2025年中国企业级AI应用行业研究报告

Core Insights - The article emphasizes the transition of enterprise-level AI applications from a technology exploration phase to a large-scale application phase, driven by advancements in large language models and the need for systematic implementation capabilities [1][14][27] - Key challenges include the integration of AI into business processes, the establishment of a robust data foundation, and the necessity for organizational changes to support AI transformation [1][23][60] Application Layer - AI Agents are identified as the core vehicle for enterprise-level AI applications, facilitating deep integration with business processes through task decomposition and various operational methods [1][29] - The focus is on creating a systematic approach to AI application delivery, emphasizing the importance of engineering capabilities alongside AI technology [10][31] Supporting Layer - A data-centric model selection process is crucial, with a focus on building a Data+AI foundation and ensuring data security for AI applications [1][41] - High-quality data sets are essential for AI development, enabling effective model training and application [41][42] Infrastructure Layer - The evolution of AI computing infrastructure is highlighted, with a shift towards heterogeneous systems and the importance of optimizing hardware and software collaboration [1][50][53] - The dominance of GPU technology in AI applications is noted, with domestic manufacturers focusing on specific optimizations to compete with international players [50][51] Organizational Layer - The article discusses the need for top-level design and role upgrades for employees to drive AI transformation within organizations [1][60] - Leadership commitment is critical for the successful implementation of AI strategies, with a focus on integrating AI into core business processes [56] Industry Landscape - The enterprise-level AI application market is characterized by a dynamic competition among vendors, including application software, technical services, cloud services, and AI model providers [2][65] - Investment trends indicate a shift towards application-level financing, particularly in sectors like healthcare, where AI applications are gaining traction [12][66] Development Trends - Future trends include the evolution of large models from single architectures to multi-architecture systems, the deep integration of AI into business processes, and the emergence of AI-native applications [2][8] - The article anticipates a significant transformation in human-machine collaboration and the expansion of AI's role in various industries [2][8] Challenges in Implementation - Key bottlenecks for scaling AI applications include weak data foundations, the lack of quantifiable business value, and a shortage of skilled talent capable of bridging technology and business insights [23][27] - The need for a structured approach to AI project ROI evaluation is emphasized, moving away from traditional financial models to more dynamic frameworks [63]