零售和电子商务市场应用人工智慧－全球产业规模、份额、趋势、机会及预测（按技术、应用、部署、最终用户、地区和竞争格局划分，2021-2031年）

全球零售和电子商务应用人工智慧市场预计将从 2025 年的 449.6 亿美元成长到 2031 年的 1,110.2 亿美元，复合年增长率达到 16.26%。

该市场涵盖将机器学习、自然语言处理和电脑视觉技术融入商业工作流程，旨在提高效率和客户参与。这些先进技术使零售商能够自动化库存管理、需求预测和个人化产品提案等关键功能。透过分析购买模式，企业可以优化供应链并部署智慧虚拟助手，从而实现与消费者的无缝全通路互动。

推动市场成长的关键因素包括降低营运成本的迫切需求，以及消费者对高度个人化体验日益增长的需求，而这需要即时数据处理。零售商也高度依赖预测模型来缓解供应链波动并优化存量基准。然而，快速扩张的一大障碍是遵守严格的资料隐私法律的复杂性，这给管理敏感消费者资料的公司带来了法律责任风险。美国零售联合会 (NRF) 的报告凸显了向自动化决策的转变，报告指出，到 2024 年，40% 的零售商将利用人工智慧动态调整其行销策略和定价。

市场驱动因素

零售业采用人工智慧的关键驱动因素是降低营运成本和实现流程自动化的迫切需求。零售商正越来越多地利用自动化来优化复杂的供应链物流、精准管理库存并减少劳力密集的行政工作。这项策略转变的驱动力在于，在经济状况波动和营运成本不断上涨的情况下，零售商需要保护利润率。这些应用带来的财务影响十分显着。根据英伟达于2025年1月发布的《零售和消费品产业人工智慧现状》报告，94%的零售商表示人工智慧已帮助他们节省了年度营运成本。此外，IBM商业价值研究院2025年的一项调查显示，81%的受访零售业主管已在其组织内部实施了中等程度到高度的人工智慧应用。

同时，人工智慧驱动的客户服务和虚拟助理正在改变零售商与基本客群互动的方式。在消费者对即时满足和跨数位管道无缝支援的需求驱动下，先进的演算法正被部署用于管理咨询、促进交易并辅助购买决策，无需人工干预。这项技术提高了用户参与度，并确保了数位原住民能够随时获得服务。这种融合的规模如此之大，以至于Honeywell2025年1月发布的《零售业人工智慧调查》发现，66%的消费者在购物过程中使用过人工智慧技术，例如聊天机器人和自动化工具。如此高的使用率迫使零售商不断升级其虚拟介面，以保持竞争优势和客户忠诚度。

市场挑战

遵守严格的资料隐私法规是全球零售和电商应用人工智慧市场的一大障碍。随着企业采用机器学习实现营运自动化，它们必须应对因地区而异的复杂合规要求。这种法律摩擦为处理敏感消费者资讯的公司带来了巨大的责任风险，并常常导致资料输入限制和预测工具部署延迟。这种犹豫不决直接削弱了零售商提供业界领先的即时、高度个人化体验的能力。

此外，普遍存在的隐私担忧限制了人工智慧强大效能所需的资料管道。如果消费者因担心被滥用而拒绝授权，智慧系统将缺乏有效优化其供应链所需的原料。国际隐私专业人士协会 (IAPP) 预测，到 2024 年，全球 57% 的消费者将认为人工智慧对其隐私构成重大威胁。这项数据凸显了严重的信任赤字，迫使企业优先考虑风险规避而非技术扩张，最终减缓了人工智慧在市场上的普及速度。

市场趋势

将生成式人工智慧整合到自动化内容生成领域正迅速成为一种变革性趋势，使零售商能够以前所未有的速度大规模生产个人化行销素材。与用于预测的传统分析型人工智慧不同，这项技术用于产生产品描述、动态电子邮件文案以及能够引起消费者偏好的客製化视觉内容。这种转变不仅加快了新宣传活动的上市速度，还使负责人能够在分散的数位管道中保持一致的品牌讯息，而无需相应增加创新人员。这项应用的规模显而易见：根据Google云端2024年10月发布的《零售和消费品产业生成式人工智慧的投资报酬率》报告，59%已在生产环境中运行生成式人工智慧的零售商将其用于销售和行销功能，包括以客户为中心的文案撰写。

与此同时，人工智慧驱动的虚拟试穿和扩增实境（AR）工具的普及正在从根本上改变电子商务介面，弥合了数位浏览和实体评估之间的鸿沟。零售商正在将电脑视觉演算法融入其行动应用程式和网站，让顾客能够在自己的环境中预览服装、化妆品和家居用品，从而有效降低导致购物车遗弃的不确定性。这种身临其境型技术具有双重作用：透过在购买前提升产品适用性，大幅提高用户参与度，同时直接解决业界长期存在的退货率居高不下的问题。 Snapchat 于 2025 年 6 月发布的《重塑服装购物的趋势》报告也反映了这一趋势，报告发现 67% 的用户认为 AR 虚拟试穿技术简化了线上购买决策。

目录

第一章概述

第二章调查方法

第三章执行摘要

第四章：客户评价

第五章：零售与电子商务应用人工智慧的全球市场展望

• 市场规模及预测
  • 按金额
• 市占率及预测
  • 透过技术（机器学习、自然语言处理（NLP）、电脑视觉、语音辨识、预测分析）
  • 按应用领域（客户服务与支援、销售与行销、供应链管理、价格优化、支付处理、产品搜寻与发现）
  • 依部署类型（本机部署、云端部署）
  • 按最终用户划分（零售商、电子商务平台、消费品製造商、物流和供应链公司）
  • 按地区
  • 按公司（2025 年）
• 市场地图

第六章：北美零售与电子商务应用人工智慧市场展望

• 市场规模及预测
• 市占率及预测
• 北美洲：国家分析
  • 我们
  • 加拿大
  • 墨西哥

7. 欧洲零售与电子商务应用人工智慧市场展望

• 市场规模及预测
• 市占率及预测
• 欧洲：国家分析
  • 德国
  • 法国
  • 英国
  • 义大利
  • 西班牙

8. 亚太地区零售与电子商务人工智慧应用市场展望

• 市场规模及预测
• 市占率及预测
• 亚太地区：国家分析
  • 中国
  • 印度
  • 日本
  • 韩国
  • 澳洲

9. 中东及非洲零售及电子商务应用人工智慧市场展望

• 市场规模及预测
• 市占率及预测
• 中东和非洲：国家分析
  • 沙乌地阿拉伯
  • 阿拉伯聯合大公国
  • 南非

第十章：南美零售与电子商务应用人工智慧市场展望

• 市场规模及预测
• 市占率及预测
• 南美洲：国家分析
  • 巴西
  • 哥伦比亚
  • 阿根廷

第十一章 市场动态

• 司机
• 任务

第十二章 市场趋势与发展

• 併购
• 产品发布
• 最新进展

13. 全球零售与电子商务应用人工智慧市场：SWOT 分析

第十四章：波特五力分析

• 产业竞争
• 新进入者的可能性
• 供应商电力
• 顾客权力
• 替代品的威胁

第十五章 竞争格局

• NVIDIA Corporation
• Alphabet Inc
• Microsoft Corporation
• IBM Corporation
• Salesforce Inc
• Oracle Corporation
• SAP SE
• Adobe Inc
• Alibaba Cloud International
• Clarifai, Inc

第十六章 策略建议

第十七章：关于研究公司及免责声明

The Global Applied AI in Retail & E-commerce Market is projected to expand from USD 44.96 Billion in 2025 to USD 111.02 Billion by 2031, achieving a CAGR of 16.26%. This market encompasses the embedding of machine learning, natural language processing, and computer vision into commercial workflows to enhance efficiency and customer engagement. These advanced technologies enable merchants to automate critical functions, such as inventory control, demand anticipation, and the curation of personalized product suggestions. By analyzing purchasing patterns, businesses can optimize their supply chains and deploy intelligent virtual assistants to ensure smooth, omnichannel interactions with shoppers.

Key drivers fueling this market growth include the urgent need to lower operational costs and the escalating consumer demand for hyper-personalized experiences that require real-time data processing. Retailers also depend heavily on predictive models to mitigate supply chain fluctuations and optimize stock levels. However, a significant obstacle to rapid expansion is the complexity of complying with strict data privacy laws, which introduce liability risks for enterprises managing sensitive consumer data. Highlighting the shift toward automated decision-making, the National Retail Federation reported in 2024 that 40% of retailers utilized AI to dynamically adjust marketing strategies and pricing.

Market Driver

The pressing need for operational cost reduction and process automation acts as a primary catalyst for AI adoption within the retail sector. Retailers are increasingly utilizing automation to refine complex supply chain logistics, manage inventory with precision, and reduce labor-intensive administrative tasks. This strategic shift is driven by the necessity to protect profit margins against fluctuating economic conditions and rising operational expenses. The financial impact of these implementations is substantial; according to NVIDIA's January 2025 'State of AI in Retail and CPG' report, 94% of retailers noted that AI helped reduce their annual operational costs. Furthermore, the IBM Institute for Business Value reported in 2025 that 81% of surveyed retail executives are already employing AI to a moderate or significant degree within their organizations.

Concurrently, the rise of AI-powered customer service and virtual assistants is transforming how merchants interact with their client base. To meet consumer demands for instant gratification and seamless support across digital channels, sophisticated algorithms are deployed to manage inquiries, facilitate transactions, and guide purchasing decisions without human intervention. This technology enhances user engagement and ensures constant availability for a digitally native demographic. The scale of this integration is significant; Honeywell's January 2025 'AI in Retail Survey' found that 66% of consumers have used AI technologies, such as chatbots and automated tools, during their shopping journey. This high usage rate compels retailers to continuously upgrade their virtual interfaces to sustain competitive advantage and customer loyalty.

Market Challenge

The challenge of complying with stringent data privacy regulations constitutes a major barrier to the Global Applied AI in Retail and E-commerce Market. As merchants integrate machine learning to automate operations, they must navigate complex compliance requirements that vary by region. This legal friction creates significant liability risks for enterprises handling sensitive consumer information, often leading them to restrict data inputs or delay the deployment of predictive tools. Such hesitation directly undermines the retailer's ability to deliver the real-time, hyper-personalized experiences that are intended to drive the sector forward.

Furthermore, widespread privacy concerns limit the data pipelines necessary for robust AI performance. If consumers withhold consent due to fear of misuse, intelligent systems lack the raw material required to optimize supply chains effectively. According to the International Association of Privacy Professionals, 57% of consumers globally agreed in 2024 that artificial intelligence posed a significant threat to their privacy. This statistic highlights a critical trust deficit that forces companies to prioritize risk mitigation over technological expansion, thereby slowing overall market adoption.

Market Trends

The integration of Generative AI for automated content creation is rapidly emerging as a transformative trend, enabling retailers to produce high volumes of personalized marketing assets with unprecedented speed. Unlike traditional analytical AI used for forecasting, this technology is deployed to generate product descriptions, dynamic email copy, and bespoke visual content that resonates with individual consumer preferences. This shift not only accelerates time-to-market for new campaigns but also allows merchants to maintain consistent brand messaging across fragmented digital channels without proportional increases in creative staff. The scale of this application is evident; according to Google Cloud's October 2024 'ROI on Gen AI for Retail and CPG' report, 59% of retailers running generative AI in production utilized it for sales and marketing functions, including crafting customer-centric copy.

In parallel, the expansion of AI-driven virtual try-on and augmented reality tools is fundamentally altering the e-commerce interface by bridging the gap between digital browsing and physical assessment. Retailers are embedding computer vision algorithms into mobile apps and websites to allow customers to visualize clothing, cosmetics, and home goods in their own environments, effectively mitigating the uncertainty that often leads to cart abandonment. This immersive technology serves a dual purpose: it significantly enhances user engagement while directly addressing the industry's chronic issue of high return rates by ensuring better product suitability prior to purchase. Reflecting this trend, Snapchat's June 2025 'Trends Reshaping Apparel Shopping' report indicated that 67% of users agreed that AR virtual try-on technology simplifies their online purchase decisions.

Key Market Players

• NVIDIA Corporation
• Alphabet Inc
• Microsoft Corporation
• IBM Corporation
• Salesforce Inc
• Oracle Corporation
• SAP SE
• Adobe Inc
• Alibaba Cloud International
• Clarifai, Inc

Report Scope

In this report, the Global Applied AI in Retail & E-commerce Market has been segmented into the following categories, in addition to the industry trends which have also been detailed below:

Applied AI in Retail & E-commerce Market, By Technology

• Machine Learning
• Natural Language Processing (NLP)
• Computer Vision
• Speech Recognition
• Predictive Analytic

Applied AI in Retail & E-commerce Market, By Application

• Customer Service & Support
• Sales & Marketing
• Supply Chain Management
• Price Optimization
• Payment Processing
• Product Search & Discovery

Applied AI in Retail & E-commerce Market, By Deployment

• On-premises
• Cloud-Based

Applied AI in Retail & E-commerce Market, By End-User

• Retailers
• E-commerce Platforms
• Consumer Goods Manufacturers
• Logistics & Supply Chain Companies

Applied AI in Retail & E-commerce Market, By Region

• North America
  • United States
  • Canada
  • Mexico
• Europe
  • France
  • United Kingdom
  • Italy
  • Germany
  • Spain
• Asia Pacific
  • China
  • India
  • Japan
  • Australia
  • South Korea
• South America
  • Brazil
  • Argentina
  • Colombia
• Middle East & Africa
  • South Africa
  • Saudi Arabia
  • UAE

Competitive Landscape

Company Profiles: Detailed analysis of the major companies present in the Global Applied AI in Retail & E-commerce Market.

Available Customizations:

Global Applied AI in Retail & E-commerce Market report with the given market data, TechSci Research offers customizations according to a company's specific needs. The following customization options are available for the report:

Company Information

• Detailed analysis and profiling of additional market players (up to five).

Table of Contents

1. Product Overview

• 1.1. Market Definition
• 1.2. Scope of the Market
  • 1.2.1. Markets Covered
  • 1.2.2. Years Considered for Study
  • 1.2.3. Key Market Segmentations

2. Research Methodology

• 2.1. Objective of the Study
• 2.2. Baseline Methodology
• 2.3. Key Industry Partners
• 2.4. Major Association and Secondary Sources
• 2.5. Forecasting Methodology
• 2.6. Data Triangulation & Validation
• 2.7. Assumptions and Limitations

3. Executive Summary

• 3.1. Overview of the Market
• 3.2. Overview of Key Market Segmentations
• 3.3. Overview of Key Market Players
• 3.4. Overview of Key Regions/Countries
• 3.5. Overview of Market Drivers, Challenges, Trends

4. Voice of Customer

5. Global Applied AI in Retail & E-commerce Market Outlook

• 5.1. Market Size & Forecast
  • 5.1.1. By Value
• 5.2. Market Share & Forecast
  • 5.2.1. By Technology (Machine Learning, Natural Language Processing (NLP), Computer Vision, Speech Recognition, Predictive Analytic)
  • 5.2.2. By Application (Customer Service & Support, Sales & Marketing, Supply Chain Management, Price Optimization, Payment Processing, Product Search & Discovery)
  • 5.2.3. By Deployment (On-premises, Cloud-Based)
  • 5.2.4. By End-User (Retailers, E-commerce Platforms, Consumer Goods Manufacturers, Logistics & Supply Chain Companies)
  • 5.2.5. By Region
  • 5.2.6. By Company (2025)
• 5.3. Market Map

6. North America Applied AI in Retail & E-commerce Market Outlook

• 6.1. Market Size & Forecast
  • 6.1.1. By Value
• 6.2. Market Share & Forecast
  • 6.2.1. By Technology
  • 6.2.2. By Application
  • 6.2.3. By Deployment
  • 6.2.4. By End-User
  • 6.2.5. By Country
• 6.3. North America: Country Analysis
  • 6.3.1. United States Applied AI in Retail & E-commerce Market Outlook
    • 6.3.1.1. Market Size & Forecast
      • 6.3.1.1.1. By Value
    • 6.3.1.2. Market Share & Forecast
      • 6.3.1.2.1. By Technology
      • 6.3.1.2.2. By Application
      • 6.3.1.2.3. By Deployment
      • 6.3.1.2.4. By End-User
  • 6.3.2. Canada Applied AI in Retail & E-commerce Market Outlook
    • 6.3.2.1. Market Size & Forecast
      • 6.3.2.1.1. By Value
    • 6.3.2.2. Market Share & Forecast
      • 6.3.2.2.1. By Technology
      • 6.3.2.2.2. By Application
      • 6.3.2.2.3. By Deployment
      • 6.3.2.2.4. By End-User
  • 6.3.3. Mexico Applied AI in Retail & E-commerce Market Outlook
    • 6.3.3.1. Market Size & Forecast
      • 6.3.3.1.1. By Value
    • 6.3.3.2. Market Share & Forecast
      • 6.3.3.2.1. By Technology
      • 6.3.3.2.2. By Application
      • 6.3.3.2.3. By Deployment
      • 6.3.3.2.4. By End-User

7. Europe Applied AI in Retail & E-commerce Market Outlook

• 7.1. Market Size & Forecast
  • 7.1.1. By Value
• 7.2. Market Share & Forecast
  • 7.2.1. By Technology
  • 7.2.2. By Application
  • 7.2.3. By Deployment
  • 7.2.4. By End-User
  • 7.2.5. By Country
• 7.3. Europe: Country Analysis
  • 7.3.1. Germany Applied AI in Retail & E-commerce Market Outlook
    • 7.3.1.1. Market Size & Forecast
      • 7.3.1.1.1. By Value
    • 7.3.1.2. Market Share & Forecast
      • 7.3.1.2.1. By Technology
      • 7.3.1.2.2. By Application
      • 7.3.1.2.3. By Deployment
      • 7.3.1.2.4. By End-User
  • 7.3.2. France Applied AI in Retail & E-commerce Market Outlook
    • 7.3.2.1. Market Size & Forecast
      • 7.3.2.1.1. By Value
    • 7.3.2.2. Market Share & Forecast
      • 7.3.2.2.1. By Technology
      • 7.3.2.2.2. By Application
      • 7.3.2.2.3. By Deployment
      • 7.3.2.2.4. By End-User
  • 7.3.3. United Kingdom Applied AI in Retail & E-commerce Market Outlook
    • 7.3.3.1. Market Size & Forecast
      • 7.3.3.1.1. By Value
    • 7.3.3.2. Market Share & Forecast
      • 7.3.3.2.1. By Technology
      • 7.3.3.2.2. By Application
      • 7.3.3.2.3. By Deployment
      • 7.3.3.2.4. By End-User
  • 7.3.4. Italy Applied AI in Retail & E-commerce Market Outlook
    • 7.3.4.1. Market Size & Forecast
      • 7.3.4.1.1. By Value
    • 7.3.4.2. Market Share & Forecast
      • 7.3.4.2.1. By Technology
      • 7.3.4.2.2. By Application
      • 7.3.4.2.3. By Deployment
      • 7.3.4.2.4. By End-User
  • 7.3.5. Spain Applied AI in Retail & E-commerce Market Outlook
    • 7.3.5.1. Market Size & Forecast
      • 7.3.5.1.1. By Value
    • 7.3.5.2. Market Share & Forecast
      • 7.3.5.2.1. By Technology
      • 7.3.5.2.2. By Application
      • 7.3.5.2.3. By Deployment
      • 7.3.5.2.4. By End-User

8. Asia Pacific Applied AI in Retail & E-commerce Market Outlook

• 8.1. Market Size & Forecast
  • 8.1.1. By Value
• 8.2. Market Share & Forecast
  • 8.2.1. By Technology
  • 8.2.2. By Application
  • 8.2.3. By Deployment
  • 8.2.4. By End-User
  • 8.2.5. By Country
• 8.3. Asia Pacific: Country Analysis
  • 8.3.1. China Applied AI in Retail & E-commerce Market Outlook
    • 8.3.1.1. Market Size & Forecast
      • 8.3.1.1.1. By Value
    • 8.3.1.2. Market Share & Forecast
      • 8.3.1.2.1. By Technology
      • 8.3.1.2.2. By Application
      • 8.3.1.2.3. By Deployment
      • 8.3.1.2.4. By End-User
  • 8.3.2. India Applied AI in Retail & E-commerce Market Outlook
    • 8.3.2.1. Market Size & Forecast
      • 8.3.2.1.1. By Value
    • 8.3.2.2. Market Share & Forecast
      • 8.3.2.2.1. By Technology
      • 8.3.2.2.2. By Application
      • 8.3.2.2.3. By Deployment
      • 8.3.2.2.4. By End-User
  • 8.3.3. Japan Applied AI in Retail & E-commerce Market Outlook
    • 8.3.3.1. Market Size & Forecast
      • 8.3.3.1.1. By Value
    • 8.3.3.2. Market Share & Forecast
      • 8.3.3.2.1. By Technology
      • 8.3.3.2.2. By Application
      • 8.3.3.2.3. By Deployment
      • 8.3.3.2.4. By End-User
  • 8.3.4. South Korea Applied AI in Retail & E-commerce Market Outlook
    • 8.3.4.1. Market Size & Forecast
      • 8.3.4.1.1. By Value
    • 8.3.4.2. Market Share & Forecast
      • 8.3.4.2.1. By Technology
      • 8.3.4.2.2. By Application
      • 8.3.4.2.3. By Deployment
      • 8.3.4.2.4. By End-User
  • 8.3.5. Australia Applied AI in Retail & E-commerce Market Outlook
    • 8.3.5.1. Market Size & Forecast
      • 8.3.5.1.1. By Value
    • 8.3.5.2. Market Share & Forecast
      • 8.3.5.2.1. By Technology
      • 8.3.5.2.2. By Application
      • 8.3.5.2.3. By Deployment
      • 8.3.5.2.4. By End-User

9. Middle East & Africa Applied AI in Retail & E-commerce Market Outlook

• 9.1. Market Size & Forecast
  • 9.1.1. By Value
• 9.2. Market Share & Forecast
  • 9.2.1. By Technology
  • 9.2.2. By Application
  • 9.2.3. By Deployment
  • 9.2.4. By End-User
  • 9.2.5. By Country
• 9.3. Middle East & Africa: Country Analysis
  • 9.3.1. Saudi Arabia Applied AI in Retail & E-commerce Market Outlook
    • 9.3.1.1. Market Size & Forecast
      • 9.3.1.1.1. By Value
    • 9.3.1.2. Market Share & Forecast
      • 9.3.1.2.1. By Technology
      • 9.3.1.2.2. By Application
      • 9.3.1.2.3. By Deployment
      • 9.3.1.2.4. By End-User
  • 9.3.2. UAE Applied AI in Retail & E-commerce Market Outlook
    • 9.3.2.1. Market Size & Forecast
      • 9.3.2.1.1. By Value
    • 9.3.2.2. Market Share & Forecast
      • 9.3.2.2.1. By Technology
      • 9.3.2.2.2. By Application
      • 9.3.2.2.3. By Deployment
      • 9.3.2.2.4. By End-User
  • 9.3.3. South Africa Applied AI in Retail & E-commerce Market Outlook
    • 9.3.3.1. Market Size & Forecast
      • 9.3.3.1.1. By Value
    • 9.3.3.2. Market Share & Forecast
      • 9.3.3.2.1. By Technology
      • 9.3.3.2.2. By Application
      • 9.3.3.2.3. By Deployment
      • 9.3.3.2.4. By End-User

10. South America Applied AI in Retail & E-commerce Market Outlook

• 10.1. Market Size & Forecast
  • 10.1.1. By Value
• 10.2. Market Share & Forecast
  • 10.2.1. By Technology
  • 10.2.2. By Application
  • 10.2.3. By Deployment
  • 10.2.4. By End-User
  • 10.2.5. By Country
• 10.3. South America: Country Analysis
  • 10.3.1. Brazil Applied AI in Retail & E-commerce Market Outlook
    • 10.3.1.1. Market Size & Forecast
      • 10.3.1.1.1. By Value
    • 10.3.1.2. Market Share & Forecast
      • 10.3.1.2.1. By Technology
      • 10.3.1.2.2. By Application
      • 10.3.1.2.3. By Deployment
      • 10.3.1.2.4. By End-User
  • 10.3.2. Colombia Applied AI in Retail & E-commerce Market Outlook
    • 10.3.2.1. Market Size & Forecast
      • 10.3.2.1.1. By Value
    • 10.3.2.2. Market Share & Forecast
      • 10.3.2.2.1. By Technology
      • 10.3.2.2.2. By Application
      • 10.3.2.2.3. By Deployment
      • 10.3.2.2.4. By End-User
  • 10.3.3. Argentina Applied AI in Retail & E-commerce Market Outlook
    • 10.3.3.1. Market Size & Forecast
      • 10.3.3.1.1. By Value
    • 10.3.3.2. Market Share & Forecast
      • 10.3.3.2.1. By Technology
      • 10.3.3.2.2. By Application
      • 10.3.3.2.3. By Deployment
      • 10.3.3.2.4. By End-User

11. Market Dynamics

• 11.1. Drivers
• 11.2. Challenges

12. Market Trends & Developments

• 12.1. Merger & Acquisition (If Any)
• 12.2. Product Launches (If Any)
• 12.3. Recent Developments

13. Global Applied AI in Retail & E-commerce Market: SWOT Analysis

14. Porter's Five Forces Analysis

• 14.1. Competition in the Industry
• 14.2. Potential of New Entrants
• 14.3. Power of Suppliers
• 14.4. Power of Customers
• 14.5. Threat of Substitute Products

15. Competitive Landscape

• 15.1. NVIDIA Corporation
  • 15.1.1. Business Overview
  • 15.1.2. Products & Services
  • 15.1.3. Recent Developments
  • 15.1.4. Key Personnel
  • 15.1.5. SWOT Analysis
• 15.2. Alphabet Inc
• 15.3. Microsoft Corporation
• 15.4. IBM Corporation
• 15.5. Salesforce Inc
• 15.6. Oracle Corporation
• 15.7. SAP SE
• 15.8. Adobe Inc
• 15.9. Alibaba Cloud International
• 15.10. Clarifai, Inc

16. Strategic Recommendations

17. About Us & Disclaimer