Artificial intelligence is no longer a distant concept from science fiction. It is already shaping the way we live and work. AI is helping companies hire employees, approve or reject loans, detect fraud, predict crimes, and even assist in court decisions.
Yet, for all its influence, most people—including business leaders, policymakers, and even AI developers—do not fully understand what AI is, how it works, or how to regulate it effectively.
This gap in understanding is not just a minor issue. It is a serious problem that leads to wasted investments, weak policies, and AI systems that are inconsistent, unreliable, or even biased.
ISO/IEC 22989:2022 was developed to address this challenge. It is not just another technical document—it is a framework that helps organizations and governments navigate AI with clarity and structure.
What is ISO/IEC 22989? And Why Does It Matter?
ISO/IEC 22989 is an international standard that defines key AI concepts, terminology, and classifications. It was designed to help businesses, governments, and researchers develop, regulate, and adopt AI in a structured way.
What Does ISO/IEC 22989 Cover?
This standard answers fundamental questions such as:
- What exactly qualifies as AI, and what does not?
- How should different types of AI systems be classified?
- How do AI models learn and function?
- What principles should AI follow to be fair, explainable, and secure?
- How should AI be managed throughout its lifecycle, from development to deployment?
- What role do soft computing and agent-based AI systems play?
- How can organizations adopt AI responsibly and ethically?
Think of AI as a high-speed race car. It is powerful, fast, and constantly evolving.
But without a seatbelt—without clear rules and safety measures—things can go terribly wrong. ISO/IEC 22989 is that seatbelt. It helps organizations harness AI’s potential while minimizing risks.
Who Is This Standard For?
ISO/IEC 22989 is designed for:
- AI Developers – To ensure AI models follow standardized principles.
- Business Leaders & Decision-Makers – To guide organizations in adopting AI responsibly.
- Regulators & Policymakers – To create AI governance that is clear and effective.
- Academics & Researchers – To align AI definitions and methodologies worldwide.
Without a shared understanding of AI, businesses will waste money, regulators will struggle to govern AI, and adoption will slow down.
Defining AI: Bringing Clarity to a Confusing Landscape
Ask ten different people to define AI, and you will likely get ten different answers. Some might describe chatbots, while others think of self-driving cars. Some imagine a world where AI operates independently, making human-like decisions.
This confusion creates real challenges when it comes to AI regulation and adoption. ISO/IEC 22989 standardizes AI definitions to remove this ambiguity.
The Core AI Concepts Defined in ISO/IEC 22989
- Perception – AI’s ability to process input from sensors, such as images, sounds, and text.
- Reasoning – How AI makes decisions based on the data it receives.
- Learning – How AI improves over time, such as through machine learning.
- Interaction – AI’s ability to communicate with users, whether through text, voice, or other methods.
- Autonomy – The extent to which AI operates independently, without human intervention.
Without clear definitions, companies and governments cannot regulate AI properly, making it harder to develop fair, safe, and effective AI systems.
Different Types of AI Systems
Not all AI is the same. ISO/IEC 22989 classifies AI systems based on their functionality and level of autonomy.
The Two Main Categories of AI
- Narrow
AI (ANI – Artificial Narrow Intelligence)
This type of AI specializes in a single task and cannot operate outside of its programmed function.
Examples: ChatGPT, AI fraud detection, self-driving car vision systems. - General
AI (AGI – Artificial General Intelligence)
This is the kind of AI that can learn, adapt, and think like a human.
Examples: Currently theoretical—no AI today has reached this level.
AI System Types Based on Functionality
ISO/IEC 22989 further categorizes AI systems based on how they function:
- Perception-Based AI – AI that focuses on visual, audio, or text processing (such as facial recognition or speech-to-text).
- Decision-Support AI – AI that provides recommendations but does not act independently (such as AI-assisted medical diagnosis).
- Autonomous AI Agents – AI that makes and acts on decisions with minimal human involvement (such as self-driving cars or AI trading bots).
Without clear classifications, organizations risk applying the wrong AI to the wrong problem.
Building Trustworthy AI
AI is powerful—but it is only useful if people can trust it. ISO/IEC 22989 outlines five key principles for AI trustworthiness:
- Fairness
& Bias – AI
should not reinforce discrimination from biased data.
Example: An AI system used in hiring automatically rejected certain candidates because it learned from historical hiring data that contained biases. - Explainability
& Transparency –
AI should provide reasons for its decisions.
Example: Doctors refused to use an AI medical tool because they could not understand how it arrived at its recommendations. - Privacy
& Security – AI
must protect user data and resist hacking.
Example: A leaked AI chatbot model exposed private financial details of users. - Reliability
& Robustness – AI
must function correctly across different scenarios.
Example: A self-driving car failed to recognize a truck’s trailer, leading to an accident. - Human
Oversight – AI should
assist, not replace, human decision-making.
Example: A criminal justice AI tool misclassified individuals' risk levels, leading to unfair sentencing.
Without trust, AI will fail to gain widespread adoption. ISO/IEC 22989 provides a structured approach to evaluating AI risks and ensuring AI systems are ethical and safe.
Managing AI from Development to Deployment
ISO/IEC 22989 provides a structured framework for managing AI across its lifecycle.
The AI Lifecycle Stages
- Defining the Problem – Identifying what AI should solve.
- Data Collection & Preparation – Ensuring data is unbiased and diverse.
- Model Development – Selecting the right algorithms and training AI models.
- Evaluation & Validation – Testing AI for accuracy, fairness, and security.
- Deployment & Monitoring – Ensuring AI continues to function correctly after launch.
AI does not stop evolving after deployment. Regular monitoring and adjustments are necessary to keep it fair, reliable, and safe.
The Future of AI Requires Clear Understanding
AI is no longer a technology of the future—it is a force shaping our world today. But without clear guidelines, organizations risk making costly mistakes.
ISO/IEC 22989 provides the foundation for understanding, regulating, and adopting AI responsibly. It separates hype from reality, ensuring that AI is developed in a way that benefits society, rather than creating unnecessary risks.
The future of AI won’t wait. Will you be ready?
