DingTalk CTO Zhu Hong: AI that doesn't enter the production process is essentially just a demo.

On the afternoon of December 23, the 2025 Dingfeng Conference – General Manufacturing Special Session, hosted by DingTalk, was successfully held. Focusing on business model transformation in the manufacturing industry during the AI era, this event brought together numerous DingTalk ecosystem partners and representatives from manufacturing enterprises to jointly explore the deep integration and value realization of AI technologies in core manufacturing processes such as order processing, production scheduling, quality control, and process optimization.

Zhu Hong, CTO of DingTalk: AI Must Penetrate Deep into Production Processes

"China is a global manufacturing powerhouse, with over 50% of the Fortune 500 manufacturing companies currently using DingTalk across more than 30 major manufacturing sectors," said Zhu Hong, CTO of DingTalk, in his opening speech. He emphasized that for AI to truly take root in manufacturing, it must become integrated into enterprise production workflows.

He pointed out that AI in manufacturing cannot rely solely on general large models; instead, it should combine industry-specific models with an agile Agent development system. To this end, DingTalk has developed Agent OS—an operating system enabling AI to run continuously within enterprises—integrating computing power, multi-model access, and low-code development platforms, working with ecosystem partners to achieve scenario-based implementation.

Zhu stressed three core principles: first, user-centricity driven by AI to solve real-world production challenges; second, empowering reusability and supporting continuous iteration; third, ensuring practical deployment—AI must enter workshops, teams, and production lines.

Taking Youcheng as an example, the Order Agent developed on DingTalk’s DEAP platform reduced unstructured order processing time from 1.4 hours to under half a minute, boosting efficiency hundreds of times. DEAP also establishes a "development mode + runtime mode" dual-paradigm framework to meet long-tail customization needs.

In terms of security and data management, DEAP supports private deployment, ensures safety through end-to-end encryption, permission controls, and full-chain auditing, and introduces a "data engineering" mechanism that transforms unstructured data into high-quality AI-ready data, creating an evolving closed loop where the system becomes “smarter with use.”

Yao Chi of YizhiweiSi: Building Zhi Xiao Q, an AI That Understands Machine Language

Yao Chi, CEO of YizhiweiSi, shared insights on the intelligent agent "Zhi Xiao Q," built on DingTalk's DEAP platform, designed to address quality control challenges in industrial settings. Integrating industrial time-series large models and vision large models, Zhi Xiao Q adopts a "large model + specialized tool plug-in" architecture capable of interpreting the physical meaning behind data such as current, voltage, and vibration.

Engineers need only issue a command like “perform SPC analysis at 6 p.m. every night,” after which Zhi Xiao Q automatically retrieves data, generates control charts, and outputs conclusions. At a Chinese factory of a globally leading sensor company, Zhi Xiao Q has independently completed approximately 40% of tasks traditionally handled by quality engineers—including process analysis and anomaly detection—all while maintaining local data deployment for enhanced security.

By combining DingTalk's Agent OS with domain-specific know-how, Zhi Xiao Q enables tacit knowledge to be made explicit and standardized, with cross-scenario reusability.

Best Practice from Leading Enterprises: Deep Integration of AI into Business Operations

Lu Zhaogang from Liuzhou Iron & Steel Group stated that AI should serve people. Liuzhou Steel has launched its "Ten Thousand AI Employees Initiative," sparking employee innovation through an AI skills competition that led to over a thousand smart assistants created within a week, covering multiple business scenarios. Meanwhile, leveraging the "Ask Data" application promotes standardized data governance, allowing AI to participate in meeting quality reviews and inspection alerts, transforming employees into intelligent "enablers."

Li Peng, IT Director at Tianzheng Electric, shared their "small steps, fast progress" strategy: using AI-powered spreadsheets to empower business departments to independently develop applications and configure over a thousand automated workflows; AI transcription reduces meeting minutes generation to just minutes; and the AI sales assistant delivers instant product selection and proposal generation, driving a shift toward a "data + AI-driven" sales model.

Shen Dongkun from Jinko Energy introduced the "1310" top-level design for AI transformation: centered on business model innovation, identifying three key AI-integrated business tracks, breaking them down into ten focus areas, and implementing X specific application scenarios. He emphasized that AI transformation is fundamentally about "redefining the relationship between human and machine intelligence." The company established a CEO-led AI Management Committee to promote an AI-driven culture. In production, AI is used for root cause analysis of quality issues, combined with "real-time detection + voice broadcast + DingTalk notifications" to ensure operational compliance, resulting in improved yield rates and reduced waste.

Deepening Collaboration to Advance AI Adoption in Manufacturing

At the closing of the event, five leading industry enterprises—Sanhua Intelligent Controls, Runjian Technology, Dahua Technology, Greebo, and Zhongjia Te Electric—signed cooperation agreements with DingTalk. They will deepen collaboration in co-creating AI agents, shop-floor management, global coordination, organizational digitalization, and intelligent sales, jointly advancing digital and intelligent transformation in the manufacturing sector.

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