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Generative AI and Edge AI in Auditing: Insights from Academia and Practice

T. L. Lin & Jerry Maginnis

Current Issues in Auditing2025https://doi.org/10.2308/ciia-2024-040article
AJG 2ABDC B
Weight
0.50

Abstract

SUMMARY Generative AI and edge AI are reshaping external audit by enabling full-population analysis, streamlining evidence gathering, and enhancing risk assessment. This article presents a practical roadmap grounded in a five-level AI maturity model, articulated by industry leaders, to depict what is operational today versus what is still emerging. Through firm-level mini-cases, we illustrate how auditors are using AI to draft memos, automate compliance checks, and structure unstructured data. The discussion also addresses real-world challenges, such as data security, professional accountability, and evolving skill sets, and offers actionable strategies to support responsible implementation. By bridging technical innovation with practitioner needs, this paper equips audit professionals to adopt AI tools with clarity, confidence, and governance.

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https://doi.org/https://doi.org/10.2308/ciia-2024-040

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@article{t.2025,
  title        = {{Generative AI and Edge AI in Auditing: Insights from Academia and Practice}},
  author       = {T. L. Lin & Jerry Maginnis},
  journal      = {Current Issues in Auditing},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.2308/ciia-2024-040},
}

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Generative AI and Edge AI in Auditing: Insights from Academia and Practice

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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

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