Policy-making Process

Policy-making Process is the systematic method of creating, implementing, and evaluating policies. In AI governance, it refers to the stages of identifying AI-related issues, setting the agenda, designing interventions, making decisions, executing policies, and evaluating their impact. This process is critical for AI because AI technology evolves faster than traditional legislation, creating a constant need for agile policy-making. International standards like ISO 42001 provide a structured approach for AI policy-making at the organizational level, while the EU AI Act represents the legislative application of these principles. For AI governance, the process must be transparent, evidence-based, and adaptable to technological advancements. Companies must understand this process to anticipate regulatory shifts, such as the upcoming AI-specific regulations in Taiwan, which will impact AI system deployment and risk-adjusted-return calculations. Effective AI governance requires integrating these policy cycles into the AI development lifecycle, ensuring that risks are mitigated before deployment, not after. This aligns with the NIST AI Risk Management Framework (AI RMF), which emphasizes the iterative nature of AI risk identification, measurement, and mitigation.

Enterprise AI risk management applies the policy-making process through four key steps: 1. Risk Identification & Monitoring — scanning the regulatory landscape (e.g., EU AI Act, Taiwan AI Basic Law); 2. Policy Formulation — creating AI-specific policies, including AI ethics principles, data-use policies, and risk-adjusted-thresholds; 3. Implementation & Control — deploying AI systems according to internal policies and external regulations; 4. Monitoring & Evaluation — auditing AI systems for compliance and performance. For example, a Taiwanese company implementing AI in customer service must first map its AI use cases against the AI Act's risk categories. High-risk use cases, such as AI-driven credit scoring, require rigorous impact assessments. Success can be measured by metrics like: AI Risk-Adjusted Compliance Rate (target >98%), AI Incident Response Time (target <4 hours), and AI Policy-to-Practice Alignment Score (target >4.5/5.0).

Taiwan enterprises face three primary challenges: 1. Regulatory Ambiguity — the current lack of a unified AI law in Taiwan creates uncertainty. Companies should be closely closely monitoring the AI Basic Law's progression. 2. Technical Expertise Gap — AI policy-making requires understanding both legal requirements and technical realities. The solution is to form cross-functional AI Governance Committees comprising legal, technical, and business experts. 3. Implementation Costs — AI governance can be resource-intensive. Companies should prioritize AI applications by risk-adjusted impact, starting with high-risk use cases to maximize ROI. The first step is to conduct a baseline AI Risk Assessment, which typically takes 30-60 days, followed by policy-to-technical requirement mapping. This phased approach ensures that resources are focused where they matter most, achieving compliance within 90 days for initial frameworks.

Winners Consulting Services Co., Ltd. specializes in Policy-making Process for Taiwan enterprises, delivering compliant management systems within 90 days. Free consultation: https://winners.com.tw/contact