AI in Retail - Personalized Shopping Market Forecasts to 2032 - Global Analysis By Retail Type (E-commerce, Omnichannel, Brick-and-Mortar), Offering, Deployment Mode, Technology, Application, End User and By Geography

According to Stratistics MRC, the Global AI in Retail - Personalized ShoppingMarket is accounted for $41.7 billion in 2025 and is expected to reach $323.5 billion by 2032 growing at a CAGR of 34.0% during the forecast period. AI in Retail - Personalized Shopping refers to the use of artificial intelligence technologies to enhance and tailor the shopping experience for individual consumers. It involves analyzing customer data such as browsing history, purchase behavior, preferences, and demographics to deliver customized product recommendations, targeted promotions, and dynamic pricing. AI tools like machine learning, natural language processing, and computer vision help retailers understand and predict customer needs in real time. This enables seamless, engaging, and efficient interactions across various channels, including online stores, mobile apps, and in-store kiosks, ultimately boosting customer satisfaction, retention, and overall retail sales performance.

Market Dynamics:

Driver:

Rising Demand for Personalized Customer Experiences

The rising demand for personalized customer experiences is significantly driving the AI in Retail Personalized Shopping Market. Consumers increasingly expect tailored recommendations, and individualized engagement across channels. AI technologies like machine learning and natural language processing empower retailers to analyze vast datasets and deliver real-time, customized shopping experiences. This shift enhances customer satisfaction, and conversion rates, prompting more retailers to invest in AI-driven personalization tools. As a result, market growth is accelerating, transforming retail into a data-driven, customer-centric ecosystem.

Restraint:

Data Privacy and Security Concerns

Data privacy and security concerns pose a significant hindrance for SMEs adopting AI in retail personalized shopping. Limited resources and technical expertise make it challenging to implement robust data protection measures, deterring the use of AI technologies reliant on sensitive customer information. Fears of data breaches and regulatory non-compliance further discourage investment, restricting SMEs from leveraging AI-driven personalization and ultimately slowing market growth and innovation in this sector.

Opportunity:

Growth of E-commerce and Omnichannel Retailing

The growth of e-commerce and omnichannel retailing is positively driving the AI in Retail - Personalized Shopping Market by creating a vast digital landscape for personalized consumer engagement. With shoppers navigating between online and offline touchpoints, retailers increasingly rely on AI to unify customer data, predict preferences, and deliver tailored experiences across channels. This seamless integration enhances customer satisfaction and loyalty, while boosting conversion rates and sales, thereby propelling the demand for AI-driven personalized shopping solutions in the retail sector.

Threat:

High Implementation Costs for SMEs

High implementation costs pose a significant barrier for small and medium-sized enterprises (SMEs) in adopting AI in retail personalized shopping. These businesses often lack the financial resources and technical expertise required for AI integration, including data infrastructure, software, and skilled personnel. As a result, SMEs struggle to compete with larger retailers, limiting market inclusivity and slowing overall growth. This cost burden hinders widespread adoption and innovation across the retail sector.

Covid-19 Impact

The COVID-19 pandemic significantly accelerated the adoption of AI in the retail personalized shopping market. As physical stores faced restrictions, retailers turned to digital channels and AI-driven tools to enhance customer engagement. AI-enabled personalized recommendations, virtual try-ons, and chatbot assistance gained traction to meet evolving consumer expectations. The crisis highlighted the need for agility, prompting retailers to invest in AI technologies to ensure continuity and deliver tailored experiences amid uncertainty.

The apparel segment is expected to be the largest during the forecast period

The apparel segment is expected to account for the largest market share during the forecast period, due to demand for customization, style recommendations, and virtual try-ons. As consumers seek personalized fashion experiences, AI technologies such as computer vision and machine learning enable retailers to deliver tailored suggestions, sizing assistance, and trend analysis. This enhances customer satisfaction, boosts conversion rates, and reduces return rates. The apparel segment's expansion thus accelerates AI adoption, transforming the shopping journey into a highly individualized experience.

The machine learning segment is expected to have the highest CAGR during the forecast period

Over the forecast period, the machine learning segment is predicted to witness the highest growth rate as it delivers hyper-personalized experiences based on real-time consumer behavior and preferences. Through advanced data analysis, machine learning algorithms can predict purchasing patterns, suggest tailored products, and enhance customer engagement. This leads to increased conversion rates, higher customer satisfaction, and brand loyalty. The continuous improvement of algorithms through self-learning capabilities ensures dynamic personalization, making machine learning a vital catalyst in the growth of personalized retail experiences.

Region with largest share:

During the forecast period, the Asia Pacific region is expected to hold the largest market share due to rapid digital transformation, increasing smartphone penetration, and growing e-commerce adoption. Retailers are leveraging AI to deliver hyper-personalized shopping experiences through real-time product recommendations, dynamic pricing, and predictive analytics. Countries like China, Japan, and India are leading innovation, supported by rising investments in AI infrastructure. This technological shift enhances customer satisfaction, drives sales growth, and strengthens brand loyalty across diverse consumer segments.

Region with highest CAGR:

Over the forecast period, the North America region is anticipated to exhibit the highest CAGR, owing to technological adoption and consumer demand for customized experiences. Retailers are leveraging AI-driven tools like recommendation engines, customer behavior analytics, and virtual assistants to deliver hyper-personalized shopping journeys. This enhances customer satisfaction, increases conversion rates, and boosts brand loyalty. The region's advanced digital infrastructure and high smartphone penetration further support AI integration, making North America a leader in personalized retail innovation.

Key players in the market

Some of the key players profiled in the AI in Retail - Personalized Shopping Market include IBM Corporation, Microsoft Corporation, Google LLC, Amazon Web Services, Inc., Salesforce, Inc., SAP SE, Oracle Corporation, Adobe Inc., Intel Corporation, NVIDIA Corporation, Infosys Limited, Cognizant Technology Solutions, Capgemini SE, Tata Consultancy Services (TCS), Wipro Limited, Shopify Inc, Sentient Technologies, ViSenze Pte Ltd. and Syte Visual Conception Ltd.

Key Developments:

In May 2025, Finanz Informatik, has renewed and expanded its partnership with IBM. Under the new multi year agreement, Finanz Informatik will deploy state of the art IBM mainframe, Power, and storage systems-alongside AI-enabled software from the watsonx portfolio-within its own data centers.

In April 2025, IBM and Tokyo Electron (TEL) have signed a new five-year extension of their longstanding semiconductor R&D partnership, originally spanning over two decades. The renewed agreement centres on advancing next-generation semiconductor nodes, chiplet architectures, and High NA EUV patterning to meet the performance and energy-efficiency demands of generative AI.

Retail Types Covered:

• E-commerce
• Omnichannel
• Brick-and-Mortar

Offerings Covered:

• Solution
• Services

Deployment Modes Covered:

• On-premise
• Cloud

Technologies Covered:

• Machine Learning
• Predictive Analytics
• Natural Language Processing (NLP)
• Computer Vision
• Chatbots & Virtual Assistants

Applications Covered:

• Personalized Product Recommendations
• Inventory Management
• Dynamic Pricing
• Customer Behavior Analytics
• Visual Search
• Virtual Fitting Rooms
• Other Applications

End Users Covered:

• Apparel
• Footwear
• Grocery
• Home Furnishing
• Beauty & Personal Care
• Electronics
• Other End Users

Regions Covered:

• North America
  • US
  • Canada
  • Mexico
• Europe
  • Germany
  • UK
  • Italy
  • France
  • Spain
  • Rest of Europe
• Asia Pacific
  • Japan
  • China
  • India
  • Australia
  • New Zealand
  • South Korea
  • Rest of Asia Pacific
• South America
  • Argentina
  • Brazil
  • Chile
  • Rest of South America
• Middle East & Africa
  • Saudi Arabia
  • UAE
  • Qatar
  • South Africa
  • Rest of Middle East & Africa

What our report offers:

• Market share assessments for the regional and country-level segments
• Strategic recommendations for the new entrants
• Covers Market data for the years 2022, 2023, 2024, 2026, and 2030
• Market Trends (Drivers, Constraints, Opportunities, Threats, Challenges, Investment Opportunities, and recommendations)
• Strategic recommendations in key business segments based on the market estimations
• Competitive landscaping mapping the key common trends
• Company profiling with detailed strategies, financials, and recent developments
• Supply chain trends mapping the latest technological advancements

Free Customization Offerings:

All the customers of this report will be entitled to receive one of the following free customization options:

• Company Profiling
  • Comprehensive profiling of additional market players (up to 3)
  • SWOT Analysis of key players (up to 3)
• Regional Segmentation
  • Market estimations, Forecasts and CAGR of any prominent country as per the client's interest (Note: Depends on feasibility check)
• Competitive Benchmarking
  • Benchmarking of key players based on product portfolio, geographical presence, and strategic alliances