US vs China Enterprise AI Agent Adoption: What 500+ Leaders Reveal in 2026

Two Surveys, Two Worlds: The Data Behind US and China Agent Adoption As of May 29, 2026, two large-scale surveys offer a rare, side-by-side look at how enterprises in the United States and China are adopting AI agents. The findings reveal a transpacific divide in model preferences, multi-agent maturity, security priorities, and return-on-investment expectations. For B2B operations leaders, the data is more than a curiosity—it’s a strategic mirror. By understanding where each ecosystem is headed, organizations can calibrate their own agent roadmaps with a clearer view of global trends. This article draws on the Material survey of over 500 US technical leaders (conducted in early 2026 and available via fxbaogao.com) and the 发现报告 (Discovery Report) covering 500+ Chinese enterprise decision-makers (published on fxbaogao.com/detail/5224007). Both reports were released in May 2026 and provide

the most current, publicly available benchmarks for enterprise AI agent adoption. The Material survey polled more than 500 technical leaders across US companies of varying sizes and industries. It focused on how organizations are using AI agents in production—model choices, architectural patterns, security practices, and expected returns. The 发现报告, meanwhile, surveyed a similar number of Chinese enterprise leaders, capturing deployment rates, preferred model families, and multi-agent system maturity. While both markets are rapidly embracing agentic AI, the paths they are taking differ markedly. The US leans heavily on open-weight models and is more cautious about multi-agent orchestration. China, by contrast, is moving faster into multi-agent architectures and relies overwhelmingly on domestic model ecosystems. These contrasts are not about one approach being “better”; they reflect diffe

rent regulatory environments, supply-chain realities, and innovation cultures. For global operations leaders, the juxtaposition offers a practical lens for evaluating their own strategy. Model Preferences: Open-Weight Dominance in the US vs. Domestic Models in China Perhaps the starkest divergence lies in model selection. According to the Material survey, 48% of US enterprises that have deployed AI agents use open-weight models —primarily Llama (Meta) and Mistral. These organizations cite flexibility, cost control, and the ability to fine-tune on proprietary data as key drivers. The remaining US deployments are split among closed-source commercial APIs (OpenAI, Anthropic, Google) and a small but growing segment of self-hosted or custom-trained models. In China, the picture is inverted. The 发现报告 shows that domestic models dominate, with Qwen (Alibaba) and DeepSeek being the most commonly

cited families . Over 70% of Chinese enterprises reported using at least one domestic model in their agent stacks, often in combination. This preference is shaped by data sovereignty regulations, government procurement policies, and the sheer performance competitiveness of Chinese open-source models. While some Chinese firms also use Western models (e.g., via cloud marketplaces), the default is overwhelmingly local. For B2B leaders, the takeaway is not to copy one region’s model strategy but to understand the trade-offs. Open-weight models offer transparency and customization but require in-house ML expertise. Domestic models can align with local compliance needs but may limit global interoperability. The survey data suggests that the most mature organizations in both regions are adopting a multi-model strategy , blending open-weight, commercial, and fine-tuned models depending on the us

e case. Multi-Agent Architectures: China’s Faster Progression One of the most striking findings is the gap in multi-agent deployment. 45% of Chinese enterprises surveyed have already implemented multi-agent systems , compared to 32% in the US . This 13-percentage-point difference is significant and points to a faster embrace of complex, collaborative agent networks in China. Why the gap? The surveys offer clues. Chinese respondents were more likely to cite “improved task decomposition” and “resilience through agent specialization” as benefits. The 发现报告 also notes that many Chinese firms are building on domestic orchestration frameworks that simplify multi-agent coordination. In the US, by contrast, the Material survey reveals a more cautious approach: many teams are still validating single-agent reliability before scaling to multi-agent patterns. Security and observability concerns are f

requently mentioned as barriers. For operations leaders, this data point is a wake-up call. Multi-agent architectures—where specialized agents collaborate on complex workflows—are not a distant future; they are already mainstream in one of the world’s largest AI markets. Organizations that delay mul