An examination of the effects of artificial intelligence-powered automation on the effectiveness of audit quality: An analytical study in U.S. organizations

Abstract

The increasing adoption of artificial intelligence (AI) has transformed organizational processes across industries, including the auditing profession. AI-powered audit automation enables organizations to enhance audit efficiency, improve analytical accuracy, and reduce operational costs, thereby improving audit quality. Despite the growing adoption of AI technologies in auditing practices, empirical evidence of their impact on audit quality in the United States remains limited. This study examines the effects of AI-powered automation on audit quality by focusing on three key dimensions: efficiency, accuracy, and cost-effectiveness. A quantitative research design was employed using a structured questionnaire distributed to auditors working in organizations across the United States. A total of 207 valid responses were collected and analyzed using correlation and multiple regression analyses to test the proposed hypotheses. The findings reveal that efficiency, accuracy, and cost-effectiveness all have significant positive effects on audit quality. Among these factors, efficiency emerged as the strongest predictor, followed by accuracy and cost-effectiveness. The results indicate that AI-enabled audit automation enhances the reliability, effectiveness, and overall quality of auditing processes by facilitating faster data processing, reducing human error, and optimizing resource utilization. This study contributes to the literature by providing empirical evidence on the role of AI-powered automation in improving audit quality within a highly regulated auditing environment. The findings offer valuable insights for audit practitioners, organizational leaders, and policymakers seeking to leverage AI technologies to strengthen audit performance and improve assurance outcomes. The study also provides a foundation for future research examining AI adoption and audit effectiveness across different industries and institutional contexts.

Keywords

Artificial intelligence / Audit automation / Audit efficiency / Audit quality / Artificial intelligence accuracy / Artificial intelligence cost effectiveness / United States

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