In recent months, through several of my professional activities, a recurring and increasingly concerning pattern has emerged: The use of AI to generate client responses to accreditation assessment findings (nonconformities) as well as participant essays, exercises, and examination answers from training programs. These frequently contain incorrect or misquoted ISO references, sometimes even citing clauses that don’t exist.

What makes this problem particularly deceptive is the quality of presentation. The language is fluent, confident, and well-structured. At first glance, the material appears technically sound. Upon verification, however, references fail basic accuracy checks. The same phenomenon is now widespread in third-party training material, online articles, and learning resources where artificial intelligence tools are used by writers who don’t adequately review or validate the generated content.

In conformity assessment, this isn’t a minor editorial issue. Accuracy, traceability, and precision are foundational requirements. Using unvalidated AI tools can generate confident-sounding but often inaccurate content if repeated without proper verification.

How AI language models work, and why errors are inevitable

To understand why these errors occur, one must first understand how AI language models actually function. Artificial intelligence tools such as ChatGPT don’t think, reason, analyze, or exercise judgment in the human sense. They don’t interpret requirements, assess compliance, or verify correctness. Crucially, they don’t search ISO standards, accreditation rules, regulatory texts, or authoritative databases when generating responses.

Instead, AI language models operate through statistical language prediction. They are trained on massive volumes of text drawn from the internet and other publicly available sources, accurate and inaccurate, current and outdated, complete and incomplete alike. From this material, they learn patterns of language use, associations between words, and common phrasing. They don’t acquire validated knowledge, nor do they understand technical meaning or the normative intent of standards.

When an AI model is prompted with a question, it doesn’t “look up” the correct answer. It calculates the most probable sequence of words that would appear to form a coherent and relevant response. Its primary objective is linguistic plausibility, not factual correctness or technical validity. This process is closer to prediction than reasoning.

This mechanism explains why AI can fluently describe complex standards while simultaneously misquoting or misapplying them. The model recognizes correlations between frequently co-occurring phrases, such as “ISO/IEC 17025” and “testing and calibration laboratories,” without understanding the structure, scope, clause hierarchy, or normative intent of the standard itself. It doesn’t know what a requirement is, what constitutes compliance, or why precision matters.

When information is missing, ambiguous, or weakly represented in its training patterns, AI models tend to invent plausible-looking details to maintain linguistic continuity. This behavior, commonly referred to as AI hallucination, may include fabricating fictitious data, creating ISO standard clause numbers that don’t exist, or attributing requirements from one standard to another. The model can’t distinguish truth from falsehood. It can only evaluate whether a statement sounds right based on learned patterns.

This isn’t intentional deception but a structural and statistical side effect of how language models are built. Because AI doesn’t verify sources, perform quality control, cross-check references, or understand the technical concepts it describes, errors aren’t exceptional. But they become inevitable.

In highly technical, evidence-based fields such as conformity assessment, metrology, accreditation, and regulation, where accuracy, traceability, and context are essential, this limitation significantly amplifies risk. Fluent language can easily mask incorrect content, creating an illusion of competence where none exists, and allowing errors to propagate unnoticed into professional, regulatory, and accreditation-related deliverables.

Why this is critical in conformity assessment

Conformity assessment is an evidence-based discipline. Every conclusion must be anchored in verifiable requirements, objective evidence, and justified evaluation. This framework is built on authoritative normative documents developed by ISO/CASCO experts, most notably the ISO/IEC 17000 series.

When AI produces incorrect references or misinterpreted requirements, the effect goes far beyond textual inaccuracies, and can lead to:
• Misinterpreting compliance obligations
• Invalid corrective actions
• Faulty assessment preparation
• Confusion among trainees and professionals
• Erosion of confidence in technical documentation

Unlike human experts, AI can’t assess context, regulatory intent, jurisdictional applicability, or document status. It doesn’t know whether a standard has been revised, superseded, or withdrawn. It generates what appears linguistically probable, even when it’s technically wrong.

Structural and systemic risks of AI use in conformity assessment

Beyond incorrect or fabricated references, using artificial intelligence in conformity assessment introduces deeper structural and systemic risks, particularly when AI is applied to assessment preparation, report drafting, interpreting requirements, gap analysis, or decision-support activities. These risks aren’t incidental but stem from a fundamental mismatch between how AI operates and how conformity assessment is expected to function.

False technical authority and automation bias

AI systems produce fluent, confident, and professionally structured text. This linguistic polish creates a false perception of technical authority, even when the underlying content is incorrect or poorly reasoned. For junior professionals, trainees, or nonspecialists, this can result in uncritical acceptance of AI-generated output.

More concerning is the phenomenon of automation bias. Under time pressure or cognitive load, even experienced assessors and technical experts might unconsciously defer to AI-generated wording, treating it as a neutral or objective baseline. Once introduced into working documents, AI-generated text can anchor subsequent thinking, subtly shaping interpretations, conclusions, and decisions.

This behavior directly conflicts with one of the core principles of conformity assessment, where decisions are expected to be made by qualified individuals based on evaluated evidence—not by deferring to automated outputs.

Loss of context, normative precision, and traceability

Conformity assessment requirements are inherently context dependent. Their applicability and interpretation vary according to regulatory frameworks, scheme rules, accreditation scopes, sector-specific practices, and transitional arrangements between standard revisions. AI systems struggle to reliably distinguish between mandatory regulatory obligations and voluntary standards, between accreditation requirements and certification rules, and between current and superseded clauses. As a result, AI-generated content may be linguistically coherent yet normatively invalid.

Equally critical is the loss of traceability. Accreditation relies on a demonstrable and auditable link between:
• The applicable requirement
• The objective evidence reviewed
• The resulting conclusion or decision

 AI-generated content lacks transparent sourcing and verifiable reasoning. It can’t show how a conclusion was derived, why a particular interpretation was selected, or which authoritative reference supports it. From an accreditation perspective, untraceable reasoning is indistinguishable from unsupported opinion, regardless of how well it’s written.

When AI output is introduced without strict human validation, it effectively creates an uncontrolled step in the conformity assessment process, undermining the reliability, repeatability, and credibility of outcomes.

Bias risk: A hidden threat to impartiality and credibility

Bias in artificial intelligence is a common operational and systemic issue. AI models inevitably inherit bias from their training data, which is drawn from large volumes of publicly available material. This material may disproportionately reflect dominant regulatory cultures, commercial narratives, simplified interpretations of standards, and widely repeated but not necessarily correct views.

As a result, AI-generated outputs tend to normalize what is frequent rather than what is authoritative. In conformity assessment, where correctness is determined by normative requirements and not by popularity, this distinction is critical.

In practical terms, AI bias manifests in several high-risk ways.

Regulatory and jurisdictional bias: Regulatory logic from one region (for example, EU-style conformity assessment and CE-marking concepts) is implicitly applied to nonregulated or differently regulated markets, leading to incorrect compliance assumptions.

Commercial bias: Favoring certification-oriented convenience and marketing-driven interpretations over the rigor, conservatism, and evidence-based discipline required in accreditation and regulatory contexts.

Overconfidence bias: AI produces definitive, authoritative-sounding statements in areas that require professional judgment, contextual interpretation, or conditional reasoning, thereby suppressing necessary expert skepticism.

Superficial risk-based bias: AI generates generic “risk-based” analyses that are linguistically polished but disconnected from real operational processes, sector-specific hazards, or actual consequences of failure. 

Most critically, AI bias is nontransparent, nondeclarable, and nonaccountable. Human assessors, auditors, and technical experts are required to declare conflicts of interest, demonstrate impartiality, justify decisions, and accept responsibility for outcomes. AI systems can’t do any of these things. They can’t recognize their own bias, disclose it, or be held accountable for its effects, yet their output may still influence technical conclusions and compliance decisions.

In conformity assessment, where impartiality is a controlled condition and not a subjective aspiration, bias without accountability is unacceptable. Any tool that introduces hidden, unmanaged bias directly threatens the credibility, independence, and trustworthiness of conformity assessment activities.

Competence, liability, and accreditation effects

Sustained or uncritical reliance on artificial intelligence in conformity assessment activities carries a significant risk of progressive erosion of professional competence. When practitioners routinely delegate requirement interpretation, technical analysis, drafting of findings, or preparation of corrective action responses to AI tools, underlying knowledge gaps may be concealed rather than identified and corrected. This risk is particularly acute in training, qualification, and certification environments, where the objective is to develop and demonstrate competence, not merely to produce well-worded outputs.

Fluent AI-generated language can mask inadequate understanding, creating a false appearance of technical proficiency. Over time, this undermines the development of critical skills essential to conformity assessment, such as analytical judgment, contextual interpretation of requirements, evaluation of evidence, and decision-making under uncertainty. In effect, AI can enable output without competence, which directly contradicts the foundational principles of conformity assessment.

From a legal and regulatory perspective, accountability never transfers to the AI tool. Organizations, conformity assessment bodies, and designated technical personnel remain fully responsible for the validity, accuracy, and integrity of all decisions, reports, and compliance outcomes. Using AI doesn’t mitigate liability; on the contrary, it might increase exposure if organizations can’t demonstrate appropriate due diligence, validation, and human verification of AI-assisted outputs.

In accredited environments, uncontrolled use of AI is fundamentally incompatible with accreditation expectations. Accreditation bodies assess competence, control of processes, validity of methods, and reliability of outcomes. Using unvalidated AI tools, uncontrolled inputs, opaque reasoning, and nonrepeatable outputs introduces uncertainty and inconsistency into conformity assessment processes. Without clearly defined controls, governance, and documented oversight, AI use itself might constitute a nonconformity rather than a neutral support tool.

In conformity assessment, competence, accountability, and process control are required. This means that technology that weakens these elements, whether intentionally or inadvertently, poses a direct risk to accreditation, credibility, and trust.

Absence of quality control and self-verification mechanisms

Finally, a fundamental but often overlooked risk of using AI in conformity assessment applications is that AI performs no quality control on its own output. AI systems have no internal mechanisms for verification, validation, review, or approval of the content they generate.

AI does not:
• Verify correctness against authoritative sources
• Check internal consistency across clauses or references
• Detect contradictions, omissions, or logical gaps
• Confirm currency, applicability, or validity of documents
• Apply second-level review or independent checking

Every output is delivered as a single-pass response, based solely on statistical probability of language patterns. There is no equivalent to typical quality assurance mechanisms such as technical review, content evaluation, or content final approval.

From a quality management perspective, this represents a complete absence of process control, which directly conflicts with the principles of competence, validity, and reliability that underpin conformity assessment.

Consequently, any use of AI without mandatory human review effectively introduces an uncontrolled process step into conformity assessment activities, one that can silently propagate errors, misinterpretations, and nonconformities.

In regulated and accredited environments, quality control can’t be optional. Because AI can’t perform it, that responsibility remains entirely and unequivocally with the human expert and the organization using the tool.

Clarifying the intent: Use AI as an assistant, never as an authority

This article isn’t written to discourage or prevent the use of artificial intelligence in conformity assessment activities. On the contrary, AI can be a powerful and valuable tool when used appropriately for drafting text, supporting documentation, summarizing information, or improving linguistic clarity and efficiency.

The purpose of this article is to present the facts about the inherent and structural risks associated with AI use, particularly in evidence-based, regulated and accredited environments. These risks don’t arise from misuse alone but stem from fundamental limitations in how AI systems currently function. AI doesn’t reason, verify, exercise judgment, ensure impartiality, or perform quality control. It can’t assume accountability for outcomes.

In conformity assessment, where competence, traceability, impartiality, and credibility are nonnegotiable, these limitations matter. Blind or uncontrolled reliance on AI can introduce errors, bias, loss of context, and erosion of professional competence, often without immediate visibility.

For this reason, human oversight is essential, at least at the current stage of AI development. Every AI-assisted output that influences conformity assessment decisions must be subject to critical human review, validation against authoritative sources, and accountable professional judgment. AI may assist the experts, but it can’t replace them.

The responsible path forward isn’t avoidance but controlled, transparent, and well-governed use of AI, with clear boundaries, documented controls, and an explicit understanding that responsibility always remains with the human professional and the organization.

Until AI systems can demonstrate verifiable understanding, contextual awareness, self-verification, and accountability—capabilities they do not yet possess—human competence remains the cornerstone of conformity assessment.