Artificial Intelligence in Auditing : Impacts on Quality, Efficiency, and Professional Judgment in Global Audit Firms
Tillder, Eva-Maria (2025)
2025
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:amk-2025100725705
https://urn.fi/URN:NBN:fi:amk-2025100725705
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The purpose of this thesis was to investigate how the integration of artificial intelligence (AI) affects audit quality, efficiency, and the role of professional judgment within large international audit firms. The objective was to contribute to the broader understanding of how AI is transforming professional audit practices and to offer insights that may benefit auditing firms, educators, and regulatory bodies navigating this digital transition. Although the thesis was not commissioned by a specific organisation, it aligns with current development initiatives in the accounting and auditing fields focused on technological innovation.
The development task was to evaluate the operational and strategic implications of AI implementation in external auditing. The theoretical framework combined the Technology Acceptance Model (TAM), sociotechnical systems theory, and algorithmic accountability theory to explore how auditors adopt, use, and interact with AI tools.
The study employed a qualitative case study method, supported by a structured literature review. The empirical material consisted of publicly available documentation and secondary sources from three global audit firms: Ernst & Young (EY), KPMG, and PricewaterhouseCoopers (PwC), each of which has implemented proprietary AI platforms—Helix, Clara, and Halo, respectively.
The findings revealed that AI enhances audit quality through full-population testing and anomaly detection, while also improving efficiency by automating routine audit tasks. At the same time, the role of professional judgment was found to be transformed rather than diminished, with auditors required to critically interpret AI outputs. The study concluded that while AI adoption offers clear benefits, it also introduces new challenges related to transparency, ethical oversight, and auditor training. Recommendations include expanding AI literacy, updating auditing standards, and ensuring explainability in AI-supported engagements.
The development task was to evaluate the operational and strategic implications of AI implementation in external auditing. The theoretical framework combined the Technology Acceptance Model (TAM), sociotechnical systems theory, and algorithmic accountability theory to explore how auditors adopt, use, and interact with AI tools.
The study employed a qualitative case study method, supported by a structured literature review. The empirical material consisted of publicly available documentation and secondary sources from three global audit firms: Ernst & Young (EY), KPMG, and PricewaterhouseCoopers (PwC), each of which has implemented proprietary AI platforms—Helix, Clara, and Halo, respectively.
The findings revealed that AI enhances audit quality through full-population testing and anomaly detection, while also improving efficiency by automating routine audit tasks. At the same time, the role of professional judgment was found to be transformed rather than diminished, with auditors required to critically interpret AI outputs. The study concluded that while AI adoption offers clear benefits, it also introduces new challenges related to transparency, ethical oversight, and auditor training. Recommendations include expanding AI literacy, updating auditing standards, and ensuring explainability in AI-supported engagements.