Artificial intelligence is creating an entirely new professional ecosystem. Organizations are beginning to need people who can assess AI governance, evaluate AI management systems, review risk and impact controls, challenge technical evidence and provide independent assurance over how artificial intelligence is developed, deployed and used.
That demand points toward a potentially important new personnel-certification market: AI auditors, AI governance professionals, AI risk specialists and other practitioners whose competence must be independently demonstrated.
The development is a natural consequence of a wider regulatory and conformity-assessment transformation. ISO/IEC 42001 has established an international framework for Artificial Intelligence Management Systems, the EU AI Act is introducing governance and competence expectations across organizations, and ISO/IEC 17024:2026 provides a framework through which professional competence can be independently certified.
The question is therefore shifting from whether organizations need AI governance to something more practical: who is competent to design it, audit it and provide assurance over it?
AI Governance Is Becoming an Organizational Discipline
Artificial intelligence was once treated primarily as a technology-development issue.
That is changing rapidly.
Organizations now need to consider AI risk, accountability, human oversight, data governance, transparency, cybersecurity, regulatory obligations, third-party systems and lifecycle monitoring.
These responsibilities increasingly extend across multiple organizational functions.
AI governance therefore requires professionals who can operate across technical, regulatory, management-system and assurance disciplines rather than focusing exclusively on model development.
ISO/IEC 42001 Creates a New Assurance Environment
ISO/IEC 42001:2023 specifies requirements for establishing, implementing, maintaining and continually improving an Artificial Intelligence Management System.
The standard provides organizations with a structured framework for managing AI-related risks and opportunities and for demonstrating responsible development, provision and use of AI systems.
As adoption of AI management systems increases, organizations will need individuals capable of evaluating whether those systems are appropriately designed and effectively implemented.
Auditors assess management systems such as quality, environmental, occupational health and safety, information security or energy management against established standards.
Auditors increasingly need to understand AI governance, lifecycle controls, risk, impacts, data, transparency, human oversight and the interaction between technical systems and organizational management.
The AI Auditor May Need a Different Competence Profile
Auditing an Artificial Intelligence Management System requires more than familiarity with management-system clauses.
AI introduces technical and governance concepts that traditional auditors may not previously have needed to evaluate.
A competent AI auditor may need to combine knowledge from several disciplines.
Understand audit principles, evidence, sampling, findings, corrective action and evaluation of management-system effectiveness.
Understand organizational responsibilities, policies, oversight structures and accountability for AI systems.
Evaluate how organizations identify, assess, treat and monitor risks associated with artificial intelligence.
Understand the role of data quality, provenance, representativeness, integrity and governance within AI systems.
Possess sufficient understanding of AI systems, models, development lifecycles and limitations to challenge technical evidence appropriately.
Understand how AI management interacts with legislation, sector requirements and conformity-assessment obligations.
AI Governance Professionals Will Be Broader Than Auditors
Auditing represents only one part of the emerging professional landscape.
Organizations also need people capable of designing governance structures, coordinating risk assessments, managing AI inventories, establishing policies, monitoring controls and supporting regulatory compliance.
This creates several potential professional profiles.
As these professional roles mature, organizations will increasingly need a reliable way to distinguish between individuals who have attended AI training and individuals who have independently demonstrated defined professional competence.
Training Alone Is Not the Same as Certification
The AI education market is expanding rapidly.
Courses, workshops, online programmes and professional training can provide valuable knowledge and awareness.
But completion of training does not automatically demonstrate competence.
Personnel certification addresses a different question.
The individual completes a defined educational programme intended to develop knowledge or skills.
An independent certification process evaluates whether the individual fulfils defined competence requirements established by a certification scheme.
This distinction may become particularly important in AI governance, where organizations need confidence that professionals can evaluate complex technical and regulatory situations rather than merely explain terminology.
ISO/IEC 17024 Provides the Certification Architecture
ISO/IEC 17024 is the international standard establishing requirements for bodies operating certification of persons.
The standard is designed to support consistent, reliable and internationally recognized personnel-certification arrangements.
For an emerging profession such as AI governance, a credible certification scheme would therefore need to define clearly:
Define precisely what professional role or competence the certificate represents.
Establish the knowledge, skills, abilities and professional judgement required for certification.
Determine appropriate education, experience, training or professional prerequisites.
Determine how competence will be evaluated through written, oral, practical or scenario-based assessment.
Ensure certification decisions remain independent, impartial and based on sufficient evidence.
Establish how continuing competence will be demonstrated in a rapidly changing AI environment.
The Most Difficult Question Is: What Is an AI Auditor?
The term “AI auditor” can mean very different things.
One professional may audit an ISO/IEC 42001 management system.
Another may technically evaluate an AI model for robustness, bias or performance.
Another may examine legal compliance under the EU AI Act.
Another may evaluate cybersecurity, data governance or algorithmic impacts.
Different AI Assurance Roles May Need Different Certifications
Evaluates whether an organization’s AI management system conforms to applicable requirements and is effectively implemented.
Designs and operates organizational governance structures, policies, responsibilities and oversight arrangements.
Focuses on identifying, assessing, treating and monitoring risks associated with AI systems and their use.
Evaluates potential impacts of AI systems on individuals, organizations or other stakeholders.
Evaluates technical characteristics such as system performance, robustness, testing, validation or other defined criteria.
Evaluates organizational or product obligations arising from AI legislation and associated conformity requirements.
AI Auditing Requires the Ability to Challenge Evidence
AI governance documentation can appear sophisticated.
Policies, risk registers, model cards, impact assessments, monitoring dashboards and technical reports may all provide evidence.
But an auditor must determine whether that evidence actually demonstrates effective control.
- Does the organization know where AI is being used?
- Are AI systems appropriately classified according to risk?
- Are responsibilities and accountability clearly assigned?
- Are AI risks evaluated throughout the lifecycle?
- Are impact assessments meaningful or merely procedural?
- Can data-governance claims be supported by evidence?
- Are human-oversight mechanisms effective in practice?
- Are third-party AI systems adequately controlled?
- Is monitoring capable of identifying emerging problems?
- Does management act when AI governance controls fail?
AI Risk Frameworks Increase the Need for Competent Professionals
ISO/IEC 42001 is not the only framework shaping organizational AI governance.
The NIST AI Risk Management Framework, for example, organizes AI risk-management activities around the functions of Govern, Map, Measure and Manage.
Organizations operating across international markets may increasingly need professionals capable of understanding multiple AI governance and risk frameworks and translating them into practical organizational controls.
The EU AI Act Adds a Competence Dimension
The EU AI Act explicitly introduces the concept of AI literacy.
Providers and deployers of AI systems are required to take measures, to their best extent, to ensure a sufficient level of AI literacy among relevant staff and other persons dealing with AI systems on their behalf.
The appropriate level depends on factors including technical knowledge, experience, education, training and the context in which the AI system is used.
Certification Schemes Must Avoid Becoming Too Superficial
The rapid growth of AI creates a commercial temptation to introduce certifications very quickly.
But credibility requires more than a short examination built around AI terminology.
A professional certification should reflect the complexity and responsibility of the role being certified.
Candidates memorize concepts, complete a short multiple-choice examination and receive a broad professional title.
Candidates demonstrate knowledge, practical interpretation, risk judgement, audit capability and the ability to evaluate realistic AI-governance situations.
Scenario-Based Assessment May Be Essential
AI governance frequently involves judgement rather than simple right-or-wrong answers.
A professional may need to determine whether an AI risk assessment is sufficient, whether an impact is material, whether evidence is credible or whether a governance control is proportionate to the risk.
Certification schemes may therefore benefit from assessment methods that extend beyond traditional knowledge testing.
Require candidates to evaluate realistic organizational AI-governance scenarios.
Assess whether candidates can identify sufficient, insufficient or contradictory evidence.
Evaluate whether candidates can recognize and prioritize AI-related risks.
Require candidates to explain and defend professional conclusions under questioning.
Observe candidates planning, conducting and reporting an AI-governance audit.
Assess how candidates respond when requirements, evidence and organizational realities are not perfectly clear.
Recertification Could Be Critical in AI
Artificial intelligence is developing faster than many traditional professional disciplines.
Regulations, standards, technologies and risk-management practices can change significantly within a short period.
A credential earned once and maintained indefinitely may therefore provide limited assurance of continuing competence.
- continuing professional development;
- changes to AI standards and regulations;
- new AI technologies and risk patterns;
- professional practice or audit experience;
- ethical and governance developments;
- updated competence assessment; and
- maintenance of relevant technical knowledge.
Auditor Independence and Impartiality Will Matter
As AI governance becomes commercialized, professionals may simultaneously provide consulting, implementation, training and auditing services.
That creates familiar conformity-assessment concerns around independence and impartiality.
Certification schemes and employers need to distinguish between professionals who help design an organization’s AI governance system and those who independently evaluate that system.
Could AI Audit the AI?
There is another dimension to the emerging profession.
AI tools themselves may increasingly assist auditors by reviewing documents, analyzing large datasets, identifying anomalies, preparing audit trails or testing selected controls.
This could significantly increase audit capability.
But it does not remove the need for auditor competence.
AI can help auditors analyze more information, identify patterns and focus attention on potential risk areas.
The professional remains responsible for determining whether conclusions are justified, evidence is reliable and findings are appropriate.
The Certification Market Could Develop in Levels
Not every AI governance professional requires the same level of expertise.
A mature certification ecosystem could therefore develop several competence levels or role-specific pathways.
Demonstrates understanding of core AI governance, risk, ethics and management-system concepts.
Demonstrates capability to implement and operate AI governance and risk-management controls.
Demonstrates competence to evaluate an organization’s AI management arrangements internally.
Demonstrates competence to plan, lead, conduct and report comprehensive AI management-system audits.
Demonstrates advanced capability in AI risk identification, evaluation, treatment and monitoring.
Demonstrates broader capability to design, manage and oversee organizational AI governance programmes.
International Recognition Could Become a Major Advantage
Artificial intelligence is inherently international.
AI platforms, suppliers, development teams and users frequently operate across multiple jurisdictions.
Personnel certification based on clearly defined competence schemes and credible conformity-assessment principles could help create greater consistency in professional expectations across markets.
This is particularly relevant for multinational organizations seeking a common competence benchmark for AI governance and assurance personnel.
The Market Will Need Credibility, Not Just More Credentials
AI certifications are likely to multiply rapidly.
The important question will not be how many certificates exist.
It will be whether employers, regulators, certification bodies and clients can distinguish credentials that genuinely demonstrate professional competence from those that primarily demonstrate course completion.
The next AI certification market should not be built around the question “Who completed an AI course?” It should be built around the question “Who can be trusted to evaluate AI governance professionally?”
What Should Certification Bodies and Scheme Owners Consider?
Organizations developing certification programmes for AI auditors and governance professionals should begin with the competence required by the profession rather than with the examination itself.
Establish exactly what activities the certified professional is expected to perform.
Determine the knowledge, skills, judgement and experience required for that role.
Maintain appropriate independence between learning activities and competence certification.
Combine knowledge testing with practical, scenario-based or audit-focused assessment where appropriate.
Ensure commercial, consulting or training interests do not compromise certification decisions.
Establish recertification arrangements capable of responding to rapid technological and regulatory change.
From AI Awareness to Professional AI Assurance
Organizations are moving beyond general AI awareness.
They increasingly need individuals who can translate AI principles into governance structures, evaluate evidence, identify risks, challenge technical claims and provide assurance that controls are operating effectively.
That transformation creates the foundation for a new professional certification market.
ISO/IEC 42001 provides an organizational management framework. ISO/IEC 17024 provides a personnel-certification framework. Regulation and risk-management frameworks are increasing the need for competent practitioners.
The next step is the development of mature, credible competence schemes capable of connecting those elements.
Are We Ready to Certify the People Governing AI?
The market opportunity is substantial, but credibility will determine its long-term value.
Certification schemes developed today can help shape what organizations understand by terms such as AI auditor, AI governance professional and AI risk specialist.
If those schemes are competence-based, impartial and aligned with recognized conformity-assessment principles, they can contribute meaningfully to confidence in the emerging AI economy.
Trustworthy AI will require more than trustworthy technology. It will require competent people capable of governing, assessing and challenging that technology.