Artificial intelligence is changing how financial audits are performed. But one question continues to come up: How much of an audit can AI actually handle?
AI can already automate several repetitive, time-consuming activities, from collecting evidence and analyzing transactions to preparing audit workpapers. Yet certain aspects of auditing still depend heavily on professional judgment, skepticism, and experience.
The real opportunity isn't replacing auditors. It's creating a more efficient division of work between AI and audit professionals.
Answers
No. AI can automate repetitive audit work such as document requests, data extraction, evidence matching and workpaper drafting. Assessing risks of material misstatement, determining materiality, evaluating contradictory evidence and complex estimates, and forming the final audit opinion remain the auditor’s responsibility.
Not automatically. AI can analyze full transaction populations, identify unusual items and support risk-focused selections, for example across 50,000 purchase transactions. But full-population analysis does not automatically eliminate the need for sampling or other audit procedures.
AI can perform materiality calculations. Selecting appropriate benchmarks, considering qualitative factors and establishing thresholds still require professional judgment from the auditor.
No. Flags such as journal entries posted outside normal hours, amounts just below approval thresholds or unusual year-end adjustments are indicators for further investigation, not evidence of fraud. Auditors interpret them in the context of the entity’s business and controls.
AI can run preliminary checks before formal manager review and flag inconsistencies, such as a workpaper concluding “no exceptions” while containing two unmatched transactions. Reviewers remain responsible for their conclusions.
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