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市场调查报告书
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1848430

全球人工智慧赋能在地商务市场：未来预测（至2032年）－按组件、部署方式、组织规模、经营模式、技术、应用、最终用户和地区进行分析

AI-Powered Local Commerce Market Forecasts to 2032 - Global Analysis By Component (Solutions, Services and Platforms), Deployment Mode, Organization Size, Business Model, Technology, Application, End User and By Geography
出版日期: | 出版商: Stratistics Market Research Consulting | 英文 200+ Pages | 商品交期: 2-3个工作天内

价格

根据 Stratistics MRC 的数据，预计到 2025 年，全球人工智慧赋能的本地商业市场规模将达到 119 亿美元，到 2032 年将达到 512 亿美元，预测期内复合年增长率为 24.3%。

人工智慧驱动的本地商务是指本地企业利用人工智慧技术来个人化客户体验并优化营运。这包括利用人工智慧分析购买历史以提供个人化促销活动、为共享出行等服务提供动态定价，以及利用库存管理系统预测本地需求。人工智慧透过提高行销相关性、改进配送物流以及创建更有效率、数据主导且以客户为中心的本地购物生态系统，帮助实体店与线上巨头竞争。

根据《麻省理工科技评论》报导，人工智慧平台正在透过为小型企业和社区零售商提供建议推荐、库存自动化和精准促销，改变本地商业格局。

超在地化零售平台的成长

人工智慧驱动的本地商业市场正受到超本地化零售平台快速扩张的推动，这些平台能够有效地连接消费者和附近的零售商。消费者对快速、便利性和个人化购物体验日益增长的需求，正在推动人工智慧技术的应用。零售商越来越多地利用机器学习进行需求预测、库存优化和精准促销。此外，都市化和智慧型手机的普及加速了数位化交易的发展，并推动了人工智慧的融合。这些因素共同促进了人工智慧解决方案在全球的应用，从而提升本地商业的效率。

小型零售商对人工智慧的采用程度有限。

由于小型和传统零售商对人工智慧的接受度较低，市场面临许多限制。技术专长有限、缺乏认知以及预算限制阻碍了小型企业有效利用人工智慧工具。许多零售商仍然依赖人工库存管理、客户参与和行销策略。此外，人工智慧平台的前期成本以及对资料隐私的担忧也进一步限制了其应用。这些限制因素降低了人工智慧解决方案在超当地语系化商业生态系统中的整体渗透率，尤其是在新兴地区。

与配送和物流平台集成

将人工智慧驱动的本地商业解决方案与配送和物流平台融合，蕴藏着巨大的成长机会。即时路线优化、需求预测规划和自动化订单履行能够提升营运效率。与第三方配送服务商和云端基础物流系统的集成，则能提高客户满意度和扩充性。此外，人工智慧驱动的分析功能可实现个人化促销，减少库存浪费，并提升盈利。此类整合使本地零售商能够与大型电商平台竞争，拓展业务范围，同时保持成本效益和高效的配送营运。

与全球电商巨头的竞争

大型全球电商平台利用先进的人工智慧和巨量资料分析技术，对市场构成重大威胁。这些公司拥有庞大的基础设施、品牌知名度和规模经济优势。它们能够提供更快的配送速度、动态定价和个人化推荐，这对规模较小的本地电商平台构成了挑战。此外，跨国公司的主导地位可能会侵蚀市场份额，限制独立人工智慧解决方案的发展机会，从而形成高度竞争的环境，迫使较小的区域性企业不断创新。

新冠疫情的影响：

新冠疫情加速了人工智慧驱动的本地商务平台的普及，因为消费者更倾向于非接触式网路购物。超当地语系化的配送网路和数位市场成为生活必需品、食品杂货和零售商品的供应关键。为了满足激增的需求，零售商迅速采用人工智慧进行需求预测、库存管理和客户参与。疫情后，消费者对便利性和个人化的消费习惯持续推动了人工智慧在本地商务领域的应用。因此，新冠疫情起到了催化剂的作用，永久改变了全球零售营运和人工智慧整合策略。

预计在预测期内，解决方案板块将成为最大的板块。

预计在预测期内，解决方案领域将占据最大的市场份额，这主要得益于市场对人工智慧驱动的库存管理、需求预测和个人化客户参与工具的需求不断增长。零售商正在寻求能够整合分析、建议引擎和营运管理的综合软体解决方案。该领域提供扩充性、适应性和持续更新，使企业能够优化绩效并高效应对动态的市场趋势，从而巩固其在人工智慧驱动的本地商业生态系统中的主导地位。

预计在预测期内，云端基础的细分市场将以最高的复合年增长率成长。

预计在预测期内，云端基础方案将保持最高的成长率，这主要得益于其灵活性、扩充性和成本效益。云端平台使零售商能够部署人工智慧应用，并支援即时数据处理和分析，而无需进行大量的基础设施投资。与行动应用和物流网路的整合可提高营运效率和客户体验。便利的远端存取和持续的软体升级使云端基础解决方案成为全球人工智慧驱动的本地商务平台的首选。

比最大的地区

预计亚太地区将在预测期内占据最大的市场份额，这主要得益于电子商务的快速成长、智慧型手机普及率的提高以及城市人口的密集分布。中国、印度和东南亚等国家正经历在超当地语系化零售平台的蓬勃发展。对数位基础设施的投资、消费者对快速配送日益增长的偏好以及该地区蓬勃发展的新兴企业生态系统，都推动了人工智慧驱动的本地商业解决方案在该地区的领先地位。

复合年增长率最高的地区：

在预测期内，北美预计将实现最高的复合年增长率，这主要得益于其强大的技术应用、先进的零售基础设施以及消费者对个人化购物体验的高期望。零售商正在利用人工智慧进行预测分析、动态定价和物流优化。主要技术供应商和人工智慧新兴企业的存在正在推动创新，而法律规范则在促进平台发展。这些因素共同作用，使北美成为人工智慧驱动的本地商业解决方案的新兴中心。

免费客製化服务

订阅本报告的用户可从以下免费自订选项中选择一项：

  • 公司简介
    • 对最多三家其他公司进行全面分析
    • 对主要企业进行SWOT分析（最多3家公司）
  • 区域分类
    • 根据客户兴趣对主要国家进行市场估算、预测和复合年增长率分析（註：基于可行性检查）
  • 竞争基准化分析
    • 基于产品系列、地域覆盖和策略联盟对主要企业基准化分析

目录

第一章执行摘要

第二章 引言

  • 概述
  • 相关利益者
  • 分析范围
  • 分析方法
    • 资料探勘
    • 数据分析
    • 数据检验
    • 分析方法
  • 分析材料
    • 原始研究资料
    • 二手研究资讯来源
    • 先决条件

第三章 市场趋势分析

  • 司机
  • 抑制因素
  • 市场机会
  • 威胁
  • 技术分析
  • 应用分析
  • 终端用户分析
  • 新兴市场
  • 感染疾病疫情的影响

第四章 波特五力分析

  • 供应商的议价能力
  • 买方议价能力
  • 替代产品的威胁
  • 新参与企业的威胁
  • 公司间的竞争

5. 全球人工智慧赋能在地商业市场（按组件划分）

  • 解决方案
  • 服务
  • 平台

第六章 全球人工智慧赋能的在地商业市场：依部署方式划分

  • 云端基础的
  • 本地部署

第七章：以组织规模分類的全球人工智慧赋能在地商业市场

  • 小型企业
  • 大公司

第八章：全球人工智慧赋能的本地商业市场（以经营模式划分）

  • B2C
  • B2B
  • C2C

9. 全球人工智慧赋能在地商业市场（依技术划分）

  • 机器学习
  • 自然语言处理
  • 电脑视觉

第十章：全球人工智慧赋能的本地商业市场（按应用划分）

  • 产品推荐
  • 动态定价
  • 库存最佳化

第十一章 全球人工智慧赋能的本地商业市场（依最终用户划分）

  • 零售商
  • 餐厅
  • 医疗保健提供者

第十二章：全球人工智慧赋能的本地商业市场（按地区划分）

  • 北美洲
    • 美国
    • 加拿大
    • 墨西哥
  • 欧洲
    • 德国
    • 英国
    • 义大利
    • 法国
    • 西班牙
    • 其他欧洲
  • 亚太地区
    • 日本
    • 中国
    • 印度
    • 澳洲
    • 纽西兰
    • 韩国
    • 亚太其他地区
  • 南美洲
    • 阿根廷
    • 巴西
    • 智利
    • 其他南美洲
  • 中东和非洲
    • 沙乌地阿拉伯
    • 阿拉伯聯合大公国
    • 卡达
    • 南非
    • 其他中东和非洲地区

第十三章 重大进展

  • 合约、商业伙伴关係和合资企业
  • 企业合併（M&A）
  • 新产品发布
  • 业务拓展
  • 其他关键策略

第十四章：公司简介

  • Amazon.com, Inc.
  • Alphabet Inc.（Google）
  • Meta Platforms, Inc.
  • Microsoft Corporation
  • Alibaba Group Holding Limited
  • Salesforce, Inc.
  • Uber Technologies, Inc.
  • DoorDash, Inc.
  • Instacart
  • Shopify Inc.
  • IBM Corporation
  • Walmart Inc.
  • Rakuten Group, Inc.
  • JD.com, Inc.
  • Meituan
  • Grab Holdings Limited
Product Code: SMRC31588

According to Stratistics MRC, the Global AI-Powered Local Commerce Market is accounted for $11.9 billion in 2025 and is expected to reach $51.2 billion by 2032 growing at a CAGR of 24.3% during the forecast period. AI-Powered Local Commerce refers to the use of artificial intelligence by local businesses to personalize customer experiences and optimize operations. This includes AI that analyzes purchase history to offer personalized promotions, dynamic pricing for services like ride-sharing, and inventory management systems that predict local demand. It enhances the relevance of marketing, improves delivery logistics, and helps brick-and-mortar stores compete with online giants by creating a more efficient, data-driven, and customer-centric local shopping ecosystem.

According to the MIT Technology Review, AI-driven platforms are transforming local commerce by personalizing recommendations, automating inventory, and enabling hyper-targeted promotions for small businesses and neighborhood retailers.

Market Dynamics:

Driver:

Growth of hyperlocal retail platforms

The AI-Powered Local Commerce Market is driven by the rapid expansion of hyperlocal retail platforms that connect nearby retailers with consumers efficiently. Rising demand for quick, convenient, and personalized shopping experiences is propelling AI adoption. Retailers are increasingly using machine learning for demand prediction, inventory optimization, and targeted promotions. Additionally, urbanization and smartphone penetration have accelerated digital transactions, encouraging AI integration. Collectively, these factors are fueling the deployment of AI solutions to enhance local commerce operations worldwide.

Restraint:

Limited AI adoption by small retailers

The market faces restraints due to low AI adoption among small and traditional retailers. Limited technological expertise, lack of awareness, and budget constraints prevent smaller players from leveraging AI tools effectively. Many retailers continue relying on manual inventory management, customer engagement, and marketing strategies. Additionally, the upfront costs of AI-enabled platforms, along with concerns about data privacy, further restrict adoption. These limitations reduce the overall penetration of AI solutions in hyperlocal commerce ecosystems, especially in emerging regions.

Opportunity:

Integration with delivery and logistics platforms

Integrating AI-powered local commerce solutions with delivery and logistics platforms presents a major growth opportunity. Real-time route optimization, predictive demand planning, and automated order fulfillment enhance operational efficiency. Collaboration with third-party delivery providers and cloud-based logistics systems improves customer satisfaction and scalability. Additionally, AI-driven analytics enable personalized promotions, reducing inventory waste and enhancing profitability. These integrations allow local retailers to compete with larger e-commerce players and expand reach while maintaining cost-effective and efficient delivery operations.

Threat:

Competition from global e-commerce giants

The market faces significant threats from large global e-commerce platforms that leverage advanced AI and big data analytics. These companies benefit from extensive infrastructure, brand recognition, and economies of scale. Their ability to offer faster delivery, dynamic pricing, and personalized recommendations challenges smaller local commerce platforms. Furthermore, the dominance of multinational players can reduce market share and limit opportunities for independent AI-powered solutions, creating a highly competitive environment that necessitates continuous innovation for smaller regional players.

Covid-19 Impact:

The COVID-19 pandemic accelerated the adoption of AI-powered local commerce platforms as consumers increasingly preferred contactless, online shopping. Hyperlocal delivery networks and digital marketplaces became critical for essential goods, groceries, and retail items. Retailers rapidly adopted AI for demand forecasting, inventory management, and customer engagement to meet surging demand. Post-pandemic, consumer habits favor convenience and personalization, sustaining AI adoption in local commerce. Consequently, COVID-19 acted as a catalyst, permanently transforming retail operations and AI integration strategies globally.

The solutions segment is expected to be the largest during the forecast period

The solutions segment is expected to account for the largest market share during the forecast period, owing to the increasing demand for AI-driven tools for inventory management, demand prediction, and personalized customer engagement. Retailers seek comprehensive software solutions that integrate analytics, recommendation engines, and operational management. This segment offers scalability, adaptability, and continuous updates, enabling businesses to optimize performance and respond to dynamic market trends efficiently, solidifying its dominance in the AI-powered local commerce ecosystem.

The cloud-based segment is expected to have the highest CAGR during the forecast period

Over the forecast period, the cloud-based segment is predicted to witness the highest growth rate, reinforced by its flexibility, scalability, and cost-efficiency. Cloud platforms allow retailers to deploy AI applications without heavy infrastructure investment, supporting real-time data processing and analytics. Integration with mobile apps and logistics networks enhances operational efficiency and customer experience. The ease of remote access and continuous software upgrades further drives adoption, making cloud-based solutions a preferred choice for AI-powered local commerce platforms globally.

Region with largest share:

During the forecast period, the Asia Pacific region is expected to hold the largest market share, ascribed to rapid e-commerce growth, widespread smartphone adoption, and a dense urban population. Countries like China, India, and Southeast Asian nations are witnessing a surge in hyperlocal retail platforms. Investments in digital infrastructure, rising consumer preference for fast delivery, and regional startup ecosystems contribute to the dominance of AI-powered local commerce solutions in the region.

Region with highest CAGR:

Over the forecast period, the North America region is anticipated to exhibit the highest CAGR associated with strong technological adoption, advanced retail infrastructure, and high consumer expectations for personalized shopping experiences. Retailers are leveraging AI for predictive analytics, dynamic pricing, and logistics optimization. The presence of major technology providers and AI startups fosters innovation, while supportive regulatory frameworks encourage platform growth. This combination positions North America as a rapidly expanding hub for AI-powered local commerce solutions.

Key players in the market

Some of the key players in AI-Powered Local Commerce Market include Marico Limited, Adani Wilmar Limited, Wilmar International Ltd, Olam International Limited, Archer Daniels Midland Company (ADM), Bunge Limited, Cargill, Incorporated, The Hain Celestial Group, Inc., Coconuts India Pvt. Ltd., NOW Foods, Nutiva, Inc., La Tourangelle, Inc., Borges International Group, Nutraj (VKC Nuts Pvt. Ltd.) and Dabur India Ltd.

Key Developments:

In August 2025, Marico reaffirmed its growth ambitions: it expects double-digit domestic growth in upcoming quarters, driven by core brands and expansion of new business lines.

In April 2025, Dabur India Ltd. announced it is weaving AI across operations: using conversational bots for consumer engagement, improving supply chain efficiency via AI forecasting, and leveraging AI to decode its Ayurvedic knowledge base to assist new product formulation.

In Feb 2025, Marico Ltd. unveiled the LoSorb Technology and other innovations at World Food India 2025, showcasing new R&D capabilities (hybrid extrusion, DOC valorisation) to push healthier and differentiated food portfolio offerings.

Components Covered:

  • Solutions
  • Services
  • Platforms

Deployment Modes Covered:

  • Cloud-Based
  • On-Premises

Organization Sizes Covered:

  • Small & Medium Enterprises (SMEs)
  • Large Enterprises

Business Models Covered:

  • B2C
  • B2B
  • C2C

Technologies Covered:

  • Machine Learning
  • Natural Language Processing
  • Computer Vision

Applications Covered:

  • Product Recommendations
  • Dynamic Pricing
  • Inventory Optimization

End Users Covered:

  • Retailers
  • Restaurants
  • Healthcare Providers

Regions Covered:

  • North America
    • US
    • Canada
    • Mexico
  • Europe
    • Germany
    • UK
    • Italy
    • France
    • Spain
    • Rest of Europe
  • Asia Pacific
    • Japan
    • China
    • India
    • Australia
    • New Zealand
    • South Korea
    • Rest of Asia Pacific
  • South America
    • Argentina
    • Brazil
    • Chile
    • Rest of South America
  • Middle East & Africa
    • Saudi Arabia
    • UAE
    • Qatar
    • South Africa
    • Rest of Middle East & Africa

What our report offers:

  • Market share assessments for the regional and country-level segments
  • Strategic recommendations for the new entrants
  • Covers Market data for the years 2024, 2025, 2026, 2028, and 2032
  • Market Trends (Drivers, Constraints, Opportunities, Threats, Challenges, Investment Opportunities, and recommendations)
  • Strategic recommendations in key business segments based on the market estimations
  • Competitive landscaping mapping the key common trends
  • Company profiling with detailed strategies, financials, and recent developments
  • Supply chain trends mapping the latest technological advancements

Free Customization Offerings:

All the customers of this report will be entitled to receive one of the following free customization options:

  • Company Profiling
    • Comprehensive profiling of additional market players (up to 3)
    • SWOT Analysis of key players (up to 3)
  • Regional Segmentation
    • Market estimations, Forecasts and CAGR of any prominent country as per the client's interest (Note: Depends on feasibility check)
  • Competitive Benchmarking
    • Benchmarking of key players based on product portfolio, geographical presence, and strategic alliances

Table of Contents

1 Executive Summary

2 Preface

  • 2.1 Abstract
  • 2.2 Stake Holders
  • 2.3 Research Scope
  • 2.4 Research Methodology
    • 2.4.1 Data Mining
    • 2.4.2 Data Analysis
    • 2.4.3 Data Validation
    • 2.4.4 Research Approach
  • 2.5 Research Sources
    • 2.5.1 Primary Research Sources
    • 2.5.2 Secondary Research Sources
    • 2.5.3 Assumptions

3 Market Trend Analysis

  • 3.1 Introduction
  • 3.2 Drivers
  • 3.3 Restraints
  • 3.4 Opportunities
  • 3.5 Threats
  • 3.6 Technology Analysis
  • 3.7 Application Analysis
  • 3.8 End User Analysis
  • 3.9 Emerging Markets
  • 3.10 Impact of Covid-19

4 Porters Five Force Analysis

  • 4.1 Bargaining power of suppliers
  • 4.2 Bargaining power of buyers
  • 4.3 Threat of substitutes
  • 4.4 Threat of new entrants
  • 4.5 Competitive rivalry

5 Global AI-Powered Local Commerce Market, By Component

  • 5.1 Introduction
  • 5.2 Solutions
  • 5.3 Services
  • 5.4 Platforms

6 Global AI-Powered Local Commerce Market, By Deployment Mode

  • 6.1 Introduction
  • 6.2 Cloud-Based
  • 6.3 On-Premises

7 Global AI-Powered Local Commerce Market, By Organization Size

  • 7.1 Introduction
  • 7.2 Small & Medium Enterprises (SMEs)
  • 7.3 Large Enterprises

8 Global AI-Powered Local Commerce Market, By Business Model

  • 8.1 Introduction
  • 8.2 B2C
  • 8.3 B2B
  • 8.4 C2C

9 Global AI-Powered Local Commerce Market, By Technology

  • 9.1 Introduction
  • 9.2 Machine Learning
  • 9.3 Natural Language Processing
  • 9.4 Computer Vision

10 Global AI-Powered Local Commerce Market, By Application

  • 10.1 Introduction
  • 10.2 Product Recommendations
  • 10.3 Dynamic Pricing
  • 10.4 Inventory Optimization

11 Global AI-Powered Local Commerce Market, By End User

  • 11.1 Introduction
  • 11.2 Retailers
  • 11.3 Restaurants
  • 11.4 Healthcare Providers

12 Global AI-Powered Local Commerce Market, By Geography

  • 12.1 Introduction
  • 12.2 North America
    • 12.2.1 US
    • 12.2.2 Canada
    • 12.2.3 Mexico
  • 12.3 Europe
    • 12.3.1 Germany
    • 12.3.2 UK
    • 12.3.3 Italy
    • 12.3.4 France
    • 12.3.5 Spain
    • 12.3.6 Rest of Europe
  • 12.4 Asia Pacific
    • 12.4.1 Japan
    • 12.4.2 China
    • 12.4.3 India
    • 12.4.4 Australia
    • 12.4.5 New Zealand
    • 12.4.6 South Korea
    • 12.4.7 Rest of Asia Pacific
  • 12.5 South America
    • 12.5.1 Argentina
    • 12.5.2 Brazil
    • 12.5.3 Chile
    • 12.5.4 Rest of South America
  • 12.6 Middle East & Africa
    • 12.6.1 Saudi Arabia
    • 12.6.2 UAE
    • 12.6.3 Qatar
    • 12.6.4 South Africa
    • 12.6.5 Rest of Middle East & Africa

13 Key Developments

  • 13.1 Agreements, Partnerships, Collaborations and Joint Ventures
  • 13.2 Acquisitions & Mergers
  • 13.3 New Product Launch
  • 13.4 Expansions
  • 13.5 Other Key Strategies

14 Company Profiling

  • 14.1 Amazon.com, Inc.
  • 14.2 Alphabet Inc. (Google)
  • 14.3 Meta Platforms, Inc.
  • 14.4 Microsoft Corporation
  • 14.5 Alibaba Group Holding Limited
  • 14.6 Salesforce, Inc.
  • 14.7 Uber Technologies, Inc.
  • 14.8 DoorDash, Inc.
  • 14.9 Instacart
  • 14.10 Shopify Inc.
  • 14.11 IBM Corporation
  • 14.12 Walmart Inc.
  • 14.13 Rakuten Group, Inc.
  • 14.14 JD.com, Inc.
  • 14.15 Meituan
  • 14.16 Grab Holdings Limited

List of Tables

  • Table 1 Global AI-Powered Local Commerce Market Outlook, By Region (2024-2032) ($MN)
  • Table 2 Global AI-Powered Local Commerce Market Outlook, By Component (2024-2032) ($MN)
  • Table 3 Global AI-Powered Local Commerce Market Outlook, By Solutions (2024-2032) ($MN)
  • Table 4 Global AI-Powered Local Commerce Market Outlook, By Services (2024-2032) ($MN)
  • Table 5 Global AI-Powered Local Commerce Market Outlook, By Platforms (2024-2032) ($MN)
  • Table 6 Global AI-Powered Local Commerce Market Outlook, By Deployment Mode (2024-2032) ($MN)
  • Table 7 Global AI-Powered Local Commerce Market Outlook, By Cloud-Based (2024-2032) ($MN)
  • Table 8 Global AI-Powered Local Commerce Market Outlook, By On-Premises (2024-2032) ($MN)
  • Table 9 Global AI-Powered Local Commerce Market Outlook, By Organization Size (2024-2032) ($MN)
  • Table 10 Global AI-Powered Local Commerce Market Outlook, By Small & Medium Enterprises (SMEs) (2024-2032) ($MN)
  • Table 11 Global AI-Powered Local Commerce Market Outlook, By Large Enterprises (2024-2032) ($MN)
  • Table 12 Global AI-Powered Local Commerce Market Outlook, By Technology (2024-2032) ($MN)
  • Table 13 Global AI-Powered Local Commerce Market Outlook, By Machine Learning (2024-2032) ($MN)
  • Table 14 Global AI-Powered Local Commerce Market Outlook, By Natural Language Processing (2024-2032) ($MN)
  • Table 15 Global AI-Powered Local Commerce Market Outlook, By Computer Vision (2024-2032) ($MN)
  • Table 16 Global AI-Powered Local Commerce Market Outlook, By Business Model (2024-2032) ($MN)
  • Table 17 Global AI-Powered Local Commerce Market Outlook, By B2C (2024-2032) ($MN)
  • Table 18 Global AI-Powered Local Commerce Market Outlook, By B2B (2024-2032) ($MN)
  • Table 19 Global AI-Powered Local Commerce Market Outlook, By C2C (2024-2032) ($MN)
  • Table 20 Global AI-Powered Local Commerce Market Outlook, By Application (2024-2032) ($MN)
  • Table 21 Global AI-Powered Local Commerce Market Outlook, By Product Recommendations (2024-2032) ($MN)
  • Table 22 Global AI-Powered Local Commerce Market Outlook, By Dynamic Pricing (2024-2032) ($MN)
  • Table 23 Global AI-Powered Local Commerce Market Outlook, By Inventory Optimization (2024-2032) ($MN)
  • Table 24 Global AI-Powered Local Commerce Market Outlook, By End User (2024-2032) ($MN)
  • Table 25 Global AI-Powered Local Commerce Market Outlook, By Retailers (2024-2032) ($MN)
  • Table 26 Global AI-Powered Local Commerce Market Outlook, By Restaurants (2024-2032) ($MN)
  • Table 27 Global AI-Powered Local Commerce Market Outlook, By Healthcare Providers (2024-2032) ($MN)

Note: Tables for North America, Europe, APAC, South America, and Middle East & Africa Regions are also represented in the same manner as above.