Overcoming the Digitalization Paradox in Manufacturing: AI-Driven Knowledge Infrastructure and Innovation Systematization

Original Equipment Manufacturers (OEMs) frequently face a “digitalization paradox” in which injecting startup innovations directly into zero-tolerance production environments may disrupt core operations. Using a 15-year longitudinal dataset, executive survey evidence, CEO interviews, organizational observations, and micro-level AI execution logs from a leading hardware OEM, this study examines the structural relationships through which asset-heavy firms may internalize open innovation while protecting operational stability. We contribute to the Dynamic Capabilities theory by conceptualizing the Innovation Translation Interface, operationalized as a corporate accelerator, as a structural buffer that helps isolate and absorb open-innovation friction. Furthermore, we introduce the concept of a domain-grounded Digital Knowledge Infrastructure. By training internal AI on accumulated operational failures and iterative engineering records, firms’ practices are associated with enhanced knowledge externalization and reduced reliance on seniority-based knowledge transfer. The findings suggest that these structural configurations are positively associated with the capability development of Systematized Innovation Capability (SIC), offering an exploratory framework for legacy manufacturers seeking to balance open innovation, AI-enabled learning, and production reliability.