中国金融智库特邀研究员余丰慧接受证券时报记者采访时表示："拓展数字服务消费，利用人工智能等技术促进服务消费，不仅是当前经济转型的重要措施， 也是推动消费升级的明确路径。" 目前文旅、医疗等服务消费场景已经率先迈向"智能新时代"。 走进医院，智能导检系统实时显示院内信息、通过算法推荐就诊者有序检查，住院患者能用智能床旁边的交互终端便捷查询费用和电子病历，甚至还能在线 点餐、调控灯光亮度和室内温度。而在智慧药房，机器人在狭窄的空间内"接力"将药品运输到发药机，机械臂快速按处方取药…… 这种以前在科幻大片里才能看到的场景正在走进现实，人工智能（AI）等信息技术正在深入服务消费的各个场景，带来消费新体验。近日，商务部等9部门联 合发布《关于扩大服务消费的若干政策措施》（以下简称《措施》），明确提出拓展数字服务消费，鼓励电商平台创新线上线下融合的数字服务消费场景， 开展"人工智能+消费"典型应用案例推荐，持续打造信息消费新产品新场景新热点。 政策的春风激荡起层层涟漪，标志着服务消费的数字化、智能化变革将从企业自发探索走向政策有力牵引的新阶段。 服务消费迈向智能新时代 什么是数字服务消费？指的是把人工智能、虚拟现实、增强 ...

中国金融智库特邀研究员余丰慧接受证券时报记者采访时表示："拓展数字服务消费，利用人工智能等 技术促进服务消费，不仅是当前经济转型的重要措施，也是推动消费升级的明确路径。" 记者了解到，目前文旅、医疗等服务消费场景已经率先迈向"智能新时代"。 走进医院，智能导检系统实时显示院内信息、通过算法推荐就诊者有序检查，住院患者能用智能床旁边 的交互终端便捷查询费用和电子病历，甚至还能在线点餐、调控灯光亮度和室内温度。而在智慧药房， 机器人在狭窄的空间内"接力"将药品运输到发药机，机械臂快速按处方取药…… 这种以前在科幻大片里才能看到的场景正在走进现实，人工智能（AI）等信息技术正在深入服务消费 的各个场景，带来消费新体验。近日，商务部等9部门联合发布《关于扩大服务消费的若干政策措施》 （以下简称《措施》），明确提出拓展数字服务消费，鼓励电商平台创新线上线下（300959）融合的数 字服务消费场景，开展"人工智能+消费"典型应用案例推荐，持续打造信息消费新产品新场景新热点。 政策的春风激荡起层层涟漪，标志着服务消费的数字化、智能化变革将从企业自发探索走向政策有力牵 引的新阶段。 服务消费迈向智能新时代 什么是数字服务消费 ...

Core Insights - The article discusses the challenges faced by the AI industry as it attempts to transition from experimental applications to widespread implementation in various sectors, highlighting three main difficulties: technology adaptability, data quality and availability, and high costs [9][10]. Group 1: AI Application Challenges - AI is entering a "deep water" phase where large-scale deployment faces significant challenges, including technology that is not specialized enough for specific industries [10]. - The current state of foundational models is still developing, with issues such as poor interpretability and high hallucination rates, making it difficult to find suitable application scenarios [10][11]. - The industrial sector faces a "three highs" dilemma: high entry barriers, high operational costs, and high safety risks, necessitating a deep understanding of complex processes and implicit knowledge [10][11]. Group 2: Data Quality Issues - High-quality data is essential for training industry-specific models, but there is a notable lack of quality data across different sectors, leading to "data islands" and inconsistent data quality [10][11]. - Legal restrictions, such as data security laws and personal information protection laws, hinder the large-scale application of existing data, particularly in the healthcare sector [11][12]. - The transition from non-digital to digital data is also constrained by intellectual property laws, further exacerbating the shortage of high-quality industry-specific data [11][12]. Group 3: Cost Barriers - The high costs associated with customized AI services, including computing power, model development, and data management, pose a significant burden for small and medium-sized enterprises (SMEs) [10][12]. - There is a need for differentiated support policies for various types of enterprises, including state-owned enterprises, industry leaders, and SMEs, to facilitate the implementation of AI initiatives [12].

Core Viewpoint - The article discusses the rapid advancement and application of artificial intelligence (AI) across various industries, highlighting both the opportunities and challenges faced in the implementation of the "AI+" initiative in China [1][4][9]. Group 1: AI Applications and Innovations - At the 2025 AGIC Shenzhen conference, AI was showcased through various applications, including a coffee-making robot that can create latte art in 90 seconds and a sorting robot that won multiple awards for its efficiency [3][6]. - The "AI+" initiative aims to integrate AI into everyday life and industries, with a goal of achieving over 90% application penetration of new intelligent terminals and agents by 2030 [6][7]. - Companies like Anno Robotics and Lingyi Intelligent Manufacturing are leading the way in automating food and beverage services, demonstrating the practical benefits of AI in enhancing operational efficiency [3][6]. Group 2: Challenges in AI Implementation - Despite the advancements, AI faces significant challenges in entering the "deep water" of application, including issues of technology adaptability, data quality, and high costs [4][9][10]. - The lack of specialized industry models and the presence of "data silos" hinder the effective training and deployment of AI systems, making it difficult to meet the specific needs of various sectors [9][10]. - High operational costs and the complexity of customized AI solutions pose barriers for small and medium-sized enterprises, limiting their ability to adopt AI technologies [10][11]. Group 3: Government Initiatives and Support - The Chinese government has initiated the "AI+" action plan, encouraging local governments to tailor their strategies based on regional strengths and industry characteristics [6][7]. - Provinces like Zhejiang and Shanghai are focusing on specific sectors such as healthcare and manufacturing to drive AI integration, showcasing the importance of localized approaches [7][8]. - Experts suggest that differentiated support policies for various types of enterprises, including public service platforms, could help reduce costs and promote innovation in AI applications [11].

Group 1: AI Applications and Innovations - The 2025 AGIC Shenzhen showcased various AI applications, including robots for beverage preparation and intelligent retail systems, indicating the rapid integration of AI into multiple industries [1][2][3] - Anno Robotics demonstrated a coffee-making robot capable of creating latte art in 90 seconds, highlighting the potential for automation in beverage services [2] - The "Linglong" robot from Lingyi Intelligent Manufacturing won multiple awards for its sorting capabilities, showcasing advancements in robotic efficiency [2][3] Group 2: Government Initiatives and Regional Developments - The State Council issued an opinion on the "Artificial Intelligence+" initiative, aiming for over 90% application penetration of new intelligent terminals and agents by 2030 [4] - Various provinces, including Zhejiang and Shanghai, have begun implementing the "Artificial Intelligence+" initiative, focusing on sectors like healthcare and manufacturing [4][5] - Zhejiang is leveraging its digital infrastructure for AI in healthcare, while Shanghai is integrating AI into high-end manufacturing sectors [5] Group 3: Challenges in AI Implementation - Experts identified three main challenges in scaling AI applications: technology adaptability, data quality issues, and high implementation costs [6][7] - The current AI models face issues such as poor interpretability and high operational costs, particularly in industrial applications [6][7] - There is a significant need for high-quality training data to enhance AI models, but many industries face data silos and varying levels of digital transformation [6][7] Group 4: Recommendations for AI Advancement - To overcome challenges, a closed-loop system involving technology, application scenarios, and ecosystem collaboration is recommended [6][7] - Tailored support policies for different types of enterprises, especially small and medium-sized ones, are essential to facilitate AI adoption [7]