Generative AI for Cost Reduction in Japanese Corporations: Cultural Insights and Deployment Framework

Abstract

Generative AI is becoming a central driver of corporate digital transformation, offering major potential for efficiency improvement and cost reduction. Japanese corporations, however, face cultural barriers, such as long-term orientation and group cohesion, while ensuring stability and quality, now create structural rigidities and higher costs. This study examines these cultural dimensions and their impact on technology adoption, proposing a macro-micro AI deployment framework aligned with Japan’s organizational context. At the micro level, agentic AI enhances employee autonomy and adaptability, mitigating procedural rigidity and improving productivity. At the macro level, large AI models integrate dispersed resources, reduce silos, and strengthen knowledge flows. Implementation involves creating internal communities of practice and redesigning incentives to promote cross-functional collaboration, supporting a shift from functional hierarchies to matrix-based teams. This study offers a culturally grounded roadmap for Japanese corporations, showing how generative AI can drive efficiency, sustainable cost reduction, and structural transformation while preserving core cultural strengths.

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