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作为促消费、稳就业与城市生活运转的核心支撑，生活服务业持续受益于人工智能、大数据及物联网对营销获客、运营调度及交付履约等环节的全方位赋能，其效能已从单纯的线上流量匹配延伸至即时零售、无人配送与智能门店等场景革新。当前，行业版图内外卖平台、生鲜电商、餐饮连锁及本地生活服务等多元模式竞合交织，平台间对用户时长、履约效率与消费心智的争夺日趋白热化，监管框架从包容审慎向常态化、精细化规范演进。在政策端，促消费与支持平台经济健康发展的信号持续释放，为生活服务企业在技术研发、模式创新及下沉市场探索中注入了新的活力。纵观行业发展脉络，生活服务业正经历从规模扩张向效率提升与体验价值创造的跃迁，AI技术与实体场景融合的广度深度将成为进一步发展的关键支点。
全球领先的新经济产业第三方数据挖掘和分析机构iiMedia Research（艾媒咨询）最新发布的《2026年中国生活服务业发展趋势及标杆企业研究报告》数据显示，AI技术在生活服务业的应用正从营销端向运营端与交付端加速渗透，三大应用方向已初具规模。在营销端，基于用户行为数据与AI算法实现千店千面的精准推荐与个性化营销，提升转化率与复购率，典型应用包括外卖平台智能推荐、商户智能发券与会员精准运营，数据显示推荐转化率提升32%，会员复购率提升28%，头部平台已部署覆盖率达65%。在运营端，AI驱动智能调度在即时配送、出行、家政等场景广泛应用，优化路线规划、运力分配与供需匹配，大幅提升履约效率与资源利用率，是平台型企业的核心竞争壁垒，数据显示配送时长缩短18%，运力利用率提升22%，订单自动分派率提升48%。在交付端，智能烹饪设备与无人餐厅、无人仓与无人配送、智能清洁机器人等在特定场景实现人工替代，缓解人力成本压力，数据显示人力成本降低40%，试点渗透率达15%，预计3-5年规模化普及。然而，行业核心挑战在于数据积累不足、场景适配复杂度高与投入产出比不明确，短期内在营销与调度环节落地最快，长期向交付端延伸。
通过对典型企业的研究，可以更直观地了解生活服务板块各具特色的布局和差异化的发展路径。美团以其在本地生活领域的平台生态优势，依托“履约网络×商户网络”双核心壁垒，构建了连接消费者、商户与骑手的三边平台生态，通过即时配送、商户评价数据沉淀与SaaS工具及供应链服务协同，实现从流量撮合向经营赋能的跃迁；叮咚买菜作为生鲜即时零售的代表，其经营情况与估值波动深度绑定于前置仓盈利模型的优化及供应链管理能力，通过“产地直采+自有品牌”提升毛利与差异化，在30分钟履约时效与高订单密度之间寻求单仓盈利平衡，持续向全品类拓展；海底捞作为餐饮连锁服务的标杆，凭借全链路标准化运营体系、蜀海供应链整合及师徒制人才培养机制，构建了以差异化服务体验为核心的品牌壁垒，实现了门店跨区域快速复制与品质管控的协同发展。三家企业的资产结构、盈利质量与估值逻辑虽有差异，但共同指向了精细化运营、持续创新与场景化服务价值的深度挖掘。
As a core pillar supporting consumption stimulation, employment stabilization, and the smooth operation of urban life, the lifestyle services sector continues to benefit from the comprehensive empowerment provided by artificial intelligence, big data, and the Internet of Things across various aspects—including marketing and customer acquisition, operational scheduling, and delivery fulfillment. Its scope has expanded beyond mere online traffic matching to encompass innovative scenarios such as instant retail, unmanned delivery, and smart retail stores. Currently, the industry landscape is characterized by a complex interplay of competitive and collaborative dynamics among diverse business models—such as food delivery platforms, fresh produce e-commerce platforms, restaurant chains, and local lifestyle service providers. The competition among these platforms for user engagement duration, fulfillment efficiency, and consumer behavior is intensifying, while the regulatory framework is evolving from a tolerant and prudent approach toward more normalization and refined oversight. On the policy front, signals encouraging consumption growth and supporting the healthy development of the platform economy continue to emerge, injecting new vitality into lifestyle service enterprises in their efforts toward technological R&D, model innovation, and exploration of lower-tier markets. Looking at the overall development trajectory of the industry, the lifestyle services sector is undergoing a transition from scale expansion to a focus on efficiency enhancement and experience value creation; the breadth and depth of integrating AI technologies into physical business scenarios will serve as a key lever for further growth.
According to data from the latest "2026 China Lifestyle Services Industry Development Trends and Benchmark Enterprise Research Report" released by iiMedia Research, a globally leading third-party data mining and analysis firm specializing in the new economy sector, the application of AI technology within the lifestyle services industry is accelerating its penetration from the marketing domain to the operational and delivery domains, with three major application areas already taking shape. On the marketing front, leveraging user behavior data and AI algorithms enables precise, personalized recommendations and tailored marketing strategies across thousands of stores, thereby boosting conversion and repeat purchase rates. Typical applications include intelligent recommendation systems on food delivery platforms, automated coupon distribution for merchants, and targeted membership management; data shows that recommendation-driven conversion rates increased by 32% and membership repeat purchase rates rose by 28%, with leading platforms having deployed these solutions across a coverage rate of 65%. On the operations front, AI-driven intelligent scheduling is widely adopted in scenarios such as instant delivery, mobility services, and household services; it optimizes route planning, transport capacity allocation, and supply-demand matching, significantly enhancing fulfillment efficiency and resource utilization – serving as a core competitive advantage for platform-based enterprises. Data indicates that delivery times were shortened by 18%, transport capacity utilization rates increased by 22%, and the automated order dispatching rate rose by 48%. On the delivery front, intelligent cooking equipment, unmanned restaurants, unmanned warehouses and delivery systems, and smart cleaning robots are being used to replace human labor in specific scenarios, alleviating the pressure of labor costs; data shows that labor costs were reduced by 40%, the pilot deployment rate reached 15%, and large-scale adoption is expected within the next 3–5 years. However, the industry's core challenges include insufficient data accumulation, high complexity in scenario adaptation, and an unclear return on investment (ROI) profile; therefore, the most rapid implementation is likely to occur in the marketing and scheduling segments in the short term, with long-term expansion expected into the delivery segment.
Through case studies of representative enterprises, one can gain a more intuitive understanding of the distinctive strategic layouts and differentiated development pathways within the lifestyle services sector. Leveraging its platform ecosystem advantages in the local living services sector and its dual-core competitive moat consisting of the "Delivery Network × Merchant Network," Meituan has built a tripartite platform ecosystem that connects consumers, merchants, and delivery riders. By integrating instant delivery services, the accumulation of merchant review data, SaaS tools, and supply chain services, the company has achieved a strategic shift from traffic matching to operational empowerment. As a representative player in the fresh produce instant retail sector, Dingdong Maicai's business performance and valuation fluctuations are closely tied to the optimization of its front-end warehouse profitability model and its supply chain management capabilities. Through a "direct sourcing from production sites + proprietary brand" strategy, the company enhances its gross margin and differentiation, seeking a profitable balance between a 30-minute delivery timeframe and high order density, while continuously expanding its portfolio across all product categories. Haidilao, as a benchmark for restaurant chain services, has established a brand barrier centered on differentiated service experiences through its end-to-end standardized operational system, Shuhai supply chain integration, and a mentorship-based talent development mechanism, enabling the simultaneous rapid cross-regional expansion of its stores and the coordinated advancement of quality control. Although the three companies differ in their asset structures, profit quality, and valuation logic, they all share a common focus on refined operations, continuous innovation, and the deep exploration of scenario-based service value.