Auditing Corporate Disclosures with the Assistance of Task-Specific Artificial Intelligence – A Survey-Based Analysis of its Effectiveness and Efficiency

Abstract

This study empirically examines auditors' perceptions of the impact of task-specific artificial intelligence (AI) on the effectiveness and efficiency of the audit of corporate disclosures. We conduct a survey-based analysis, gathering insights from employees of a Big 4 audit firm in Germany. The Big 4 audit firm introduced an AI tool designed to assist in detecting material misstatements in management reports, e.g., by automatically identifying and matching disclosure requirements with the disclosures prepared by the client. Our results indicate that the AI tool enhances audit effectiveness and efficiency although this result is less pronounced in supporting the detection of more complex qualitative issues (e.g., misleading presentation). This study highlights that perceptions of audit effectiveness and efficiency improvements are not solely driven by technological features but are also moderated by auditors' roles, expertise, and engagement with digital transformation. Trust in the tool and its perceived educational value similarly vary along these lines, emphasizing the relevance of implementation strategies, training, and transparent communication when integrating AI into audit workflows.

Keywords: Artificial intelligence, audit quality, audit efficiency, audit effectiveness

JEL Classification: D83, O33, M42