Group 1: GenAI Current Status and Trends - By 2027, 80% of Chinese enterprises will adopt multi-model generative AI strategies to diversify model functions, meet local deployment requirements, and achieve cost-effectiveness [16] - By 2028, investment in AI-ready data (especially unstructured data) by Chinese enterprises will reach 20 times that of 2024 [16] - By 2029, 60% of Chinese enterprises will integrate AI into their main products and services, making these AI capabilities a major driver of revenue growth [16] Group 2: GenAI Opportunities - Companies can enhance products, services, processes, and business models using AI to improve product capabilities and customer experience while reducing costs [22] - The emergence of open-source large models like Llama, Mistral, and GLM is significantly lowering inference costs [18] - AI applications can expand product applicability and create new business opportunities [24] Group 3: Implementation and Tools - Internal tools such as programming assistants (Cursor, Claude Sonnet 4) and operational ticket assistants are being utilized to streamline processes [26] - The implementation of retrieval-augmented generation (RAG) combines retrieval technology with large language models (LLMs) to enhance product capabilities [38] Group 4: AI and Security - The need for SASE (Secure Access Service Edge) is emphasized due to the distributed nature of AI applications, requiring high performance and low latency [50] - AI-native architecture is necessary to optimize network security for AI traffic, ensuring fine-grained access control and data security [51]