2024年企业AI大模型应用落地白皮书

Investment Rating - The report does not explicitly state an investment rating for the industry Core Insights - The demand for AI large model applications is increasing among enterprises driven by policy support, technological breakthroughs, and digital transformation needs [5][6] - Enterprises face multiple challenges in implementing AI large model applications, including insufficient tools/solutions, diverse model adaptation, and complex end-to-end development processes [14][15] Summary by Sections 1. Demand and Pain Points Analysis - The application of AI large models is becoming crucial for enterprise transformation, with higher demands for accuracy, effectiveness, and deployment efficiency [4][5] - Enterprises are exploring AI's potential to empower their operations while facing challenges in the application process [4][14] 2. Exploration and Successful Pathways - Market attempts to address the pain points of AI large model application include the need for comprehensive solutions and professional capabilities from service providers [20][25] - Successful practices in the industry emphasize the importance of one-stop solutions [20][25] 3. Future Trends and Strategic Recommendations - Enterprises are expected to increasingly focus on the ROI of AI large models, with a significant portion of CIOs planning to increase AI budgets in 2024 [46] - The integration of multi-modal models is seen as a key trend to address complex business problems [47] - Combining Retrieval-Augmented Generation (RAG) with knowledge graphs is anticipated to enhance performance in complex query processing [49] - The development of intelligent agents is moving towards expanding single-agent capabilities and fostering multi-agent collaboration [52] 4. Core Capabilities Required for AI Large Model Application - Service providers need to offer flexible and efficient data processing tools, high-quality data generation capabilities, and robust privacy protection [27][28] - The ability to adapt to diverse computing power and model matching is essential for service providers [28][29] - Full-process service capabilities are necessary to ensure the successful deployment of AI large models [29][30] 5. Best Practices for AI Large Model Deployment - Companies should focus on understanding their business needs and selecting appropriate models for deployment [40][41] - Continuous learning and iteration mechanisms are crucial for optimizing AI model performance [59][60]