Dynamic Capability Theory

Dynamic Capability Theory, introduced by Teece, Pisano, and Shuen (1997), is a strategic management concept explaining how firms sustain competitive advantage in rapidly changing environments. It posits that success depends not on existing resources, but on an organization's ability to integrate, build, and reconfigure competencies. This capability comprises three core processes: (1) Sensing: identifying opportunities and threats; (2) Seizing: mobilizing resources to capture value; and (3) Reconfiguring: continuously transforming the organization's assets. In AI risk management, this theory provides a framework for adapting to evolving regulations like the EU AI Act and standards such as ISO/IEC 42001. It enables firms to proactively build a resilient AI Management System (AIMS) that can manage risks related to bias, privacy, and security, ensuring long-term compliance and responsible innovation.

Applying dynamic capability theory in enterprise AI risk management involves a three-step, iterative process. First, **Sensing**: Establish a continuous intelligence-gathering process to monitor AI-related regulatory changes (e.g., GDPR updates, NIST AI RMF), technological advancements, and emerging ethical risks. This involves creating a cross-functional AI governance committee. Second, **Seizing**: Based on sensed risks, make strategic investments. For instance, a global bank, anticipating stricter fairness regulations, invested in Explainable AI (XAI) platforms and bias detection tools, improving its model validation pass rate by over 20%. Third, **Reconfiguring**: Integrate responsible AI principles into core business processes. This means embedding ethical risk assessments into the Machine Learning Operations (MLOps) pipeline, aligning it with the AI system lifecycle management requirements of ISO/IEC 42001. This proactive reconfiguration has been shown to reduce post-deployment compliance-related rework by up to 30%.

Taiwan enterprises face several challenges in implementing dynamic capability theory for AI governance. First, **Regulatory Ambiguity**: Taiwan's domestic AI-specific legislation is still evolving, creating uncertainty. To overcome this, firms should proactively adopt stringent international standards like ISO/IEC 42001 or the NIST AI RMF as a baseline to build a future-proof governance framework. An immediate action is to draft internal AI principles within one quarter. Second, **Resource Constraints**: SMEs often lack the dedicated personnel and budget for a comprehensive AI risk function. Mitigation involves leveraging external consultants and automated Governance, Risk, and Compliance (GRC) tools. A priority is to conduct an AI risk maturity assessment. Third, **Lack of Data-Driven Culture**: A reliance on intuition over data-driven insights can cripple the 'Sensing' capability. The solution is top-down advocacy for AI literacy and risk awareness training, with AI governance metrics integrated into performance evaluations.

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