For more than 30 years I’ve audited management systems in manufacturing, automotive, laboratory, service, and nuclear environments. During that time I’ve watched internal auditing change shape several times. Paper checklists gave way to process-based auditing. Filing cabinets gave way to digital document control. Strictly onsite work gave way to remote and hybrid methods, codified now in ISO/IEC TS 17012:2024.

Each transition created some discomfort at first. Auditors questioned whether the new method would weaken the audit. Organizations questioned whether it would add value. Over time, the profession adjusted.

Artificial intelligence is the next major shift, and it’s larger than the previous ones. The question isn’t whether AI will replace internal auditors—it will not. The better question is whether internal auditors will know how to use AI without losing professional judgment, professional skepticism, and control of the audit process.

For quality professionals, this matters because AI is no longer just an IT topic. It’s entering document control, corrective action, supplier monitoring, inspection, training, customer feedback analysis, production planning, and management review. Once AI becomes part of a quality process, it also becomes part of the control environment. That means it must be understood, governed, and audited.

In 2026, internal auditing won’t be simply checking whether procedures are followed. It will increasingly be asking whether the system is producing reliable signals, whether data are trustworthy, and whether automated tools are helping the organization see risk earlier—or hiding it behind polished outputs.

The internal audit model is changing

Traditional internal audits were designed for a slower environment. Auditors worked with limited time, limited records, and limited visibility. We selected samples, interviewed process owners, reviewed procedures, checked records, and wrote reports after the fact. That model still has value. But it’s no longer enough.

Most organizations now generate continuous signals: customer complaints, overdue corrective actions, supplier issues, calibration status, training records, scrap trends, late approvals, repeat nonconformities, and KPI movement. I’ve seen these signals sitting inside organizations long before they appeared in any audit report. The data were already telling the story. The audit was just the last to know.

The data were already telling the story. The audit was just the last to know.

AI changes the starting point. Instead of asking, “Which process is due for audit this quarter?” stronger audit programs will begin asking where risk is increasing, and where the system is drifting.

In a QMS environment, this could mean using AI tools to review three years of customer complaints and identify repeated product, supplier, or process issues. It could mean scanning CAPA records for corrective actions that close administratively but never address recurrence.

One precision manufacturer I’m familiar with ran AI across an entire procurement-to-pay cycle and found that 15% of transactions had bypassed the required purchase-order approval. 15%! A traditional auditor sampling 25 transactions might have found nothing and signed off with a clean report. That gap—between what a sample tells you and what the population actually contains—is the gap that’s closing.

This doesn’t remove the auditor. It gives the auditor a better starting point. The strongest internal auditors in 2026 will not be the ones who simply collect more records. They will be the ones who can see more of the system and ask better questions.

From sampling to system visibility

Sampling has always been a practical necessity. No auditor can manually review every transaction, every complaint, every training record, every work order, or every supplier issue. We select reasonable samples and use our judgment to decide whether the process is effective.

But every experienced auditor knows the weakness in that approach. Sometimes the sample doesn’t tell the full story. AI-supported auditing can change this. It enables organizations to screen larger populations of records before the auditor selects where to focus. The auditor may still sample, but the sample is informed by actual risk signals rather than random selection or convenience.

For example, instead of selecting 20 corrective actions and hoping they represent the system, an AI-supported review could scan all corrective actions from the last two years and identify repeat failure modes, late closures, repeated root-cause wording, departments or processes with recurring issues, or actions that closed without meaningful effectiveness checks.

The future audit will still require interviews, observation, evidence review, and professional judgment. But it will be supported by broader visibility. What this really overcomes is what I’ve always called auditor’s luck—the case where a genuinely bad process passes simply because the sample didn’t catch the problem. A weak process shouldn’t be able to hide behind a lucky pull of 20 records, and in 2026 it won’t have to.

The three practical waves of AI in auditing

I see AI entering internal auditing in three practical waves. The first is automation: tools that help collect evidence, extract data, compare documents, track actions, and prepare audit files. These tools might not change the nature of auditing, but they reduce the administrative burden. Auditors spend less time chasing records and more time evaluating whether controls actually work.

The second wave is text and pattern analysis—generative AI and natural language processing applied to procedures, complaints, audit findings, management review minutes, supplier records, and corrective actions. Used properly, this can change audit preparation entirely. An auditor can enter fieldwork already aware of recurring issues, document inconsistencies, and weak follow-up patterns.

Used poorly, the same tools create false confidence. AI can produce wording that looks credible but isn’t tied to real evidence. Quality professionals need to be careful here. I’ve seen generative AI produce perfect-looking calibration records for equipment that doesn’t exist. Perfect formatting. Correct units. Plausible values. Entirely fabricated.

A well-written CAPA response isn’t the same as an effective corrective action, and a well-formatted record isn’t the same as a real one. The auditor’s job is shifting from, “Does the record exist?” to, “Is this record’s metadata traceable to something that actually happened?”

The third wave is continuous assurance. AI-supported systems monitor process indicators all the time and alert the organization when risk is rising. In a mature system, internal audit planning wouldn’t rely only on an annual schedule—it would also be influenced by live signals from the business. If supplier defects rise, the supplier process moves up the audit plan. If customer complaints repeat after a CAPA closure, the effectiveness of corrective action is challenged automatically. The audit calendar won’t disappear, but it will become less static. Risk intelligence will play a larger role in deciding what to audit, when, and how deeply.

AI itself becomes auditable

There’s another issue quality professionals can’t afford to ignore. If AI is helping classify complaints, recommend corrective actions, prioritize suppliers, inspect products, summarize audit results, draft procedures, or support management review, it’s influencing the quality system. That means auditors must ask how it is controlled.

The questions are practical ones. Who approved the use of the AI tool? What is it allowed to do? What data do it rely on, and how are those data validated and kept current? How are changes to prompts, models, software settings, or vendor updates managed? Can the organization explain why the AI produced a recommendation? What happens when the AI is wrong?

These aren’t exotic questions. They’re the same questions a competent auditor would ask of any other process-critical input—measuring equipment, inspection methods, production parameters. We wouldn’t allow uncontrolled measuring devices on the shop floor. AI tools that influence quality decisions deserve the same discipline.

If the AI tool hasn’t been reviewed against current conditions, its recommendations are drifting, too.

One concept quality professionals should learn is model drift. A measuring device drifts out of calibration over time, and we all know how to deal with that—schedule recalibration, tag it, trace it. An AI model behaves the same way. Its accuracy degrades as the operating environment shifts: new suppliers, different customer behavior, changed materials, updated regulation. If the AI tool hasn’t been reviewed against current conditions, its recommendations are drifting, too. Model drift is the AI equivalent of an out-of-calibration gauge, and in quality language it’s nothing mysterious. It’s control of externally provided processes, organizational knowledge, change management, monitoring, and effectiveness applied to AI-enabled processes.

The standards direction is clear

The timing of this shift matters. ISO 9001 is expected to be revised by September 2026. Whether the final changes turn out to be large or moderate, one thing is already clear: Quality management systems are operating in a much more digital, data-driven, and automated environment than they were in 2015. The standard is being updated to reflect that.

ISO 19011 is also moving. The 2026 revision is expected to formalize remote and hybrid auditing methods that are already established in ISO/IEC TS 17012:2024, sharpen risk-based audit planning, and broaden auditor competence to include digital and analytical capability. The auditor described in these revisions isn’t the same auditor most of us were trained to be.

ISO/IEC 42001, the AI management system standard, adds another important layer. It gives organizations a management system framework for AI governance, including oversight, risk, transparency, and control. For auditors, the combined effect is a new competence expectation. It won’t be enough to know the clause structure of a standard. Auditors will need to understand how digital evidence is created, how AI-supported decisions are made, and how technology controls affect the reliability of the management system. The next decade of the profession will be defined by auditors who can connect quality, risk, data, technology, and human judgment in the same audit.

The risks are real

I would be a poor auditor not to turn the same scrutiny on AI’s risks.

The most discussed is “hallucination”—confident, polished outputs that are factually wrong. In auditing, this could mean incorrect clause references, weak finding wording, unsupported conclusions, or root-cause language that sounds professional but has no evidence behind it.

Closely related is the black box problem. If an AI tool flags a risk but can’t explain the basis for that conclusion, the auditor must be careful. Audit conclusions need objective evidence. “The system said so” is not adequate audit evidence, and certification bodies won’t accept it.

There’s also the risk of overreliance. Auditors can become impressed by speed and formatting, and forget to challenge the output, which would be a serious mistake. AI should support professional judgment, not replace it.

The competence gap may be the biggest of all. Many organizations are adopting AI faster than their audit teams are being trained to evaluate it. Internal audit is asked to provide assurance over processes it doesn’t fully understand. That’s not sustainable. Auditors don’t need to become software engineers, but they do need basic AI literacy—data quality, model limitations, change control, and validation of outputs.

Above all of that, they need what I call algorithmic skepticism. If an auditor can’t challenge an AI-generated conclusion, the auditor isn’t using AI as a tool. The auditor is being led by it.

What quality professionals should do now

Organizations don’t need to transform their entire internal audit program overnight. The practical approach is to start with one bounded pilot—a low-risk but useful area such as complaint trend analysis, CAPA review, supplier issue tracking, or document comparison. Use AI to support analysis, but require human review before any conclusion is accepted. From there, define governance before scaling: Which AI tools are approved, what data may be used, who reviews the outputs, and how will records be retained. Internal audit checklists should be updated to bring AI-enabled processes within audit scope; if AI is being used in quality activities, it should be auditable like everything else in the management system.

Training is where most programs will fall behind without noticing. The training itself doesn’t have to be technical, but it does have to be practical. An auditor needs to know where AI is genuinely helping, where it’s quietly leading them off course, and how to test an AI-supported output against the kind of objective evidence we’ve always required. Throughout the audit, the auditor’s role must stay clear. AI can collect, compare, summarize, and detect patterns. Auditors still interview people, test implementation, and reach evidence-based conclusions about whether the system actually works.

Final thought

AI won’t make internal auditing less important. It will make weak auditing easier to see.

A checklist audit that only confirms records exist will not be enough in an AI-enabled environment. Quality professionals will need audit programs that can evaluate data, controls, risks, digital evidence, and AI-supported decisions in the same conversation.

Twenty years ago, I was one of a small number of auditors in Canada arguing that remote auditing could be a legitimate methodology. The reception was cool, to put it generously. A pandemic settled that argument for everyone.

AI in auditing is a similar inflection point. The window is narrower this time, and the consequences come faster for the auditors who wait. The internal auditor of 2026 won’t be replaced by AI—but the auditor who understands AI will be far more useful, and far more credible, than the one who avoids it.