This article provides a systematic review of the implementation of Artificial Intelligence (AI) and Robotic Process Automation (RPA) in the accounting and auditing professions. It uniquely and holistically identifies the benefits, challenges, drivers, and endorsement levels of implementing these emerging technologies and investigates gaps in the literature to prioritize future studies.
The systematic review method was chosen to critically assess the body of literature following Tranfield et al. (2003) systematic review approach. The years 2016–2022 have been considered to gain insights into recent advancements in AI and RPA deployments. A rigorous selection process was designed to identify the relevant literature and ensure the quality and standard of the reviewed results.
The study shows that the potential benefits of implementing AI and RPA can be categorized into four categories: monetary, quality reporting, operational, and customer-related benefits, and the challenges and limitations can be categorized into four categories: ethical, regulatory, societal, and technical challenges. Initial studies generally show that accountants and auditors are not fully endorsing these implementations, mainly due to concerns about job loss and the trustworthiness of these technologies.
The coding process of the examined articles was done manually, which could introduce subjectivity despite efforts to prevent this through multiple coders and rounds of review. Also, the methodological approach used may be criticized for eliminating articles with a journal ranking below 2. M, the review only included articles written in English, which may exclude relevant studies. Moreover, the review did not examine if certain regions were over-represented in the sample and a recommendation based on this has been made in the future research directions section.
Our systematic review shows that auditors, their clients, and regulatory bodies are working independently. This highlights the importance of a framework or a mechanism to govern the relationship between them and build common expectations to scale up the utilization of RPA and AI in a well-regulated profession. This review would help audit firms and organizations to build strategies to better understand the vital factors and limitations towards scaling up the deployment of such technologies.
This review can be seen as a guide for managers and policy makers and offers future research direction for scholars. Our study facilitates (1) understanding which type of technology to utilize, given the different potential benefits of these emerging technologies in a well-regulated profession, and (2) realizing the factors holding back the potential of scaling up the adoption of AI and RPA.
