Taxonomy of AI Applications

A taxonomy of AI applications is a systematic framework for classifying an organization's AI systems based on predefined criteria such as function, technology, data sensitivity, and, most importantly, risk level. It is a foundational tool for AI governance, essential for implementing standards like ISO/IEC 42001 (AI Management System) and operationalizing the 'Map' function of the NIST AI Risk Management Framework (AI RMF). The EU AI Act's risk-based tiers (unacceptable, high, limited, minimal) are a prime example of a regulatory taxonomy. Unlike a simple inventory, a taxonomy provides a structured, hierarchical view that enables consistent, risk-differentiated governance policies and controls across the entire AI portfolio.

Applying an AI taxonomy in ERM involves three key steps. First, establish classification criteria based on business context and regulatory requirements, such as the risk tiers from the EU AI Act or data sensitivity under GDPR. Second, conduct a comprehensive inventory of all AI systems and categorize each one according to the established criteria. Third, link these categories to specific risk controls and governance procedures. For instance, applications classified as 'High-Risk,' like an automated credit scoring system, would mandate rigorous bias audits and human oversight, while 'Minimal-Risk' applications would have a streamlined review process. This approach helps enterprises allocate resources efficiently, improve compliance rates, and reduce risk incidents by focusing efforts on the most critical systems.

Taiwan enterprises face three main challenges: 1) Lack of a unified internal framework, as AI is often adopted in a decentralized manner, leading to inconsistent risk management. 2) Organizational and data silos, which hinder the creation of a complete AI inventory and accurate risk assessment. 3) Keeping pace with the dynamic regulatory landscape, such as the EU AI Act and anticipated local legislation, which requires continuous monitoring and adaptation. To overcome these, companies should establish a cross-functional AI governance committee, adopt an international framework like the NIST AI RMF as a baseline, create a centralized AI system registry, and build flexibility into the taxonomy to accommodate future regulatory changes.

Winners Consulting specializes in taxonomy of AI applications for Taiwan enterprises, delivering compliant management systems within 90 days. We have successfully assisted over 100 local companies. Request a free consultation: https://winners.com.tw/contact