2025年中国企业级AI应用行业研究报告

Core Insights - The enterprise-level AI application industry is transitioning from a technology exploration phase to a large-scale application phase, driven by advancements in large language models [1][14] - Companies face complex challenges in implementing AI applications, which require not only technological breakthroughs but also systematic, end-to-end implementation capabilities [1][27] - AI Agents are becoming the core vehicle for enterprise-level AI applications, facilitating deep integration with business processes [1][29] Application Layer - AI Agents are central to the deployment of enterprise-level AI applications, breaking down tasks into smaller units and integrating with business processes through various methods [1][29] - The focus is on enhancing efficiency in processes, amplifying knowledge, and innovating value through AI applications [17][27] Supporting Layer - A data-centric approach is essential for model selection, emphasizing the construction of a Data+AI foundation and a data security system [1][41] - High-quality datasets are crucial for AI development, enabling businesses to convert data into unique competitive advantages [41][42] Infrastructure Layer - AI infrastructure is evolving towards a multi-dimensional and heterogeneous model, highlighting the importance of deep collaboration between software and hardware in the context of domestic substitution [1][53] - The dominance of GPU chips in AI applications is solidifying, with domestic manufacturers focusing on optimizing interconnectivity and inference capabilities [50][51] Organizational Layer - Leadership commitment is critical for the success of AI applications, with high-level management playing a significant role in driving AI strategy and resource allocation [56] - Employees need to transition from being passive users to active collaborators in AI processes, requiring a shift in organizational roles and skills [60] Industry Trends - The enterprise-level AI application market is characterized by a layered collaboration and dynamic competition among vendors, including application software, technical services, cloud services, and AI model providers [2][65] - The financing landscape is shifting towards application-level investments, with AI in healthcare emerging as a popular sector for funding [12][14] Policy and Regulatory Support - The Chinese government is actively promoting AI integration across various sectors, setting specific goals for AI application coverage and deep integration by 2027 [8][9] - Policies are focused on releasing data value and constructing industry-specific model systems to enhance AI application deployment [8][9] Challenges in Implementation - Key bottlenecks in scaling AI applications include weak data foundations, lack of quantifiable business value, and a shortage of skilled talent capable of bridging technology and business insights [23][27] - The transition from model-centric approaches to agent-driven frameworks is essential for ensuring reliable AI application delivery [10][31]