AI systems

An AI system, as defined by Article 3(1) of the EU AI Act, is a machine-based system designed to operate with varying levels of autonomy and that, for explicit or implicit objectives, infers from its input how to generate outputs like predictions or decisions that influence environments. This definition aligns with international standards like ISO/IEC 22989:2022. In enterprise risk management, identifying a technology as an 'AI system' is the critical first step. This classification triggers specific regulatory obligations based on its risk level (e.g., unacceptable, high, limited). For high-risk systems, this includes mandatory conformity assessments, risk management system implementation (guided by ISO/IEC 23894), and comprehensive technical documentation, distinguishing them from traditional, deterministic software.

Applying AI systems in enterprise risk management involves a systematic process. Key steps include: 1) **Inventory and Classification**: Identify all AI systems in use and classify them according to a risk-based framework, such as the one in the EU AI Act. For instance, an AI tool for medical diagnosis would be classified as high-risk. 2) **Risk Assessment and Mitigation**: For high-risk systems, conduct assessments using frameworks like the NIST AI Risk Management Framework (AI RMF 1.0) to identify potential harms like bias or security flaws, and implement mitigation measures. 3) **Governance and Documentation**: Establish an AI governance structure and create detailed technical documentation as required by Article 11 of the EU AI Act. A Taiwanese medical device firm successfully applied this process, achieving 100% compliance for EU market entry and reducing potential risk incidents by an estimated 40%.

Taiwanese enterprises face three primary challenges: 1) **Regulatory Gaps**: The absence of a dedicated domestic AI law creates uncertainty, forcing export-oriented companies to navigate complex international regulations like the EU AI Act. 2) **Immature Data Governance**: Many firms lack robust data governance, leading to risks of biased AI models that could violate Taiwan's Personal Data Protection Act. 3) **Interdisciplinary Talent Shortage**: There is a scarcity of professionals with combined expertise in AI technology, legal compliance, and ethics. To overcome these, firms should adopt the EU AI Act as a baseline standard (Priority: High), implement a data governance framework based on ISO/IEC 42001 (Priority: High), and partner with external experts for specialized training and framework implementation (Priority: Medium).

Winners Consulting specializes in AI systems for Taiwan enterprises, delivering compliant management systems within 90 days. Free consultation: https://winners.com.tw/contact