AI In Retail Market Size, Share, Opportunities, And Trends By Deployment Type (Cloud, On-Premise), By Technology (Large language model, Machine Learning, Chatbots, Others), By Application (Demand forecasting, Recommendations, Inventory management, Sentiment analysis, Others), And By Geography - Forecasts From 2024 To 2029

The AI in the retail market is expected to grow at a CAGR of 36.60%, reaching a market size of US$53.271 billion in 2029 from US$19.508 billion in 2024.

The emergence of surveillance and monitoring at a physical retail location, the constant rise of internet users and smart gadgets, and the government's stance toward digitization are contributing to AI in the retail industry’s growth.

Moreover, the way companies have operated in the past few decades lies at the heart of artificial intelligence in the retail industry. AI and big data analytics are the core components of any digitalized business, as they can enhance services, processes, and even the entire business. The growing awareness and adoption of big data analytics and AI applications in retail is also driven by the advancement of technology such as IoT, machine learning services, and increased usage of applications and smart devices, among others.

What are AI in retail market drivers?

• E-commerce growth is contributing to the AI in retail market growth

With the boom of e-commerce and digital experiences, there has been a call for using Artificial Intelligence in the retail sector. Most online retailers use AI-based recommendation systems, chatbots, and virtual assistants to enhance the online shopping experience while engaging consumers to drive conversations. Additionally, even physical stores are enhancing their operations with artificial intelligence to bridge the gap left in the customers' shopping trips.

Moreover, Build Your Own Brain (BYOB) is an AI-supportive tool for all data and decision-making processes. It extends your analyst’s workload. It will unsystematically deep dive, curate, and develop a repository. It presents analytics and actionable insights in real-time according to key metrics and statistical trends.

The growth of e-commerce also promotes the use of artificial intelligence in the retail sector. New markets are accompanied by a wealth of data, leading to expectations for improved service, greater operational efficiency, and enhanced security. This business environment creates opportunities for more effective use of AI in retail.

• Advancements in AI technologies are contributing to the AI in retail market growth

There is a significant correlation between AI in the retail market’s growth and the different components of artificial intelligence. For example, the increasing evolution of technologies such as machine learning, natural language processing, image recognition, deep learning, and a few others have availed more efficient, inexpensive, and scalable AIs. Hence, retailers can implement advanced levels of artificial intelligence-graded applications in handling automating processes, customer interaction, and improving business systems, among other functions.

For instance, KIQ Customer Assist employs advanced language models to give precise, conversational responses to client inquiries. Its code-free design enables simple deployment of chatbot routines, which transfer conversations to a dedicated human support staff for uninterrupted service.

Moreover, advances in AI technology enable retailers to provide personalized experiences, optimize processes, manage risks, and drive company development in a highly competitive retail environment. As AI evolves, its revolutionary influence on the retail business is projected to rise, resulting in more acceptance and innovation in the retail market.

• Rising use of AI-driven visual and voice search is contributing to the AI in retail market growth

Virtual stores and e-commerce platforms are expanding quickly. Consumers can now search for new products using voice, video, and product images. Through query processing and metadata mining, AI in visual search maximizes its capabilities. The visual search engine improves the experience and engagement of customers by using AI features to analyze, track, and forecast emerging shopping trends.

Artificial Intelligence in retail can become more natural and impromptu with the growing use of digital tools. Retailers must now use AI-based search engines to improve customer service and increase revenue growth. Additionally, AI-powered searches help retailers make better business decisions by providing them with insightful information about consumer trends.  

Major restraints in AI in the retail market:

• Lack of infrastructure and higher implementation costs hamper the market growth

To enhance customer interaction, major retail brand names spend significant amounts of money on the latest technology; nonetheless, certain factors are expected to hinder the market's growth. For instance, large corporations and global retailers such as Walmart have already deployed advanced AI technology to manage the operations of their online platforms and physical stores.

However, not all small and medium-sized enterprises and new business ventures can fully embrace the technology due to the lack of appropriate infrastructure and technical expertise. In addition, the high costs associated with implementing AI technology in retail solutions are another barrier that small businesses face. These factors are likely to slow the growth rate of artificial intelligence in the retail business.

Geographical outlook of AI in the retail market:

• North America is witnessing exponential growth during the forecast period

North America is home to many leading technology companies and research institutions driving innovation in AI and retail, like Intel, Nvidia, and Accenture. These improvements aid in creating and using artificial intelligence in the retail industry.

Retailers in North America are employing AI technology to improve operations such as personalized advertising, customer service, inventory management, and price optimization. As this region is characterized by a buoyant retail industry, the presence of traditional retailers, e-commerce players, and brick-and-mortar shops, it offers a perfect ground for adopting AI to stay ahead of the competition in an ever-dynamic environment.

North America's vast customer data is critical for AI algorithms and predictive analytics, allowing merchants to create more personalized shopping experiences. The region's enabling environment, which includes venture capital investment, government initiatives, university research, and a trained workforce, fosters innovation and growth in the AI and retail industries.

Key developments in AI in the retail market:

• In January 2024, Salesforce developed new customer-centric data and AI-based solutions to streamline the shopping experience. All retailers and marketing companies can understand customer behavior and preferences in real-time by developing generative tools and integrating them into marketing and commerce clouds. They use this information to refine every customer interaction through AI.
• In January 2024, in an attempt to enhance customer experience, drive more business, and minimize loss, Lenovo presented its fully open AI solutions for retailers and consumers across the board.

AI In Retail Market Scope:

Report Metric	Details
AI In Retail Market Size in 2024	US$19.508 billion
AI In Retail Market Size in 2029	US$53.271 billion
Growth Rate	CAGR of 36.60%
Study Period	2019 to 2029
Historical Data	2019 to 2022
Base Year	2024
Forecast Period	2024 – 2029
Forecast Unit (Value)	USD Billion
Segmentation	Deployment Type Technology Application Geography
Geographical Segmentation	North America, South America, Europe, Middle East and Africa, Asia Pacific
List of Major Companies in AI In Retail Market	Hitachi Solutions BYOB Intel Accenture Nvidia
Customization Scope	Free report customization with purchase

 

The AI in retail market is analyzed into the following segments:

• By Deployment Type
  • Cloud
  • On-Premise
• By Technology
  • Large language model
  • Machine Learning
  • Chatbots
  • Others
• By Application
  • Demand forecasting
  • Recommendations
  • Inventory management
  • Sentiment analysis
  • Others
• By Geography
  • North America
    • USA
    • Canada
    • Mexico
  • South America
    • Brazil
    • Argentina
    • Others
  • Europe
    • Germany
    • France
    • UK
    • Spain
    • Others
  • Middle East and Africa
    • Saudi Arabia
    • UAE
    • Israel
    • Others
  • Asia Pacific
    • China
    • Japan
    • India
    • South Korea
    • Indonesia
    • Taiwan
    • Others

1. INTRODUCTION

1.1. Market Overview

1.2. Market Definition

1.3. Scope of the Study

1.4. Market Segmentation

1.5. Currency

1.6. Assumptions

1.7. Base and Forecast Years Timeline

1.8. Key Benefits to the Stakeholder

2. RESEARCH METHODOLOGY  

2.1. Research Design

2.2. Research Processes

3. EXECUTIVE SUMMARY

3.1. Key Findings

3.2. CXO Perspective

4. MARKET DYNAMICS

4.1. Market Drivers

4.2. Market Restraints

4.3. Porter’s Five Forces Analysis

4.3.1. Bargaining Power of Suppliers

4.3.2. Bargaining Power of Buyers

4.3.3. Threat of New Entrants

4.3.4. Threat of Substitutes

4.3.5. Competitive Rivalry in the Industry

4.4. Industry Value Chain Analysis

4.5. Analyst View

5. AI IN THE RETAIL MARKET BY DEPLOYMENT TYPE

5.1. Introduction

5.2. Cloud

5.3. On-Premise

6. AI IN THE RETAIL MARKET BY TECHNOLOGY

6.1. Introduction

6.2. Large language model

6.3. Machine Learning

6.4. Chatbots

6.5. Others

7. AI IN THE RETAIL MARKET BY APPLICATION

7.1. Introduction

7.2. Demand forecasting

7.3. Recommendations

7.4. Inventory management

7.5. Sentiment analysis

7.6. Others

8. AI IN THE RETAIL MARKET BY GEOGRAPHY

8.1. Introduction

8.2. North America

8.2.1. By Deployment Type

8.2.2. By Technology

8.2.3. By Application

8.2.4. By Country

8.2.4.1. USA

8.2.4.2. Canada

8.2.4.3. Mexico

8.3. South America

8.3.1. By Deployment Type

8.3.2. By Technology

8.3.3. By Application

8.3.4. By Country

8.3.4.1. Brazil

8.3.4.2. Argentina

8.3.4.3. Others

8.4. Europe

8.4.1. By Deployment Type

8.4.2. By Technology

8.4.3. By Application

8.4.4. By Country

8.4.4.1. Germany

8.4.4.2. France

8.4.4.3. UK

8.4.4.4. Spain

8.4.4.5. Others

8.5. Middle East and Africa

8.5.1. By Deployment Type

8.5.2. By Technology

8.5.3. By Application

8.5.4. By Country

8.5.4.1. Saudi Arabia

8.5.4.2. UAE

8.5.4.3. Others

8.6. Asia Pacific

8.6.1. By Deployment Type

8.6.2. By Technology

8.6.3. By Application

8.6.4. By Country

8.6.4.1. China

8.6.4.2. Japan

8.6.4.3. India

8.6.4.4. South Korea

8.6.4.5. Indonesia

8.6.4.6. Taiwan

8.6.4.7. Others

9. COMPETITIVE ENVIRONMENT AND ANALYSIS

9.1. Major Players and Strategy Analysis

9.2. Market Share Analysis

9.3. Mergers, Acquisitions, Agreements, and Collaborations

9.4. Competitive Dashboard

10. COMPANY PROFILES

10.1. Hitachi Solutions

10.2. BYOB

10.3. Intel

10.4. Accenture

10.5. Nvidia

10.6. Kustomer

10.7. HPE

10.8. Adeppto

10.9. H2O.ai

10.10. Matellio

10.11. BCG

Hitachi Solutions

BYOB

Intel

Accenture

Nvidia

Kustomer

HPE

Adeppto

H2O.ai

Matellio

BCG