institutional path dependence

Originating from economic history and sociology, institutional path dependence explains how past decisions create self-reinforcing regulations, institutions, and organizational routines that are difficult to change due to high switching costs, a phenomenon known as 'lock-in.' In the context of AI governance, it is an analytical lens for legal and organizational behavior. For example, the EU AI Act is a product of path dependence, merging the EU's established traditions of product safety regulation (e.g., CE marking) and fundamental rights protection (e.g., GDPR). This hybrid nature requires companies implementing standards like ISO/IEC 42001 (AI Management System) to understand and navigate these distinct regulatory logics, which is a challenge beyond merely addressing technical legacy systems.

In risk management, the goal is not to 'implement' path dependence but to 'manage' the risks it creates. The practical steps are: 1. **Diagnostic Identification:** Audit existing risk management frameworks (e.g., based on ISO 31000) and IT governance processes to identify rigidities rooted in past decisions that are ill-suited for dynamic AI risks. 2. **Impact & Gap Analysis:** Benchmark these legacy processes against emerging regulations like the EU AI Act and standards like ISO/IEC 23894 (AI Risk Management) to quantify compliance gaps and operational inefficiencies. 3. **Implement Adaptive Governance:** Guided by the NIST AI Risk Management Framework (RMF), establish agile, cross-functional AI governance bodies empowered to make rapid decisions. For example, a Taiwanese financial firm used this approach to reduce its AI model compliance review cycle from 3 months to 4 weeks, achieving a 15% reduction in risk event false positives.

Taiwanese enterprises face three main challenges in addressing the AI governance issues arising from institutional path dependence: 1. **Inertia in SMEs:** Many local SMEs operate on established, cost-effective models, making them resistant to the investment required for new AI governance frameworks like ISO/IEC 42001 due to organizational inertia. 2. **Complex Regulatory Overlap:** Enterprises must already comply with local laws like the Personal Data Protection Act. Integrating the novel requirements of extraterritorial regulations like the EU AI Act with these pre-existing, path-dependent compliance workflows is a major challenge. 3. **Interdisciplinary Talent Gap:** Effective AI risk management requires a blend of legal, technical, and ethical expertise, which is often siloed or lacking in firms, hindering collaborative responses to systemic AI risks. **Solution:** A phased approach is recommended, starting with a C-level sponsored, cross-functional task force to pilot a framework like the NIST AI RMF on a high-risk application. This can build a successful case study within 3-6 months to overcome resistance and facilitate broader adoption.

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