ISO/IEC 25059:2023 addresses the quality model for AI systems within the Systems and software Quality Requirements and Evaluation (SQuaRE) framework. For engineering, testing, and compliance teams, it provides a structured reference for evaluating how AI-related quality characteristics are defined and assessed. As a derived document connected to ISO/IEC 25059, it is relevant when organizations need a technical document that supports documented evaluation, technical review, and quality workflows around AI system assurance.

Overview of ISO/IEC 25059:2023

ISO/IEC 25059:2023 focuses on the quality model used to describe and evaluate AI systems in a consistent way. It is typically useful where teams need to align product evaluation criteria, verification activities, and engineering documentation with a recognized quality framework. The document helps support technical assessment by providing terminology and structure for comparing quality requirements, which may be important during early design review, supplier evaluation, or internal compliance preparation.

Compliance applications of ISO/IEC 25059:2023

Organizations can use ISO/IEC 25059:2023 when defining evaluation criteria for AI-enabled products, reviewing supplier claims, or preparing conformity assessment evidence. It is particularly relevant in documented evaluation workflows where quality characteristics must be tracked across development, testing, and approval stages. In procurement and regulatory preparation, the reference may help teams establish a clearer compliance reference for AI systems, especially when operational consistency and traceable assessment methods are needed.

Importance of compliance with ISO/IEC 25059:2023

Compliance with ISO/IEC 25059:2023 can improve quality assurance by giving teams a common model for discussing and measuring AI system quality. That can reduce ambiguity during technical validation, support repeatable testing workflows, and strengthen risk management decisions. For procurement and conformity assessment preparation, a clear quality model may also help organizations compare offerings more consistently and document how engineering requirements are interpreted and verified across the lifecycle of an AI system.

• Quality model guidance for AI system evaluation within the SQuaRE framework
• Useful for documented assessment, technical review, and verification activities
• Supports procurement checks and supplier comparison for AI-related products
• Helps structure conformity assessment evidence and compliance workflows
• Relevant to quality assurance, risk management, and technical validation planning