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Adopting artificial intelligence in accounting: prerequisites and applications

David Roos et al.

Journal of Electronic Business & Digital Economics2025https://doi.org/10.1108/jebde-03-2025-0023article
ABDC C
Weight
0.37

What the paper says

Purpose The rapid development of artificial intelligence (AI) is transforming various fields, including accounting. AI-driven solutions have the potential to enhance efficiency and accuracy. This study examines the integration of AI in accounting by addressing two key research questions: (1) What are the prerequisites for incorporating AI solutions in accounting? and (2) Which accounting processes are suitable for AI adoption? Design/methodology/approach To answer the research questions, a multivocal literature review (MLR) was conducted, systematically analysing both academic and practice-oriented sources. Findings The findings indicate that the successful implementation of AI in accounting depends on several prerequisites, including a suitable IT infrastructure, access to high-quality data, regulatory compliance and workforce upskilling. Furthermore, key accounting processes benefiting from AI integration include invoice processing, anomaly detection, financial forecasting and tax compliance. Originality/value This study contributes to the existing body of knowledge by providing a comprehensive and up-to-date analysis of AI in accounting, bridging the gap between research and practice.

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https://doi.org/https://doi.org/10.1108/jebde-03-2025-0023

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@article{david2025,
  title        = {{Adopting artificial intelligence in accounting: prerequisites and applications}},
  author       = {David Roos et al.},
  journal      = {Journal of Electronic Business & Digital Economics},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.1108/jebde-03-2025-0023},
}

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Adopting artificial intelligence in accounting: prerequisites and applications

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Evidence weight

0.37

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.16 × 0.4 = 0.06
M · momentum0.53 × 0.15 = 0.08
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.