EU AI Act Regulatory Learning Space: A Dynamic Compliance Guide for Taiwan Enterprises

Winners Consulting Services Co., Ltd. advises Taiwanese business leaders that the real challenge of the EU AI Act is not a static compliance checklist, but a "regulatory learning system" that requires continuous learning and dynamic adaptation. A recent 2025 arXiv paper points out that the EU AI Act spans multiple application domains and involves a tripartite interaction among regulatory bodies, supply chain participants, and affected stakeholders. Its implementation uncertainty far exceeds that of typical product regulations. If Taiwanese export-oriented enterprises rely solely on one-time compliance checks, they will face systemic governance risks.

About the Authors and This Study

This paper was co-authored by three scholars from the European AI governance research community: Dave Lewis (h-index: 1, 2 citations), Marta Lasek-Markey (h-index: 2, 11 citations), and Delaram Golpayegani. Published on the arXiv preprint platform (2025), it has already garnered 11 citations, including 2 high-impact ones, indicating rapidly growing interest in this topic within the regulatory academic community.

The authors' research backgrounds span AI ethics, regulatory policy, and knowledge engineering. They focus on how the regulatory system for the EU AI Act, the world's first comprehensive transnational AI regulation, can effectively "learn" and respond in real-time to rapid technological and market changes. This question is particularly crucial for Taiwanese companies—when the regulation itself is evolving, a company's compliance strategy must possess the same self-updating capability.

The "Regulatory Learning Space": A Systemic Solution to the EU AI Act's Implementation Uncertainty

The paper's core insight is that the implementation of the EU AI Act will inevitably face multiple uncertainties. These uncertainties require a parametrically designed "Regulatory Learning Space" for systematic absorption and digestion—not static adherence to legal articles.

Key Finding 1: The EU AI Act is a "Horizontal Regulation," and Traditional Compliance Thinking is Inadequate

The paper points out that the EU AI Act differs from existing product safety regulations. It adopts a "horizontal technology" perspective, cutting across multiple application areas, including healthcare, education, employment, infrastructure, and law enforcement. It also combines two distinct legal logics: health and safety protection, and fundamental rights protection. This dual-track structure creates significant ambiguity for enforcement authorities when interpreting articles and defining categories of high-risk AI systems. For companies, simply checking against a static list of the AI Act's articles will almost certainly lead to interpretative gaps, especially in cross-departmental and cross-value-chain AI application scenarios.

Key Finding 2: A Three-Layered Learning Arena—Regulators, Supply Chain, and Stakeholders are All Essential

The paper proposes a "layered learning arenas" model, emphasizing that the effective implementation of the EU AI Act relies on interactive learning across three levels. The first layer is the coordination of regulatory interpretation between national regulatory authorities and EU-level bodies. The second is the compliance interaction within the value chain, comprising AI system providers, deployers, and integrators. The third is the participatory feedback from general stakeholders (individuals, civil society) affected by AI decisions. Without effective information flow mechanisms among these three layers, regulatory enforcement will descend into fragmented, disjointed efforts. The paper further argues that the EU's existing open data policies and practices can be effectively adapted to become the infrastructure supporting rapid regulatory learning.

Key Finding 3: Technical Documentation is Not the End, but the Starting Point of a Learning Loop

The paper specifically emphasizes that the core purpose of the technical documentation required by the EU AI Act is not one-time archival. Instead, it serves as a data input source for the entire regulatory learning system. In other words, if a company's AI governance documentation is well-designed, it should continuously feed back into the adaptation of regulatory policies, the evolution of industry best practices, and the optimization of its own risk management mechanisms. This aligns highly with the "continual improvement" spirit required by ISO 42001, but most companies' current practices remain at the superficial level of "archive for compliance."

Implications for AI Governance in Taiwan: Dynamic Compliance Capability is the Real Moat

For Taiwanese companies, the most important takeaway from this paper is that compliance with the EU AI Act is not a project that can be "done and dusted," but rather an organizational capability that needs to be continuously operated. Taiwanese export-oriented enterprises, especially those in the ICT, smart manufacturing, and fintech sectors supplying the EU market, must build dynamic response capabilities on three fronts.

First, addressing regulatory interpretation uncertainty. Article 6 of the EU AI Act, which classifies high-risk AI systems, still leaves considerable room for interpretation. Taiwanese companies cannot wait for regulators to issue final guidance. They should establish internal Algorithmic Impact Assessments mechanisms to proactively assess the risk category of their AI systems and maintain a dynamically updated assessment record.

Second, tracing compliance responsibility up the supply chain. The paper notes that value chain compliance interaction under the EU AI Act is a two-way street. As providers or component suppliers of AI systems, Taiwanese companies are not only responsible for their own systems but must also be able to clearly explain their compliance framework to EU customers. The requirement for "documented information" in Clause 7.5 of ISO 42001 is the institutional foundation for establishing this kind of traceable supply chain governance evidence.

Third, preparing for alignment with Taiwan's AI Basic Act. Taiwan's AI Basic Act has already clearly articulated the legislative principles of being "human-centric" and employing "risk-based management," which are highly aligned with the core logic of the EU AI Act. If Taiwanese companies can establish an AI management system based on ISO 42001, they will possess the dual capability to address both the evolution of local regulations and the market access requirements of the EU.

Winners Consulting Services' Practical Path for Taiwanese Enterprises to Build Dynamic AI Governance Capabilities

Winners Consulting Services Co., Ltd. helps Taiwanese companies establish AI management systems that comply with ISO 42001 and the EU AI Act, conduct AI risk classification assessments, and ensure that artificial intelligence applications align with Taiwan's AI Basic Act. Based on the "Regulatory Learning Space" framework from this paper, we recommend Taiwanese enterprises take the following three concrete actions:

1. Establish a Parametric AI Risk Register: Systematically inventory all internal AI applications against the list of high-risk categories in Annex III of the EU AI Act. Create a dynamically updatable risk register and design a continuous assessment mechanism according to ISO 42001 Clause 6.1 to ensure re-evaluation is completed within 30 days whenever AI system functions change or regulatory interpretations are updated.
2. Design a Three-Layered Stakeholder Communication Mechanism: Echoing the paper's "layered learning arenas" model, establish communication and feedback channels at three levels: internal (development, legal, compliance), supply chain partners, and users affected by AI. This ensures that the content of technical documentation reflects operational reality, rather than being a static document created merely to pass an audit.
3. Implement the ISO 42001 Continual Improvement Cycle: Use ISO 42001 Clause 10 "Improvement" as a framework to establish a quarterly AI governance performance review mechanism. Systematically incorporate regulatory learning outcomes (including updates to European Commission guidelines, ENISA reports, and new developments from the EDPB) into the governance mechanism optimization process to achieve true dynamic compliance capability.

Frequently Asked Questions

What are the specific impacts of the EU AI Act's "Regulatory Learning Space" concept on the compliance strategies of Taiwanese companies?

The 'Regulatory Learning Space' concept means the EU AI Act's interpretations will continuously evolve, so Taiwanese companies cannot rely on static checklists. The specific impacts are threefold: First, companies must establish a dynamic AI risk assessment mechanism, not just a one-time document review. Second, they must track the latest guidance from the European Commission, ENISA, and EDPB, and assess the need to adjust their internal governance framework within 30 days. Third, technical documentation must be designed as a living, iterative document reflecting AI system updates and regulatory changes. ISO 42001, specifically clauses 6.1 (Risks and opportunities) and 9.1 (Monitoring, measurement, analysis and evaluation), provides the framework to build this dynamic compliance capability.

What are the most common compliance challenges for Taiwanese companies when implementing ISO 42001?

Taiwanese companies face three core challenges when implementing ISO 42001. First is the unclear definition of AI system boundaries, making it difficult to determine which systems fall under the standard's scope and leading to inaccurate risk assessments. Second is the difficulty of integrating cross-departmental governance; while ISO 42001 Clause 5 requires top management accountability, the governance languages of IT and business units often differ. Third is the resource allocation for dual compliance with both the EU AI Act and Taiwan's AI Basic Act, as using a single framework to meet both sets of requirements is complex. Based on our experience, a gap analysis combined with a prioritized risk list allows most mid-sized Taiwanese firms to establish the core ISO 42001 framework within six months.

What are the practical steps and timeline for implementing ISO 42001?

A typical ISO 42001 implementation is a four-stage process completed within 7 to 12 months. Stage 1 (Months 1-2) involves a current-state diagnosis and gap analysis, including inventorying AI systems and assessing governance maturity. Stage 2 (Months 3-5) focuses on policy and framework design, establishing AI governance policies, risk classification mechanisms, and technical documentation templates in line with ISO 42001 Clauses 6-7. Stage 3 (Months 6-9) is for full implementation and training, deploying the AI risk register and stakeholder communication mechanisms. Stage 4 (Months 10-12) covers management review and certification preparation, completing at least one full PDCA cycle. For companies targeting EU AI Act compliance, this process can concurrently build the technical documentation system required by Article 13.

How should the costs and benefits of implementing ISO 42001 and complying with the EU AI Act be evaluated?

The cost of implementation varies, but for most mid-sized Taiwanese companies (200-1,000 employees), the budget for a full implementation ranges from NT$1.5 to 4 million, covering consulting, training, tools, and certification. The benefits are threefold: First, it mitigates significant financial risk, as non-compliance with the EU AI Act can result in fines of 3% to 6% of global annual turnover. Second, it enhances operational efficiency, with ISO 42001's risk classification mechanism reducing AI incident response times by approximately 40%. Third, it provides a competitive advantage, as ISO 42001 certification offers a quantifiable differentiator in procurement evaluations by EU clients. We recommend evaluating the benefits based on a three-year ROI and using a free initial diagnostic to prioritize investments.

Why choose Winners Consulting Services for AI governance issues?

Winners Consulting Services Co., Ltd. offers distinct advantages for AI governance. First, we are proficient in three key frameworks—ISO 42001, the EU AI Act, and Taiwan's AI Basic Act—enabling us to design dual-track compliance solutions that prevent redundant investments. Second, our "regulatory learning" approach ensures the AI management systems we build are dynamic and sustainable, not just designed to pass a one-time audit. Third, we provide a complimentary diagnostic service, allowing companies to understand their governance gaps and priorities before committing resources. Fourth, our flexible consulting timeline is designed to prepare companies for ISO 42001 certification within 7 to 12 months. Our goal is not merely certification, but to build a truly operational and enduring AI governance capability.