Implementation of AI-driven automation: A game-changer in accounting research

Purpose: This study examined the implementation of Artificial Intelligence-driven Automation as a game changer in accounting research. Specifically, this study assessed the advantages and disadvantages of AI-driven automation in enhancing the quality of accounting research. Methods: A descriptive survey design was used in the study. The study sample comprised of 137 accounting academics. Primary data for this study were collected using a structured questionnaire. The collected data were assigned quantitative measurements using a Likert scale system of ranks. Descriptive analytical tools (frequency and mean-point analyses) were used to analyze the data with the aid of the SPSS version 25 software. Results: The findings show a general consensus that AI-driven automation enhances the accuracy, efficiency, and comprehensiveness of accounting research, with high acceptance of its benefits. However, there are notable concerns about potential drawbacks such as reduced originality, difficulties in validation, and the risk of introducing biases or compromising ethical standards. Limitations: This study’s limitations include a narrow sample of academics, potential response biases, and the inability to assess long-term AI impacts across diverse accounting professionals. Contribution: The implementation of AI-driven automation represents a game-changer in accounting research because it offers new opportunities to enhance the quality, efficiency, and scope of academic inquiry, as well as challenges and risks that must be carefully managed to ensure that the benefits of AI are fully realized while maintaining the integrity and rigor of the research process. Therefore, this study recommends that academic institutions and research ethics committees develop workable training programs that emphasize the importance of maintaining human oversight, creativity, and ethical standards when utilizing AI-driven automation in accounting research.