As active corporate AI agents reach a projected 5 million in 2026, Chinese enterprises are abandoning isolated pilots to build unified, full-stack AI foundations.
The Chinese market for artificial intelligence agents is executing a decisive leap in 2026, transitioning rapidly from small-scale proofs-of-concept to large-scale, mission-critical deployments. According to data from International Data Corporation (IDC), the volume of active enterprise AI agents in China is projected to surge from nearly 2 million in 2025 to 5 million in 2026. This dramatic growth marks a fundamental shift in corporate strategy: organizations are no longer content with isolated, single-use AI applications. Instead, they are prioritizing the construction of unified, enterprise-level AI platforms designed to embed intelligence directly into core business operations.
The Paradigm Shift to Enterprise Asset Status
In its research report, “The Rise of New Enterprise-Level AI Platforms” (Doc# CHC54691926, July 2026), IDC highlights that AI’s value proposition has evolved from an experimental “technological toy” into a vital “operational asset”. Enterprise applications have progressed beyond superficial Q&A interfaces toward deep integration within primary business workflows. Concurrently, employee roles are shifting from system operators to “intelligence commanders,” transforming organizational structures from collections of human personnel into collaborative human-AI ecosystems.
To support this evolution, the “new enterprise AI platform” has emerged. Rather than a simple layering of models atop software, it features a comprehensive six-layer architecture:
- AI Foundation Layer
- Data Layer
- Intelligent Agent Development Layer
- Process and Business Layer
- Application Layer
- Governance and Security Layer
Two Primary Implementation Paths
IDC identifies two clear architectural approaches dominating the enterprise AI landscape:
- Intelligent Process Automation: Centered on business processes, this approach embeds AI capabilities into the entire Business Process Management (BPM) lifecycle. AI functions as a “digital employee node” handling pre-examinations, form completion, and risk detection. Its primary benefits are rapid time-to-value and seamless integration with legacy systems.
- Integration of Data and AI: Grounded in data infrastructure, this path establishes an end-to-end foundation connecting data lakes and warehouses through governance, model training, inference, and agent creation. It suits data-intensive sectors requiring long-term data assetization and native AI capabilities.
Vendor Ecosystem and Ecosystem Implementation
Leading Chinese technology providers are demonstrating diverse implementations across both paths. Within intelligent process automation, Aozhe leverages a “AI + Data + Low-Code” approach via its AI Designer, AI Agent, and AI Discover modules, offering tiered deployment for large enterprises (Cloud Hub) and SMBs (Tritium Cloud). Yanhuang Yingdong delivers a full-stack engine covering aPaaS, bpmPaaS, and iPaaS with differentiated L1–L4 risk-level solutions. Langchao Tongruan encapsulates complex processes into callable business capabilities while creating knowledge graphs to enable contextual AI understanding—demonstrating success in Sichuan Jiuzhou’s contract audits and mine safety maintenance.
For data-AI integration, Puyuan provides a complete application-to-data chain, utilizing MCP service delivery to link legacy platforms with AI and engineering-based programming to enforce code control. Dipu Technology focuses on enterprise AI worker infrastructure through an ontology methodology and a “Token Factory” model that increases effective model tokens from 5 to 9, achieving hallucination-free output in manufacturing diagnostics and retail replenishment. KeJie Technology utilizes an integrated lakehouse architecture via its KDP and Keen AI platforms to support both traditional machine learning and LLMs. Similarly, Shuxin Intelligence offers a unified “Data+AI” matrix supporting hybrid CPU/GPU/NPU workloads, serving major state-owned enterprises like PetroChina and China Energy Engineering.
Strategic Recommendations for Enterprise Adoption
IDC outlines six crucial recommendations for building enterprise platforms:
- Integrate governance and security end-to-end across the full stack from inception.
- Recognize that data quality and semantization set the upper limit of AI performance.
- Maintain task-level precision over costs and operational economics.
- Avoid lock-in to single vendors or rigid technology paths.
- Build capabilities progressively without skipping structural steps.
- Align business-IT integration with strong organizational structures and talent development.
Lu Yanxia, Research Director for AI and Big Data at IDC, emphasized: “Transitioning from pilot phases to native intelligence depends heavily on an enterprise’s governance and risk management capabilities. Organizations that anchor their AI capabilities within data, processes, and competency layers—rather than binding themselves to a specific model—will capture true competitive advantages in the era of intelligent systems.”
Dr. Jakob Jung is Editor-in-Chief of Security Storage and Channel Germany. He has been working in IT journalism for more than 20 years. His career includes Computer Reseller News, Heise Resale, Informationweek, Techtarget (storage and data center) and ChannelBiz. He also freelances for numerous IT publications, including Computerwoche, Channelpartner, IT-Business, Storage-Insider and ZDnet. His main topics are channel, storage, security, data center, ERP and CRM.
Contact via Mail: jakob.jung@security-storage-und-channel-germany.de