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Auditing Corporate Disclosures with the Assistance of Task-Specific Artificial Intelligence—Evidence on Effectiveness and Efficiency

Jette Fabian & Nicole V. S. Ratzinger-Sakel

Journal of Information Systems2026https://doi.org/10.2308/isys-2025-054article
AJG 1ABDC A
Weight
0.50

Abstract

This study examines auditors’ perceptions of how task-specific artificial intelligence (AI) impacts the effectiveness and efficiency of auditing corporate disclosures, particularly management reports. Based on a survey of employees of a Big 4 audit firm in Germany, we analyze experiences with an AI tool designed to assist in detecting misstatements in management reports, e.g., by automatically identifying and matching disclosure requirements with reported content. The 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. Perceptions of the AI tool’s impact are shaped not only by its technological features but also by auditors’ roles, expertise, and engagement with digital transformation. Auditors’ perceptions of potential deskilling effects and level of trust in AI outputs also vary across these attributes, emphasizing the relevance of implementation strategies, training, and transparent communication when integrating AI into audit workflows. Data Availability: The participants of this study consented only to the publication of aggregated data. Therefore, individual response data cannot be shared publicly. JEL Classifications: M4; M42; O33; D83.

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https://doi.org/https://doi.org/10.2308/isys-2025-054

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@article{jette2026,
  title        = {{Auditing Corporate Disclosures with the Assistance of Task-Specific Artificial Intelligence—Evidence on Effectiveness and Efficiency}},
  author       = {Jette Fabian & Nicole V. S. Ratzinger-Sakel},
  journal      = {Journal of Information Systems},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.2308/isys-2025-054},
}

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.