从酷炫功能到真实产业应用，AI卡在了哪里?

Core Insights - The rapid development of generative AI since the release of ChatGPT in November 2022 has led to a heated competition among large model vendors, with claims that the era of Artificial General Intelligence (AGI) is approaching. However, the commercial adoption of AI has shown signs of stagnation, with a recent decline in the proportion of U.S. companies using paid AI products [1][4]. Group 1: Business Process Reconstruction and AI Path Planning - AI model performance metrics do not directly translate into commercial value, as AI often fails to provide end-to-end solutions. Successful AI implementation requires identifying business segments where AI capabilities are mature and data accumulation is sufficient [4][5]. - The process of AI application requires a restructuring of business workflows, where tasks suited for AI are delegated to it, while remaining tasks that require human judgment and emotional interaction are managed by people [5][6]. - The path planning analogy illustrates that AI can enhance certain business segments, but human involvement is necessary to connect different AI functions and ensure task completion [6]. Group 2: Who Leads AI Implementation - Effective AI application necessitates both AI expertise and industry insight. This can be achieved either by having AI experts learn about the industry or by industry professionals acquiring AI skills [7][8]. - The rise of Forward Deployed Engineers (FDE) represents a model where engineers familiar with AI are embedded within client companies to identify value creation points that align with business needs [8][11]. Group 3: AI Programming Activating Industry Self-Transformation - The advancement of AI programming tools has significantly lowered the barriers to software development, allowing non-experts to create functional prototypes using natural language [12][13]. - This shift indicates a potential transition in AI implementation from being driven by technical experts to being led by industry practitioners who can autonomously utilize AI tools to address specific business challenges [12][14]. - Small and medium-sized enterprises (SMEs) are positioned to become key players in AI implementation due to their agility and reduced complexity in decision-making processes [13][14]. Group 4: Conclusion - AI implementation is a gradual process that requires alignment between AI technology and industry needs. Companies should focus on specific, high-adaptability scenarios to create effective AI applications [14]. - The growing capabilities of AI programming tools will empower more individuals to leverage technology for problem-solving, ultimately enhancing productivity across various sectors [14].