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市场调查报告书
商品编码
2021751

2034年零售市场人工智慧市场预测：按组件、技术、部署模式、销售管道、应用、最终用户和地区分類的全球分析

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个工作天内

价格

根据 Stratistics MRC 的数据，预计到 2026 年，全球零售业人工智慧市场规模将达到 165 亿美元，并在预测期内以 26.1% 的复合年增长率增长，到 2034 年将达到 1059 亿美元。

在零售业，人工智慧指的是利用机器学习、数据分析和电脑视觉等先进技术来提升营运效率和客户体验的各项措施。这使得零售商能够分析大量数据，从而实现需求预测、个人化建议、库存管理和动态定价。透过流程自动化和即时洞察生成，企业可以改善决策、提高效率并支援无缝的全通路客户互动，从而更深入地了解客户行为并优化整体零售业绩。

电子商务和全通路零售的快速扩张。

网路购物的快速成长以及实体店与线上销售管道的融合，正迫使零售商采用人工智慧（AI）来实现即时库存同步和个人化客户参与。 AI驱动的推荐引擎会分析浏览历史和购买模式，进而提高转换率；聊天机器人则能即时回应大量咨询。此外，动态定价演算法会根据需求波动和竞争对手的动态调整产品价格。随着消费者期望在行动应用、网站和实体店之间获得无缝体验，零售商越来越依赖AI进行资料流整合、库存需求预测和履约流程。这种营运上的必然需求，是推动AI在整个零售生态系中普及应用的主要动力。

高昂的实施成本和资料整合成本

在零售业实施人工智慧解决方案需要对云端基础设施、资料仓储以及资料科学家和机器学习工程师等专业人才进行大量投资。许多中小型零售商难以承担这些初期成本，尤其是在将人工智慧与现有POS和ERP系统整合时。仓库、线上平台和实体店之间的资料孤岛进一步加剧了实施难度，因为清理和标准化各种资料集既耗时又昂贵。此外，模型重新训练、软体更新和网路安全措施等持续成本也加重了财务负担。由于许多传统零售商无法预期短期内获得明确的投资回报，他们推迟了人工智慧的采用，儘管人工智慧具有潜在的长期效率提升潜力，但这反而阻碍了市场成长。

无人商店和智慧结帐系统的发展

包括无人商店和「即买即走」技术在内的自主零售模式的兴起，为人工智慧在零售业的应用带来了巨大的成长机会。电脑视觉感测器、货架重量检测器和深度学习演算法能够追踪顾客的选择，并在顾客离开时自动为其电子钱包收费。这不仅消除了排队结帐的环节，也降低了人事费用。大型零售商和Start-Ups正在便利商店和校园商店试点应用这些系统。此外，配备人工智慧物件辨识功能的智慧结帐终端机正在加速快餐店和超级市场的支付处理。随着消费者偏好转向「无摩擦」购物体验，对基于视觉的人工智慧和边缘运算的投资将会增加，从而为技术提供者创造新的收入来源。

资料隐私问题和监管合规风险

零售业的人工智慧系统严重依赖收集和分析顾客行为数据、购买历史和生物识别资讯（例如无人商店中的面部表情）。这引发了严重的隐私担忧，尤其是在欧洲的《一般资料保护规范》(GDPR) 和加州的《消费者隐私法案》(CCPA) 等法规的限制下。如果人工智慧模型无意中洩露敏感数据或在未经透明同意的情况下使用这些数据，零售商可能面临诉讼和巨额罚款。此外，针对人工智慧资料库的网路攻击可能导致大规模身分盗窃。消费者对过度追踪（例如店内脸部辨识）的抵制可能会损害品牌声誉。这些合规性和信任的挑战威胁着人工智慧的普及，并迫使零售商在联邦学习和匿名化工具等隐私保护技术方面投入大量资金。

新冠疫情的影响：

新冠疫情大大加速了人工智慧在零售业的应用。封锁措施导致实体店关闭，消费者行为转向非接触式购物。零售商迅速部署人工智慧聊天机器人来应对激增的线上客户咨询。同时，需求预测模型有助于管理中断的供应链和恐慌性抢购。自助结帐和路边取货系统普及开来，尽量减少人与人之间的接触。然而，预算限制延缓了一些中小型零售商的人工智慧专案。即使在经济活动重启后，混合购物模式也已确立，人工智慧驱动着个人化促销和库存视觉化。疫情永久改变了零售业的预期，使人工智慧投资从一种实验性的奢侈品转变为一项策略重点。

在预测期内，解决方案领域预计将占据最大的市场份额。

在预测期内，解决方案领域预计将占据最大的市场份额。这包括客户服务平台、库存管理工具、价格优化引擎、诈欺检测系统和建议演算法。零售商优先采购可快速部署的人工智慧解决方案，以应对诸如库存过剩、购物车遗弃和退货处理等紧迫的营运挑战。这些解决方案透过提升销售额和降低成本，带来可衡量的投资报酬率。此外，基于云端的解决方案订阅模式降低了中型零售商的进入门槛。

在预测期内，机器学习和深度学习领域预计将呈现最高的复合年增长率。

在预测期内，机器学习和深度学习领域预计将呈现最高的成长率。这些技术透过识别交易和库存资料中的复杂模式，支援需求预测、个人化建议、动态定价和诈欺侦测。深度学习模型，尤其是循环神经网络，在供应链优化的时间序列分析方面表现出色。自动化机器学习 (AutoML) 的进步使得即使是非专业使用者也能轻鬆部署模型。

市占率最大的地区：

在整个预测期内，北美预计将保持最大的市场份额，这主要得益于IBM、微软、谷歌和亚马逊网路服务等领先的人工智慧技术供应商的存在。该地区的零售业正走向成熟，无人商店、人工智慧驱动的建议引擎和自动化仓库等技术已得到早期应用。美国和加拿大零售人工智慧Start-Ups的大量创业投资投资正在加速创新。此外，沃尔玛、塔吉特和好市多等大型零售商正透过持续投资人工智慧来增强供应链韧性并实现个人化行销，从而巩固其在北美的领先地位。

复合年增长率最高的地区：

在预测期内，亚太地区预计将呈现最高的复合年增长率，这主要得益于中国、印度和东南亚零售业的快速数字化转型。庞大的人口基数、智慧型手机普及率的不断提高以及政府对人工智慧发展的支持，都在推动人工智慧的应用。阿里巴巴和京东在人工智慧物流和虚拟试穿技术领域发挥主导作用。此外，无现金购物模式在日本和韩国也迅速扩张。中产阶级可支配收入的不断增长，也带动了人们对个人化购物需求日益增长的增长。

免费客製化服务：

所有购买此报告的客户均可享受以下免费自订选项之一：

  • 企业概况
    • 对其他市场参与者（最多 3 家公司）进行全面分析
    • 对主要企业进行SWOT分析（最多3家公司）
  • 区域细分
    • 应客户要求，我们提供主要国家和地区的市场估算和预测，以及复合年增长率（註：需进行可行性检查）。
  • 竞争性标竿分析
    • 根据产品系列、地理覆盖范围和策略联盟对主要企业进行基准分析。

目录

第一章执行摘要

  • 市场概览及主要亮点
  • 驱动因素、挑战与机会
  • 竞争格局概述
  • 战略洞察与建议

第二章：研究框架

  • 研究目标和范围
  • 相关人员分析
  • 研究假设和限制
  • 调查方法

第三章 市场动态与趋势分析

  • 市场定义与结构
  • 主要市场驱动因素
  • 市场限制与挑战
  • 投资成长机会和重点领域
  • 产业威胁与风险评估
  • 技术与创新展望
  • 新兴市场/高成长市场
  • 监管和政策环境
  • 新冠疫情的影响及復苏前景

第四章：竞争环境与策略评估

  • 波特五力分析
    • 供应商的议价能力
    • 买方的议价能力
    • 替代品的威胁
    • 新进入者的威胁
    • 竞争公司之间的竞争
  • 主要企业市占率分析
  • 产品基准评效和效能比较

第五章 全球零售市场：按组成部分划分

  • 解决方案
    • 客户服务解决方案
    • 库存管理解决方案
    • 价格最佳化解决方案
    • 诈欺检测解决方案
    • 建议引擎
  • 服务
    • 专业服务
    • 託管服务

第六章 全球零售市场：依技术划分

  • 机器学习和深度学习
  • 自然语言处理（NLP）
  • 聊天机器人和虚拟助手
  • 影像和影片分析
  • 群体智能

第七章 全球零售市场：依部署模式划分

  • 基于云端的
  • 现场

第八章 全球零售市场：依销售管道划分

  • 全通路零售
  • 实体店面
  • 仅限线上销售的零售商

第九章 全球零售市场：依应用划分

  • 客户关係管理（CRM）
  • 供应炼和物流
  • 库存管理和需求预测
  • 产品优化及商品行销
  • 店内导航和智慧货架
  • 付款、定价和结帐分析
  • 诈欺检测和损失预防
  • 虚拟助理和聊天机器人

第十章 全球零售市场：依最终用户划分

  • 超级市场和大卖场
  • 专卖店
  • 便利商店
  • 百货公司
  • 电子商务零售商

第十一章 全球零售市场：按地区划分

  • 北美洲
    • 我们
    • 加拿大
    • 墨西哥
  • 欧洲
    • 英国
    • 德国
    • 法国
    • 义大利
    • 西班牙
    • 荷兰
    • 比利时
    • 瑞典
    • 瑞士
    • 波兰
    • 其他欧洲国家
  • 亚太地区
    • 中国
    • 日本
    • 印度
    • 韩国
    • 澳洲
    • 印尼
    • 泰国
    • 马来西亚
    • 新加坡
    • 越南
    • 其他亚太国家
  • 南美洲
    • 巴西
    • 阿根廷
    • 哥伦比亚
    • 智利
    • 秘鲁
    • 其他南美国家
  • 世界其他地区（RoW）
    • 中东
      • 沙乌地阿拉伯
      • 阿拉伯聯合大公国
      • 卡达
      • 以色列
      • 其他中东国家
    • 非洲
      • 南非
      • 埃及
      • 摩洛哥
      • 其他非洲国家

第十二章 策略市场资讯

  • 工业价值网络和供应链评估
  • 空白区域和机会地图
  • 产品演进与市场生命週期分析
  • 通路、经销商和打入市场策略的评估

第十三章 产业趋势与策略倡议

  • 併购
  • 伙伴关係、联盟和合资企业
  • 新产品发布和认证
  • 扩大生产能力和投资
  • 其他策略倡议

第十四章：公司简介

  • 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.