2025年中国企业级AI应用行业研究报告

Core Viewpoint - 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 and the need for systematic, end-to-end implementation capabilities [1][14]. Application Layer - Agents are becoming 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 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 tailored for AI [1][41]. - High-quality datasets are crucial for AI development, enabling the transformation of business data into unique competitive advantages [42][45]. Infrastructure Layer - AI computing 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][50][53]. - The dominance of GPU chips in AI applications is solidifying, with domestic manufacturers focusing on optimizing interconnectivity and inference capabilities to achieve differentiation [50][51]. Organizational Layer - The success of AI applications is heavily influenced by top management's commitment and strategic involvement, which is critical for driving AI investment returns [56]. - Employees must transition from being passive users to active collaborators in AI processes, necessitating a shift in organizational roles and capabilities [60]. Vendor Landscape - The enterprise-level AI application market consists of four main categories: application software, technical services and solutions, cloud services, and AI model providers, creating a dynamic competitive landscape [2][65]. - Established companies leverage their industry expertise to extend AI applications, while startups focus on specific scenarios to complement existing systems [65][66]. Development Trends - The evolution of large models is moving from single Transformer architectures to multi-architecture parallel iterations, allowing for flexible and efficient adaptation to various scenarios [2]. - AI is expected to deeply intervene and reconstruct enterprise processes, leading to a transformation in human-machine collaboration models [2][8].