Mapping the factors influencing artificial intelligence adoption in auditing: a bibliometric analysis

Abstract

Artificial intelligence has emerged as a decisive force in the auditing profession because it enhances automation, improves fraud detection, and strengthens professional judgment. However, the academic literature still lacks an integrated view of the factors that shape its adoption in auditing. This study addresses this gap by examining the intellectual structure and research trends on artificial intelligence adoption in auditing from 2016 to 2025 through a bibliometric approach. Data were obtained from the Dimensions database, and 210 English-language journal articles were retained after screening. The analysis employed text-based co-occurrence techniques to identify the main research themes and conceptual linkages. The results reveal five dominant lines of work: the transformation of internal and financial audits, the use of data analytics and digital tools, the adoption of new technologies and their effects on efficiency within audit firms, auditors’ perceptions and behavioral responses, and the broader opportunities and challenges facing the auditing profession. These findings show a progression from conceptual discussions toward empirical examinations that consider organizational, ethical, and strategic implications. The study offers a consolidated overview of how artificial intelligence adoption has evolved in auditing and provides a reference point for future investigations seeking to promote responsible and sustainable technological integration in assurance practices.