used to enable probabilistic, machine-based interrogation of large volumes of literature (Aziz et al., 2022). This study applies this LDA approach to the abstracts of peer reviewed academic publications from a variety of disciplines, to identify the most significant AI and accounting topics discussed in the literature.The paper contributes a comprehensive typology of AI and accounting research and represents one of the first applications of probabilistic topic modelling to the accounting literature
