Applied AI in Retail and E-Commerce Market Size, Share, and Trends 2026 to 2035

Applied AI in Retail and E-Commerce Market (By Solution Type: Personalized recommendations, Search and discovery, Dynamic pricing, Demand forecasting, Inventory optimization, Fraud detection, Customer service automation, Visual search, Marketing automation, Supply chain optimization; By Component: Software, Services; By Deployment: Cloud On-premises; By End-use Vertical: Grocery and food retail, Fashion and apparel, Consumer electronics, Beauty and personal care, Home and furniture, General merchandise, Others) - Global Industry Analysis, Size, Trends, Leading Companies, Regional Outlook, and Forecast 2026 to 2035

Last Updated : 23 Mar 2026  |  Report Code : 8194  |  Category : ICT   |  Format : PDF / PPT / Excel

Chapter 1. Introduction

1.1. Research Objective

1.2. Scope of the Study

1.3. Definition

Chapter 2. Research Methodology

2.1. Research Approach

2.2. Data Sources

2.3. Assumptions & Limitations

Chapter 3. Executive Summary

3.1. Market Snapshot

Chapter 4. Market Variables and Scope 

4.1. Introduction

4.2. Market Classification and Scope

4.3. Industry Value Chain Analysis

4.3.1. Raw Material Procurement Analysis 

4.3.2. Sales and Distribution Channel Analysis

4.3.3. Downstream Buyer Analysis

Chapter 5. COVID 19 Impact on Applied AI in Retail and E-Commerce Market 

5.1. COVID-19 Landscape: Applied AI in Retail and E-Commerce Industry Impact

5.2. COVID 19 - Impact Assessment for the Industry

5.3. COVID 19 Impact: Global Major Government Policy

5.4. Market Trends and Opportunities in the COVID-19 Landscape

6.1. Market Dynamics

6.1.1. Market Drivers

6.1.2. Market Restraints

6.1.3. Market Opportunities

6.2. Porter’s Five Forces Analysis

6.2.1. Bargaining power of suppliers

6.2.2. Bargaining power of buyers

6.2.3. Threat of substitute

6.2.4. Threat of new entrants

6.2.5. Degree of competition

Chapter 7. Competitive Landscape

7.1.1. Company Market Share/Positioning Analysis

7.1.2. Key Strategies Adopted by Players

7.1.3. Vendor Landscape

7.1.3.1. List of Suppliers

7.1.3.2. List of Buyers

Chapter 8. Global Applied AI in Retail and E-Commerce Market, By Solution Type

8.1. Applied AI in Retail and E-Commerce Market, by Solution Type

8.1.1. Personalized recommendations

8.1.1.1. Market Revenue and Forecast

8.1.2. Search and discovery

8.1.2.1. Market Revenue and Forecast

8.1.3. Dynamic pricing

8.1.3.1. Market Revenue and Forecast

8.1.4. Demand forecasting

8.1.4.1. Market Revenue and Forecast

8.1.5. Inventory optimization

8.1.5.1. Market Revenue and Forecast

8.1.6. Fraud detection

8.1.6.1. Market Revenue and Forecast

8.1.7. Customer service automation

8.1.7.1. Market Revenue and Forecast

8.1.8. Visual search

8.1.8.1. Market Revenue and Forecast

8.1.9. Marketing automation

8.1.9.1. Market Revenue and Forecast

8.1.10. Supply chain optimization

8.1.10.1. Market Revenue and Forecast

Chapter 9. Global Applied AI in Retail and E-Commerce Market, By Component

9.1. Applied AI in Retail and E-Commerce Market, by Component

9.1.1. Software

9.1.1.1. Market Revenue and Forecast

9.1.2. Services

9.1.2.1. Market Revenue and Forecast

Chapter 10. Global Applied AI in Retail and E-Commerce Market, By Deployment

10.1. Applied AI in Retail and E-Commerce Market, by Deployment

10.1.1. Cloud

10.1.1.1. Market Revenue and Forecast

10.1.2. On-premises

10.1.2.1. Market Revenue and Forecast

Chapter 11. Global Applied AI in Retail and E-Commerce Market, By End-use Vertical

11.1. Applied AI in Retail and E-Commerce Market, by End-use Vertical

11.1.1. Grocery and food retail

11.1.1.1. Market Revenue and Forecast

11.1.2. Fashion and apparel

11.1.2.1. Market Revenue and Forecast

11.1.3. Consumer electronics

11.1.3.1. Market Revenue and Forecast

11.1.4. Home and furniture

11.1.4.1. Market Revenue and Forecast

11.1.5. General merchandise

11.1.5.1. Market Revenue and Forecast

11.1.6. Others

11.1.6.1. Market Revenue and Forecast  

Chapter 12. Global Applied AI in Retail and E-Commerce Market, Regional Estimates and Trend Forecast

12.1. North America

12.1.1. Market Revenue and Forecast, by Solution Type

12.1.2. Market Revenue and Forecast, by Component

12.1.3. Market Revenue and Forecast, by Deployment

12.1.4. Market Revenue and Forecast, by End-use Vertical

12.1.5. U.S.

12.1.5.1. Market Revenue and Forecast, by Solution Type

12.1.5.2. Market Revenue and Forecast, by Component

12.1.5.3. Market Revenue and Forecast, by Deployment

12.1.5.4. Market Revenue and Forecast, by End-use Vertical

12.1.6. Rest of North America

12.1.6.1. Market Revenue and Forecast, by Solution Type

12.1.6.2. Market Revenue and Forecast, by Component

12.1.6.3. Market Revenue and Forecast, by Deployment

12.1.6.4. Market Revenue and Forecast, by End-use Vertical

12.2. Europe

12.2.1. Market Revenue and Forecast, by Solution Type

12.2.2. Market Revenue and Forecast, by Component

12.2.3. Market Revenue and Forecast, by Deployment

12.2.4. Market Revenue and Forecast, by End-use Vertical

12.2.5. UK

12.2.5.1. Market Revenue and Forecast, by Solution Type

12.2.5.2. Market Revenue and Forecast, by Component

12.2.5.3. Market Revenue and Forecast, by Deployment

12.2.5.4. Market Revenue and Forecast, by End-use Vertical

12.2.6. Germany

12.2.6.1. Market Revenue and Forecast, by Solution Type

12.2.6.2. Market Revenue and Forecast, by Component

12.2.6.3. Market Revenue and Forecast, by Deployment

12.2.6.4. Market Revenue and Forecast, by End-use Vertical

12.2.7. France

12.2.7.1. Market Revenue and Forecast, by Solution Type

12.2.7.2. Market Revenue and Forecast, by Component

12.2.7.3. Market Revenue and Forecast, by Deployment

12.2.7.4. Market Revenue and Forecast, by End-use Vertical

12.2.8. Rest of Europe

12.2.8.1. Market Revenue and Forecast, by Solution Type

12.2.8.2. Market Revenue and Forecast, by Component

12.2.8.3. Market Revenue and Forecast, by Deployment

12.2.8.4. Market Revenue and Forecast, by End-use Vertical

12.3. APAC

12.3.1. Market Revenue and Forecast, by Solution Type

12.3.2. Market Revenue and Forecast, by Component

12.3.3. Market Revenue and Forecast, by Deployment

12.3.4. Market Revenue and Forecast, by End-use Vertical

12.3.5. India

12.3.5.1. Market Revenue and Forecast, by Solution Type

12.3.5.2. Market Revenue and Forecast, by Component

12.3.5.3. Market Revenue and Forecast, by Deployment

12.3.5.4. Market Revenue and Forecast, by End-use Vertical

12.3.6. China

12.3.6.1. Market Revenue and Forecast, by Solution Type

12.3.6.2. Market Revenue and Forecast, by Component

12.3.6.3. Market Revenue and Forecast, by Deployment

12.3.6.4. Market Revenue and Forecast, by End-use Vertical

12.3.7. Japan

12.3.7.1. Market Revenue and Forecast, by Solution Type

12.3.7.2. Market Revenue and Forecast, by Component

12.3.7.3. Market Revenue and Forecast, by Deployment

12.3.7.4. Market Revenue and Forecast, by End-use Vertical

12.3.8. Rest of APAC

12.3.8.1. Market Revenue and Forecast, by Solution Type

12.3.8.2. Market Revenue and Forecast, by Component

12.3.8.3. Market Revenue and Forecast, by Deployment

12.3.8.4. Market Revenue and Forecast, by End-use Vertical

12.4. MEA

12.4.1. Market Revenue and Forecast, by Solution Type

12.4.2. Market Revenue and Forecast, by Component

12.4.3. Market Revenue and Forecast, by Deployment

12.4.4. Market Revenue and Forecast, by End-use Vertical

12.4.5. GCC

12.4.5.1. Market Revenue and Forecast, by Solution Type

12.4.5.2. Market Revenue and Forecast, by Component

12.4.5.3. Market Revenue and Forecast, by Deployment

12.4.5.4. Market Revenue and Forecast, by End-use Vertical

12.4.6. North Africa

12.4.6.1. Market Revenue and Forecast, by Solution Type

12.4.6.2. Market Revenue and Forecast, by Component

12.4.6.3. Market Revenue and Forecast, by Deployment

12.4.6.4. Market Revenue and Forecast, by End-use Vertical

12.4.7. South Africa

12.4.7.1. Market Revenue and Forecast, by Solution Type

12.4.7.2. Market Revenue and Forecast, by Component

12.4.7.3. Market Revenue and Forecast, by Deployment

12.4.7.4. Market Revenue and Forecast, by End-use Vertical

12.4.8. Rest of MEA

12.4.8.1. Market Revenue and Forecast, by Solution Type

12.4.8.2. Market Revenue and Forecast, by Component

12.4.8.3. Market Revenue and Forecast, by Deployment

12.4.8.4. Market Revenue and Forecast, by End-use Vertical

12.5. Latin America

12.5.1. Market Revenue and Forecast, by Solution Type

12.5.2. Market Revenue and Forecast, by Component

12.5.3. Market Revenue and Forecast, by Deployment

12.5.4. Market Revenue and Forecast, by End-use Vertical

12.5.5. Brazil

12.5.5.1. Market Revenue and Forecast, by Solution Type

12.5.5.2. Market Revenue and Forecast, by Component

12.5.5.3. Market Revenue and Forecast, by Deployment

12.5.5.4. Market Revenue and Forecast, by End-use Vertical

12.5.6. Rest of LATAM

12.5.6.1. Market Revenue and Forecast, by Solution Type

12.5.6.2. Market Revenue and Forecast, by Component

12.5.6.3. Market Revenue and Forecast, by Deployment

12.5.6.4. Market Revenue and Forecast, by End-use Vertical

Chapter 13. Company Profiles

13.1. Microsoft

13.1.1. Company Overview

13.1.2. Product Offerings

13.1.3. Financial Performance

13.1.4. Recent Initiatives

13.2. Amazon Web Services

13.2.1. Company Overview

13.2.2. Product Offerings

13.2.3. Financial Performance

13.2.4. Recent Initiatives

13.3. Google Cloud

13.3.1. Company Overview

13.3.2. Product Offerings

13.3.3. Financial Performance

13.3.4. Recent Initiatives

13.4. Salesforce

13.4.1. Company Overview

13.4.2. Product Offerings

13.4.3. Financial Performance

13.4.4. Recent Initiatives

13.5. Adobe

13.5.1. Company Overview

13.5.2. Product Offerings

13.5.3. Financial Performance

13.5.4. Recent Initiatives

13.6. Oracle

13.6.1. Company Overview

13.6.2. Product Offerings

13.6.3. Financial Performance

13.6.4. Recent Initiatives

13.7. SAP

13.7.1. Company Overview

13.7.2. Product Offerings

13.7.3. Financial Performance

13.7.4. Recent Initiatives

13.8. IBM

13.8.1. Company Overview

13.8.2. Product Offerings

13.8.3. Financial Performance

13.8.4. Recent Initiatives

13.9. NVIDIA

13.9.1. Company Overview

13.9.2. Product Offerings

13.9.3. Financial Performance

13.9.4. Recent Initiatives

13.10. Shopify

13.10.1. Company Overview

13.10.2. Product Offerings

13.10.3. Financial Performance

13.10.4. Recent Initiatives

Chapter 14. Research Methodology

14.1. Primary Research

14.2. Secondary Research

14.3. Assumptions

Chapter 15. Appendix

15.1. About Us

15.2. Glossary of Terms

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Frequently Asked Questions

Answer : The applied AI in retail and e-commerce market size is expected to increase from USD 60.30 billion in 2025 to USD 376.48 billion by 2035.

Answer : The applied AI in retail and e-commerce market is expected to grow at a compound annual growth rate (CAGR) of around 20.10% from 2026 to 2035.

Answer : The major players in the applied AI in retail and e-commerce market include Microsoft, Amazon Web Services, Google Cloud, Salesforce, Adobe, Oracle, SAP, IBM, NVIDIA, Shopify, Dynamic Yield, Algolia, Criteo, Bloom reach and Coveo.

Answer : The driving factors of the applied AI in retail and e-commerce market are the rising demand for a personalized shopping experience and a strong focus on improving supply chain operations.

Answer : North America region will lead the global applied AI in retail and e-commerce market during the forecast period 2026 to 2035.

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