Global AI Shopping Assistant Market Size is valued at USD 4.3 Bn in 2024 and is predicted to reach USD 41.9 Bn by the year 2034 at a 26.1% CAGR during the forecast period for 2025-2034.
AI shopping assistants are smart digital agents that help customers find, compare, and buy products online by offering personalized recommendations and answering questions in real-time. They make shopping easier, faster, and more tailored to each user's preferences. These assistants help users navigate eCommerce sites with ease by simulating human-like interactions through the use of technologies such as machine learning (ML), natural language processing (NLP) and data analytics.
AI shopping assistants respond to customer inquiries in real-time, offering solutions and recommendations that are specific to each user's tastes and habits. With capabilities such as product discovery, order updates, and website navigation support, these AI assistants act as virtual sales representatives, guiding customers through the purchasing process. The market for AI shopping assistants is expanding due to rising disposable income worldwide. According to a report released by the Bureau of Economic Analysis (BEA), the United States' disposable personal income grew by USD 194.3 billion, or 0.9%, in January 2025. As their discretionary income increases, people are driven to seek efficient and customized purchasing experiences. Additionally, the increasing digitalization is driving demand for AI shopping assistants. Customers now demand smooth, individualized, and effective shopping experiences as a result of digitalization. These expectations are met by AI shopping assistants, who offer real-time assistance, tailored product recommendations, and prompt answers to questions—all of which are frequently difficult for traditional customer service models to provide.
Notwithstanding their promise, AI shopping assistants have drawbacks like data security and privacy difficulties. Fearing misuse, customers could be reluctant to divulge personal information. However, by putting strong security protections and open data standards into place, developers have a chance to innovate. By resolving these issues, businesses can foster greater adoption and establish trust. Additionally, as technology advances, artificial intelligence (AI) has the potential to enhance customer service and optimize operations, thereby creating new opportunities for the expansion of the AI shopping assistant market.
Some Major Key Players In The AI Shopping Assistant Market:
The AI Shopping Assistant market is segmented based on type, technology, and application. Based on type, the market is segmented into Recommendation Engines, Virtual Shopping Assistants, Voice-activated Assistants, Chatbot-based Al Assistants, and Visual Search Al Assistants. By technology, the market segmentation includes Computer Vision, Natural Language Processing (NLP), Machine Learning, and Speech Recognition. By application, the overall market is categorized into Online Retail, Mobile Applications, In-store Retail, E-commerce Platforms, and Social Media Platforms.
The virtual shopping assistant category is expected to hold a major global market share in 2024. The virtual shopping assistant is becoming more and more well-liked since it may act as a personal shopper, making personalized suggestions and guiding clients through product catalogues. Another important market is the chatbot-based AI assistant, which companies frequently employ to provide immediate customer service, respond to inquiries, and assist customers during their trips. Voice-activated assistants are becoming more & more popular, particularly as voice-enabled gadgets like smart speakers and smartphones gain traction. The user experience is improved by these assistants, which enable hands-free, comfortable shopping.
One of the most popular technologies is Natural Language Processing (NLP), which allows AI assistants to naturally comprehend and process human language. This enhances user-friendliness and engagement by enabling conversational interactions between users and chatbots or voice assistants. Because it allows AI assistants to learn from user behaviour and gradually improve their predictions and recommendations, machine learning is crucial. In order to forecast trends and preferences, it also aids in the analysis of enormous volumes of consumer data.
The North American AI Shopping Assistant market is expected to register the highest market share in revenue in the near future, stimulated by the region's high rates of e-commerce customer acceptance and sophisticated technological infrastructure. AI-powered solutions are being increasingly adopted by retailers in the US and Canada to enhance customer service, offer personalized shopping experiences, and streamline both in-person and online retail processes. In addition, Europe is projected to grow rapidly in the global AI Shopping Assistant market, attributed to rising smartphone adoption and the rapidly growing e-commerce sector. Local shops are using AI shopping assistants to meet the increasing customers' needs for speed, convenience, and personalization.
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Report Attribute |
Specifications |
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Market Size Value In 2024 |
USD 4.3 Bn |
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Revenue Forecast In 2034 |
USD 41.9 Bn |
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Growth Rate CAGR |
CAGR of 26.1% from 2025 to 2034 |
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Quantitative Units |
Representation of revenue in US$ Bn 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 Type, Technology, And Application. |
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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; India; Japan; Brazil; Mexico; France; Italy; Spain; South East Asia; South Korea |
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Competitive Landscape |
Alibaba, Shopify, Salesforce, eBay, ByteDance (TikTok), Amazon, Meta (Instagram & Facebook), Microsoft, Adobe, Zalando, Naver (LINE Shopping), Rakuten, Google, Walmart, Pinterest, Snap Inc. (Snapchat), Coupang, Wayfair, and Best Buy. |
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Customization Scope |
Free customization report with the procurement of the report and 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 Shopping Assistant Market Snapshot
Chapter 4. Global AI Shopping Assistant 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. Porter's Five Forces Analysis
4.7. Incremental Opportunity Analysis (US$ MN), 2025-2034
4.8. Global AI Shopping Assistant Market Penetration & Growth Prospect Mapping (US$ Mn), 2024-2034
4.9. Competitive Landscape & Market Share Analysis, By Key Player (2024)
4.10. Use/impact of AI on AI Shopping Assistant Market Industry Trends
Chapter 5. AI Shopping Assistant Market Segmentation 1: By Technology, Estimates & Trend Analysis
5.1. Market Share by Technology, 2024 & 2034
5.2. Market Size (Value (US$ Mn) & Forecasts and Trend Analyses, 2021 to 2034 for the following Technology:
5.2.1. Natural Language Processing (NLP)
5.2.2. Machine Learning
5.2.3. Computer Vision
5.2.4. Speech Recognition
Chapter 6. AI Shopping Assistant Market Segmentation 2: By Application, Estimates & Trend Analysis
6.1. Market Share by Application, 2024 & 2034
6.2. Market Size (Value (US$ Mn) & Forecasts and Trend Analyses, 2021 to 2034 for the following Application:
6.2.1. Online Retail
6.2.2. In-store Retail
6.2.3. E-commerce Platforms
6.2.4. Mobile Applications
6.2.5. Social Media Platforms
Chapter 7. AI Shopping Assistant Market Segmentation 3: By Type, Estimates & Trend Analysis
7.1. Market Share by Type, 2024 & 2034
7.2. Market Size (Value (US$ Mn) & Forecasts and Trend Analyses, 2021 to 2034 for the following Type:
7.2.1. Virtual Shopping Assistant
7.2.2. Chatbot-based AI Assistant
7.2.3. Voice-activated Assistant
7.2.4. Recommendation Engine
7.2.5. Visual Search AI Assistant
Chapter 8. AI Shopping Assistant Market Segmentation 4: Regional Estimates & Trend Analysis
8.1. Global AI Shopping Assistant Market , Regional Snapshot 2024 & 2034
8.2. North America
8.2.1. North America AI Shopping Assistant Market Revenue (US$ Mn) Estimates and Forecasts by Country, 2021-2034
8.2.1.1. US
8.2.1.2. Canada
8.2.2. North America AI Shopping Assistant Market Revenue (US$ Mn) Estimates and Forecasts by Technology, 2021-2034
8.2.3. North America AI Shopping Assistant Market Revenue (US$ Mn) Estimates and Forecasts by Application, 2021-2034
8.2.4. North America AI Shopping Assistant Market Revenue (US$ Mn) Estimates and Forecasts by Type, 2021-2034
8.3. Europe
8.3.1. Europe AI Shopping Assistant Market Revenue (US$ Mn) Estimates and Forecasts by Country, 2021-2034
8.3.1.1. Germany
8.3.1.2. U.K.
8.3.1.3. France
8.3.1.4. Italy
8.3.1.5. Spain
8.3.1.6. Rest of Europe
8.3.2. Europe AI Shopping Assistant Market Revenue (US$ Mn) Estimates and Forecasts by Technology, 2021-2034
8.3.3. Europe AI Shopping Assistant Market Revenue (US$ Mn) Estimates and Forecasts by Application, 2021-2034
8.3.4. Europe AI Shopping Assistant Market Revenue (US$ Mn) Estimates and Forecasts by Type, 2021-2034
8.4. Asia Pacific
8.4.1. Asia Pacific AI Shopping Assistant Market Revenue (US$ Mn) Estimates and Forecasts by Country, 2021-2034
8.4.1.1. India
8.4.1.2. China
8.4.1.3. Japan
8.4.1.4. Australia
8.4.1.5. South Korea
8.4.1.6. Hong Kong
8.4.1.7. Southeast Asia
8.4.1.8. Rest of Asia Pacific
8.4.2. Asia Pacific AI Shopping Assistant Market Revenue (US$ Mn) Estimates and Forecasts by Technology, 2021-2034
8.4.3. Asia Pacific AI Shopping Assistant Market Revenue (US$ Mn) Estimates and Forecasts by Application, 2021-2034
8.4.4. Asia Pacific AI Shopping Assistant Market Revenue (US$ Mn) Estimates and Forecasts by Type, 2021-2034
8.5. Latin America
8.5.1. Latin America AI Shopping Assistant Market Revenue (US$ Mn) Estimates and Forecasts by Country, 2021-2034
8.5.1.1. Brazil
8.5.1.2. Mexico
8.5.1.3. Rest of Latin America
8.5.2. Latin America AI Shopping Assistant Market Revenue (US$ Mn) Estimates and Forecasts by Technology, 2021-2034
8.5.3. Latin America AI Shopping Assistant Market Revenue (US$ Mn) Estimates and Forecasts by Application, 2021-2034
8.5.4. Latin America AI Shopping Assistant Market Revenue (US$ Mn) Estimates and Forecasts by Type, 2021-2034
8.6. Middle East & Africa
8.6.1. Middle East & Africa AI Shopping Assistant Market Revenue (US$ Mn) Estimates and Forecasts by country, 2021-2034
8.6.1.1. GCC Countries
8.6.1.2. Israel
8.6.1.3. South Africa
8.6.1.4. Rest of Middle East and Africa
8.6.2. Middle East & Africa AI Shopping Assistant Market Revenue (US$ Mn) Estimates and Forecasts by Technology, 2021-2034
8.6.3. Middle East & Africa AI Shopping Assistant Market Revenue (US$ Mn) Estimates and Forecasts by Application, 2021-2034
8.6.4. Middle East & Africa AI Shopping Assistant Market Revenue (US$ Mn) Estimates and Forecasts by Type, 2021-2034
Chapter 9. Competitive Landscape
9.1. Major Mergers and Acquisitions/Strategic Alliances
9.2. Company Profiles
9.2.1. Amazon
9.2.1.1. Business Overview
9.2.1.2. Key Product/Application
9.2.1.3. Financial Performance
9.2.1.4. Geographical Presence
9.2.1.5. Recent Developments with Business Strategy
9.2.2. Google
9.2.3. Alibaba
9.2.4. Shopify
9.2.5. Walmart
9.2.6. Meta (Instagram & Facebook)
9.2.7. Microsoft
9.2.8. Salesforce
9.2.9. eBay
9.2.10. ByteDance (TikTok)
9.2.11. Adobe
9.2.12. Zalando
9.2.13. com
9.2.14. Pinterest
9.2.15. Snap Inc. (Snapchat)
9.2.16. Naver (LINE Shopping)
9.2.17. Rakuten
9.2.18. Coupang
9.2.19. Wayfair
9.2.20. Best Buy
Segmentation of AI Shopping Assistant Market-
AI Shopping Assistant Market By Type-
AI Shopping Assistant Market
AI Shopping Assistant Market By Application-
AI Shopping Assistant Market By Region-
North America-
Europe-
Asia-Pacific-
Latin America-
Middle East & Africa-
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.
