Artificial Intelligence-Driven Digital Transformation and Sustainable Innovation Governance: A Comparative Analysis of Scientific and Innovation Systems in Germany and South

Abstract

The accelerating convergence of artificial intelligence (AI), digital transformation, sustainability imperatives, and innovation governance is reshaping contemporary scientific systems and socio-technical development pathways. While numerous studies examine AI adoption and digital innovation independently, limited scholarship has comparatively analyzed how national scientific systems translate technological capabilities into sustainable societal outcomes through governance and innovation coordination mechanisms. This study investigates the relationship between scientific capacity, digital transformation, innovation governance, and sustainability performance through a comparative analysis of Germany and South Korea. These cases were selected due to their advanced technological infrastructures, strong research and development ecosystems, and distinct institutional approaches to innovation governance. Drawing upon comparative scientific systems analysis and interdisciplinary evidence synthesis, the study integrates evidence from OECD, UNESCO, World Bank, International Energy Agency, and peer-reviewed scientific literature. The findings demonstrate that technological advancement alone does not guarantee sustainable innovation outcomes. Instead, governance quality, institutional coordination, data interoperability, scientific collaboration, and mission-oriented innovation policies mediate the relationship between technological implementation and societal impact. Germany exhibits strengths in sustainability-oriented industrial transformation and collaborative governance, whereas South Korea demonstrates superior digital integration, AI deployment, and state-coordinated innovation acceleration. The comparative analysis reveals that sustainable socio-technical development emerges from the alignment of scientific capacity, technological implementation, innovation governance, and societal adaptation mechanisms. The article contributes to interdisciplinary science by proposing an integrated conceptual model linking scientific systems, AI-enabled innovation, governance coordination, and sustainability outcomes.