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Artificial intelligence in accounting professions: The young chartered accountants' experience

Annalisa Sentuti et al.

Management Control2025https://doi.org/10.3280/maco2025-001-s1003article
ABDC C
Weight
0.50

What the paper says

This study investigates how young Chartered Accountants (CAs) approach AI in their professional practices. Using a qualitative research design, data were collected through semi-structured interviews with young Italian CAs. Findings highlight that AI adoption among CAs follows two main approaches: horizontal and vertical. The horizontal approach focuses on improving efficiency in routine or peripheral tasks (e.g., scheduling and content creation) through self-directed learning and experimentation with general-purpose AI tools. It represents an entry point into digital transformation, fostering AI literacy. The vertical approach applies AI to strategic, high-value tasks (e.g., forecasting and market analysis), requiring structured training in data analytics and predictive modelling. It reflects a more profound professional evolution, where AI becomes a "cognitive assistant" for decision-making, strategic analysis, and innovation. While both approaches offer significant benefits, they also share risks and challenges, including data privacy issues and output reliability, and have a different impact on the CAs-Client relationships. The two approaches are also analysed using a functional and an evolutionary perspective.

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https://doi.org/https://doi.org/10.3280/maco2025-001-s1003

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@article{annalisa2025,
  title        = {{Artificial intelligence in accounting professions: The young chartered accountants' experience}},
  author       = {Annalisa Sentuti et al.},
  journal      = {Management Control},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.3280/maco2025-001-s1003},
}

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Artificial intelligence in accounting professions: The young chartered accountants' experience

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Evidence weight

0.50

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

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
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.