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whereas isolated use does not produce the same effect. The study also finds that AI
adoption is linked to a lower likelihood of financial restatements, with improvements in
audit quality largely reflecting increased audit effort.
At the same time, recent studies also highlight that AI adoption in auditing remains
uneven and faces several practical challenges. Kokina et al. (2025) show that simpler
applications such as data extraction and optical character recognition are widely used,
while more advanced tools are still under development. The study also identifies key
challenges related to transparency, explainability, data privacy, and the risk of
overreliance on AI, suggesting that effective adoption depends not only on technological
capability but also on governance and professional guidance.
Overall, prior research suggests that artificial intelligence can enhance productivity,
accuracy, and work quality in accounting and auditing, while supporting more data-driven
approaches. However, these benefits depend on how effectively the technology is
implemented and integrated within the professional environment.
2.2. The impact of AI on employment and skill requirements in accounting and
auditing
In addition to its impact on work efficiency, an important stream of research
focuses on examining the effects of AI on employment and skill requirements in the
accounting & auditing profession. Almufadda & Almezeini (2022) argue that it is necessary
to assess the extent to which AI applications influence current recruitment practices, as
well as their potential to reshape auditors’ job positions in the future. This observation
reflects a growing concern regarding adjustments in workforce structure and competency
requirements as technology becomes more deeply integrated into professional processes.
Luthfiani (2024) suggests that the expanding application of AI may improve
efficiency and productivity, but it may also bring about challenges such as increasing
income inequality, the displacement of certain traditional job positions, and shortages of
relevant skills. This perspective indicates that the impact of AI extends beyond technical
aspects and raises significant labor and social issues, particularly in economies undergoing
structural transformation. In the Vietnamese research context, Nguyen et al. (2024) argue
that auditors may shift toward advisory and data analytics roles in the future, although
they are unlikely to be completely replaced by technology. This view is consistent with the
findings of Dalwai et al. (2022), who maintain that AI plays a supportive rather than a
substitutive role in auditing activities.
Overall, existing studies suggest that AI may reshape professional structures and
skill requirements, yet there is no convincing evidence that it will completely replace the
roles of accountants and auditors. Instead, the prevailing trend points to a shift in job
content and competency requirements toward stronger capabilities in data analytics,
critical thinking, and the governance and application of technology.
2.3. Institutional, governance, and organizational conditions influencing the
implementation of AI
The implementation of AI in accounting & auditing does not depend solely on
technological capabilities but is also significantly influenced by institutional and
organizational factors. Eisikovits et al. (2025) emphasize that issues related to data
ownership, governance mechanisms, and the risk of algorithmic bias need to be carefully
considered when applying AI in this field. This implies that accountants and auditors are
required not only to possess technological skills but also to have the ability to identify and
manage risks associated with the use of AI.

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