Artificial Intelligence In Retail Market Size, Share, and Trends 2024 to 2033

Artificial Intelligence (AI) in Retail Market (By Component: Solution, Service, By Technology: Image and Video Analytics, Machine Learning, Natural Language Processing, Swarm Intelligence, Chatbots; By Sales Channel: Brick and Motor, Omnichannel, Pure-play Online Retailers; By Application) - Global Industry Analysis, Size, Share, Growth, Trends, Regional Outlook, and Forecast 2024-2033

  • Last Updated : July 2024
  • Report Code : 2495
  • Category : ICT

Artificial Intelligence In Retail Market Size and Growth

The global artificial intelligence (AI) in retail market size was USD 9.97 billion in 2023, accounted for USD 11.83 billion in 2024, and is expected to reach around USD 54.92 billion by 2033, expanding at a CAGR of 18.6% from 2024 to 2033.

Artificial Intelligence in Retail Market Size 2024 to 2033

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Artificial Intelligence In Retail Market Key Takeaways

  • By geography, The North America region accounted for more than 39.14% of revenue share in 2023.
  • By components, the solution segment contributed more than 72% of revenue share in 2023. 
  • By technology, the machine learning segment generated more than 31% of revenue share in 2023. 
  • By sales channels, the pure-play market segment gained a sizeable revenue share.
  • By application, the customer relationship management segment captured around 21.50% of revenue share in 2023.

U.S. Artificial Intelligence In Retail Market Size and Growth 2024 to 2033

The U.S. artificial intelligence (AI) in retail market size was estimated at USD 2.74 billion in 2023 and is predicted to be worth around USD 15.90 billion by 2033, at a CAGR of 19.2% from 2024 to 2033.

U.S. Artificial Intelligence In Retail Market Size 2024 to 2033

Artificial intelligence in the retail sector generated most of its revenue in North America. There is potential for industrial growth as significant investments are being made in AI projects and related R&D activities initiated by the companies. To improve the efficacy of their customer service, regional retail providers are concentrating on obtaining insightful consumer preferences. The industry leaders use both inorganic & organic methods to expand. The United States pioneered AI technology adoption and is seeing significant investment in the field. Due to the country's growing need for technology, new start-ups and small businesses are also emerging. This should accelerate the growth of artificial intelligence in the retail sector.

Regarding AI in retail market share dominance, Europe is probably in second place. The region’s significant merchants, including those in the apparel, cosmetics, and fashion industries, actively invest in cutting-edge technology to improve the customer experience. Thus, the need for artificial intelligence in the retail sector is anticipated to increase. Additionally, due to the increasing digitalization, Asia Pacific is predicted to experience rapid growth during the projection period. The regional retail market is experiencing an immediate era of change, and the demand for cutting-edge technologies to enhance operations and customer experience is propelling the market.

Artificial Intelligence In Retail Market Share, By Region, 2023 (%)

Market Overview

As physical storefronts continue to dominate the retail industry, they face intense competition. As with traditional stores, digital platforms may quickly access their competitors in the market where they compete. Retailers may use AI to improve the customer shopping experience and gain the competitive advantage they need to stay relevant. The most widely utilized AI technologies are those based on machine learning and deep learning. Businesses in the retail sector use machine learning and deep learning technology to provide end consumers with a more personalized experience and an engaging setting. 

However, a reluctance to adopt technology advancements may hinder the market's expansion. The market expansion is also being hampered by a need for a competent workforce to integrate AI in retail. Artificial intelligence (AI) is making advances in the retail sector, and retailers may utilize AI to interact with customers and operate more efficiently. Techniques include using computer vision to adjust marketing in real-time and machine learning for inventory management.

Artificial Intelligence (AI) in Retail Market Scope

Report Coverage Details
Market Size in 2023 USD 9.97 Billion
Market Size in 2024 USD 11.83 Billion
Market Size by 2033 USD 54.92 Billion
Growth Rate from 2024 to 2033 CAGR of 18.6%
Base Year 2023
Forecast Period 2024 to 2033
Segments Covered By Component, By Technology, By Sales Channel, By Application
Regions Covered North America, Europe, Asia-Pacific, Latin America, Middle East & Africa


Artificial Intelligence (AI) in Retail Market Dynamics

In the coming years, AI has the potential to significantly change the retail industry, impacting the value chain from cost elements to shopping participation. Adopting AI is crucial because e-commerce and AI work in tandem and the coronavirus outbreak has increased e-commerce growth rates. The benefits of AI will convert the industry. Sellers must subsequently begin planning as soon as it is practical, and these plans must include both technology and company strategy.

The main advantage of AI is that it can help consumers with tedious, repetitive activities. A large number of workers think that productivity has grown as a result of the greater use of AI at work. The usage of AI in retail may result in the same outcome. Artificial intelligence is a tool drivers in the logistics sector can use to determine the optimal delivery routes. Robots can also assist with order selection and packing, freeing staff employees to focus on other essential duties.

Numerous factors are expected to impede the spread of artificial intelligence in the retail sector, even though well-known retail companies continue to invest in cutting-edge technology to enhance client engagement. Large firms and significant retailers like Walmart have already included artificial intelligence technologies for managing online portals and in-store operations. Small and medium-sized firms and new start-ups need more infrastructure and technological know-how to utilize the technology. A lack of AI knowledge is a barrier to deploying such technology. Also, the adoption of the intelligent retail solution needs to be improved by its high implementation costs, which represent substantial challenges for small retailers. These factors are expected to limit market expansion. 

Artificial intelligence in the retail market has a wealth of lucrative potential due to the increased usage of IoT, Big Data analytics, and e-commerce marketing. Computer vision and other technological developments in the retail industry are becoming more popular in brick-and-mortar stores. This development creates possibilities for new retail in areas such as customer experience, demand forecasting, and inventory management. Furthermore, using AI in retail will increasingly focus on planning and product recommendations. Growth in artificially intelligent products and services across various industrial domains and verticals will be fueled by developments in big data analytics.

COVID-19 Impact

The COVID-19 epidemic negatively impacted global economies. Governments worldwide have been compelled to shut down retail facilities, shops, and import-export operations, disrupting global supply chains. Retail, manufacturing, and logistical sectors have all been significantly impacted by the COVID-19 epidemic, as have customer behavior and product demand. The retail industry, especially brick-and-mortar retailers, has been severely damaged by the temporary closures of non-essential stores, bars, and venues in several nations, except food and grocery stores and pharmacies.

Since customers view internet platforms as their primary shopping channel, the COVID-19 outbreak has consequently enhanced the significance of online retail channels. Retailers and consumer products companies now have a fantastic opportunity to develop sustainability efforts connected with their online presence. Retailers are embracing e-commerce platforms and online marketplaces to profit from this shifting trend.

Component Insights

Based on components, artificial intelligence (AI) in retail market is segmented into services and solutions. The solution segment occupies the retail market's highest share of global artificial intelligence (AI) in the estimated year. New automated technology is being developed in response to the management problems many retail companies are experiencing. With AI-powered technologies, retailers can manage supply chain operations, logistics, and warehouse management while improving the consumer experience. 

On the hand, the services segment is expected to see strong growth in the projected period. This growth is attributed to the rapid adoption of AI solutions as it contributes to the creation of intelligent functions, improves the customer experience, increases the potential for revenue development, leads to faster innovation, and lowers human error.

Technology Insights

Based on technology, the market is bifurcated into natural language processing, machine learning (ML), image & video analytics, chatbots, and swarm intelligence. Amongst the mentioned segments, the ML segment acquired the largest revenue share of the market. Machine learning technology's greater precision and flexibility contribute to the segment’s increasing expansion. As it serves data rapidly and deeply, machine learning is excellent for offering personalized experiences to customers. Furthermore, it aids merchants in streamlining supply chain strategies and demand projections to increase inventory productivity. For instance, the fully managed service - Amazon Sage Maker enables the deployment of machine learning models for any activity, from customer experience to predictive analytics. 

Additionally, NLP is expected to progress as data analysis increases and chatbots powered by AI become more popular. Thus, demand for natural language processing will be expected to increase rapidly throughout the forecast period. 

Sales Channel Insights

Based on sales channels, the market is segregated into brick-and-mortar, omnichannel, and pure-play online retailers. The pure-play market category gained a sizeable revenue share of AI in the retail industry. The increased acceptance of online and virtual purchasing would hasten the growth of pure-play internet businesses. Social media, IoT, and AI would grow in popularity, boosting AI in the retail industry.

Application Insights

Based on application, the global market for AI in retail is segmented into customer relationship management (CRM), inventory management, supply chain & logistics, product optimization, payment & pricing analytics, in-store navigation, virtual assistant (VA), and others. CRM in the retail market saw the highest revenue share. The CRM market would rise to prominence if there were a pressing need to enhance customer service & retention. With the use of chatbots, search engines, and other technologies, retail providers may encourage customer loyalty and strong relationships.

Virtual assistant technologies have ample opportunities to explore in the retail industry, such as streamlining the supply chain, invoicing, ordering inventory, and bookkeeping. Therefore, virtual assistance is expected to experience rapid growth in the projected period.

Artificial Intelligence (AI) in Retail Market Companies

  • IBM Corporation 
  • Microsoft  
  • SAP SE 
  • Amazon Web Services 
  • Oracle
  • Salesforce Inc.
  • Intel
  • NVIDIA
  • Google LLC
  • Sentient Technology 
  • ViSenze 

Recent Developments

  • In September 2022, Microsoft collaborated with the Indian global IT business Infosys. The organizations wanted to make it possible for enterprises to reimagine customer experiences quickly, augment systems with cloud & data, and update processes through this alliance.
  • In August 2022, the company introduced a new personalized e-commerce product suggestions solution called ViSenze’s Session-Based Recommendations. With the new approach, the clients would receive a more customized experience without providing personal information.
  • In July 2022, novel reference kits were released by Intel. The new solution sought to make it easier for data scientists and engineers to understand how to implement AI in various settings, including manufacturing, retail, healthcare, and other fields.
  • In July 2022, Askdata, a search-driven analytics business, was acquired by SAP. The company's goal after the acquisition was to improve its capacity to support businesses in making educated decisions through AI-based natural language searches.
  • In June 2022, Google and the retail outlet H&M entered a partnership. The company intended to design and create a corporate data backbone through this partnership, including cutting-edge AI & ML capabilities, a core data platform, and data products.
  • In June 2022, Oracle and the retail operator Komax collaborated. As a result of this partnership, Oracle would provide Komax with access to its retail bouquet of services via its cloud infrastructure to assist the company in introducing the newest apparel, accessories, and footwear from various well-known brands to clients throughout Latin America.
  • In June 2022, NVIDIA and German multinational firm Siemens established a collaboration. The firms hoped to combine Siemens Xcelerator and NVIDIA Omniverse through this alliance to provide an industrial metaverse in addition to physics-based digital models. 
  • In April 2022, SAP and Kyndryl, a well-known supplier of IT infrastructure services, joined forces. Through this agreement, the businesses planned to concentrate on delivering cutting-edge solutions to customers’ most demanding digital business transformation concerns. 
  • In March 2022, Microsoft acquired Nuance Communications, an American multinational company that develops computer software. The company’s goal after this acquisition was to incorporate Nuance’s conversational AI and ambient intelligence, which were best in class, into its reputable and secure industry cloud products.
  • In January 2022, Federos, a provider of IT consulting and services, was purchased by Oracle. With network analytics, assurance, ad automated orchestration, and AI-optimized services, this acquisition aims to give service providers more leverage.

Segments Covered in the Report 

By Component 

  • Solution 
  • Service 

By Technology 

  • Image and Video Analytics 
  • Machine Learning 
  • Natural Language Processing 
  • Swarm Intelligence 
  • Chatbots

By Sales Channel 

  • Brick and Motor
  • Omnichannel 
  • Pure-play Online Retailers 

By Application 

  • In-Store Navigation
  • Payment and Pricing Analytics 
  • Customer Relationship Management (CRM)
  • Supply Chain and Logistics
  • Inventory Management 
  • Product Optimization 
  • Virtual Assistant 
  • Others

By Geography

  • North America
  • Europe
  • Asia-Pacific
  • Latin America
  • Middle East & Africa (MEA)

Frequently Asked Questions

The global artificial intelligence (AI) inretail market size was estimated at USD 9.97 billion in 2023 and it is expected to reach around USD 54.92 billion by 2033.

The global artificial intelligence (AI) in retail market is poised to grow at a CAGR of 18.6% from 2024 to 2033.

The major players operating in the artificial intelligence (AI) in retail market are IBM Corporation, Microsoft, SAP SE, Amazon Web Services, Oracle, Salesforce Inc., Intel, NVIDIA, Google LLC, Sentient Technology, ViSenze and Others.

The growing demand for better security and monitoring in offline stores, increasing demand for enhanced consumer experience with AI-powered chatbots and increased knowledge of using AI in retail will drive market growth.

North America region will lead the global artificial intelligence (AI) in retail market during the forecast period 2024 to 2033.

Chapter 1. Introduction

1.1. Research Objective

1.2. Scope of the Study

1.3. Definition

Chapter 2. Research Methodology

2.1. Research Approach

2.2. Data Sources

2.3. Assumptions & Limitations

Chapter 3. Executive Summary

3.1. Market Snapshot

Chapter 4. Market Variables and Scope 

4.1. Introduction

4.2. Market Classification and Scope

4.3. Industry Value Chain Analysis

4.3.1. Raw Material Procurement Analysis 

4.3.2. Sales and Distribution Channel Analysis

4.3.3. Downstream Buyer Analysis

Chapter 5. COVID 19 Impact on Artificial Intelligence In Retail Market 

5.1. COVID-19 Landscape: Artificial Intelligence In Retail Industry Impact

5.2. COVID 19 - Impact Assessment for the Industry

5.3. COVID 19 Impact: Global Major Government Policy

5.4. Market Trends and Opportunities in the COVID-19 Landscape

Chapter 6. Market Dynamics Analysis and Trends

6.1. Market Dynamics

6.1.1. Market Drivers

6.1.2. Market Restraints

6.1.3. Market Opportunities

6.2. Porter’s Five Forces Analysis

6.2.1. Bargaining power of suppliers

6.2.2. Bargaining power of buyers

6.2.3. Threat of substitute

6.2.4. Threat of new entrants

6.2.5. Degree of competition

Chapter 7. Competitive Landscape

7.1.1. Company Market Share/Positioning Analysis

7.1.2. Key Strategies Adopted by Players

7.1.3. Vendor Landscape

7.1.3.1. List of Suppliers

7.1.3.2. List of Buyers

Chapter 8. Global Artificial Intelligence In Retail Market, By Component

8.1. Artificial Intelligence In Retail Market, by Component, 2024-2033

8.1.1. Solution

8.1.1.1. Market Revenue and Forecast (2021-2033)

8.1.2. Service

8.1.2.1. Market Revenue and Forecast (2021-2033)

Chapter 9. Global Artificial Intelligence In Retail Market, By Technology

9.1. Artificial Intelligence In Retail Market, by Technology, 2024-2033

9.1.1. Image and Video Analytics

9.1.1.1. Market Revenue and Forecast (2021-2033)

9.1.2. Machine Learning

9.1.2.1. Market Revenue and Forecast (2021-2033)

9.1.3. Natural Language Processing

9.1.3.1. Market Revenue and Forecast (2021-2033)

9.1.4. Swarm Intelligence

9.1.4.1. Market Revenue and Forecast (2021-2033)

9.1.5. Chatbots

9.1.5.1. Market Revenue and Forecast (2021-2033)

Chapter 10. Global Artificial Intelligence In Retail Market, By Sales Channel 

10.1. Artificial Intelligence In Retail Market, by Sales Channel, 2024-2033

10.1.1. Brick and Motor

10.1.1.1. Market Revenue and Forecast (2021-2033)

10.1.2. Omnichannel

10.1.2.1. Market Revenue and Forecast (2021-2033)

10.1.3. Pure-play Online Retailers

10.1.3.1. Market Revenue and Forecast (2021-2033)

Chapter 11. Global Artificial Intelligence In Retail Market, By Application 

11.1. Artificial Intelligence In Retail Market, by Application, 2024-2033

11.1.1. In-Store Navigation

11.1.1.1. Market Revenue and Forecast (2021-2033)

11.1.2. Payment and Pricing Analytics

11.1.2.1. Market Revenue and Forecast (2021-2033)

11.1.3. Customer Relationship Management (CRM)

11.1.3.1. Market Revenue and Forecast (2021-2033)

11.1.4. Supply Chain and Logistics

11.1.4.1. Market Revenue and Forecast (2021-2033)

11.1.5. Inventory Management

11.1.5.1. Market Revenue and Forecast (2021-2033)

11.1.6. Product Optimization

11.1.6.1. Market Revenue and Forecast (2021-2033)

11.1.7. Virtual Assistant

11.1.7.1. Market Revenue and Forecast (2021-2033)

11.1.8. Others

11.1.8.1. Market Revenue and Forecast (2021-2033)

Chapter 12. Global Artificial Intelligence In Retail Market, Regional Estimates and Trend Forecast

12.1. North America

12.1.1. Market Revenue and Forecast, by Component (2021-2033)

12.1.2. Market Revenue and Forecast, by Technology (2021-2033)

12.1.3. Market Revenue and Forecast, by Sales Channel (2021-2033)

12.1.4. Market Revenue and Forecast, by Application (2021-2033)

12.1.5. U.S.

12.1.5.1. Market Revenue and Forecast, by Component (2021-2033)

12.1.5.2. Market Revenue and Forecast, by Technology (2021-2033)

12.1.5.3. Market Revenue and Forecast, by Sales Channel (2021-2033)

12.1.5.4. Market Revenue and Forecast, by Application (2021-2033)

12.1.6. Rest of North America

12.1.6.1. Market Revenue and Forecast, by Component (2021-2033)

12.1.6.2. Market Revenue and Forecast, by Technology (2021-2033)

12.1.6.3. Market Revenue and Forecast, by Sales Channel (2021-2033)

12.1.6.4. Market Revenue and Forecast, by Application (2021-2033)

12.2. Europe

12.2.1. Market Revenue and Forecast, by Component (2021-2033)

12.2.2. Market Revenue and Forecast, by Technology (2021-2033)

12.2.3. Market Revenue and Forecast, by Sales Channel (2021-2033)

12.2.4. Market Revenue and Forecast, by Application (2021-2033)

12.2.5. UK

12.2.5.1. Market Revenue and Forecast, by Component (2021-2033)

12.2.5.2. Market Revenue and Forecast, by Technology (2021-2033)

12.2.5.3. Market Revenue and Forecast, by Sales Channel (2021-2033)

12.2.5.4. Market Revenue and Forecast, by Application (2021-2033)

12.2.6. Germany

12.2.6.1. Market Revenue and Forecast, by Component (2021-2033)

12.2.6.2. Market Revenue and Forecast, by Technology (2021-2033)

12.2.6.3. Market Revenue and Forecast, by Sales Channel (2021-2033)

12.2.6.4. Market Revenue and Forecast, by Application (2021-2033)

12.2.7. France

12.2.7.1. Market Revenue and Forecast, by Component (2021-2033)

12.2.7.2. Market Revenue and Forecast, by Technology (2021-2033)

12.2.7.3. Market Revenue and Forecast, by Sales Channel (2021-2033)

12.2.7.4. Market Revenue and Forecast, by Application (2021-2033)

12.2.8. Rest of Europe

12.2.8.1. Market Revenue and Forecast, by Component (2021-2033)

12.2.8.2. Market Revenue and Forecast, by Technology (2021-2033)

12.2.8.3. Market Revenue and Forecast, by Sales Channel (2021-2033)

12.2.8.4. Market Revenue and Forecast, by Application (2021-2033)

12.3. APAC

12.3.1. Market Revenue and Forecast, by Component (2021-2033)

12.3.2. Market Revenue and Forecast, by Technology (2021-2033)

12.3.3. Market Revenue and Forecast, by Sales Channel (2021-2033)

12.3.4. Market Revenue and Forecast, by Application (2021-2033)

12.3.5. India

12.3.5.1. Market Revenue and Forecast, by Component (2021-2033)

12.3.5.2. Market Revenue and Forecast, by Technology (2021-2033)

12.3.5.3. Market Revenue and Forecast, by Sales Channel (2021-2033)

12.3.5.4. Market Revenue and Forecast, by Application (2021-2033)

12.3.6. China

12.3.6.1. Market Revenue and Forecast, by Component (2021-2033)

12.3.6.2. Market Revenue and Forecast, by Technology (2021-2033)

12.3.6.3. Market Revenue and Forecast, by Sales Channel (2021-2033)

12.3.6.4. Market Revenue and Forecast, by Application (2021-2033)

12.3.7. Japan

12.3.7.1. Market Revenue and Forecast, by Component (2021-2033)

12.3.7.2. Market Revenue and Forecast, by Technology (2021-2033)

12.3.7.3. Market Revenue and Forecast, by Sales Channel (2021-2033)

12.3.7.4. Market Revenue and Forecast, by Application (2021-2033)

12.3.8. Rest of APAC

12.3.8.1. Market Revenue and Forecast, by Component (2021-2033)

12.3.8.2. Market Revenue and Forecast, by Technology (2021-2033)

12.3.8.3. Market Revenue and Forecast, by Sales Channel (2021-2033)

12.3.8.4. Market Revenue and Forecast, by Application (2021-2033)

12.4. MEA

12.4.1. Market Revenue and Forecast, by Component (2021-2033)

12.4.2. Market Revenue and Forecast, by Technology (2021-2033)

12.4.3. Market Revenue and Forecast, by Sales Channel (2021-2033)

12.4.4. Market Revenue and Forecast, by Application (2021-2033)

12.4.5. GCC

12.4.5.1. Market Revenue and Forecast, by Component (2021-2033)

12.4.5.2. Market Revenue and Forecast, by Technology (2021-2033)

12.4.5.3. Market Revenue and Forecast, by Sales Channel (2021-2033)

12.4.5.4. Market Revenue and Forecast, by Application (2021-2033)

12.4.6. North Africa

12.4.6.1. Market Revenue and Forecast, by Component (2021-2033)

12.4.6.2. Market Revenue and Forecast, by Technology (2021-2033)

12.4.6.3. Market Revenue and Forecast, by Sales Channel (2021-2033)

12.4.6.4. Market Revenue and Forecast, by Application (2021-2033)

12.4.7. South Africa

12.4.7.1. Market Revenue and Forecast, by Component (2021-2033)

12.4.7.2. Market Revenue and Forecast, by Technology (2021-2033)

12.4.7.3. Market Revenue and Forecast, by Sales Channel (2021-2033)

12.4.7.4. Market Revenue and Forecast, by Application (2021-2033)

12.4.8. Rest of MEA

12.4.8.1. Market Revenue and Forecast, by Component (2021-2033)

12.4.8.2. Market Revenue and Forecast, by Technology (2021-2033)

12.4.8.3. Market Revenue and Forecast, by Sales Channel (2021-2033)

12.4.8.4. Market Revenue and Forecast, by Application (2021-2033)

12.5. Latin America

12.5.1. Market Revenue and Forecast, by Component (2021-2033)

12.5.2. Market Revenue and Forecast, by Technology (2021-2033)

12.5.3. Market Revenue and Forecast, by Sales Channel (2021-2033)

12.5.4. Market Revenue and Forecast, by Application (2021-2033)

12.5.5. Brazil

12.5.5.1. Market Revenue and Forecast, by Component (2021-2033)

12.5.5.2. Market Revenue and Forecast, by Technology (2021-2033)

12.5.5.3. Market Revenue and Forecast, by Sales Channel (2021-2033)

12.5.5.4. Market Revenue and Forecast, by Application (2021-2033)

12.5.6. Rest of LATAM

12.5.6.1. Market Revenue and Forecast, by Component (2021-2033)

12.5.6.2. Market Revenue and Forecast, by Technology (2021-2033)

12.5.6.3. Market Revenue and Forecast, by Sales Channel (2021-2033)

12.5.6.4. Market Revenue and Forecast, by Application (2021-2033)

Chapter 13. Company Profiles

13.1. IBM Corporation

13.1.1. Company Overview

13.1.2. Product Offerings

13.1.3. Financial Performance

13.1.4. Recent Initiatives

13.2. Microsoft 

13.2.1. Company Overview

13.2.2. Product Offerings

13.2.3. Financial Performance

13.2.4. Recent Initiatives

13.3. SAP SE

13.3.1. Company Overview

13.3.2. Product Offerings

13.3.3. Financial Performance

13.3.4. Recent Initiatives

13.4. Amazon Web Services

13.4.1. Company Overview

13.4.2. Product Offerings

13.4.3. Financial Performance

13.4.4. Recent Initiatives

13.5. Oracle

13.5.1. Company Overview

13.5.2. Product Offerings

13.5.3. Financial Performance

13.5.4. Recent Initiatives

13.6. Salesforce Inc.

13.6.1. Company Overview

13.6.2. Product Offerings

13.6.3. Financial Performance

13.6.4. Recent Initiatives

13.7. Intel

13.7.1. Company Overview

13.7.2. Product Offerings

13.7.3. Financial Performance

13.7.4. Recent Initiatives

13.8. NVIDIA

13.8.1. Company Overview

13.8.2. Product Offerings

13.8.3. Financial Performance

13.8.4. Recent Initiatives

13.9. Google LLC

13.9.1. Company Overview

13.9.2. Product Offerings

13.9.3. Financial Performance

13.9.4. Recent Initiatives

13.10. Sentient Technology

13.10.1. Company Overview

13.10.2. Product Offerings

13.10.3. Financial Performance

13.10.4. Recent Initiatives

Chapter 14. Research Methodology

14.1. Primary Research

14.2. Secondary Research

14.3. Assumptions

Chapter 15. Appendix

15.1. About Us

15.2. Glossary of Terms

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