与爱为舞张怀亭：在AI应用领域创业，要先有业务闭环、再用模型接管

Core Viewpoint - The core viewpoint of the article emphasizes the potential of generative AI technology to transform the service industry into a manufacturing-like model, addressing the challenges of providing high-quality, low-cost services at scale, which is currently seen as a paradox in many service sectors [4][7][8]. Summary by Sections AI Application Opportunities - The article discusses the entrepreneurial opportunities in AI applications, particularly in converting service industries into manufacturing-like operations, thereby overcoming the "impossible triangle" of low cost, high quality, and large-scale service delivery [4][7]. - Generative AI is seen as a solution to provide personalized services at scale, which has not yet been fully realized in the service sector [7][8]. Challenges in AI Implementation - The current lack of explosive commercialization of AI applications is attributed to issues such as model hallucinations, inaccurate reasoning, and uncertain outcomes [4][10]. - The need for teams to balance model uncertainty with business tolerance is highlighted, emphasizing the importance of understanding both business and AI technology [4][10]. Historical Context and Comparisons - A comparison is made to the mobile application explosion over a decade ago, which was facilitated by the maturity of foundational technologies like 5G and smartphones, suggesting that similar foundational advancements are needed for AI applications to thrive [9][10]. Business Transformation Pathway - The article outlines a pragmatic approach for AI application development, starting with establishing a business loop to validate application scenarios, followed by gradually integrating AI models into the business processes [12][13]. - The importance of cloud-based data collection and high-quality feature sets for training AI models is emphasized [12]. Organizational Structure for AI Applications - The article stresses the necessity of having a high density of talent that combines industry expertise with AI knowledge, as well as fostering a culture of practical innovation [15][16]. - Human-machine collaboration is identified as a foundational operational paradigm for companies in the intelligent era [15][16]. Conclusion - The article concludes with a summary of guiding principles for AI application development: "business-driven, intelligent-driven, human-machine collaboration, and practical innovation" [16].