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strengthening professional competencies in parallel with investments in technological
systems.
6.1. Foundational competencies for practice-oriented application
In the enterprise sector, particularly among small and medium-sized enterprises,
the primary requirement is to ensure that accountants are able to review and evaluate
outputs suggested by software systems. The most significant risk is not merely technical
errors but excessive reliance on automated systems when users lack the capability to
critically assess their outputs.
Therefore, the emphasis should be placed on developing practical competencies
and internal control awareness that ensure the principle that humans remain ultimately
responsible for critical accounting operations.
6.2. Advanced analytical competencies in professional practice
For independent auditing, the key issue is to strengthen the level of data analytics
competency across the profession. The focus should not be limited to providing analytical
tools but should also include developing the core competencies required of professionals
in a digital environment: understanding data sources and structures, evaluating the
completeness and integrity of data, identifying risks arising from analytical models, and
interpreting results in a manner consistent with professional standards and evidence.
The development of these competencies should take into account differences in
resources among organizations.
6.3. Competencies related to governance, transparency and risk control
In the public sector, professional competency requirements extend further to
include data governance and accountability. When AI is used to support the selection of
audit subjects or to assess risks at a systemic level, professionals must be capable of
explaining the basis of analytical outcomes and evaluating the limitations of algorithms.
Compared with independent auditing, the focus here is not only on audit evidence but
also on transparency and risk governance at the policy level.
Overall, the integration of AI requires adjustments to professional competency
frameworks toward strengthening data literacy, analytical thinking, and the governance
of technological risks. The key implication is not to train purely technical specialists, but to
ensure that accounting & auditing professionals possess sufficient expertise and
professional judgment to use technology cautiously and responsibly in the context of
digital transformation.
7. Conclusion
This study examines the application of artificial intelligence in the accounting &
auditing sector in Vietnam within the context of ongoing digital transformation and
institutional adjustment. The findings suggest that AI has been introduced into several
operational processes through accounting software at the enterprise level, data analytics
tools in independent auditing, and policy orientations for application in the public sector.
However, the level of integration remains fragmented and has not yet formed a relatively
consistent level of adoption across different stakeholder groups.
In practice, AI has not resulted in the direct replacement of accountants and
auditors. Its primary role at present remains to support data processing, information
screening, and the strengthening of risk control. The more notable change lies in the need
to adjust professional competencies: practitioners are required to work effectively with
data, understand the operational principles of analytical systems, and independently
evaluate outputs generated by technological tools. Therefore, the integration of AI is not

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