DIGIWIN(SZ:300378) 硬AI·2025-06-24 12:28Core Viewpoint - The article discusses the emergence of Industrial AI as a significant revolution in the manufacturing sector, emphasizing the need to bridge the gap between traditional craftsmanship and modern AI technologies [1][2]. Group 1: Industrial AI and Its Importance - Industrial AI is seen as a deeper and more impactful revolution compared to generative AI, which has dominated discussions in content creation and software [1]. - The challenge lies in transferring the tacit knowledge of experienced craftsmen to the next generation without loss, which is crucial for the future of Chinese manufacturing [1][14]. Group 2: Challenges Faced by Manufacturing Enterprises - Manufacturing companies are caught between the risks of "rushing ahead" with AI technology without a clear strategy and the danger of falling behind if they do not adapt [4]. - Many enterprises invest heavily in technology without understanding the fundamental purpose of transformation, leading to a disconnect between application and business needs [4]. Group 3: The Solution Proposed by Dingjie - Dingjie Smart aims to create a "thinking system" that decouples knowledge from action, allowing for independent upgrades of AI's knowledge base and execution capabilities [4][5]. - The company has developed a "three-layer rocket" product matrix to integrate the experience of craftsmen with large model reasoning [5]. Group 4: Product Features and Capabilities - The first layer, the Intelligent Data Suite, acts like a "data CT" for factories, addressing the issue of data silos between operational technology and information technology [6][7]. - The second layer, the Enterprise Intelligent Agent Generation Suite, utilizes the MAC P protocol to enable collaboration among digital employees, enhancing decision-making processes [9][10]. - The third layer, the AIoT Command Center and Industrial Mechanism AI, connects various production and facility devices, allowing for real-time data processing and action [11][12]. Group 5: Digitalization of Industrial Knowledge - Dingjie focuses on digitizing industrial knowledge through contextualization, capturing non-structured experience, and creating an industrial knowledge graph [15]. - The use of RAG technology ensures that sensitive core process documents are protected while still allowing AI to provide accurate insights [15]. Group 6: Real-World Applications and Success Stories - Case study of Jiali Co., a leader in automotive tail lights, shows significant improvements in productivity and efficiency after implementing Dingjie’s AI solutions [18]. - Another case with Yingfei highlights the robustness of Dingjie’s platform in building a new global IT system under tight deadlines, demonstrating the platform's capabilities [20][21]. Group 7: Transformation of Business Models - The shift from project-based revenue models to subscription-based models with AI capabilities is highlighted as a significant change in the industrial software landscape [22]. - The emergence of data flywheels and network effects is expected to enhance the value proposition of platforms like Dingjie’s Athena, attracting more clients and partners [22]. Group 8: Future Outlook and Challenges - The article concludes with the notion that the future of Industrial AI will depend on addressing key challenges such as algorithm trust, continuous knowledge acquisition, and ecosystem vitality [27].