Advisera editorial teamThis article was created with the assistance of AI based on the episode “How AI is Transforming Audits | Interview with Carlos Cruz” of the AI Transformation & Governance podcast. Prefer listening instead? Listen to the audio version below.
Artificial intelligence is changing how management system audits are performed. Auditors can already use AI to prepare audit plans, identify potential problems, and improve reports. However, these applications may represent only the beginning of a much bigger transformation.
AI could eventually automate most auditing activities, including document reviews, interviews, evidence collection, and reporting. However, human auditors will likely remain essential for verifying results, exercising professional judgment, and taking responsibility for audit conclusions.
The development of AI agents raises an important question: Could AI eventually perform almost the entire auditing process? This could fundamentally change how audits are conducted, the role of human auditors, and the cost of auditing.
How are auditors using AI today?
AI is already useful for auditors, particularly during audit preparation and reporting. Several practical applications demonstrate how AI can improve efficiency and help auditors perform more thorough assessments.
Preparing audit plans and interview sequences
An effective audit requires more than a checklist. Auditors must decide which departments to visit, whom to interview, which questions to ask, and how to connect information collected at different stages.
AI can help design this sequence. By providing information about the organization, its activities, and the audit objectives, auditors can ask AI to suggest an appropriate audit flow and identify evidence that could be collected along the way.
The approach may depend on the management system’s maturity. For an established system, auditors might start with customer complaints and trace problems backward. For a newly implemented system, following processes from beginning to end might be more appropriate.
Identifying risks and blind spots
Another valuable application is identifying issues that an auditor might otherwise overlook, particularly when auditing organizations in unfamiliar industries or dealing with specialized processes.
For example, when preparing an ISO 14001 audit, an auditor could ask AI to identify potentially significant environmental aspects or emergency situations associated with the organization’s activities.
The purpose is not to accept every AI suggestion, but to broaden the auditor’s perspective and identify potential blind spots that deserve further investigation.
Improving audit reports
AI can help auditors communicate their findings more clearly. Individual findings are sometimes interconnected, and documenting them separately might prevent the organization from understanding the broader problem.
For example, observations concerning employee training, operational controls, and monitoring activities might indicate a common weakness in process management. AI could help organize these observations into a coherent finding, provided the relationship is supported by evidence.
AI can also improve the wording of nonconformities by clearly presenting the requirement, observed deviation, and supporting evidence. However, auditors must verify that the final wording accurately reflects the facts.
Can AI automate the entire auditing process?
While current AI applications primarily support auditors, AI agents could eventually perform much more of the auditing process. Several activities that currently require substantial human effort could potentially be automated.
Reviewing management system documentation
AI could review an organization’s policies, procedures, and other documents, compare them against the applicable standard, and identify missing or potentially noncompliant elements.
For example, during an ISO 27001 audit, AI could examine whether documentation addresses relevant requirements and identify inconsistencies requiring further investigation.
This could automate a significant portion of the document review associated with a Stage 1 certification audit. However, reviewing documentation alone cannot establish whether a management system is effectively implemented.
Creating audit plans and checklists
AI could use information about an organization to prepare audit plans and checklists. It could identify areas requiring investigation, suggest employees to interview, and determine an appropriate sequence of activities.
Unlike a generic checklist, an AI-generated plan could consider the organization’s industry, size, management system maturity, and identified risks. It could also incorporate the preferred methodology of an experienced auditor.
Conducting interviews and collecting evidence
Auditors typically collect evidence through interviews, observations, and examination of documents or records. In principle, AI could support all three methods.
- Interviews: AI agents could question employees through messaging applications or conversational interfaces, evaluate responses, and request additional information.
- Observations: AI could analyze videos showing how activities are performed and identify potential deviations from procedures.
- Documentary evidence: AI could examine records, screenshots, system logs, and other information to verify whether requirements are being followed.
Advanced AI agents could adapt their investigations based on the evidence collected. For example, if an employee’s response suggested a process weakness, an agent could request additional records or interview another employee.
Identifying nonconformities and preparing reports
If AI understands the applicable requirements and has collected sufficient evidence, it could identify potential nonconformities. Each finding could describe the requirement, explain the deviation, and reference supporting evidence.
AI could then organize these findings into an audit report, identifying recurring problems and relationships between observations. This could substantially reduce the manual effort involved in reporting.
However, technical feasibility does not automatically mean that fully autonomous auditing would be reliable or acceptable in every situation.
What will human auditors still be needed for?
Even if AI performs most auditing activities, human auditors will remain important for verifying results and taking responsibility for audit conclusions.
AI can misunderstand specialized terminology, misinterpret requirements, or generate findings unsupported by evidence. For example, it might confuse the meaning of “work environment” in quality management with environmental management, overlooking factors such as temperature, humidity, or vibration.
Human judgment is particularly important when evaluating management system effectiveness. In the future, auditors may spend less time collecting routine evidence and more time investigating complex situations, validating AI-generated findings, and determining whether management systems genuinely achieve their objectives.
What are the biggest risks of AI-powered auditing?
Greater automation introduces risks that must be addressed before organizations can rely extensively on AI-powered audits. These include confidentiality, inaccurate findings, and incorrect interpretation of information.
Confidentiality of client information
Auditors access sensitive information, including internal procedures, employee information, and security-related documentation. Uploading these materials into AI systems without appropriate safeguards could expose confidential data.
Organizations using AI for auditing need appropriate controls for information processing, access, retention, and confidentiality. Auditors should understand how AI providers handle client information before uploading it.
Incorrect findings and hallucinations
AI may generate nonexistent requirements or draw conclusions unsupported by evidence. Such mistakes could lead to unjustified nonconformities or incorrect compliance assessments.
Therefore, AI-generated findings should be traceable to actual requirements and verifiable evidence. Human review remains particularly important for significant findings and final audit conclusions.
Misinterpretation and information contamination
AI may misunderstand auditing terminology or use irrelevant information from previous conversations or unrelated projects, potentially influencing its analysis.
Clear instructions, controlled information sources, and separation between audit engagements can reduce these risks. Expert-validated knowledge bases could also improve reliability by helping AI apply established auditing practices.
Will AI reduce the demand for human auditors?
AI automation could affect employment in two opposite ways. It might reduce the number of auditors needed for routine activities, but it could also increase demand for audits by making them more affordable.
Fewer traditional auditing jobs
If AI automates document reviews, evidence collection, and checklist-based assessments, organizations may need fewer auditors for these activities.
However, new specializations could emerge. Auditors might increasingly focus on investigations, management system improvement, and evaluating whether organizations have designed effective systems.
Instead of primarily checking whether procedures are followed, auditors could spend more time examining whether those procedures are appropriate and help achieve organizational objectives.
More audits because auditing becomes cheaper
Auditing costs can discourage smaller organizations from pursuing certification or conducting frequent internal audits. AI could substantially reduce these costs, allowing more companies to adopt management system standards.
Lower costs could also enable organizations to conduct more frequent, targeted audits following incidents, complaints, significant changes, or increased risks.
Consequently, even if AI reduces the effort required for each audit, the total number of audits could increase significantly. The overall impact on employment therefore remains uncertain.
How should auditors prepare for the future?
The most practical recommendation is to start experimenting with AI now. Auditors can already use it to prepare agendas, design interview sequences, identify blind spots, develop checklists, and improve reports.
These experiments help auditors understand the technology’s capabilities and limitations. Future auditors will also need stronger skills in information security, remote auditing, and supervising AI tools and agents.
At the same time, they should strengthen competencies that remain valuable as routine tasks become automated: professional judgment, critical thinking, investigation, and evaluating management system effectiveness.
Conclusion: Will AI replace human auditors?
AI could eventually automate a substantial proportion of auditing activities, from reviewing documentation to collecting evidence and identifying nonconformities. This could make auditing faster, less expensive, and accessible to more organizations.
However, organizations must still ensure that findings are accurate and that someone remains accountable for audit conclusions. Human auditors are likely to continue playing an important role in interpreting complex situations, validating results, and exercising professional judgment.
Rather than eliminating the auditing profession, AI could fundamentally transform it. Auditors who learn to use AI effectively while developing higher-value skills will be better prepared for this future.
Want to learn how to audit AI management systems and evaluate whether AI is being used responsibly? Enroll in Advisera’s free ISO 42001 Lead Auditor course to develop the knowledge and skills needed to audit AI management systems against ISO 42001 requirements.
