ISO 42001 - Competence (Clause 7.2)

ISO 42001: Competence (Clause 7.2 of ISO 42001) with TurboAudit by [Kimova AI](https://kimova.ai)

Responsible AI isn’t just about systems—it’s about people. Clause 7.2 of ISO/IEC 42001 recognizes that the success of an AI Management System (AIMS) depends heavily on whether the people involved have the competence to manage AI-related risks, responsibilities, and requirements effectively.

This clause focuses on building and maintaining the skills, experience, and knowledge necessary across the organization to ensure that AI systems are developed, deployed, and governed in a trustworthy, compliant, and ethical manner.

What Clause 7.2 Requires

ISO 42001 requires organizations to:

  • Determine the necessary competence of persons doing work under its control that affects AI system performance or AIMS effectiveness
  • Ensure that these persons are competent, based on appropriate education, training, or experience
  • Take actions to acquire and maintain competence, where gaps are identified
  • Retain documented information as evidence of competence

This is not limited to technical roles. It includes individuals across compliance, legal, ethics, data governance, operations, and even leadership.

Why Competence Is Critical in AI Governance

AI presents unique challenges—such as algorithmic bias, data privacy risks, explainability, and evolving regulations—that require specialized, interdisciplinary knowledge. Without proper competence:

  • Risks may be underestimated or mishandled
  • AI models may fail to meet ethical, legal, or performance expectations
  • The AIMS may lose credibility and become ineffective
  • Stakeholders may misinterpret AI decisions, leading to poor outcomes or loss of trust

Competence ensures that decisions about AI systems are informed, contextualized, and accountable.

Areas of Competence That May Be Required

Depending on the organization’s AI use cases and governance scope, key competence areas may include:

Domain Examples of Required Competence
Technical AI/ML development, data science, MLOps, software engineering
Ethical Bias identification, impact assessments, value-sensitive design
Legal & Regulatory AI regulations (e.g. EU AI Act), data protection (e.g. GDPR), IP law
Risk Management Risk assessment, mitigation planning, incident response
Security & Privacy Adversarial robustness, data minimization, access control
Communication Model explainability, stakeholder engagement, documentation writing

How to Build and Maintain Competence

  1. Perform a skills gap assessment: Identify current competence levels and compare them against the roles and responsibilities defined in the AIMS.
  2. Develop a training and upskilling program: Provide formal training on AI ethics, regulatory compliance, data handling, and emerging technologies.
  3. Encourage cross-functional learning: Facilitate collaboration between technical, legal, and business teams to build holistic understanding.
  4. Support certifications and professional development: Encourage certifications in AI governance, data privacy, or ISO standards.
  5. Document competence evidence: Keep records of qualifications, training completion, and experience relevant to AI-related work.

Common Pitfalls

  • Assuming that technical competence alone is enough to manage AI responsibly
  • Failing to keep up with fast-changing AI technologies and regulatory expectations
  • Not recognizing the need for ethical and human rights literacy within AI teams
  • Lacking documented evidence of competence during audits

Conclusion

Clause 7.2 emphasizes that effective AI governance depends not only on frameworks and policies, but also on the capabilities of people. Organizations that prioritize competence build more resilient, adaptable, and ethically grounded AI systems.


In tomorrow’s article by Kimova.AI, we’ll explore Clause 7.3: Awareness—how to ensure that all relevant staff are aware of their role in AI governance, the implications of their work, and the broader impact of the AI systems they support.


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