Core Insights - The evolution of AI large models is marked by a shift from general models to specialized vertical models, enhancing AI's ability to solve specific problems in various industries while changing commercialization methods and potential [1][2] - By 2024, vertical models are expected to achieve commercial applications in manufacturing, finance, and healthcare, becoming crucial for companies to enhance efficiency and reduce costs in their digital transformation efforts [1][2] - The current application of vertical models is limited to specific operational segments, which restricts their overall impact on business processes, highlighting the need for a transition from "AI+" to "+AI" to unlock greater value from AI applications [1][2][3] Industry Trends - The use of AI in enterprises has remained around 50% from 2018 to 2023, with a projected increase to 75% in 2024, indicating a significant growth in AI adoption across business functions [2] - The three essential elements of business—production, management, and sales—are undergoing transformation due to AI, leading to substantial cost reductions in production and potential improvements in management efficiency through digital tools [2][3] Application and Development - Current AI applications in manufacturing focus on three main areas: process management to enhance execution efficiency, intelligent Q&A for knowledge acquisition, and decision support through complex data analysis [3][4] - The development of a digital operating space that facilitates collaboration between humans and AI agents is essential for advancing AI applications beyond single-task execution to more integrated solutions [4][5] Ethical Considerations - The establishment of a reusable, interconnected, and trustworthy AI ecosystem is critical, requiring collaboration on standardization and ethical practices within the industry [4][5][6] - Ensuring that AI operates within ethical boundaries is vital for maintaining system stability and preventing issues such as "AI hallucinations," which can undermine trust in AI-generated content [5][6]