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Assessing the Current State of Artificial Intelligence Adoption in Accounting at Enterprises in Hanoi City

Aug 2026 · Journal of Accounting and Financial Management · 0 citations

TL;DR

Evaluating the adoption of AI in accounting at enterprises in Hanoi City indicates that, although Hanoi possesses favorable environmental conditions, the real bottleneck lies in the internal digital capabilities of enterprises - data quality, workforce skills, and organizational readiness.

Abstract

The Fourth Industrial Revolution is reshaping the nature of the accounting profession, with Artificial Intelligence (AI) shifting the role of accountants from manual record-keeping toward data-driven analysis and advisory. In Vietnam, this wave is spreading rapidly but unevenly, creating a need to assess the current state at the local level. This study evaluates the adoption of AI in accounting at enterprises in Hanoi City - an economic center with a high density of enterprises and a structure dominated by small and medium-sized enterprises (SMEs). Using desk research with synthesis, comparison, and content-analysis techniques, the study systematizes secondary data from peer-reviewed articles, industry reports, official statistics, and legal documents, through the lens of the Technology–Organization–Environment (TOE) framework combined with the Diffusion of Innovation (DOI) theory. The results show that AI adoption is clearly tiered according to the complexity of accounting functions and firm size: basic automation is widely adopted owing to legal-compliance pressure, whereas predictive analytics and strategic AI remain nascent and are almost confined to the Big 4 and foreign-invested enterprises. The findings indicate that, although Hanoi possesses favorable environmental conditions, the real bottleneck lies in the internal digital capabilities of enterprises - data quality, workforce skills, and organizational readiness. On this basis, the study proposes stakeholder-anchored recommendations for enterprises, policymakers, and training institutions to promote effective AI adoption, while suggesting directions for subsequent quantitative research.

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