AI is entering audit at a time when trust in corporate reporting is already under scrutiny. That makes the question less about whether auditors can use AI, and more about how they can use it without weakening the confidence audit is designed to provide.
This matters because AI-generated output can be wrong, incomplete or misleading while still appearing confident and credible. If an audit team relies on these outputs without proper review, it could affect the quality of audit evidence and, in serious cases, contribute to an inappropriate audit conclusion.
Not just efficiency
In this context, audit quality means more than completing procedures. It means identifying the right risks, obtaining persuasive evidence, challenging management where needed and reaching conclusions that are properly supported.
The judgment must still belong to the audit team
The real value of AI in audit should be seen through the lens of quality. Used carefully, it may help teams review board minutes, contracts, accounting papers, correspondence or transaction data and identify inconsistencies, unusual wording, missing information or items that merit further enquiry. For example, it may help highlight inconsistencies between board minutes and management’s going concern assessment, unusual contract terms relevant to revenue recognition, or journal patterns that deserve further enquiry.
This does not mean that AI replaces the auditor. Its value is in helping auditors ask better questions, focus attention on higher-risk areas and consider a wider range of evidence. The judgment must still belong to the audit team.
Where risks arise
The risks arise because AI does not understand audit evidence in the way an auditor does. It generates responses based on patterns, but it does not exercise professional scepticism, understand client-specific context or take responsibility for the conclusion.
One of the most obvious risks is hallucination. AI may produce an answer that sounds reasonable but is not supported by the underlying evidence. It may invent a reference, misstate a fact or provide an explanation that appears technically sound but is wrong.
There is also omission risk. An AI tool may summarise a long document but leave out a key clause, exception or contradictory point. In audit, what is missing can be just as important as what is included.
The danger is that auditors may gradually stop challenging outputs that appear credible
Interpretation risk is another important concern. AI may misunderstand the commercial substance of a contract, the tone of board minutes or the accounting impact of a management explanation. Small differences in wording or context can change the audit response required.
Other risks include using incomplete or outdated information, entering confidential client data into unapproved tools and placing too much reliance on AI output. There is also a methodology risk if AI-enabled procedures are used without considering whether they provide sufficient appropriate audit evidence.
The danger is not that AI will replace the auditor overnight but that auditors may gradually stop challenging outputs that appear credible.
Safeguards are key
These risks can be managed, but only through controlled adoption. Audit firms need clear policies setting out which tools are approved, what they may be used for and what information can be entered into them.
AI output should be treated as a starting point, not as a conclusion. Auditors should check back to original source evidence, consider whether important information has been missed, and challenge whether the output is consistent with relevant accounting and auditing requirements.
Documentation matters, too. Where AI has been used, the audit file should explain how it was used, what information was considered, what output was generated and how the team reviewed it. Without this, it may be difficult to show how the audit conclusion was reached.
Leadership responsibility
Senior leadership also has an important role. The use of AI in audit should not be left to informal experimentation by individual teams. It should be supported by policy, training, approved use cases and monitoring. This is not only a technology issue; it is a quality, ethics, risk management and public-interest issue.
In addition, senior management should decide the firm’s risk appetite for AI use, approve higher-risk use cases and monitor whether AI is actually improving audit quality rather than simply increasing usage.
Responsible adoption also includes transparency about how AI is used. Where AI tools assist audit work, firms should consider whether it is appropriate to explain that use to clients, particularly if client information is involved or if AI materially shapes a deliverable. In most cases, AI in audit is an internal aid rather than a decision-maker, and professional judgment remains with the audit team. But openness about safeguards, human oversight and data protection can help reinforce trust and demonstrate that innovation is being applied responsibly.
The future
For stakeholders, confidence will not come from knowing that an audit firm uses AI. It will come from knowing that AI is used within a framework of evidence, scepticism, accountability and review.
Used well, AI can support stronger audit evidence, better audit challenge and more reliable reporting. Used poorly, it can create new risks that undermine the confidence audit is designed to provide. That is why firms not only need technical controls, but clear governance over confidentiality, approved tools and the responsible communication of AI-assisted work.
