Today, with the emergence of GenAI, large language models can efficiently process high-value, complex, and creative forms of knowledge and automatically generate new knowledge and data, accelerating the accumulation of enterprise data assets by tenfold or even a hundredfold, entering an "perpetual motion era."

At the same time, enterprises' digitalization will also face challenges stemming from the explosive growth of data assets, continuous AI-driven application innovation, and the complex situation and challenges brought by multi-cloud heterogeneity and industry-specific attributes. As a result, a management system based on multi-cloud heterogeneity will also become necessary.

Industry-based data governance tools (DaaS) help companies address the complexities of data asset pricing and classification, which arise due to differences in the value derived from data and the prices paid for it.

Industry-based large language model implementation tools (MaaS)，assist enterprises in accelerating the deployment of GenAI in real-world scenarios.

A series of specialized tools, such as industry-powered application infrastructure (aPaaS) and platforms for AI-based agent engineering and knowledge engineering, support enterprises in achieving continuous innovation.

At the infrastructure level, commercially or open-source-based cloud-native development tools and technical components (PaaS), along with AI-optimized underlying public infrastructure resources (IaaS), will together support enterprises in completing digital transformation.

On a broader scale, Digital China's 'Data Cloud Integration' will evolve into a growth flywheel that drives enterprise growth.

In the new era of integrated development across cloud-native, digital-native, and AI-native technologies, Digital China helps enterprises fully leverage these three paradigms (cloud-native, digital-native, AI-native) to innovate their own businesses, create a second growth curve, and achieve leapfrog growth.