Generative AI in Retail Market By Technology Type (Machine Learning, Natural Language Processing, Computer Vision, Generative Adversarial Networks), By Deployment Type (On-premise, Cloud-based), By Application (Personalized Marketing, Inventory Management, Customer Service, Price Optimization, Visual Merchandising, Demand Forecasting, Fraud Detection), By End-use Industry (Apparel & Fashion, Electronics, Groceries & Consumer Goods, Beauty & Cosmetics, Home Furnishings), By Solution Type (Software, Services), By Business Size (Large Enterprises, SMEs); Global Insights & Forecast (2023 – 2030)

As per Intent Market Research, the Generative AI In Retail Market was valued at USD 3.0 billion in 2024-e and will surpass USD 67.5 billion by 2030; growing at a CAGR of 56.3% during 2025 - 2030.

The generative AI in retail market is growing rapidly, transforming the way retailers operate, personalize, and engage with customers. This technological advancement enables businesses to automate various aspects of retail, from personalized marketing and inventory management to customer service and demand forecasting. Driven by the increasing need to provide a tailored shopping experience and optimize operational efficiency, generative AI has gained significant traction across various segments of the retail industry. As retailers continue to focus on enhancing customer experiences and improving efficiency, certain subsegments within this market are emerging as key drivers of growth.

Technology Type: Machine Learning is Leading the Way

Machine Learning (ML) stands out as the largest and most impactful technology in the generative AI retail market. ML algorithms enable retailers to process vast amounts of data and make real-time decisions that improve customer experience, marketing strategies, and operational efficiency. From predicting customer preferences to optimizing inventory and pricing, machine learning is at the core of many AI-driven solutions in the retail sector.

The growth of machine learning can be attributed to its versatility and scalability. ML models are capable of analyzing consumer data, recognizing patterns, and making predictions with remarkable accuracy. As retail businesses increasingly adopt personalized marketing strategies and automate processes, the demand for ML solutions continues to expand. Furthermore, with the rise of e-commerce and omni-channel retailing, ML’s ability to enhance the customer journey from online browsing to purchase has made it indispensable for retailers.

Deployment Type: Cloud-Based Solutions are Dominating

Cloud-based deployment is the fastest-growing segment within the generative AI in retail market. The transition from on-premise infrastructure to cloud-based platforms has enabled retailers to scale AI applications more effectively and reduce upfront costs. Cloud solutions offer flexibility, accessibility, and a more cost-efficient approach to integrating generative AI technologies, making them attractive to both large enterprises and SMEs.

Cloud-based platforms allow retailers to access advanced AI models and tools without the need for substantial on-site infrastructure or maintenance. As the demand for real-time data analytics, seamless integrations, and scalability increases, cloud-based AI solutions are becoming essential. Retailers are leveraging these solutions to enhance personalized marketing, optimize supply chains, and improve customer engagement across multiple touchpoints. This trend is expected to continue, as more retailers move toward cloud-based services to stay competitive in an increasingly digital landscape.

Application: Personalized Marketing is the Key to Customer Engagement

Personalized marketing is the largest and most impactful application of generative AI in retail. With the growing need to deliver tailored customer experiences, retailers are increasingly relying on AI-driven personalized marketing strategies to engage customers, drive conversions, and build loyalty. Generative AI technologies enable businesses to create personalized offers, product recommendations, and targeted advertisements based on individual customer preferences and behaviors.

The adoption of personalized marketing is fueled by the ability of generative AI to process and analyze vast amounts of consumer data, providing deeper insights into customer desires and purchasing patterns. Retailers use AI-powered tools to deliver relevant content at the right time, across multiple channels, ensuring a seamless customer experience. This application not only improves conversion rates but also enhances customer satisfaction and brand loyalty, making it a cornerstone of modern retail strategies.

End-use Industry: Apparel & Fashion is the Largest Segment

The apparel and fashion industry is the largest end-use segment within the generative AI retail market. As fashion retailers face the challenge of keeping up with rapidly changing consumer preferences, AI technologies are helping them stay ahead of trends, optimize inventory, and deliver personalized shopping experiences. With generative AI, fashion retailers can better predict customer demand, customize product recommendations, and even design clothing items that align with emerging fashion trends.

Machine learning algorithms and generative design tools are widely used in the apparel sector to streamline product development, marketing, and distribution. Retailers are increasingly using AI for visual merchandising, automated customer service, and personalized shopping experiences. As fashion businesses continue to innovate and respond to shifting consumer demands, generative AI remains a critical enabler of growth in this competitive industry.

Solution Type: Software Solutions are Leading the Charge

Software solutions dominate the generative AI in retail market, as businesses look for scalable, flexible, and customizable AI tools to integrate into their existing operations. Software platforms for personalized marketing, inventory optimization, and customer service automation are being widely adopted across the retail industry. These platforms are typically cloud-based, enabling retailers to access powerful AI-driven capabilities without the need for extensive on-premise infrastructure.

AI-powered software solutions offer a range of functionalities, from data analytics and predictive modeling to automated customer engagement and visual merchandising. The growth of e-commerce and online retail has driven demand for AI tools that enhance customer interaction, streamline operations, and improve the overall shopping experience. Retailers that adopt these solutions are gaining a competitive edge by leveraging AI to stay agile in a fast-paced market.

Business Size: Large Enterprises are Leading Adoption

Large enterprises are leading the adoption of generative AI in retail, owing to their greater resources and ability to invest in advanced technologies. These companies are leveraging AI to optimize their entire supply chain, from demand forecasting and inventory management to personalized marketing and customer service. Large retailers, such as e-commerce giants and multinational brands, have the infrastructure and data to implement generative AI solutions at scale.

The advantage of AI adoption for large enterprises is clear: they can process large volumes of data and automate key functions across their operations. This allows them to improve efficiency, reduce costs, and enhance the customer experience on a global scale. While SMEs are also adopting AI technologies, large enterprises are able to integrate more complex AI systems and realize a quicker return on investment, making them the leaders in this space.

Region: North America is the Largest Market

North America is the largest region for the generative AI in retail market, driven by the presence of leading technology companies, a strong retail sector, and rapid adoption of AI solutions. The U.S. is home to some of the largest global retailers and AI solution providers, contributing to the region's dominance in the generative AI market. With the rise of e-commerce, North American retailers are heavily investing in AI technologies to improve customer engagement, streamline operations, and enhance the shopping experience.

Furthermore, North America benefits from a highly developed technology ecosystem, which supports innovation and accelerates AI adoption in the retail sector. The region's strong digital infrastructure, skilled workforce, and consumer demand for personalized experiences make it a key market for AI-driven retail solutions. This trend is expected to continue as companies in the region continue to embrace generative AI for competitive advantage.

Leading Companies and Competitive Landscape

The competitive landscape of the generative AI in retail market is shaped by the presence of leading technology providers, including global giants like Google Cloud, Microsoft, IBM, and Amazon Web Services (AWS). These companies offer cutting-edge AI platforms that enable retailers to integrate machine learning, natural language processing, and other AI technologies into their operations. The market also features several specialized AI firms and solution providers, such as Salesforce and Adobe, which focus on retail-specific applications like personalized marketing and customer engagement.

As the market continues to grow, competition among these players is intensifying. Companies are investing in research and development to enhance their AI offerings and differentiate themselves from competitors. Strategic partnerships, acquisitions, and collaborations are also common, as leading firms look to expand their portfolios and gain a larger market share. The ongoing evolution of AI technology will likely fuel further competition, with businesses continually seeking to improve their AI capabilities to better serve retail clients

Recent Developments:

  • Amazon Web Services (AWS) launched new AI tools tailored to retail, enabling businesses to personalize shopping experiences and optimize product recommendations.
  • Salesforce announced an acquisition of a machine learning company to enhance its generative AI capabilities in customer service automation for retail clients.
  • Microsoft Corporation introduced a new cloud-based solution aimed at automating inventory management, leveraging AI to streamline stock optimization in retail.
  • IBM Corporation expanded its generative AI offerings, unveiling an advanced platform for predictive analytics in retail, aiming to enhance sales forecasting accuracy.
  • Accenture collaborated with a leading global retailer to implement AI-driven merchandising strategies, boosting the retailer's ability to generate more targeted and effective product displays.

List of Leading Companies:

  • Google Cloud
  • IBM Corporation
  • Microsoft Corporation
  • Amazon Web Services (AWS)
  • Adobe Inc.
  • Salesforce
  • SAP SE
  • Oracle Corporation
  • Accenture
  • Nvidia Corporation
  • Infosys Limited
  • HCL Technologies
  • ServiceNow
  • Cognizant Technology Solutions
  • Deloitte

Report Scope:

Report Features

Description

Market Size (2024-e)

USD 3.0 Billion

Forecasted Value (2030)

USD 67.5 Billion

CAGR (2025 – 2030)

56.3%

Base Year for Estimation

2024-e

Historic Year

2023

Forecast Period

2025 – 2030

Report Coverage

Market Forecast, Market Dynamics, Competitive Landscape, Recent Developments

Segments Covered

Generative AI in Retail Market By Technology Type (Machine Learning, Natural Language Processing, Computer Vision, Generative Adversarial Networks), By Deployment Type (On-premise, Cloud-based), By Application (Personalized Marketing, Inventory Management, Customer Service, Price Optimization, Visual Merchandising, Demand Forecasting, Fraud Detection), By End-use Industry (Apparel & Fashion, Electronics, Groceries & Consumer Goods, Beauty & Cosmetics, Home Furnishings), By Solution Type (Software, Services), By Business Size (Large Enterprises, SMEs)

Regional Analysis

North America (US, Canada, Mexico), Europe (Germany, France, UK, Italy, Spain, and Rest of Europe), Asia-Pacific (China, Japan, South Korea, Australia, India, and Rest of Asia-Pacific), Latin America (Brazil, Argentina, and Rest of Latin America), Middle East & Africa (Saudi Arabia, UAE, Rest of Middle East & Africa)

Major Companies

Google Cloud, IBM Corporation, Microsoft Corporation, Amazon Web Services (AWS), Adobe Inc., Salesforce, SAP SE, Oracle Corporation, Accenture, Nvidia Corporation, Infosys Limited, HCL Technologies, ServiceNow, Cognizant Technology Solutions, Deloitte

Customization Scope

Customization for segments, region/country-level will be provided. Moreover, additional customization can be done based on the requirements

1. Introduction

   1.1. Market Definition

   1.2. Scope of the Study

   1.3. Research Assumptions

   1.4. Study Limitations

2. Research Methodology

   2.1. Research Approach

      2.1.1. Top-Down Method

      2.1.2. Bottom-Up Method

      2.1.3. Factor Impact Analysis

  2.2. Insights & Data Collection Process

      2.2.1. Secondary Research

      2.2.2. Primary Research

   2.3. Data Mining Process

      2.3.1. Data Analysis

      2.3.2. Data Validation and Revalidation

      2.3.3. Data Triangulation

3. Executive Summary

   3.1. Major Markets & Segments

   3.2. Highest Growing Regions and Respective Countries

   3.3. Impact of Growth Drivers & Inhibitors

   3.4. Regulatory Overview by Country

4. Generative AI In Retail Market, by Technology Type (Market Size & Forecast: USD Million, 2023 – 2030)

   4.1. Machine Learning

   4.2. Natural Language Processing

   4.3. Computer Vision

   4.4. Generative Adversarial Networks (GANs)

5. Generative AI In Retail Market, by Deployment Type (Market Size & Forecast: USD Million, 2023 – 2030)

   5.1. On-premise

   5.2. Cloud-based

6. Generative AI In Retail Market, by Application (Market Size & Forecast: USD Million, 2023 – 2030)

   6.1. Personalized Marketing

   6.2. Inventory Management

   6.3. Customer Service

   6.4. Price Optimization

   6.5. Visual Merchandising

   6.6. Demand Forecasting

   6.7. Fraud Detection

7. Generative AI In Retail Market, by End-use Industry (Market Size & Forecast: USD Million, 2023 – 2030)

   7.1. Apparel & Fashion

   7.2. Electronics

   7.3. Groceries & Consumer Goods

   7.4. Beauty & Cosmetics

   7.5. Home Furnishings

8. Generative AI In Retail Market, by Solution Type (Market Size & Forecast: USD Million, 2023 – 2030)

   8.1. Software

   8.2. Services

9. Generative AI In Retail Market, by Business Size (Market Size & Forecast: USD Million, 2023 – 2030)

   9.1. Large Enterprises

   9.2. SMEs (Small and Medium Enterprises)

10. Regional Analysis (Market Size & Forecast: USD Million, 2023 – 2030)

   10.1. Regional Overview

   10.2. North America

      10.2.1. Regional Trends & Growth Drivers

      10.2.2. Barriers & Challenges

      10.2.3. Opportunities

      10.2.4. Factor Impact Analysis

      10.2.5. Technology Trends

      10.2.6. North America Generative AI In Retail Market, by Technology Type

      10.2.7. North America Generative AI In Retail Market, by Deployment Type

      10.2.8. North America Generative AI In Retail Market, by Application

      10.2.9. North America Generative AI In Retail Market, by End-use Industry

      10.2.10. North America Generative AI In Retail Market, by Solution Type

      10.2.11. North America Generative AI In Retail Market, by Business Size

      10.2.12. By Country

         10.2.12.1. US

               10.2.12.1.1. US Generative AI In Retail Market, by Technology Type

               10.2.12.1.2. US Generative AI In Retail Market, by Deployment Type

               10.2.12.1.3. US Generative AI In Retail Market, by Application

               10.2.12.1.4. US Generative AI In Retail Market, by End-use Industry

               10.2.12.1.5. US Generative AI In Retail Market, by Solution Type

               10.2.12.1.6. US Generative AI In Retail Market, by Business Size

         10.2.12.2. Canada

         10.2.12.3. Mexico

    *Similar segmentation will be provided for each region and country

   10.3. Europe

   10.4. Asia-Pacific

   10.5. Latin America

   10.6. Middle East & Africa

11. Competitive Landscape

   11.1. Overview of the Key Players

   11.2. Competitive Ecosystem

      11.2.1. Level of Fragmentation

      11.2.2. Market Consolidation

      11.2.3. Product Innovation

   11.3. Company Share Analysis

   11.4. Company Benchmarking Matrix

      11.4.1. Strategic Overview

      11.4.2. Product Innovations

   11.5. Start-up Ecosystem

   11.6. Strategic Competitive Insights/ Customer Imperatives

   11.7. ESG Matrix/ Sustainability Matrix

   11.8. Manufacturing Network

      11.8.1. Locations

      11.8.2. Supply Chain and Logistics

      11.8.3. Product Flexibility/Customization

      11.8.4. Digital Transformation and Connectivity

      11.8.5. Environmental and Regulatory Compliance

   11.9. Technology Readiness Level Matrix

   11.10. Technology Maturity Curve

   11.11. Buying Criteria

12. Company Profiles

   12.1. Google Cloud

      12.1.1. Company Overview

      12.1.2. Company Financials

      12.1.3. Product/Service Portfolio

      12.1.4. Recent Developments

      12.1.5. IMR Analysis

    *Similar information will be provided for other companies 

   12.2. IBM Corporation

   12.3. Microsoft Corporation

   12.4. Amazon Web Services (AWS)

   12.5. Adobe Inc.

   12.6. Salesforce

   12.7. SAP SE

   12.8. Oracle Corporation

   12.9. Accenture

   12.10. Nvidia Corporation

   12.11. Infosys Limited

   12.12. HCL Technologies

   12.13. ServiceNow

   12.14. Cognizant Technology Solutions

   12.15. Deloitte

13. Appendix

A comprehensive market research approach was employed to gather and analyze data on the Generative AI in Retail Market. In the process, the analysis was also done to analyze the parent market and relevant adjacencies to measure the impact of them on the Generative AI in Retail Market. The research methodology encompassed both secondary and primary research techniques, ensuring the accuracy and credibility of the findings.

Research Approach -

Secondary Research

Secondary research involved a thorough review of pertinent industry reports, journals, articles, and publications. Additionally, annual reports, press releases, and investor presentations of industry players were scrutinized to gain insights into their market positioning and strategies.

Primary Research

Primary research involved conducting in-depth interviews with industry experts, stakeholders, and market participants across the E-Waste Management ecosystem. The primary research objectives included:

  • Validating findings and assumptions derived from secondary research
  • Gathering qualitative and quantitative data on market trends, drivers, and challenges
  • Understanding the demand-side dynamics, encompassing end-users, component manufacturers, facility providers, and service providers
  • Assessing the supply-side landscape, including technological advancements and recent developments

Market Size Assessment

A combination of top-down and bottom-up approaches was utilized to analyze the overall size of the Generative AI in Retail Market. These methods were also employed to assess the size of various subsegments within the market. The market size assessment methodology encompassed the following steps:

  1. Identification of key industry players and relevant revenues through extensive secondary research
  2. Determination of the industry's supply chain and market size, in terms of value, through primary and secondary research processes
  3. Calculation of percentage shares, splits, and breakdowns using secondary sources and verification through primary sources

Bottom Up and Top Down -

Data Triangulation

To ensure the accuracy and reliability of the market size, data triangulation was implemented. This involved cross-referencing data from various sources, including demand and supply side factors, market trends, and expert opinions. Additionally, top-down and bottom-up approaches were employed to validate the market size assessment.

NA

Please state your requirements.


I have read the Terms & Conditions and Privacy Policy. I agree to its terms.

Report Buying Options