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Article . 2025 . Peer-reviewed
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Article . 2025
Data sources: mEDRA
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Artificial intelligence in accounting professions: The young chartered accountants' experience

Authors: Sentuti, Annalisa; Sgro, Francesca; Cesaroni, Francesca Maria;

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

Abstract

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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Keywords

Chartered accountants, Artificial intelligence, Accounting, Italy

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
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