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occurs in a partial and uneven manner across organizations. Although the policy
framework for digital transformation has been relatively well established, the practical
implementation of AI still primarily focuses on improving short-term operational
efficiency rather than restructuring the scope of work or fundamentally transforming
professional practices.
Within the enterprise sector, particularly among small and medium-sized
enterprises, AI is mainly present in the form of integrated features within commercial
accounting software. The standardization of electronic invoices has created an important
data foundation; however, current applications mainly focus on automating document
processing, suggesting accounting entries, and strengthening internal controls. These
improvements help reduce manual errors and save operational time, but they have not
yet led to a clear shift from recording functions toward analytical and advisory roles. In
other words, AI is improving the way work is performed but has not fundamentally
altered the content and scope of accountants’ responsibilities in most enterprises.
In the field of independent auditing, the level of technology integration is more
visible among large audit firms. The use of data analytics tools with broader coverage
than traditional sampling methods reflects a shift in auditing approaches. However,
differences in investment resources, technological capabilities, and governance systems
among audit firms may lead to stratification within the profession. When only a group of
organizations has the capacity to implement advanced analytical tools while others
continue to rely largely on traditional processes, the gap in professional capacity and
service quality may widen. In this context, AI is not only a tool for improving efficiency but
also a factor highlighting differences in technological development among professional
entities.
In the public sector, the orientation toward applying AI in the State Audit indicates
an increasing recognition of the role of data and technology in public financial oversight.
However, the public audit environment requires a higher level of transparency and
accountability compared with the private sector. The use of AI to support risk
identification or suggest audit priorities can only be effective when accompanied by
mechanisms that ensure traceability, explainability, and control of analytical outcomes.
Without an appropriate governance foundation, the application of AI may create risks
related to bias, system dependence, or lack of transparency in decision-making.
Overall, the integration of AI in accounting & auditing in Vietnam is currently
unfolding along three parallel trends: operational automation at the enterprise level;
methodological standardization and convergence among large audit firms; and policy-
oriented initiatives in the public sector. Nevertheless, these trends remain relatively
fragmented and have not yet formed a closely connected structure. The gap between the
level of technological investment and the ability to effectively utilize technology, as well
as between analytical tools and corresponding professional competencies, continues to
represent a significant challenge during the current phase of transformation.
6. Implications for professional competencies development in Vietnam’s
accounting and auditing sector
From the current application of AI in accounting & auditing in Vietnam, it can be
observed that the core issue does not lie in the availability of technology itself, but in the
capability of professionals to use and control that technology. As the level of adoption
remains partial and uneven across stakeholder groups, the key implication lies in

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