The AI-Enabled E-Commerce Solutions Market Size is valued at USD 6.90 Billion in 2024 and is predicted to reach USD 31.43 Billion by the year 2034 at a 16.5% CAGR during the forecast period for 2025-2034.
In the recent years, Machine learning and artificial intelligence technologies are creating complex software development processes easy. Machine learning technology allows software applications to function more accurately in terms of predictive analysis. The recent Covid-19 outbreak has significantly impacted the AI-enabled E-Commerce solutions market as it has created the need for warehouse automation and management. Understanding customers' needs based on shopping history, product searches, and demographic details makes the market more competitive.
The AI-based platform enables the seller to optimize their sales target by reaching the right customer with fundamental analysis based on gathered information. E-commerce AI is changing the online shopping field through the features like image search, customer-centric search, retargeting potential customers, virtual buying assistants, and extensive data analysis. AI applications analyze customer data to estimate future shopping trends and make product recommendations based on browsing patterns, ultimately driving the market growth.
Multiple factors that drive the AI-enabled E-Commerce solutions market are rising adoption of advanced technologies, manual error reductions in development processes due to the use of machine learning-based applications, cost-effective procedures, fast implementation of cloud-based platforms, and easy access to real-time data, various government initiatives for the R&D, and increasing awareness regarding advanced technologies. In addition, instant customer services related to product delivery, return, and complaints can be quickly resolved through artificial intelligence-enabled chat boxes. However, factors like the high cost of AI Solutions, shortage of skilled professionals, and complex and time-consuming procedures may downscale the AI-enabled E-Commerce solutions market's growth over the forecast period 2024-2031.
Segmentation of AI-enabled E-Commerce solutions market includes Technology, Applications, Deployment, and Region. The Technology segment comprises Deep Learning, Machine Learning, and NLP. Deep learning is a widely used technology in the market due to its benefits and useful features. In terms of Applications, the market is segmented into Customer Relationship Management, Supply Chain Analysis, Fake Review Analysis, Warehouse Automation, Merchandizing, Product Recommendation, and Customer Service. The Warehouse Automation segment is further bifurcated into Sorting and Placing and Inventory Storage. The Merchandizing segment is divided into Facets and Filter Selection and Multi Device Interaction. The Customer Service segment is subdivided into Chatbots. Out of these applications, customer relationship management, customer service, and product recommendation are the majorly used features. By Deployment, the market is divided into On-Premises and Cloud. Cloud Service has dominated this market. At regional level, the AI-enabled E-Commerce solutions market can be segmented into North America, Europe, Asia-Pacific, Latin America, and the Middle East and Africa. North America is anticipated to be the major market shareholder of this market over the forecast period, followed by Europe, Asia-Pacific, Latin America, and Rest-of-the-World.
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Report Attribute |
Specifications |
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Market Size Value In 2024 |
USD 6.90 Billion |
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Revenue Forecast In 2034 |
USD 31.43 Billion |
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Growth Rate CAGR |
CAGR of 16.5% from 2025 to 2035 |
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Quantitative Units |
Representation of revenue in US$ Billion and CAGR from 2025 to 2034 |
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Historic Year |
2021 to 2024 |
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Forecast Year |
2025-2034 |
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Report Coverage |
The forecast of revenue, the position of the company, the competitive market structure, growth prospects, and trends |
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Segments Covered |
By Technology, By Application, By Deployment |
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Regional Scope |
North America; Europe; Asia Pacific; Latin America; Middle East & Africa |
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Country Scope |
U.S.; Canada; U.K.; Germany; China; Japan; Brazil; Mexico ;The UK; France; Italy; Spain; Japan; India; South Korea; South East Asia |
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Competitive Landscape |
Riskified, Reflektion, Inc., Shelf.ai, Osaro, Sift, AntVoice SAS, Appier Inc, Eversight, Inc., Granify Inc., LivePerson, Inc., Manthan Software Services Pvt. Ltd., PayPal, Inc., Sidecar Interactive, Inc., Tinyclues SAS, Twiggle Ltd., Celect, Inc., Cortexica Vision Systems Ltd., Crobox B.V., Deepomatic SAS, Dynamic Yield Ltd., Emarsys eMarketing, Systems AG, Satisfi Labs, Inc., Staqu Technologies Pvt. Ltd., ViSenze Pte Ltd., and Other Prominent Players. |
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Customization Scope |
Free customization report with the procurement of the report, Modifications to the regional and segment scope. Particular Geographic competitive landscape. |
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Pricing And Available Payment Methods |
Explore pricing alternatives that are customized to your particular study requirements. |
Chapter 1. Methodology and Scope
1.1. Research Methodology
1.2. Research Scope & Assumptions
Chapter 2. Executive Summary
Chapter 3. Global AI Enabled E-Commerce Solutions Market Snapshot
Chapter 4. Global AI Enabled E-Commerce Solutions Market Variables, Trends & Scope
4.1. Market Segmentation & Scope
4.2. Drivers
4.3. Challenges
4.4. Trends
4.5. Investment and Funding Analysis
4.6. Industry Analysis – Porter’s Five Forces Analysis
4.7. Competitive Landscape & Market Share Analysis
4.8. Impact of Covid-19 Analysis
Chapter 5. Market Segmentation 1: by Technology Estimates & Trend Analysis
5.1. by Technology & Market Share, 2024 & 2034
5.2. Market Size (Value US$ Mn) & Forecasts and Trend Analyses, 2021 to 2034 for the following by Technology:
5.2.1. Deep Learning
5.2.2. Machine Learning
5.2.3. NLP
Chapter 6. Market Segmentation 2: by Applications Estimates & Trend Analysis
6.1. by Applications & Market Share, 2024 & 2034
6.2. Market Size (Value US$ Mn) & Forecasts and Trend Analyses, 2021 to 2034 for the following by Applications:
6.2.1. Customer Relationship Management
6.2.2. Supply Chain Analysis
6.2.3. Fake Review Analysis
6.2.4. Warehouse Automation
6.2.4.1. Sorting and Placing
6.2.4.2. Inventory Storage
6.2.5. Merchandizing
6.2.5.1. Facets and Filter Selection
6.2.5.2. Multi Device Interaction
6.2.6. Product Recommendation
6.2.7. Customer Service
6.2.7.1. Chatbots
Chapter 7. Market Segmentation 3: by Deployment Estimates & Trend Analysis
7.1. by Deployment & Market Share, 2024 & 2034
7.2. Market Size (Value US$ Mn) & Forecasts and Trend Analyses, 2021 to 2034 for the following by Deployment:
7.2.1. On-Premise
7.2.2. Cloud
Chapter 8. AI Enabled E-Commerce Solutions Market Segmentation 4: Regional Estimates & Trend Analysis
8.1. North America
8.1.1. North America AI Enabled E-Commerce Solutions Market Revenue (US$ Million) Estimates and Forecasts by Technology, 2021-2034
8.1.2. North America AI Enabled E-Commerce Solutions Market Revenue (US$ Million) Estimates and Forecasts by Applications, 2021-2034
8.1.3. North America AI Enabled E-Commerce Solutions Market Revenue (US$ Million) Estimates and Forecasts by Deployment, 2021-2034
8.1.4. North America AI Enabled E-Commerce Solutions Market Revenue (US$ Million) Estimates and Forecasts by country, 2021-2034
8.2. Europe
8.2.1. Europe AI Enabled E-Commerce Solutions Market revenue (US$ Million) by Technology, 2021-2034
8.2.2. Europe AI Enabled E-Commerce Solutions Market revenue (US$ Million) by Applications, 2021-2034
8.2.3. Europe AI Enabled E-Commerce Solutions Market Revenue (US$ Million) Estimates and Forecasts by Deployment, 2021-2034
8.2.4. Europe AI Enabled E-Commerce Solutions Market revenue (US$ Million) by country, 2021-2034
8.3. Asia Pacific
8.3.1. Asia Pacific AI Enabled E-Commerce Solutions Market revenue (US$ Million) by Technology, 2021-2034
8.3.2. Asia Pacific AI Enabled E-Commerce Solutions Market revenue (US$ Million) by Applications, 2021-2034
8.3.3. Asia Pacific AI Enabled E-Commerce Solutions Market Revenue (US$ Million) Estimates and Forecasts by Deployment, 2021-2034
8.3.4. Asia Pacific AI Enabled E-Commerce Solutions Market revenue (US$ Million) by country, 2021-2034
8.4. Latin America
8.4.1. Latin America AI Enabled E-Commerce Solutions Market revenue (US$ Million) by Technology, 2021-2034
8.4.2. Latin America AI Enabled E-Commerce Solutions Market revenue (US$ Million) by Applications, 2021-2034
8.4.3. Latin America AI Enabled E-Commerce Solutions Market Revenue (US$ Million) Estimates and Forecasts by Deployment, 2021-2034
8.4.4. Latin America AI Enabled E-Commerce Solutions Market revenue (US$ Million) by country, 2021-2034
8.5. Middle East & Africa
8.5.1. Middle East & Africa AI Enabled E-Commerce Solutions Market revenue (US$ Million) by Technology, 2021-2034
8.5.2. Middle East & Africa AI Enabled E-Commerce Solutions Market revenue (US$ Million) by Applications, 2021-2034
8.5.3. Middle East & Africa AI Enabled E-Commerce Solutions Market Revenue (US$ Million) Estimates and Forecasts by Deployment, 2021-2034
8.5.4. Middle East & Africa AI Enabled E-Commerce Solutions Market revenue (US$ Million) by country, 2021-2034
Chapter 9. Competitive Landscape
9.1. Major Mergers and Acquisitions/Strategic Alliances
9.2. Company Profiles
9.2.1. Riskified
9.2.2. Reflektion, Inc.
9.2.3. Shelf.ai
9.2.4. Osaro
9.2.5. Sift
9.2.6. AntVoice SAS
9.2.7. Appier Inc
9.2.8. Eversight, Inc.
9.2.9. Granify Inc.
9.2.10. LivePerson, Inc.
9.2.11. Manthan Software Services Pvt. Ltd.
9.2.12. PayPal, Inc.
9.2.13. Sidecar Interactive, Inc.
9.2.14. Tinyclues SAS
9.2.15. Twiggle Ltd.
9.2.16. Celect, Inc.
9.2.17. Cortexica Vision Systems Ltd.
9.2.18. Crobox B.V.
9.2.19. Deepomatic SAS
9.2.20. Dynamic Yield Ltd.
9.2.21. Emarsys eMarketing Systems AG
9.2.22. Satisfi Labs, Inc.
9.2.23. Staqu Technologies Pvt. Ltd.
9.2.24. ViSenze Pte Ltd.
9.2.25. Other Prominent Players
Market Size (Value US$ Mn) & Forecasts and Trend Analyses, by Technology
Market Size (Value US$ Mn) & Forecasts and Trend Analyses, by Applications
Market Size (Value US$ Mn) & Forecasts and Trend Analyses, by Deployment
Market Size (Value US$ Mn) & Forecasts and Trend Analyses, by Region
North America AI enabled E-Commerce solutions market revenue by Country
Europe AI enabled E-Commerce solutions market revenue by Country
Asia Pacific AI enabled E-Commerce solutions market revenue by Country
Latin America AI enabled E-Commerce solutions market revenue by Country
Middle East & Africa AI enabled E-Commerce solutions market revenue by Country
InsightAce Analytic follows a standard and comprehensive market research methodology focused on offering the most accurate and precise market insights. The methods followed for all our market research studies include three significant steps – primary research, secondary research, and data modeling and analysis - to derive the current market size and forecast it over the forecast period. In this study, these three steps were used iteratively to generate valid data points (minimum deviation), which were cross-validated through multiple approaches mentioned below in the data modeling section.
Through secondary research methods, information on the market under study, its peer, and the parent market was collected. This information was then entered into data models. The resulted data points and insights were then validated by primary participants.
Based on additional insights from these primary participants, more directional efforts were put into doing secondary research and optimize data models. This process was repeated till all data models used in the study produced similar results (with minimum deviation). This way, this iterative process was able to generate the most accurate market numbers and qualitative insights.
Secondary research
The secondary research sources that are typically mentioned to include, but are not limited to:
The paid sources for secondary research like Factiva, OneSource, Hoovers, and Statista
Primary Research:
Primary research involves telephonic interviews, e-mail interactions, as well as face-to-face interviews for each market, category, segment, and subsegment across geographies
The contributors who typically take part in such a course include, but are not limited to:
Data Modeling and Analysis:
In the iterative process (mentioned above), data models received inputs from primary as well as secondary sources. But analysts working on these models were the key. They used their extensive knowledge and experience about industry and topic to make changes and fine-tuning these models as per the product/service under study.
The standard data models used while studying this market were the top-down and bottom-up approaches and the company shares analysis model. However, other methods were also used along with these – which were specific to the industry and product/service under study.
To know more about the research methodology used for this study, kindly contact us/click here.
