General-Purpose AI

General-Purpose AI (GPAI), often referred to as a foundation model, is an AI model capable of performing a wide variety of distinct tasks. This concept is formally defined and regulated under the EU AI Act (see Article 3). Unlike narrow AI designed for a single function, GPAI serves as a base that can be adapted for numerous downstream applications. Under the EU AI Act, providers of GPAI models have specific obligations, including maintaining comprehensive technical documentation (Annex XI), providing downstream deployers with necessary information, and establishing policies to comply with copyright law. In risk management, GPAI is considered a source of systemic risk, as its flaws or biases can propagate across many systems. Therefore, its governance is a critical upstream control point for the entire AI ecosystem, distinguishing it from the risk classification of final 'high-risk AI systems,' which is based on the context of use. Proper management of GPAI is essential for ensuring trustworthy AI development and deployment.

Enterprises must integrate GPAI governance into their risk management frameworks. A practical approach involves three key steps. First, **Model Inventory and Classification**: Identify all AI models in use and classify those with broad capabilities as GPAI according to the EU AI Act's criteria. Second, **Transparency and Documentation**: Develop and maintain detailed technical documentation for each GPAI model, as required by regulations like the EU AI Act (e.g., Annex XI), covering its architecture, training data, performance, and limitations. This information must be clearly communicated to downstream users. Third, **Continuous Risk Assessment**: Implement a systematic risk assessment process using frameworks like the NIST AI Risk Management Framework (AI RMF 100-1) to evaluate bias, robustness, and security. For example, a global FinTech firm using its proprietary GPAI for credit scoring and fraud detection implemented this process, reducing potential compliance penalties by 40% and improving its model audit pass rate to over 95%, thereby building market trust and ensuring regulatory adherence.

Taiwanese enterprises face three primary challenges with GPAI adoption. First, a **Regulatory Gap**: Taiwan's own AI legislation is still in draft, leaving companies without local guidance on how to align with stringent international standards like the EU AI Act. Second, **Resource Constraints**: Many small and medium-sized enterprises (SMEs) lack the in-house AI expertise, computational power, and financial resources to properly train, evaluate, and document large GPAI models. Third, **Supply Chain Opacity**: As downstream users of GPAI, many firms struggle to obtain the necessary transparency and technical documentation from upstream providers, such as major cloud service platforms. To mitigate these, companies should proactively adopt international standards like ISO/IEC 42001 to build a robust AI management system. They must also enhance supplier due diligence by contractually requiring providers to furnish compliance documentation and collaborate with expert consultancies to bridge knowledge gaps and ensure alignment with global best practices.

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