ai standards in manufacturing
Why Standards Matter for SMEs in AI-Driven Manufacturing
As artificial intelligence (AI) and machine learning (ML) advance, they bring immense potential and inevitable questions. While global discussions on AI often spotlight societal impacts and ethical concerns, one thing is clear: AI can be a powerful tool to enhance manufacturing, particularly for small and medium-sized enterprises (SMEs). In this space, standards play an essential role.
For manufacturing SMEs, adopting AI has the potential to automate, streamline tasks and provide critical decision support, making operations smoother and more responsive. But, like any emerging technology, AI's full potential hinges on standards that ensure reliability, safety, and integration into existing industrial systems.
How can these standards help me?
- Build Trust: Standards create a reliable baseline, reassuring clients and stakeholders that AI is used responsibly and effectively.
- Simplify Compliance: Clear guidelines make it easier to meet regulatory requirements and align with industry best practices.
- Facilitate Integration: Standards ensure that AI tools can work seamlessly with existing manufacturing systems, maximizing productivity.
- Reduce Risks: With standardized protocols, the potential risks associated with AI implementation are minimized, making the technology more accessible and beneficial for smaller players.
In summary, standards are not just regulatory hurdles; they’re tools that help you confidently bring AI into your operations, promoting innovation with clear boundaries and structured support.
Who makes the standards?
European AI Standards are developped by the CEN-CENELEC Joint Technical Committee 21 (JTC 21) on Artificial Intelligence. Their work consists in both developing standards from scratch and adopting existing ones for AI and related data. They are designed to address European market needs and support EU legislation, including the upcoming AI Act. JTC 21 builds on frameworks such as the CEN-CENELEC AI Roadmap and the German AI Standardization Roadmap.
It collaborates with international bodies like ISO/IEC (speficifally JTC 1 SC 42) to adopt and align standards while ensuring they reflect European principles and societal values. By doing so, JTC 21 aims to provide manufacturers with a basis for presumed conformity with EU AI regulations.
What standards apply to my SME?
In this section, we reviewed several existing standards published by CEN and CENELEC, identified their key interesting features.
Operational Aspects
EN ISO/IEC 23894:2024
This standard offers guidance on managing AI-specific risks for organizations that develop, deploy, or use AI-based products and services. It includes strategies for integrating risk management into AI-related activities and processes, adaptable to any organization’s unique needs. For SMEs, this guidance helps in building safer, more reliable AI applications by systematically addressing potential risks.
Engineering Aspects
(CEN/CLC ISO/IEC/TR 24027:2023)
Read this report if you want to identify and reduce bias in your AI systems, especially for decision-making processes. It offers practical methods for detecting and measuring bias, covering the entire AI lifecycle—from data collection to ongoing use and evaluation. Follow these guidelines to ensure their AI tools are fair, reliable, and free from unintended biases that could impact decisions.
CEN/CLC ISO/IEC/TR 24029-1:2023
This report gives an overview of methods for evaluating the robustness of neural networks. It covers established techniques to test how well these AI systems can handle unexpected inputs and perform reliably. By following this guidance, SMEs can ensure their neural networks are resilient, reducing the risk of failures in real-world applications.
prCEN/CLC ISO/IEC/TS 12791
This document provides mitigation techniques that can be applied throughout the AI system life cycle in order to treat unwanted bias. This document describes how to address unwanted bias in AI systems that use machine learning to conduct classification and regression tasks. This document is applicable to all types and sizes of organization.
CEN/CLC/TR 18115:2024
This document provides an overview on AI-related standards, with a focus on data and data life cycles, to organizations, agencies, enterprises, developers, universities, researchers, focus groups, users, and other stakeholders that are experiencing this era of digital transformation. It describes links among the many international standards and regulations published or under development, with the aim of promoting a common language, a greater culture of quality, giving an information framework. It addresses the following areas: - data governance; - data quality; - elements for data, data sets properties to provide unbiased evaluation and information for testing.
prEN ISO/IEC 8183
This document defines the stages and identifies associated actions for data processing throughout the artificial intelligence (AI) system life cycle, including acquisition, creation, development, deployment, maintenance, and decommissioning. This document does not define specific services, platforms, or tools. This document is applicable to all organizations, regardless of type, size, or nature, that use data in the development and use of AI systems.
Other Aspects
EN ISO/IEC 22989:2023
This standard provides a clear glossary of AI terms and concepts, making communication easier between different stakeholders—whether in commercial enterprises, government, or non-profits. By establishing a shared language, it supports consistent understanding and application of AI principles, useful for SMEs looking to align with industry standards and communicate effectively with partners.
EN ISO/IEC 23053:2023
This standard outlines a framework for understanding AI systems built with machine learning (ML). It describes the key components and their roles within an AI ecosystem, offering a clear structure for any organization—public or private, large or small—that is implementing or using AI systems. For SMEs, this framework provides essential guidance on building and managing effective AI solutions.
prEN ISO/IEC 25059
This document outlines a quality model for AI systems and is an application-specific extension to the SQuaRE series. The characteristics and sub-characteristics detailed in the model provide consistent terminology for specifying, measuring, and evaluating AI system quality. The characteristics and sub-characteristics detailed in the model also provide a set of quality characteristics against which stated quality requirements can be compared for completeness.