Core Insights - The article discusses the anxiety traditional industries face in the age of AI, questioning the practical benefits AI can bring to businesses beyond superficial applications [2] - It emphasizes the need for a deeper integration of AI into core business processes such as R&D, production, and supply chain management to address issues like inventory, efficiency, and information asymmetry [2][5] Group 1: AI Empowerment in Traditional Industries - The true path for empowering traditional industries with AI is explored, moving beyond theoretical discussions to practical insights from industry insiders [3] - Liu Chen, the CEO of Lingdi Technology Style3D, shares his journey from traditional garment manufacturing to leading a tech unicorn, highlighting the pain points of high inventory, slow response times, and thin margins in the apparel industry [4][5] - Liu argues that while companies should fully embrace AI, it should not be viewed as a catch-all solution; instead, it should be combined with core technologies to drive real change [5][8] Group 2: Future of Manufacturing and Supply Chains - A provocative future scenario is presented where, by 2050, advanced AI and robotics could shift manufacturing locations closer to consumers, challenging the current logic of low-cost labor in countries like Vietnam and Cambodia [6][7] - Liu believes that China's current advantage lies in exporting products, but future opportunities will focus on exporting AI-driven manufacturing capabilities [7] Group 3: AI Integration Strategies - The article outlines a strategic framework for integrating AI with core technologies, emphasizing the importance of proprietary data and specialized models in achieving successful AI implementation [11][23] - Liu introduces a three-step transformation path for industries: enhancing tools, reengineering processes, and building ecosystems [11][20] - The combination of AI and core technologies can lead to exponential improvements in productivity, allowing tasks that once required large teams to be completed by a single individual in a fraction of the time [14] Group 4: Organizational Transformation - The organizational structure is shifting from linear to networked models, necessitating a new approach to collaboration and workflow [15] - Liu stresses the importance of data governance and the role of CEOs as "AI architects" in leading these transformations, ensuring that AI initiatives are not relegated to IT departments [17][18] - The article raises critical questions about how to effectively combine general models, specialized models, and proprietary data to leverage decades of industry experience into AI-driven solutions [22]