Artificial intelligence is rapidly entering the world of auditing. Yet the biggest challenge may not be the technology itself. It is whether auditors are prepared to use it well.
According to our research into the value of food safety audits, digitalization points to an important gap. While organizations increasingly experiment with digital tools and AI, auditor capability and training remain among the most significant barriers to adoption. This becomes particularly visible when AI moves beyond administrative support into areas requiring professional judgment. When it comes to activities such as risk assessment, trust in AI-generated conclusions remains limited.
This lack of trust is not necessarily a weakness. Auditors are trained to challenge evidence, question assumptions, and remain professionally skeptical. The objective, therefore, should not be to teach auditors to trust AI. It should be to teach them when to trust it, how far to trust it, and how to verify what it produces.
From Tool Training to AI Literacy
Many organizations approach digital training by teaching employees how to operate a particular tool. AI requires something broader.
Auditors need AI literacy: an understanding of what AI can do, what it cannot do, how outputs are generated, and where errors and bias might enter the process. An auditor using AI to analyze historical findings, identify patterns or support risk assessment should understand that a convincing answer is not necessarily a correct answer.
Training should therefore begin with the fundamentals: how generative AI works, the limitations of models, hallucination and bias, confidentiality risks, and the importance of validating sources. Prompting skills matter, but critical evaluation of the output matters more.
Auditors should learn to ask three questions whenever AI contributes to their work: What data was used? What assumptions are behind the output? How can I independently validate the conclusion?
These questions are very close to the thinking auditors already apply to other forms of evidence. AI training should build on that professional mindset rather than create an entirely separate discipline.
Data Quality Is The Foundation
Trust in AI cannot be separated from trust in data.
If audit findings are inconsistently classified, risk categories are poorly defined, historical records are incomplete, or different sites use different terminology, even sophisticated AI will struggle to generate reliable insights.
Organizations therefore need to treat data quality as part of their AI strategy. Standardized taxonomies, clear data ownership, consistent classification of findings, reliable CAPA information and appropriate access controls are not simply IT issues. They determine the quality of AI-supported audit decisions.
This also creates an important new competency for auditors: data literacy. Future auditors should be able to challenge not only the AI output but also the dataset behind it.
Learning AI Through Auditing
The most effective training will probably not come from generic AI courses.
Auditors should practice AI using realistic audit scenarios: reviewing previous findings before an audit, identifying recurring weaknesses, preparing interview questions, comparing evidence, analyzing CAPA trends, drafting reports, or supporting risk-based audit scope and planning.
Auditors might first use AI for low-risk activities such as summarization and document comparison. They can then move toward pattern recognition and trend analysis before experimenting with higher-judgment applications such as risk assessment.
At each stage, AI output should initially be compared with an experienced auditor’s independent assessment. Differences can be discussed through calibration sessions. This creates something particularly important: evidence-based trust rather than blind trust.
Governance Alongside Capability
There is another challenge that organizations should not underestimate. As some research indicates, companies have limited visibility or control over how employees use AI, including publicly available AI platforms.
Employees may already be using AI to summarize documents, draft reports, interpret findings, or analyze information, sometimes without clear guidance about what information can be entered into external systems.
Banning these tools entirely is unlikely to create AI capability. Ignoring their use creates a different risk.
Organizations need clear AI governance: approved tools, rules for confidential information, defined human-review requirements, transparency about where AI has contributed to audit work, and clear accountability for final decisions. Auditors should know where AI assistance is acceptable and where human judgment remains mandatory.
The Auditor Role Will Transform
AI will inevitably automate parts of auditing, particularly repetitive activities such as document review, information extraction, comparison, and classification.
But this does not make the auditor less important. It changes where the auditor creates value.
The future advantage will belong to auditors who can combine technological capability with professional skepticism, contextual understanding, communication, and judgment. They will know how to use AI to process more information and identify patterns faster while recognizing when the technology should be challenged.
Perhaps the most important capability we can teach future auditors is therefore neither prompting nor a particular AI application. It is augmented judgment: knowing how to combine the speed and analytical power of AI with the skepticism, experience and accountability of a human auditor.
The question is no longer whether auditors will use AI. Many already do.
The question is whether we are preparing them to use it responsibly, critically and exceptionally well.
