ISO/IEC 22989:2022 is the international standard that establishes a common conceptual framework and terminology for the field of Artificial Intelligence (AI). It defines fundamental AI concepts, clarifies relationships among them, and provides a consistent vocabulary to be used across diverse disciplines, industries, and regulatory environments globally. This standardization aims to improve communication, aid in the development and deployment of reliable AI systems, and support international trade and collaboration in AI. The standard encompasses terms related to AI systems, models, data, and various AI application areas, serving as a foundational document for other AI standards.

Use Case

A global technology conglomerate is developing a novel large-scale AI platform designed for deployment across numerous disparate business units, from autonomous logistics to predictive financial modeling. To ensure interoperability, regulatory compliance, and consistent technical documentation across these specialized teams, the conglomerate mandates the adoption of ISO/IEC 22989:2022.

1. Standardized Communication: The data science team, the software engineering unit, and the legal/governance department can rely on universally understood definitions. For instance, the terms "AI system, "machine learning model, and "training data" are used consistently, preventing ambiguity that could arise from internal jargon or regional linguistic differences. This is critical when specifying system requirements, auditing model performance, or drafting data privacy impact assessments.
2. Regulatory Compliance: When preparing the system for deployment in various markets with evolving AI regulations, the governance team uses the standard's terminology to accurately describe the system's components and capabilities to regulatory bodies. This ensures that legal declarations regarding "transparency" or "explainability" are grounded in an internationally recognized definition, streamlining the approval process.
3. Streamlined Development: Developers across different geographic teams integrate components built by others. By referencing the standard, they ensure that key concepts like "bias," "fairness," and "robustness" are approached with a common technical understanding, facilitating seamless integration and reducing integration errors or misinterpretations of design specifications. This accelerates the development lifecycle and improves the overall quality and safety of the final AI platform.