查看購物車
  • English
  • Japanese
封面
市場調查報告書
商品編碼
2021751

2034年零售市場人工智慧市場預測：按組件、技術、部署模式、銷售管道、應用、最終用戶和地區分類的全球分析

AI in Retail Market Forecasts to 2034 - Global Analysis By Component (Solutions, and Services), Technology, Deployment Mode, Sales Channel, Application, End User and By Geography
出版日期: | 出版商: Stratistics Market Research Consulting | 英文 | 商品交期: 2-3個工作天內

價格

根據 Stratistics MRC 的數據，預計到 2026 年，全球零售業人工智慧市場規模將達到 165 億美元，並在預測期內以 26.1% 的複合年成長率成長，到 2034 年將達到 1059 億美元。

在零售業，人工智慧指的是利用機器學習、數據分析和電腦視覺等先進技術來提升營運效率和客戶體驗的各項措施。這使得零售商能夠分析大量數據，從而實現需求預測、個人化建議、庫存管理和動態定價。透過流程自動化和即時洞察生成，企業可以改善決策、提高效率並支援無縫的全通路客戶互動，從而更深入地了解客戶行為並最佳化整體零售業績。

電子商務和全通路零售的快速擴張。

網路購物的快速成長以及實體店與線上銷售管道的融合，正迫使零售商採用人工智慧（AI）來實現即時庫存同步和個人化客戶參與。 AI驅動的推薦引擎會分析瀏覽歷史和購買模式，進而提高轉換率；聊天機器人則能即時回應大量諮詢。此外，動態定價演算法會根據需求波動和競爭對手的動態調整產品價格。隨著消費者期望在行動應用、網站和實體店之間獲得無縫體驗，零售商越來越依賴AI進行資料流整合、庫存需求預測和履約流程。這種營運上的必然需求，是推動AI在整個零售生態系中普及應用的主要動力。

高昂的實施成本和資料整合成本

在零售業實施人工智慧解決方案需要對雲端基礎設施、資料倉儲以及資料科學家和機器學習工程師等專業人才進行大量投資。許多中小型零售商難以承擔這些初期成本，尤其是在將人工智慧與現有POS和ERP系統整合時。倉庫、線上平台和實體店之間的資料孤島進一步加劇了實施難度，因為清理和標準化各種資料集既耗時又昂貴。此外，模型重新訓練、軟體更新和網路安全措施等持續成本也加重了財務負擔。由於許多傳統零售商無法預期短期內獲得明確的投資回報，他們推遲了人工智慧的採用，儘管人工智慧具有潛在的長期效率提升潛力，但這反而阻礙了市場成長。

無人商店和智慧結帳系統的發展

包括無人商店和「即買即走」技術在內的自主零售模式的興起，為人工智慧在零售業的應用帶來了巨大的成長機會。電腦視覺感測器、貨架重量檢測器和深度學習演算法能夠追蹤顧客的選擇，並在顧客離開時自動為其電子錢包收費。這不僅消除了排隊結帳的環節，也降低了人事費用。大型零售商和Start-Ups正在便利商店和校園商店試點應用這些系統。此外，配備人工智慧物件辨識功能的智慧結帳終端機正在加速快餐店和超級市場的支付處理。隨著消費者偏好轉向「無摩擦」購物體驗，對基於視覺的人工智慧和邊緣運算的投資將會增加，從而為技術提供者創造新的收入來源。

資料隱私問題和監管合規風險

零售業的人工智慧系統嚴重依賴收集和分析顧客行為數據、購買歷史和生物識別資訊（例如無人商店中的面部表情）。這引發了嚴重的隱私擔憂，尤其是在歐洲的《一般資料保護規範》(GDPR) 和加州的《消費者隱私法案》(CCPA) 等法規的限制下。如果人工智慧模型無意中洩露敏感數據或在未經透明同意的情況下使用這些數據，零售商可能面臨訴訟和巨額罰款。此外，針對人工智慧資料庫的網路攻擊可能導致大規模身分盜竊。消費者對過度追蹤（例如店內臉部辨識）的抵制可能會損害品牌聲譽。這些合規性和信任的挑戰威脅著人工智慧的普及，並迫使零售商在聯邦學習和匿名化工具等隱私保護技術方面投入大量資金。

新冠疫情的影響：

新冠疫情大大加速了人工智慧在零售業的應用。封鎖措施導致實體店關閉，消費者行為轉向非接觸式購物。零售商迅速部署人工智慧聊天機器人來應對激增的線上客戶諮詢。同時，需求預測模型有助於管理中斷的供應鏈和恐慌性搶購。自助結帳和路邊取貨系統普及開來，盡量減少人與人之間的接觸。然而，預算限制延緩了一些中小型零售商的人工智慧專案。即使在經濟活動重啟後，混合購物模式也已確立，人工智慧驅動著個人化促銷和庫存視覺化。疫情永久改變了零售業的預期，使人工智慧投資從一種實驗性的奢侈品轉變為一項策略重點。

在預測期內，解決方案領域預計將佔據最大的市場佔有率。

在預測期內，解決方案領域預計將佔據最大的市場佔有率。這包括客戶服務平台、庫存管理工具、價格最佳化引擎、詐欺檢測系統和建議演算法。零售商優先採購可快速部署的人工智慧解決方案，以應對諸如庫存過剩、購物車遺棄和退貨處理等緊迫的營運挑戰。這些解決方案透過提升銷售額和降低成本，帶來可衡量的投資報酬率。此外，基於雲端的解決方案訂閱模式降低了中型零售商的進入門檻。

在預測期內，機器學習和深度學習領域預計將呈現最高的複合年成長率。

在預測期內，機器學習和深度學習領域預計將呈現最高的成長率。這些技術透過識別交易和庫存資料中的複雜模式，支援需求預測、個人化建議、動態定價和詐欺偵測。深度學習模型，尤其是循環神經網路，在供應鏈最佳化的時間序列分析方面表現出色。自動化機器學習 (AutoML) 的進步使得即使是非專業使用者也能輕鬆部署模型。

市佔率最大的地區：

在整個預測期內，北美預計將保持最大的市場佔有率，這主要得益於IBM、微軟、谷歌和亞馬遜網路服務等領先的人工智慧技術供應商的存在。該地區的零售業正走向成熟，無人商店、人工智慧驅動的建議引擎和自動化倉庫等技術已得到早期應用。美國和加拿大零售人工智慧Start-Ups的大量創業投資投資正在加速創新。此外，沃爾瑪、塔吉特和好市多等大型零售商正透過持續投資人工智慧來增強供應鏈韌性並實現個人化行銷，從而鞏固其在北美的領先地位。

複合年成長率最高的地區：

在預測期內，亞太地區預計將呈現最高的複合年成長率，這主要得益於中國、印度和東南亞零售業的快速數字化轉型。龐大的人口基數、智慧型手機普及率的不斷提高以及政府對人工智慧發展的支持，都在推動人工智慧的應用。阿里巴巴和京東在人工智慧物流和虛擬試穿技術領域發揮主導作用。此外，無現金購物模式在日本和韓國也迅速擴張。中產階級可支配收入的不斷成長，也帶動了人們對個人化購物需求日益成長的成長。

免費客製化服務：

所有購買此報告的客戶均可享受以下免費自訂選項之一：

  • 企業概況
    • 對其他市場參與者（最多 3 家公司）進行全面分析
    • 對主要企業進行SWOT分析（最多3家公司）
  • 區域細分
    • 應客戶要求，我們提供主要國家和地區的市場估算和預測，以及複合年成長率（註：需進行可行性檢查）。
  • 競爭性標竿分析
    • 根據產品系列、地理覆蓋範圍和策略聯盟對主要企業進行基準分析。

目錄

第1章執行摘要

  • 市場概覽及主要亮點
  • 促進因素、挑戰與機遇
  • 競爭格局概述
  • 戰略洞察與建議

第2章：研究框架

  • 研究目標和範圍
  • 相關人員分析
  • 研究假設和限制
  • 調查方法

第3章 市場動態與趨勢分析

  • 市場定義與結構
  • 主要市場促進因素
  • 市場限制與挑戰
  • 投資成長機會和重點領域
  • 產業威脅與風險評估
  • 技術與創新展望
  • 新興市場/高成長市場
  • 監管和政策環境
  • 新冠疫情的影響及復甦前景

第4章：競爭環境與策略評估

  • 波特五力分析
    • 供應商的議價能力
    • 買方的議價能力
    • 替代品的威脅
    • 新進入者的威脅
    • 競爭公司之間的競爭
  • 主要企業市佔率分析
  • 產品基準評效和效能比較

第5章 全球零售市場：按組成部分分類

  • 解決方案
    • 客戶服務解決方案
    • 庫存管理解決方案
    • 價格最佳化解決方案
    • 詐欺檢測解決方案
    • 建議引擎
  • 服務
    • 專業服務
    • 託管服務

第6章 全球零售市場：依技術分類

  • 機器學習和深度學習
  • 自然語言處理（NLP）
  • 聊天機器人和虛擬助手
  • 影像和影片分析
  • 群體智慧

第7章 全球零售市場：依部署模式分類

  • 基於雲端的
  • 現場

第8章 全球零售市場：依銷售管道分類

  • 全通路零售
  • 實體店面
  • 僅限線上銷售的零售商

第9章 全球零售市場：依應用分類

  • 客戶關係管理（CRM）
  • 供應鍊和物流
  • 庫存管理和需求預測
  • 產品最佳化及商品行銷
  • 店內導航和智慧貨架
  • 付款、定價和結帳分析
  • 詐欺檢測和損失預防
  • 虛擬助理和聊天機器人

第10章 全球零售市場：依最終用戶分類

  • 超級市場和大賣場
  • 專賣店
  • 便利商店
  • 百貨公司
  • 電子商務零售商

第11章 全球零售市場：按地區分類

  • 北美洲
    • 美國
    • 加拿大
    • 墨西哥
  • 歐洲
    • 英國
    • 德國
    • 法國
    • 義大利
    • 西班牙
    • 荷蘭
    • 比利時
    • 瑞典
    • 瑞士
    • 波蘭
    • 其他歐洲國家
  • 亞太地區
    • 中國
    • 日本
    • 印度
    • 韓國
    • 澳洲
    • 印尼
    • 泰國
    • 馬來西亞
    • 新加坡
    • 越南
    • 其他亞太國家
  • 南美洲
    • 巴西
    • 阿根廷
    • 哥倫比亞
    • 智利
    • 秘魯
    • 其他南美國家
  • 世界其他地區（RoW）
    • 中東
      • 沙烏地阿拉伯
      • 阿拉伯聯合大公國
      • 卡達
      • 以色列
      • 其他中東國家
    • 非洲
      • 南非
      • 埃及
      • 摩洛哥
      • 其他非洲國家

第12章 策略市場資訊

  • 工業價值網路和供應鏈評估
  • 空白區域和機會地圖
  • 產品演進與市場生命週期分析
  • 通路、經銷商和打入市場策略的評估

第13章 產業趨勢與策略舉措

  • 併購
  • 夥伴關係、聯盟和合資企業
  • 新產品發布和認證
  • 擴大生產能力和投資
  • 其他策略舉措

第14章：公司簡介

  • Amazon Web Services
  • Microsoft Corporation
  • Google LLC
  • IBM Corporation
  • Oracle Corporation
  • SAP SE
  • Salesforce, Inc.
  • NVIDIA Corporation
  • Intel Corporation
  • Accenture plc
  • Capgemini SE
  • Infosys Limited
  • Tata Consultancy Services
  • Wipro Limited
  • SymphonyAI
Product Code: SMRC35019

According to Stratistics MRC, the Global AI in Retail Market is accounted for $16.5 billion in 2026 and is expected to reach $105.9 billion by 2034 growing at a CAGR of 26.1% during the forecast period. AI in retail involves the use of advanced technologies such as machine learning, data analytics, and computer vision to enhance operations and customer experiences. It enables retailers to analyze large volumes of data for demand forecasting, personalized recommendations, inventory management, and dynamic pricing. By automating processes and generating real-time insights, it improves decision-making, boosts efficiency, and supports seamless omnichannel interactions, helping businesses better understand customer behavior and optimize overall retail performance.

Market Dynamics:

Driver:

Rapid expansion of e-commerce and omnichannel retailing

The exponential growth of online shopping and the integration of physical and digital sales channels are forcing retailers to adopt AI for real-time inventory synchronization and personalized customer engagement. AI-driven recommendation engines analyze browsing history and purchase patterns to boost conversion rates, while chatbots handle high-volume inquiries instantly. Additionally, dynamic pricing algorithms adjust product costs based on demand fluctuations and competitor actions. As consumers expect seamless experiences across mobile apps, websites, and brick-and-mortar stores, retailers increasingly rely on AI to unify data streams, forecast stock needs, and automate fulfillment processes. This operational necessity is a primary driver accelerating AI adoption across the retail ecosystem.

Restraint:

High implementation and data integration costs

Deploying AI solutions in retail requires substantial investment in cloud infrastructure, data warehousing, and skilled personnel such as data scientists and ML engineers. Many small and mid-sized retailers struggle to afford these upfront costs, especially when integrating AI with legacy point-of-sale and enterprise resource planning systems. Data silos across warehouses, online platforms, and physical stores further complicate implementation, as cleaning and standardizing diverse datasets is time-consuming and expensive. Additionally, ongoing expenses for model retraining, software updates, and cybersecurity measures add financial pressure. Without clear short-term ROI, many traditional retailers delay AI adoption, restraining market growth despite long-term efficiency benefits.

Opportunity:

Growth of cashierless stores and smart checkout systems

The emergence of autonomous retail formats, including cashierless stores and just-walk-out technology, presents a significant growth opportunity for AI in retail. Computer vision sensors, shelf weight detectors, and deep learning algorithms track customer selections and automatically charge digital wallets upon exit. This eliminates checkout queues and reduces labor costs. Major retailers and startups are testing these systems in convenience stores and campus shops. Furthermore, smart checkout kiosks equipped with AI-powered object recognition accelerate payment processing in quick-service restaurants and supermarkets. As consumer preference shifts toward frictionless shopping experiences, investment in vision-based AI and edge computing will expand, creating new revenue streams for technology providers.

Threat:

Data privacy concerns and regulatory compliance risks

AI systems in retail rely heavily on collecting and analyzing customer behavioral data, purchase histories, and biometric information (e.g., facial expressions in cashierless stores). This raises serious privacy concerns, especially under regulations like GDPR in Europe and CCPA in California. Retailers face potential lawsuits and heavy fines if AI models inadvertently expose sensitive data or use it without transparent consent. Additionally, cyberattacks targeting AI databases can lead to large-scale identity theft. Consumer backlash over intrusive tracking-such as in-store facial recognition-can damage brand reputation. These compliance and trust challenges threaten AI deployment, forcing retailers to invest heavily in privacy-preserving technologies like federated learning and anonymization tools.

Covid-19 Impact:

The COVID-19 pandemic drastically accelerated AI adoption in retail as lockdowns shuttered physical stores and shifted consumer behavior toward contactless shopping. Retailers rapidly deployed AI-powered chatbots to handle surge in online customer queries, while demand forecasting models helped manage disrupted supply chains and panic buying. Cashierless checkout and curbside pickup systems gained traction to minimize human contact. However, budget constraints delayed some AI projects for smaller retailers. As economies reopened, hybrid shopping models remained, with AI driving personalized promotions and inventory visibility. The pandemic permanently changed retail expectations, making AI investment a strategic priority rather than an experimental luxury.

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. This includes customer service platforms, inventory management tools, pricing optimization engines, fraud detection systems, and recommendation algorithms. Retailers prioritize purchasing ready-to-deploy AI solutions to address immediate operational challenges such as overstocking, cart abandonment, and returns processing. Solutions offer measurable ROI through sales lift and cost reduction. Additionally, cloud-based solution subscriptions lower entry barriers for mid-sized retailers.

The machine learning & deep learning segment is expected to have the highest CAGR during the forecast period

Over the forecast period, the machine learning & deep learning segment is predicted to witness the highest growth rate. These technologies power demand forecasting, personalized recommendations, dynamic pricing, and fraud detection by identifying complex patterns in transaction and inventory data. Deep learning models, especially recurrent neural networks, excel at time-series analysis for supply chain optimization. Advances in automated machine learning (AutoML) allow non-experts to deploy models.

Region with largest share:

During the forecast period, the North America region is expected to hold the largest market share, driven by the presence of major AI technology vendors such as IBM, Microsoft, Google, and Amazon Web Services. The region has a mature retail landscape with early adoption of cashierless stores, AI-powered recommendation engines, and automated warehouses. Strong venture capital funding for retail AI startups in the US and Canada accelerates innovation. Additionally, large retailers like Walmart, Target, and Costco continuously invest in AI for supply chain resilience and personalized marketing, solidifying North America's leadership.

Region with highest CAGR:

Over the forecast period, the Asia Pacific region is anticipated to exhibit the highest CAGR, fueled by rapid digitalization of retail in China, India, and Southeast Asia. Massive populations, rising smartphone penetration, and government support for AI development drive adoption. Alibaba and JD.com lead in AI-powered logistics and virtual try-on technologies. Additionally, cashierless store formats are expanding rapidly in Japan and South Korea. Growing middle-class disposable income increases demand for personalized shopping.

Key players in the market

Some of the key players in AI in Retail Market include Amazon Web Services, Microsoft Corporation, Google LLC, IBM Corporation, Oracle Corporation, SAP SE, Salesforce, Inc., NVIDIA Corporation, Intel Corporation, Accenture plc, Capgemini SE, Infosys Limited, Tata Consultancy Services, Wipro Limited, and SymphonyAI.

Key Developments:

In March 2026, Oracle announced the latest updates to Oracle AI Agent Studio for Fusion Applications, a complete development platform for building, connecting, and running AI automation and agentic applications. The latest updates to Oracle AI Agent Studio include a new agentic applications builder as well as new capabilities that support workflow orchestration, content intelligence, contextual memory, and ROI measurement.

In April 2026, IBM announced a strategic collaboration with Arm to develop new dual-architecture hardware that helps enterprises run future AI and data intensive workloads with greater flexibility, reliability, and security. IBM's leadership in system design, from silicon to software and security, has helped enterprises adopt emerging technologies with the scale and reliability required for mission-critical workloads.

Components Covered:

  • Solutions
  • Services

Technologies Covered:

  • Machine Learning & Deep Learning
  • Natural Language Processing (NLP)
  • Chatbots & Virtual Assistants
  • Image & Video Analytics
  • Swarm Intelligence

Deployment Modes Covered:

  • Cloud-based
  • On-Premise

Sales Channels Covered:

  • Omnichannel Retail
  • Brick-and-Mortar Stores
  • Pure-play Online Retailers

Applications Covered:

  • Customer Relationship Management (CRM)
  • Supply Chain & Logistics
  • Inventory Management & Demand Forecasting
  • Product Optimization & Merchandising
  • In-store Navigation & Smart Shelves
  • Payment, Pricing & Checkout Analytics
  • Fraud Detection & Loss Prevention
  • Virtual Assistants & Chatbots

End Users Covered:

  • Supermarkets & Hypermarkets
  • Specialty Stores
  • Convenience Stores
  • Department Stores
  • E-commerce Retailers

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 2023, 2024, 2025, 2026, 2027, 2028, 2029, 2030, 2032 and 2034
  • 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

  • 1.1 Market Snapshot and Key Highlights
  • 1.2 Growth Drivers, Challenges, and Opportunities
  • 1.3 Competitive Landscape Overview
  • 1.4 Strategic Insights and Recommendations

2 Research Framework

  • 2.1 Study Objectives and Scope
  • 2.2 Stakeholder Analysis
  • 2.3 Research Assumptions and Limitations
  • 2.4 Research Methodology
    • 2.4.1 Data Collection (Primary and Secondary)
    • 2.4.2 Data Modeling and Estimation Techniques
    • 2.4.3 Data Validation and Triangulation
    • 2.4.4 Analytical and Forecasting Approach

3 Market Dynamics and Trend Analysis

  • 3.1 Market Definition and Structure
  • 3.2 Key Market Drivers
  • 3.3 Market Restraints and Challenges
  • 3.4 Growth Opportunities and Investment Hotspots
  • 3.5 Industry Threats and Risk Assessment
  • 3.6 Technology and Innovation Landscape
  • 3.7 Emerging and High-Growth Markets
  • 3.8 Regulatory and Policy Environment
  • 3.9 Impact of COVID-19 and Recovery Outlook

4 Competitive and Strategic Assessment

  • 4.1 Porter's Five Forces Analysis
    • 4.1.1 Supplier Bargaining Power
    • 4.1.2 Buyer Bargaining Power
    • 4.1.3 Threat of Substitutes
    • 4.1.4 Threat of New Entrants
    • 4.1.5 Competitive Rivalry
  • 4.2 Market Share Analysis of Key Players
  • 4.3 Product Benchmarking and Performance Comparison

5 Global AI in Retail Market, By Component

  • 5.1 Solutions
    • 5.1.1 Customer Service Solutions
    • 5.1.2 Inventory Management Solutions
    • 5.1.3 Pricing Optimization Solutions
    • 5.1.4 Fraud Detection Solutions
    • 5.1.5 Recommendation Engines
  • 5.2 Services
    • 5.2.1 Professional Services
    • 5.2.2 Managed Services

6 Global AI in Retail Market, By Technology

  • 6.1 Machine Learning & Deep Learning
  • 6.2 Natural Language Processing (NLP)
  • 6.3 Chatbots & Virtual Assistants
  • 6.4 Image & Video Analytics
  • 6.5 Swarm Intelligence

7 Global AI in Retail Market, By Deployment Mode

  • 7.1 Cloud-based
  • 7.2 On-Premise

8 Global AI in Retail Market, By Sales Channel

  • 8.1 Omnichannel Retail
  • 8.2 Brick-and-Mortar Stores
  • 8.3 Pure-play Online Retailers

9 Global AI in Retail Market, By Application

  • 9.1 Customer Relationship Management (CRM)
  • 9.2 Supply Chain & Logistics
  • 9.3 Inventory Management & Demand Forecasting
  • 9.4 Product Optimization & Merchandising
  • 9.5 In-store Navigation & Smart Shelves
  • 9.6 Payment, Pricing & Checkout Analytics
  • 9.7 Fraud Detection & Loss Prevention
  • 9.8 Virtual Assistants & Chatbots

10 Global AI in Retail Market, By End User

  • 10.1 Supermarkets & Hypermarkets
  • 10.2 Specialty Stores
  • 10.3 Convenience Stores
  • 10.4 Department Stores
  • 10.5 E-commerce Retailers

11 Global AI in Retail Market, By Geography

  • 11.1 North America
    • 11.1.1 United States
    • 11.1.2 Canada
    • 11.1.3 Mexico
  • 11.2 Europe
    • 11.2.1 United Kingdom
    • 11.2.2 Germany
    • 11.2.3 France
    • 11.2.4 Italy
    • 11.2.5 Spain
    • 11.2.6 Netherlands
    • 11.2.7 Belgium
    • 11.2.8 Sweden
    • 11.2.9 Switzerland
    • 11.2.10 Poland
    • 11.2.11 Rest of Europe
  • 11.3 Asia Pacific
    • 11.3.1 China
    • 11.3.2 Japan
    • 11.3.3 India
    • 11.3.4 South Korea
    • 11.3.5 Australia
    • 11.3.6 Indonesia
    • 11.3.7 Thailand
    • 11.3.8 Malaysia
    • 11.3.9 Singapore
    • 11.3.10 Vietnam
    • 11.3.11 Rest of Asia Pacific
  • 11.4 South America
    • 11.4.1 Brazil
    • 11.4.2 Argentina
    • 11.4.3 Colombia
    • 11.4.4 Chile
    • 11.4.5 Peru
    • 11.4.6 Rest of South America
  • 11.5 Rest of the World (RoW)
    • 11.5.1 Middle East
      • 11.5.1.1 Saudi Arabia
      • 11.5.1.2 United Arab Emirates
      • 11.5.1.3 Qatar
      • 11.5.1.4 Israel
      • 11.5.1.5 Rest of Middle East
    • 11.5.2 Africa
      • 11.5.2.1 South Africa
      • 11.5.2.2 Egypt
      • 11.5.2.3 Morocco
      • 11.5.2.4 Rest of Africa

12 Strategic Market Intelligence

  • 12.1 Industry Value Network and Supply Chain Assessment
  • 12.2 White-Space and Opportunity Mapping
  • 12.3 Product Evolution and Market Life Cycle Analysis
  • 12.4 Channel, Distributor, and Go-to-Market Assessment

13 Industry Developments and Strategic Initiatives

  • 13.1 Mergers and Acquisitions
  • 13.2 Partnerships, Alliances, and Joint Ventures
  • 13.3 New Product Launches and Certifications
  • 13.4 Capacity Expansion and Investments
  • 13.5 Other Strategic Initiatives

14 Company Profiles

  • 14.1 Amazon Web Services
  • 14.2 Microsoft Corporation
  • 14.3 Google LLC
  • 14.4 IBM Corporation
  • 14.5 Oracle Corporation
  • 14.6 SAP SE
  • 14.7 Salesforce, Inc.
  • 14.8 NVIDIA Corporation
  • 14.9 Intel Corporation
  • 14.10 Accenture plc
  • 14.11 Capgemini SE
  • 14.12 Infosys Limited
  • 14.13 Tata Consultancy Services
  • 14.14 Wipro Limited
  • 14.15 SymphonyAI

List of Tables

  • Table 1 Global AI in Retail Market Outlook, By Region (2023-2034) ($MN)
  • Table 2 Global AI in Retail Market Outlook, By Component (2023-2034) ($MN)
  • Table 3 Global AI in Retail Market Outlook, By Solutions (2023-2034) ($MN)
  • Table 4 Global AI in Retail Market Outlook, By Customer Service Solutions (2023-2034) ($MN)
  • Table 5 Global AI in Retail Market Outlook, By Inventory Management Solutions (2023-2034) ($MN)
  • Table 6 Global AI in Retail Market Outlook, By Pricing Optimization Solutions (2023-2034) ($MN)
  • Table 7 Global AI in Retail Market Outlook, By Fraud Detection Solutions (2023-2034) ($MN)
  • Table 8 Global AI in Retail Market Outlook, By Recommendation Engines (2023-2034) ($MN)
  • Table 9 Global AI in Retail Market Outlook, By Services (2023-2034) ($MN)
  • Table 10 Global AI in Retail Market Outlook, By Professional Services (2023-2034) ($MN)
  • Table 11 Global AI in Retail Market Outlook, By Managed Services (2023-2034) ($MN)
  • Table 12 Global AI in Retail Market Outlook, By Technology (2023-2034) ($MN)
  • Table 13 Global AI in Retail Market Outlook, By Machine Learning & Deep Learning (2023-2034) ($MN)
  • Table 14 Global AI in Retail Market Outlook, By Natural Language Processing (NLP) (2023-2034) ($MN)
  • Table 15 Global AI in Retail Market Outlook, By Chatbots & Virtual Assistants (2023-2034) ($MN)
  • Table 16 Global AI in Retail Market Outlook, By Image & Video Analytics (2023-2034) ($MN)
  • Table 17 Global AI in Retail Market Outlook, By Swarm Intelligence (2023-2034) ($MN)
  • Table 18 Global AI in Retail Market Outlook, By Deployment Mode (2023-2034) ($MN)
  • Table 19 Global AI in Retail Market Outlook, By Cloud-based (2023-2034) ($MN)
  • Table 20 Global AI in Retail Market Outlook, By On-Premise (2023-2034) ($MN)
  • Table 21 Global AI in Retail Market Outlook, By Sales Channel (2023-2034) ($MN)
  • Table 22 Global AI in Retail Market Outlook, By Omnichannel Retail (2023-2034) ($MN)
  • Table 23 Global AI in Retail Market Outlook, By Brick-and-Mortar Stores (2023-2034) ($MN)
  • Table 24 Global AI in Retail Market Outlook, By Pure-play Online Retailers (2023-2034) ($MN)
  • Table 25 Global AI in Retail Market Outlook, By Application (2023-2034) ($MN)
  • Table 26 Global AI in Retail Market Outlook, By Customer Relationship Management (CRM) (2023-2034) ($MN)
  • Table 27 Global AI in Retail Market Outlook, By Supply Chain & Logistics (2023-2034) ($MN)
  • Table 28 Global AI in Retail Market Outlook, By Inventory Management & Demand Forecasting (2023-2034) ($MN)
  • Table 29 Global AI in Retail Market Outlook, By Product Optimization & Merchandising (2023-2034) ($MN)
  • Table 30 Global AI in Retail Market Outlook, By In-store Navigation & Smart Shelves (2023-2034) ($MN)
  • Table 31 Global AI in Retail Market Outlook, By Payment, Pricing & Checkout Analytics (2023-2034) ($MN)
  • Table 32 Global AI in Retail Market Outlook, By Fraud Detection & Loss Prevention (2023-2034) ($MN)
  • Table 33 Global AI in Retail Market Outlook, By Virtual Assistants & Chatbots (2023-2034) ($MN)
  • Table 34 Global AI in Retail Market Outlook, By End User (2023-2034) ($MN)
  • Table 35 Global AI in Retail Market Outlook, By Supermarkets & Hypermarkets (2023-2034) ($MN)
  • Table 36 Global AI in Retail Market Outlook, By Specialty Stores (2023-2034) ($MN)
  • Table 37 Global AI in Retail Market Outlook, By Convenience Stores (2023-2034) ($MN)
  • Table 38 Global AI in Retail Market Outlook, By Department Stores (2023-2034) ($MN)
  • Table 39 Global AI in Retail Market Outlook, By E-commerce Retailers (2023-2034) ($MN)

Note: Tables for North America, Europe, APAC, South America, and Rest of the World (RoW) are also represented in the same manner as above.