By Yelna Yuristiary
For decades, quality auditing has depended heavily on human auditors.
Auditors review documents, interview employees, observe processes, examine records and evaluate evidence. Their experience helps them identify weaknesses that may not be obvious from a checklist.
But the amount of information available to organizations is changing rapidly.
Digital QMS platforms can contain thousands of inspection records, test results, complaints, NCRs, supplier evaluations and performance indicators. Artificial intelligence can analyse large amounts of information much faster than a human auditor.
This raises an important question:
If AI can analyse the data, what will happen to the quality auditor?
The answer may not be that AI replaces auditors. Instead, the future may be a combination of human judgement and artificial intelligence.
Auditing Is Already Becoming More Digital
Quality auditing itself is changing.
The newly published ISO 19011:2026 provides guidance for auditing management systems and reflects changes in organizational operations, including technology, digitalization and virtual environments. The new edition also places greater emphasis on risk analysis and mitigation.
This is important because auditing is no longer limited to reviewing paper documents in a physical office.
Auditors increasingly encounter electronic records, cloud-based systems, digital workflows, remote operations, automated inspections and AI-supported processes.
The audit therefore needs to understand not only what the organization does, but increasingly how technology influences what the organization does.
AI Can Help Auditors Find Patterns
Imagine an auditor reviewing five years of quality data from a construction company.
There may be thousands of inspection records, hundreds of NCRs and numerous supplier performance reports.
A human auditor can review samples and investigate specific areas. AI, however, can potentially analyse a much larger dataset and identify unusual patterns.
For example, an AI system might identify that certain types of defects occur more frequently with a particular supplier, subcontractor, location, material or process.
The auditor can then investigate whether the pattern represents a genuine systemic issue.
This changes the role of the auditor.
Instead of spending most of the audit trying to find where the information is, the auditor can spend more time understanding why the pattern exists and what it means for the QMS.
From Sampling to Intelligent Sampling
Sampling will remain important in auditing.
Auditors cannot normally examine every single record, particularly in large organizations.
However, AI can potentially help make sampling more data-driven.
Suppose a project has 10,000 inspection records.
Instead of selecting samples only through traditional methods, an analytical system could identify records with unusual characteristics, repeated failures or other patterns that deserve attention.
The auditor can then investigate those areas.
The purpose is not to allow AI to decide that something is nonconforming.
The purpose is to help the auditor identify where further audit attention may be useful.
Human judgement remains essential.
AI Can Support Evidence Analysis
Audit evidence is another area where AI can be useful.
A modern organization may have information in many formats: inspection reports, emails, meeting minutes, photographs, test results, dashboards, complaints and corrective-action records.
AI can help organize and analyse this information.
For example, it may identify repeated references to the same problem across different records.
A quality auditor can then follow the evidence back to the relevant process and determine whether the issue represents an isolated event or a systemic weakness.
This can make audits more efficient.
But efficiency does not automatically mean effectiveness.
The auditor still needs to evaluate whether the evidence is reliable, relevant and sufficient.
AI Can Also Audit AI
An especially interesting development is that organizations are increasingly using AI within their own management systems.
This creates a new auditing question:
How do you audit a QMS that uses AI?
The ISO 9001 Auditing Practices Group published a paper in June 2026 specifically titled “Auditing a QMS that uses Artificial Intelligence.” The group explains that the paper is intended to provide insights for auditors dealing with QMS environments that use AI.
This is significant because AI is no longer simply an external tool used by auditors.
It can become part of the organization's actual operational processes.
For example, an organization may use AI to predict equipment failures, classify customer complaints, inspect products, evaluate suppliers or identify quality anomalies.
The auditor then needs to understand how that AI-supported process is controlled.
What Should an Auditor Ask About AI?
The auditor may need to understand where the AI system gets its data, how the system is used, who is responsible for decisions and what happens when the AI produces an unexpected result.
For example, if an AI system identifies a potential quality problem, does a qualified person review the result?
If the AI produces a false warning, is there a process for correcting it?
If the AI fails to identify a real defect, how is that failure detected?
And if the AI model changes over time, does the organization understand the consequences?
These questions move auditing beyond traditional document checking.
They require auditors to understand the relationship between data, technology, people and processes.
The Problem of “Black Box” Decisions
One challenge is explainability.
Some AI systems can produce highly sophisticated predictions, but the reasoning behind a particular result may not always be easy for a user to understand.
This can create difficulties in quality management.
Quality decisions may affect product acceptance, customer satisfaction, safety, cost and contractual obligations.
An organization therefore needs to understand the appropriate level of human oversight for the AI applications it uses.
This is also consistent with the broader development of AI management standards. ISO/IEC 42006:2025 establishes requirements for bodies that audit and certify AI management systems against ISO/IEC 42001, reflecting the need for appropriate competence and rigor when evaluating AI-related risks and controls.
The Auditor Still Needs Professional Judgement
AI can analyse data.
But auditing is not simply data analysis.
An auditor needs to understand the organizational context, process interactions, requirements, evidence and consequences.
Imagine that an AI system identifies an unusually high number of concrete quality issues in one project area.
The number alone does not explain the situation.
Perhaps the area contains the largest volume of concrete work.
Perhaps the testing frequency is higher there.
Perhaps the project has introduced a new supplier.
Perhaps the data collection method changed.
Or perhaps there really is a systemic quality problem.
The auditor needs to investigate.
This is where professional judgement remains important.
AI Should Support the Auditor, Not Become the Auditor
A useful way to think about the future is:
AI finds patterns.
The auditor investigates.
AI processes information.
The auditor evaluates evidence.
AI can identify anomalies.
The auditor determines their significance.
This creates a human-AI partnership rather than a competition.
The objective is not to make the auditor unnecessary.
It is to allow the auditor to spend less time on repetitive information processing and more time on analysis, questioning and understanding the effectiveness of the management system.
Quality Auditors Will Need New Skills
This transformation will also change auditor competence.
The traditional auditor needs knowledge of auditing principles, management systems, processes and relevant requirements.
The future auditor may additionally need a basic understanding of data analytics and AI.
They do not necessarily need to become programmers.
But they should be able to ask important questions.
Where did the data come from?
Is the data reliable?
What assumptions does the system use?
What are the limitations of the AI?
Who reviews the output?
How are errors detected?
How does the organization control changes to the system?
These questions will become increasingly relevant as AI becomes embedded in business processes.
The Future Audit May Be More Continuous
Traditional audits often happen at defined intervals.
An organization may conduct an internal audit once or twice a year and then produce a report.
Digital systems create the possibility of more continuous monitoring.
Quality data can be monitored throughout the year, while AI or analytics can identify unusual trends between formal audits.
The auditor can then use this information when planning future audit activities.
This does not necessarily mean that formal audits disappear.
Instead, the audit programme may become more connected to continuously available performance information.
ISO 19011:2026 continues to emphasize managing audit programmes, risk and opportunities, conducting audits and improving audit programmes.
The Future Quality Auditor
The quality auditor of the future may therefore look quite different from the traditional image of an auditor carrying a checklist.
The future auditor may work with dashboards, data analytics, digital records and AI-supported analysis.
But the fundamental purpose remains.
The auditor still needs to determine whether the management system is implemented effectively, whether evidence supports conclusions and whether the organization can achieve its intended results.
Technology changes the tools.
It does not remove the need for professional judgement.
From Checklist Auditor to Quality Intelligence Professional
This may be one of the biggest changes in quality auditing.
The traditional audit question is often:
“Can you show me the procedure?”
A more data-driven audit may ask:
“Show me what the data says about how this process actually performs.”
And an AI-supported audit may go one step further:
“What patterns or risks can we identify before they become significant problems?”
This does not make the checklist irrelevant.
Instead, the checklist becomes only one part of a much broader evidence-based approach.
The auditor becomes increasingly involved in understanding how the organization's processes actually perform.
Human + AI Is the Real Future
The future of quality auditing is therefore unlikely to be simply human versus AI.
It is more likely to be human plus AI.
AI can process enormous amounts of information, identify patterns and support risk-based audit planning.
Human auditors can provide context, professional judgement, questioning, interpretation and ethical responsibility.
Neither side provides the complete solution by itself.
The strongest audit environment may be one where technology helps auditors see more, while human judgement helps organizations understand what those findings actually mean.
The question for the future is therefore not:
“Will AI replace the quality auditor?”
A more useful question is:
“How can quality auditors use AI to conduct more evidence-based, risk-focused and valuable audits while maintaining human judgement and accountability?”
That is where the next generation of quality auditing is beginning.
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