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1848430

全球人工智慧賦能在地商務市場：未來預測（至2032年）－按組件、部署方式、組織規模、經營模式、技術、應用、最終用戶和地區進行分析

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個工作天內

價格

根據 Stratistics MRC 的數據，預計到 2025 年，全球人工智慧賦能的本地商業市場規模將達到 119 億美元，到 2032 年將達到 512 億美元，預測期內複合年成長率為 24.3%。

人工智慧驅動的本地商務是指本地企業利用人工智慧技術來個人化客戶體驗並最佳化營運。這包括利用人工智慧分析購買歷史以提供個人化促銷活動、為共享出行等服務提供動態定價，以及利用庫存管理系統預測本地需求。人工智慧透過提高行銷相關性、改進配送物流以及創建更有效率、數據主導且以客戶為中心的本地購物生態系統，幫助實體店與線上巨頭競爭。

根據《麻省理工科技評論》報導，人工智慧平台正在透過為小型企業和社區零售商提供建議推薦、庫存自動化和精準促銷，改變本地商業格局。

超在地化零售平台的成長

人工智慧驅動的本地商業市場正受到超本地化零售平台快速擴張的推動，這些平台能夠有效地連接消費者和附近的零售商。消費者對快速、便利性和個人化購物體驗日益成長的需求，正在推動人工智慧技術的應用。零售商擴大利用機器學習進行需求預測、庫存最佳化和精準促銷。此外，都市化和智慧型手機的普及加速了數位化交易的發展，並推動了人工智慧的融合。這些因素共同促進了人工智慧解決方案在全球的應用，從而提升本地商業的效率。

小型零售商對人工智慧的採用程度有限。

由於小型和傳統零售商對人工智慧的接受度較低，市場面臨許多限制。技術專長有限、缺乏認知以及預算限制阻礙了小型企業有效利用人工智慧工具。許多零售商仍然依賴人工庫存管理、客戶參與和行銷策略。此外，人工智慧平台的前期成本以及對資料隱私的擔憂也進一步限制了其應用。這些限制因素降低了人工智慧解決方案在超當地語系化商業生態系統中的整體滲透率，尤其是在新興地區。

與配送和物流平台整合

將人工智慧驅動的本地商業解決方案與配送和物流平台融合，蘊藏著巨大的成長機會。即時路線最佳化、需求預測規劃和自動化訂單履行能夠提升營運效率。與第三方配送服務商和雲端基礎物流系統的整合，則能提高客戶滿意度和擴充性。此外，人工智慧驅動的分析功能可實現個人化促銷，減少庫存浪費，並提升盈利。此類整合使本地零售商能夠與大型電商平台競爭，拓展業務範圍，同時保持成本效益和高效的配送營運。

與全球電商巨頭的競爭

大型全球電商平台利用先進的人工智慧和巨量資料分析技術，對市場構成重大威脅。這些公司擁有龐大的基礎設施、品牌知名度和規模經濟優勢。它們能夠提供更快的配送速度、動態定價和個人化推薦，這對規模較小的本地電商平台構成了挑戰。此外，跨國公司的主導地位可能會侵蝕市場佔有率，限制獨立人工智慧解決方案的發展機會，從而形成高度競爭的環境，迫使較小的區域性企業不斷創新。

新冠疫情的影響：

新冠疫情加速了人工智慧驅動的本地商務平台的普及，因為消費者更傾向於非接觸式網路購物。超當地語系化的配送網路和數位市場成為生活必需品、食品雜貨和零售商品的供應關鍵。為了滿足激增的需求，零售商迅速採用人工智慧進行需求預測、庫存管理和客戶參與。疫情後，消費者對便利性和個人化的消費習慣持續推動了人工智慧在本地商務領域的應用。因此，新冠疫情起到了催化劑的作用，永久改變了全球零售營運和人工智慧整合策略。

預計在預測期內，解決方案板塊將成為最大的板塊。

預計在預測期內，解決方案領域將佔據最大的市場佔有率，這主要得益於市場對人工智慧驅動的庫存管理、需求預測和個人化客戶參與工具的需求不斷成長。零售商正在尋求能夠整合分析、建議引擎和營運管理的綜合軟體解決方案。該領域提供擴充性、適應性和持續更新，使企業能夠最佳化績效並高效應對動態的市場趨勢，從而鞏固其在人工智慧驅動的本地商業生態系統中的主導地位。

預計在預測期內，雲端基礎的細分市場將以最高的複合年成長率成長。

預計在預測期內，雲端基礎方案將保持最高的成長率，這主要得益於其靈活性、擴充性和成本效益。雲端平台使零售商能夠部署人工智慧應用，並支援即時數據處理和分析，而無需進行大量的基礎設施投資。與行動應用和物流網路的整合可提高營運效率和客戶體驗。便利的遠端存取和持續的軟體升級使雲端基礎解決方案成為全球人工智慧驅動的本地商務平台的首選。

比最大的地區

預計亞太地區將在預測期內佔據最大的市場佔有率，這主要得益於電子商務的快速成長、智慧型手機普及率的提高以及城市人口的密集分佈。中國、印度和東南亞等國家正經歷在超當地語系化零售平台的蓬勃發展。對數位基礎設施的投資、消費者對快速配送日益成長的偏好以及該地區蓬勃發展的新興企業生態系統，都推動了人工智慧驅動的本地商業解決方案在該地區的領先地位。

複合年成長率最高的地區：

在預測期內，北美預計將實現最高的複合年成長率，這主要得益於其強大的技術應用、先進的零售基礎設施以及消費者對個人化購物體驗的高期望。零售商正在利用人工智慧進行預測分析、動態定價和物流最佳化。主要技術供應商和人工智慧新興企業的存在正在推動創新，而法律規範則在促進平台發展。這些因素共同作用，使北美成為人工智慧驅動的本地商業解決方案的新興中心。

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訂閱本報告的用戶可從以下免費自訂選項中選擇一項：

  • 公司簡介
    • 對最多三家其他公司進行全面分析
    • 對主要企業進行SWOT分析（最多3家公司）
  • 區域分類
    • 根據客戶興趣對主要國家進行市場估算、預測和複合年成長率分析（註：基於可行性檢查）
  • 競爭基準化分析
    • 基於產品系列、地域覆蓋和策略聯盟對主要企業基準化分析

目錄

第1章執行摘要

第2章 引言

  • 概述
  • 相關利益者
  • 分析範圍
  • 分析方法
    • 資料探勘
    • 數據分析
    • 數據檢驗
    • 分析方法
  • 分析材料
    • 原始研究資料
    • 二手研究資訊來源
    • 先決條件

第3章 市場趨勢分析

  • 促進要素
  • 抑制因素
  • 市場機遇
  • 威脅
  • 技術分析
  • 應用分析
  • 終端用戶分析
  • 新興市場
  • 感染疾病疫情的影響

第4章 波特五力分析

  • 供應商的議價能力
  • 買方議價能力
  • 替代產品的威脅
  • 新參與企業的威脅
  • 公司間的競爭

5. 全球人工智慧賦能在地商業市場（按組件分類）

  • 解決方案
  • 服務
  • 平台

第6章 全球人工智慧賦能的在地商業市場：依部署方式分類

  • 雲端基礎的
  • 本地部署

第7章：以組織規模分類的全球人工智慧賦能在地商業市場

  • 小型企業
  • 主要企業

第8章：全球人工智慧賦能的本地商業市場（以經營模式分類）

  • B2C
  • B2B
  • C2C

9. 全球人工智慧賦能在地商業市場（依技術分類）

  • 機器學習
  • 自然語言處理
  • 電腦視覺

第10章：全球人工智慧賦能的本地商業市場（按應用分類）

  • 產品推薦
  • 動態定價
  • 庫存最佳化

第11章 全球人工智慧賦能的本地商業市場（依最終用戶分類）

  • 零售商
  • 餐廳
  • 醫療保健提供者

第12章：全球人工智慧賦能的本地商業市場（按地區分類）

  • 北美洲
    • 美國
    • 加拿大
    • 墨西哥
  • 歐洲
    • 德國
    • 英國
    • 義大利
    • 法國
    • 西班牙
    • 其他歐洲
  • 亞太地區
    • 日本
    • 中國
    • 印度
    • 澳洲
    • 紐西蘭
    • 韓國
    • 亞太其他地區
  • 南美洲
    • 阿根廷
    • 巴西
    • 智利
    • 其他南美洲
  • 中東和非洲
    • 沙烏地阿拉伯
    • 阿拉伯聯合大公國
    • 卡達
    • 南非
    • 其他中東和非洲地區

第13章 重大進展

  • 合約、商業夥伴關係和合資企業
  • 企業合併（M&A）
  • 新產品發布
  • 業務拓展
  • 其他關鍵策略

第14章：公司簡介

  • 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.