The State of Enterprise-Level AI Commercialization in China: Insights from 2025 Trends and Global Comparisons

Abstract

The rapid advancement of artificial intelligence (AI) technologies has positioned China as a global frontrunner in AI development, yet the commercialization of enterprise-level AI remains a critical challenge amid evolving policy landscapes, international competition, and market dynamics. This survey synthesizes empirical data from the 2025 China Enterprise-Level AI Commercialization Progress Report, based on a mixed-methods survey of 229 enterprises (81.6% SaaS vendors, 15.4% AI-native firms), with complementary insights from global benchmarks such as the Stanford HAI AI Index 2025 and the World Economic Forum's Blueprint to Action on China's AI-Powered Industry Transformation 2025. Key findings highlight a "scale exploration phase" in commercialization, where AI penetration exceeds 70% in internal operations but value realization is fragmented, with only 31.4% of firms achieving mature expansion. Structural divergences emerge between SaaS firms, which dominate market breadth through incremental "+AI" integrations, and AI-native entities pursuing disruptive "AI+" reconstructions, amid regional concentration in five major hubs (Beijing, Shanghai, Shenzhen, Hangzhou, Guangzhou) capturing 70% of resources. Challenges include demand-side bottlenecks like fragmented scenarios (29.7-30% cited as primary barriers) and low client penetration (54.5% below 10%), while trends point to vertical specialization, value-based pricing, and ecosystem integration as growth engines. Comparative analysis with MERICS' Report on China's AI Stack  and RAND's analysis of China's AI industrial policy  reveals China's shift toward self-reliant stacks (e.g., Huawei Ascend chips) and policy-driven innovation, contrasting U.S. capital-intensive models where private AI investment reached $109.1 billion in 2024 versus China's $9.3 billion . Contributions include a synthesized framework for assessing commercialization maturity, identification of eight core insights (e.g., organizational readiness as a new threshold), and strategic recommendations for enterprises, investors, and policymakers. This work addresses gaps in prior surveys, such as CEIBS' AI Industry Landscape Report 2025 , by emphasizing granular enterprise data and future trajectories, fostering informed decision-making in AI's transformative era.