Generative AI as a process of socio-technical co-evolution: a structured review of the transition from content generation to operational intelligence

In the era of the digital economy, generative artificial intelligence (GenAI) has become a transformative force, and its impact extends far beyond the scope of content creation. GenAI has rapidly evolved from generating text, images, and code to assuming operational roles across industries, science, healthcare, and governance. Tracing this evolution requires more than a catalog of technical milestones. A dedicated analytical framework is therefore needed. This study constructs a comprehensive analytical framework organized around three intertwined axes: technology generation, operational intelligence, and socio-technical co-evolution. The framework is applied comparatively across five domains: industry, healthcare, education, finance, and public governance. Three cross-cutting principles emerge from this cross-domain analysis. These are situation-sensitive anchoring, dynamic trust calibration, and responsibility-aware delegation. Risk tolerance, cultural background, and the depth of domain-specific knowledge are identified as key moderators that shape the transition from generative tools to operational intelligence. By synthesizing evidence across these dimensions, this review identifies future directions for research, design, and governance of GenAI systems.