Impact of artificial intelligence on auditing: an evaluation from the profession in Jordan

Abstract This study looks at how profession thinks artificial intelligence (AI) has changed auditing activities in the Jordan Free and Development Zones Group. It focuses on how AI has improved auditing processes, communication with clients, and task management. A total of 336 auditors participated in a structured survey addressing topics such as enhancing audit efficiency, leveraging AI for client interactions, training programs, and ensuring data security. Data was analyzed with Smart PLS 4, revealing that AI significantly boosts audit efficiency, accuracy, and client communication quality. The findings show that integrating AI into audits enhances work quality and streamlines governance practices. However, challenges persist, including inadequate technological infrastructure, data privacy concerns, and ethical compliance. Auditor training is a key factor in maximizing AI’s benefits and improving governance and audit efficiency. This study uniquely contributes to the literature by highlighting AI’s role in transforming audit quality and governance within a specific context—Jordan’s Free and Development Zones Group. It underscores tailored strategies for the adoption of AI, distinguishing it from broader studies. Recommendations include strengthening regulatory frameworks, investing in secure AI systems, and offering comprehensive training to ensure ethical, efficient audits. These findings provide practical implications for audit firms and policymakers. Future studies could examine the long-term effects of AI on audit results and compare its application across various industries.

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Publication Details

Journal
Discover Sustainability
Published
2025-04-07
DOI
https://doi.org/10.1007/s43621-025-01058-3
Citations
7
Primary Topic
Organizational and Employee Performance
Type
article
Field-Weighted Citation Impact
17.37
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article

Impact of artificial intelligence on auditing: an evaluation from the profession in Jordan

Patricia Milanés Montero, Esteban Pérez Calderón, Samer Alrahamneh
7 citations
Discover Sustainability
Organizational and Employee Performance
17.37
article

Impact of artificial intelligence on auditing: an evaluation from the profession in Jordan

Patricia Milanés Montero, Esteban Pérez Calderón, Samer Alrahamneh
article en
7 citations

Abstract

Abstract This study looks at how profession thinks artificial intelligence (AI) has changed auditing activities in the Jordan Free and Development Zones Group. It focuses on how AI has improved auditing processes, communication with clients, and task management. A total of 336 auditors participated in a structured survey addressing topics such as enhancing audit efficiency, leveraging AI for client interactions, training programs, and ensuring data security. Data was analyzed with Smart PLS 4, revealing that AI significantly boosts audit efficiency, accuracy, and client communication quality. The findings show that integrating AI into audits enhances work quality and streamlines governance practices. However, challenges persist, including inadequate technological infrastructure, data privacy concerns, and ethical compliance. Auditor training is a key factor in maximizing AI’s benefits and improving governance and audit efficiency. This study uniquely contributes to the literature by highlighting AI’s role in transforming audit quality and governance within a specific context—Jordan’s Free and Development Zones Group. It underscores tailored strategies for the adoption of AI, distinguishing it from broader studies. Recommendations include strengthening regulatory frameworks, investing in secure AI systems, and offering comprehensive training to ensure ethical, efficient audits. These findings provide practical implications for audit firms and policymakers. Future studies could examine the long-term effects of AI on audit results and compare its application across various industries.

Discover SustainabilityVol. 6(1)
Universidad de Extremadura (ES)
Openalex Percentile: Top 1%
Organizational and Employee Performance
17.37
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