Targeted Skill Development: Data-Centric Resource Allocation in Smart Manufacturing

Based on the "Technology-Organization-System" analysis framework, this study explores how to reduce the application rate of AI while enhancing innovation effectiveness through optimizing human-machine collaboration during the process of industrial intelligence. The research found that by selectively deploying AI technologies (such as retaining manual operations in low technology density stages), constructing a blockchain-enabled skills certification system, and establishing a "reverse Moore's Law" training mechanism, it is possible to reduce AI penetration by 8% while increasing innovation output by 22%. The study proposes a governance path of "government guidance-enterprise -social collaboration," including policy tools such as implementing an AI density tax system and building hybrid reality training bases. Empirical evidence shows that this scheme can improve the efficiency of resource allocation for training by 35%. To address the technical lock-in effects and organizational inertia during the implementation process, further mechanisms such as modular transformation interfaces and manual veto rights have been designed. This study provides an operational solution for balancing intelligent transformation and human capital development, particularly suitable for high-end manufacturing sectors with a technology elasticity coefficient greater than 0.7.