<![CDATA[Artificial intelligence is being incorporated into accounting and auditing practice at a pace that has outrun the profession's settled understanding of what the technology changes and what it leaves intact. This article synthesizes recent empirical, field-based, and conceptual literature on how artificial intelligence affects audit quality, reconfigures professional judgment, and raises unresolved questions of accountability and governance. Using a systematic thematic review of studies published between 2017 and 2026, the analysis finds consistent evidence, from large-sample archival studies, cross-country surveys, and audit-office hiring data, that artificial intelligence investment is associated with measurable improvements in audit quality, including fewer restatements, lower audit fees, and more accurate audit opinions. At the same time, the literature is equally consistent that artificial intelligence augments rather than replaces the human auditor: judgment quality mediates much of the technology's effect on audit outcomes, and an emerging hybrid professional model combines technical assurance skills with data literacy and ethical accountability. The review further identifies significant unresolved challenges, including unclear legal responsibility for algorithmically informed judgments, the opacity of complex models, workforce displacement concentrated in specific job categories, and laboratory evidence that automated recommendations can anchor auditor judgment and suppress independent hypothesis generation among less experienced practitioners. The synthesis concludes that realizing artificial intelligence's benefits for audit quality depends on regulatory frameworks, workforce training, and explainability standards that have not yet caught up with the technology's practical adoption.]]>
Copyrights © 2026
