This report provides a comprehensive analysis of the global artificial intelligence in retail market, tracking its rapid expansion driven by the rising demand for hyper-personalized shopping experiences. It examines key technological applications, including visual search, generative AI, and conversational tools, while highlighting the marketing and sales function as a dominant segment. The study explores regional dynamics, noting significant growth potential in the Middle East and Africa, alongside a detailed competitive assessment of major industry players and emerging market trends.

Market Overview

The global Artificial Intelligence (AI) in retail market is estimated to be valued at USD 31.12 billion in 2024 and is projected to reach USD 164.74 billion by 2030, growing at a CAGR of 32.0% from 2024 to 2030.

Key Market Drivers

• Personalized Shopping Experiences: A primary driver is the rising consumer demand for hyper-personalization. AI technologies, including machine learning and natural language processing, enable retailers to analyze massive volumes of consumer data to understand preferences and behavior patterns.
• Data-Driven Insights: These insights allow retailers to deliver personalized recommendations, targeted promotions, and tailor-made marketing strategies, which are essential for enhancing customer engagement and satisfaction.

Market Segmentation and Insights

By Business Function: Marketing and Sales

During the forecast period, the marketing and sales business function is expected to contribute the largest market share to the AI in retail market. AI is transforming this function through:

• Enhanced Engagement: AI Chatbots and virtual assistants improve customer experience by providing prompt support and assisting through the buying process.
• Hyper-Personalized Campaigns: Companies like Amazon and eBay utilize AI to analyze customer data to deliver personalized ads, product suggestions, and promotions.
• Dynamic Pricing: AI enables real-time price adjustments based on demand, competition, and consumer behavior.
• Loyalty Management: AI assists in managing loyalty programs by targeting relevant customers with specific messages.
• Content Automation: Generative AI is employed to automate the creation of marketing content, such as emails and advertisements.
• Key Players in Marketing & Sales: Alibaba, H&M, and Nike.

By Technology: Visual Search

Visual search is projected to register the highest CAGR during the forecast period. This technology changes the shopping experience by allowing customers to upload images to find similar products.

• Industry Applications: High usage observed in the fashion and home decor industries.
• Functionality: AI-driven visual search tracks search history to provide customized solutions and interconnects online and offline shopping experiences.
• Consumer Convenience: Customers can snap images of items to find details online, facilitating easier purchasing.
• Retailer Benefits: Retailers can utilize visual search for stock management, monitoring current inventory and identifying when products need renewal.
• Key Player in Visual Search: ASOS.

Regional Analysis

Middle East & Africa

The Middle East & Africa region is anticipated to register the highest growth rate during the forecast period.

• Growth Drivers: Government promotion of AI adoption and heavy business investments in the UAE and KSA.
• E-commerce Influence: The growth of the e-commerce sector is compelling retailers to adopt AI to understand online consumer behavior and optimize digital marketing.
• In-store Optimization: Retailers are leveraging data analytics to improve visual merchandising and in-store layouts.
• Strategic Developments: Presight's strategic alliance with Intel aims to foster advanced AI solutions in the region, focusing on customer insights and enhanced in-store experiences.
• Notable Growth Markets: Developing nations such as South Africa and the UAE are expected to see significant growth driven by e-commerce advancements.

Competitive Landscape

Major Market Players

The following companies are key players in the market, employing strategies such as partnerships, collaborations, agreements, new product launches, enhancements, and acquisitions to expand their footprint:

• United States: Microsoft, IBM, Google, Amazon, Oracle, Salesforce, NVIDIA, ServiceNow, Intel, AMD, Talkdesk, Symphony AI, Bloomreach, C3.AI, Pathr.ai, Vue.AI, Cresta, Mason, Feedzai.
• Germany: SAP
• Ireland: Accenture
• India: Infosys, TCS
• China: Alibaba
• Japan: Fujitsu
• France: Capgemini
• Singapore: Visenze, Trax
• Spain: Nextail
• Canada: Daisy Intelligence
• Israel: Syte, Shopic

Breakdown of Primaries (Industry Expert Insights)

• By Company Type: Tier 1 (62%), Tier 2 (23%), Tier 3 (15%)
• By Designation: C-level (50%), D-level (30%), Managers (20%)
• By Region: North America (38%), Asia Pacific (35%), Europe (15%), Middle East & Africa (7%), Latin America (5%)

Research Coverage and Report Benefits

Scope of Study

The report estimates market size and growth potential across several dimensions:

• Segments: Offering, infrastructure platform, application performance platform, security platform, digital experience platform, workforce operations platform, vertical, and region.
• Analysis Depth: Includes in-depth competitive analysis, company profiles, product/business offering observations, recent developments, and market strategies.

Key Report Insights

• Market Drivers: Increasing adoption of conversational AI for advice/recommendations, evolving consumer expectations, social commerce integration, enhanced checkout automation, and data-driven decision making.
• Restraints: High implementation costs, data privacy, and security concerns.
• Opportunities: AI-powered customer engagement, enhanced decision-making via predictive analytics, and supply chain optimization.
• Challenges: Addressing rising theft and fraud, integration with legacy systems, and ethical concerns in AI.
• Product Development/Innovation: Insights into upcoming technologies, R&D activities, and new product/service launches.
• Market Development: Analysis of lucrative regional markets.
• Market Diversification: Information on new products, services, untapped geographies, and recent investments.
• Competitive Assessment: Detailed assessment of market shares, growth strategies, and service offerings of leading global players.

• 1 INTRODUCTION 35

  • 1.1 STUDY OBJECTIVES 35

  • 1.2 MARKET DEFINITION 35

  • 1.3 STUDY SCOPE 36

    • 1.3.1 MARKET SEGMENTATION 36

    • 1.3.2 INCLUSIONS AND EXCLUSIONS 37

  • 1.4 YEARS CONSIDERED 37

  • 1.5 CURRENCY CONSIDERED 38

  • 1.6 STAKEHOLDERS 38

• 2 RESEARCH METHODOLOGY 39

  • 2.1 RESEARCH DATA 39

    • 2.1.1 SECONDARY DATA 40

      • 2.1.1.1 Key data from secondary sources 40

    • 2.1.2 PRIMARY DATA 40

      • 2.1.2.1 Breakup of primary interviews 41
      • 2.1.2.2 Primary interviews with experts 41
      • 2.1.2.3 Key insights from industry experts 41

  • 2.2 MARKET SIZE ESTIMATION METHODOLOGY 42

    • 2.2.1 TOP-DOWN APPROACH 42

      • 2.2.1.1 Supply-side analysis 42

    • 2.2.2 BOTTOM-UP APPROACH 43

      • 2.2.2.1 Demand-side analysis 43

  • 2.3 DATA TRIANGULATION 45

  • 2.4 RESEARCH ASSUMPTIONS 46

  • 2.5 RESEARCH LIMITATIONS 47

  • 2.6 RISK ASSESSMENT 47

• 3 EXECUTIVE SUMMARY 48

• 4 PREMIUM INSIGHTS 50

  • 4.1 ATTRACTIVE OPPORTUNITIES FOR KEY PLAYERS IN ARTIFICIAL INTELLIGENCE IN RETAIL MARKET 50

  • 4.2 ARTIFICIAL INTELLIGENCE IN RETAIL MARKET, BY OFFERING 50

  • 4.3 ARTIFICIAL INTELLIGENCE IN RETAIL MARKET, BY SERVICE 51

  • 4.4 ARTIFICIAL INTELLIGENCE IN RETAIL MARKET, BY BUSINESS FUNCTION 51

  • 4.5 ARTIFICIAL INTELLIGENCE IN RETAIL MARKET, BY TYPE 51

  • 4.6 ARTIFICIAL INTELLIGENCE IN RETAIL MARKET, BY SOLUTION 52

  • 4.7 ARTIFICIAL INTELLIGENCE IN RETAIL MARKET, BY END USER 52

  • 4.8 NORTH AMERICA: ARTIFICIAL INTELLIGENCE IN RETAIL MARKET, TOP THREE SOLUTIONS AND SERVICES 53

• 5 MARKET OVERVIEW AND INDUSTRY TRENDS 54

  • 5.1 INTRODUCTION 54

  • 5.2 MARKET DYNAMICS 54

    • 5.2.1 DRIVERS 55

      • 5.2.1.1 Increasing adoption of conversational AI in retail for advice and recommendations 55
      • 5.2.1.2 Evolving consumer expectations and social media integration 55
      • 5.2.1.3 Enhancing checkout experiences with AI-powered automation 56
      • 5.2.1.4 Data-driven decision-making 56

    • 5.2.2 RESTRAINTS 56

      • 5.2.2.1 High implementation costs 56
      • 5.2.2.2 Data privacy and security 57

    • 5.2.3 OPPORTUNITIES 57

      • 5.2.3.1 AI-powered customer engagement 57
      • 5.2.3.2 Enhanced decision-making with predictive analytics 57
      • 5.2.3.3 AI in supply chain optimization 57

    • 5.2.4 CHALLENGES 58

      • 5.2.4.1 Rising theft and fraud issues 58
      • 5.2.4.2 Complexity in integrating with legacy systems 58
      • 5.2.4.3 Ethical concerns in AI 58

  • 5.3 TRENDS/DISRUPTIONS IMPACTING CUSTOMER BUSINESS 59

  • 5.4 PRICING ANALYSIS 59

    • 5.4.1 AVERAGE SELLING PRICE TREND OF KEY PLAYERS, BY SOLUTION 59

    • 5.4.2 INDICATIVE PRICING ANALYSIS OF ARTIFICIAL INTELLIGENCE IN RETAIL KEY PLAYERS 61

  • 5.5 SUPPLY CHAIN ANALYSIS 62

  • 5.6 ECOSYSTEM 63

  • 5.7 TECHNOLOGY ANALYSIS 65

    • 5.7.1 KEY TECHNOLOGIES 65

      • 5.7.1.1 Conversational AI 65
      • 5.7.1.2 Autonomous AI & autonomous agent 65
      • 5.7.1.3 AutoML 66

    • 5.7.2 COMPLEMENTARY TECHNOLOGIES 66

      • 5.7.2.1 Edge computing 66
      • 5.7.2.2 Big data analytics 66
      • 5.7.2.3 Cloud computing 66

    • 5.7.3 ADJACENT TECHNOLOGIES 66

      • 5.7.3.1 Blockchain 66
      • 5.7.3.2 Cybersecurity solutions 67

  • 5.8 PATENT ANALYSIS 67

    • 5.8.1 LIST OF MAJOR PATENTS 68

  • 5.9 TRADE ANALYSIS 69

    • 5.9.1 EXPORT SCENARIO OF PROCESSORS AND CONTROLLERS 69

    • 5.9.2 IMPORT SCENARIO OF PROCESSORS AND CONTROLLERS 71

  • 5.10 KEY CONFERENCES AND EVENTS, 2024-2026 72

  • 5.11 TARIFF AND REGULATORY LANDSCAPE 73

    • 5.11.1 TARIFF DATA (HSN: 854231) - PROCESSORS AND CONTROLLERS 73

    • 5.11.2 REGULATORY BODIES, GOVERNMENT AGENCIES, AND OTHER ORGANIZATIONS 75

    • 5.11.3 KEY REGULATIONS 79

      • 5.11.3.1 North America 79
        • 5.11.3.1.1 SCR 17: Artificial Intelligence Bill (California) 79
        • 5.11.3.1.2 S1103: Artificial Intelligence Automated Decision Bill (Connecticut) 79
        • 5.11.3.1.3 National Artificial Intelligence Initiative Act (NAIIA) 79
        • 5.11.3.1.4 The Artificial Intelligence and Data Act (AIDA) - Canada 79
      • 5.11.3.2 Europe 80
        • 5.11.3.2.1 The European Union (EU) - Artificial Intelligence Act (AIA) 80
        • 5.11.3.2.2 General Data Protection Regulation (Europe) 80
      • 5.11.3.3 Asia Pacific 81
        • 5.11.3.3.1 Interim Administrative Measures for Generative Artificial Intelligence Services (China) 81
        • 5.11.3.3.2 The National AI Strategy (Singapore) 82
        • 5.11.3.3.3 The Hiroshima AI Process Comprehensive Policy Framework (Japan) 82
      • 5.11.3.4 Middle East & Africa 83
        • 5.11.3.4.1 The National Strategy for Artificial Intelligence (UAE) 83
        • 5.11.3.4.2 The National Artificial Intelligence Strategy (Qatar) 83
        • 5.11.3.4.3 The AI Ethics Principles and Guidelines (Dubai) 83
      • 5.11.3.5 Latin America 84
        • 5.11.3.5.1 The Santiago Declaration (Chile) 84
        • 5.11.3.5.2 The Brazilian Artificial Intelligence Strategy (EBIA) 84

  • 5.12 PORTER’S FIVE FORCES’ ANALYSIS 85

    • 5.12.1 THREAT OF NEW ENTRANTS 86

    • 5.12.2 THREAT OF SUBSTITUTES 86

    • 5.12.3 BARGAINING POWER OF BUYERS 86

    • 5.12.4 BARGAINING POWER OF SUPPLIERS 86

    • 5.12.5 INTENSITY OF COMPETITIVE RIVALRY 86

  • 5.13 KEY STAKEHOLDERS AND BUYING CRITERIA 87

    • 5.13.1 KEY STAKEHOLDERS IN BUYING PROCESS 87

    • 5.13.2 BUYING CRITERIA 88

  • 5.14 EVOLUTION OF ARTIFICIAL INTELLIGENCE IN RETAIL 89

  • 5.15 CASE STUDY ANALYSIS 90

    • 5.15.1 TARGET LEVERAGED GOOGLE CLOUD TO ENHANCE CUSTOMER EXPERIENCES AND ACHIEVE SIGNIFICANT REVENUE GROWTH 90

    • 5.15.2 PRADA GROUP IMPROVED CUSTOMER EXPERIENCE USING ORACLE'S CLOUD SOLUTIONS FOR PERSONALIZED RETAIL STRATEGIES 91

    • 5.15.3 PEPE JEANS INDIA AUGMENTED ONLINE SHOPPING WITH SALESFORCE BY FOCUSING ON DIRECT CONSUMER ENGAGEMENT AND PERSONALIZATION 91

    • 5.15.4 WALMART ENHANCED DIGITAL SHOPPING WITH MICROSOFT’S GENERATIVE AI FOR PERSONALIZED SEARCH AND IMPROVED CX 92

    • 5.15.5 AI-POWERED CHECKOUT-FREE SHOPPING SOLUTION TRANSFORMED RETAIL OPERATIONS OF ITREX GROUP 92

    • 5.15.6 SNITCH LEVERAGED VISENZE TO OFFER PERSONALIZED SHOPPING EXPERIENCE 93

    • 5.15.7 MYNTRA LEVERAGED VISENZE’S DISCOVERY SUITE TO POWER ITS ‘VIEW SIMILAR’ CAROUSEL 93

    • 5.15.8 MACY’S AI-DRIVEN SHOPPING ASSISTANT HIGHLIGHTED INCREASING IMPORTANCE OF MOBILE IN IN-STORE SHOPPING EXPERIENCE 94

    • 5.15.9 MARKS & SPENCER ACHIEVED 185% ROAS INCREASE WITH MICROSOFT PERFORMANCE MAX 95

    • 5.15.10 WEIS MARKETS PARTNERED WITH SYMPHONYAI CINDE FOR ADVANCED AI-POWERED CUSTOMER ANALYTICS TO ENHANCE CATEGORY MANAGEMENT 95

• 6 ARTIFICIAL INTELLIGENCE IN RETAIL MARKET, BY OFFERING 96

  • 6.1 INTRODUCTION 97

    • 6.1.1 OFFERINGS: ARTIFICIAL INTELLIGENCE IN RETAIL MARKET DRIVERS 97

  • 6.2 SOLUTIONS 98

    • 6.2.1 PERSONALIZED PRODUCT RECOMMENDATIONS 100

      • 6.2.1.1 AI to help tailor product suggestions based on customer behavior and drive engagement and sales in retail 100

    • 6.2.2 CUSTOMER RELATIONSHIP MANAGEMENT 101

      • 6.2.2.1 AI-driven CRM to automate personalized marketing and customer segmentation and churn prevention strategies 101

    • 6.2.3 VISUAL SEARCH 102

      • 6.2.3.1 Visual search to enable customers find products using images and enhance discovery and shopping experiences 102

    • 6.2.4 VIRTUAL CUSTOMER ASSISTANT 103

      • 6.2.4.1 AI-powered virtual assistants to offer real-time customer support, improving response times and personalization 103

    • 6.2.5 PRICE OPTIMIZATION 104

      • 6.2.5.1 AI-powered price optimization to help retailers adjust prices dynamically based on competition, demand, and market conditions 104

    • 6.2.6 SUPPLY CHAIN MANAGEMENT & DEMAND PLANNING 105

      • 6.2.6.1 AI to optimize retail supply chains by predicting demand and streamlining inventory management 105

    • 6.2.7 VIRTUAL STORES 106

      • 6.2.7.1 AI to offer immersive shopping experiences with AR and VR technologies 106

    • 6.2.8 SMART CHECKOUT 107

      • 6.2.8.1 AI to eliminate wait times and enable frictionless shopping experiences 107

    • 6.2.9 OTHER SOLUTIONS 108

  • 6.3 SERVICES 109

    • 6.3.1 PROFESSIONAL SERVICES 110

      • 6.3.1.1 Professional services in AI for retail to help businesses effectively integrate advanced AI technologies into their operations 110
      • 6.3.1.2 Training & consulting 112
        • 6.3.1.2.1 Optimizing IT operations for improved business performance to propel market 112
      • 6.3.1.3 System integration & deployment 112
        • 6.3.1.3.1 System integration & deployment services to help retailers seamlessly incorporate AI solutions into their existing infrastructure 112
      • 6.3.1.4 Support & maintenance 113
        • 6.3.1.4.1 Support & maintenance services in AI for retail to ensure that AI systems function optimally post-deployment 113

    • 6.3.2 MANAGED SERVICES 113

      • 6.3.2.1 Managed services in AI to provide continuous monitoring and management of AI systems for scalability and efficiency 113

• 7 ARTIFICIAL INTELLIGENCE IN RETAIL MARKET, BY TYPE 115

  • 7.1 INTRODUCTION 116

    • 7.1.1 TYPES: ARTIFICIAL INTELLIGENCE IN RETAIL MARKET DRIVERS 116

  • 7.2 GENERATIVE AI 117

  • 7.3 OTHER AI 118

    • 7.3.1 DISCRIMINATIVE MACHINE LEARNING 119

      • 7.3.1.1 ML to optimize retail with personalized recommendations, dynamic pricing, and efficient demand forecasting 119

    • 7.3.2 NATURAL LANGUAGE PROCESSING 119

      • 7.3.2.1 NLP to enhance customer service with AI chatbots and sentiment analysis for personalized, real-time engagement 119

    • 7.3.3 COMPUTER VISION 119

      • 7.3.3.1 Computer vision to revolutionize retail with smart checkouts, visual search, and in-store analytics to boost efficiency 119

    • 7.3.4 PREDICTIVE ANALYTICS 120

      • 7.3.4.1 Predictive analytics to improve demand forecasting, price optimization, and customer targeting in retail operations 120

• 8 ARTIFICIAL INTELLIGENCE IN RETAIL MARKET, BY BUSINESS FUNCTION 121

  • 8.1 INTRODUCTION 122

    • 8.1.1 BUSINESS FUNCTIONS: ARTIFICIAL INTELLIGENCE IN RETAIL MARKET DRIVERS 122

  • 8.2 MARKETING & SALES 123

    • 8.2.1 AI TO IMPROVE PERSONALIZED CAMPAIGNS, PRODUCT RECOMMENDATIONS, AND DYNAMIC PRICING IN RETAIL 123

  • 8.3 HUMAN RESOURCES 124

    • 8.3.1 AI TO AUTOMATE RECRUITMENT, WORKFORCE OPTIMIZATION, AND PERSONALIZED TRAINING IN RETAIL HR 124

  • 8.4 FINANCE & ACCOUNTING 125

    • 8.4.1 AI TO SYSTEMATIZE FINANCIAL PROCESSES, SUCH AS BILLING, FORECASTING, AND FRAUD DETECTION IN RETAIL 125

  • 8.5 OPERATIONS 126

    • 8.5.1 AI TO ENHANCE SUPPLY CHAIN OPTIMIZATION, INVENTORY MANAGEMENT, AND LOGISTICS IN RETAIL OPERATIONS 126

  • 8.6 CYBERSECURITY 127

    • 8.6.1 AI TO STRENGTHEN FRAUD DETECTION, DATA SECURITY, AND BIOMETRIC AUTHENTICATION IN RETAIL CYBERSECURITY 127

• 9 ARTIFICIAL INTELLIGENCE IN RETAIL MARKET, BY END USER 129

  • 9.1 INTRODUCTION 130

    • 9.1.1 END USERS: ARTIFICIAL INTELLIGENCE IN RETAIL MARKET DRIVERS 130

  • 9.2 ONLINE 131

    • 9.2.1 AI TO REVOLUTIONIZE ONLINE RETAIL BY IMPROVING SHOPPING EXPERIENCE THROUGH PERSONALIZATION, INVENTORY MANAGEMENT, AND REAL-TIME CUSTOMER SUPPORT 131

  • 9.3 OFFLINE 132

    • 9.3.1 ESSENTIAL SECURITY TOOLS TO MONITOR NETWORK TRAFFIC FOR THREATS 132

    • 9.3.2 SUPERMARKETS & HYPERMARKETS 134

      • 9.3.2.1 AI to improve inventory management, customer experience, and operational efficiency with smart checkout and predictive analytics 134

    • 9.3.3 SPECIALTY STORES 135

      • 9.3.3.1 AI to personalize shopping experiences and optimize inventory management in specialty stores 135

    • 9.3.4 CONVENIENCE STORES 136

      • 9.3.4.1 Smart checkout, dynamic pricing, and improved inventory management to ensure operational efficiency and quick service in convenience stores 136

    • 9.3.5 OTHER OFFLINE STORES 137

• 10 ARTIFICIAL INTELLIGENCE IN RETAIL MARKET, BY REGION 138

  • 10.1 INTRODUCTION 139

  • 10.2 NORTH AMERICA 140

    • 10.2.1 NORTH AMERICA: MACROECONOMIC OUTLOOK 140

    • 10.2.2 US 146

      • 10.2.2.1 Technological advancements and strategic partnerships to propel market 146

    • 10.2.3 CANADA 151

      • 10.2.3.1 Need for predicting product demand, optimizing inventory, and enhancing personalized customer experiences to drive market 151

  • 10.3 EUROPE 151

    • 10.3.1 EUROPE: MACROECONOMIC OUTLOOK 151

    • 10.3.2 UK 157

      • 10.3.2.1 Need to enhance customer experiences, streamline operations, and optimize inventory management to accelerate market growth 157

    • 10.3.3 ITALY 162

      • 10.3.3.1 Increasing demand for enhanced customer experiences, operational efficiency, and data-driven decision-making to fuel market growth 162

    • 10.3.4 GERMANY 167

      • 10.3.4.1 Need to enhance operational efficiency, customer engagement, and government initiatives to enhance market growth 167

    • 10.3.5 FRANCE 167

      • 10.3.5.1 Integration of AI to enhance customer experiences through personalized recommendations, dynamic pricing, and improved inventory management 167

    • 10.3.6 SPAIN 168

      • 10.3.6.1 Strong emphasis on predictive analytics and focus on mitigating risks and enhancing decision-making investments in retail sector to boost market growth 168

    • 10.3.7 NORDIC COUNTRIES 168

      • 10.3.7.1 Increasing consumer expectations for personalized experiences and operational efficiency to foster market growth 168

    • 10.3.8 REST OF EUROPE 168

  • 10.4 ASIA PACIFIC 169

    • 10.4.1 ASIA PACIFIC: MACROECONOMIC OUTLOOK 169

    • 10.4.2 CHINA 175

      • 10.4.2.1 Strong government support for AI technology, rapid digitalization, and growing consumer demand for personalized and efficient retail experiences to fuel market growth 175

    • 10.4.3 JAPAN 180

      • 10.4.3.1 Labor shortages arising due to aging population, push for operational efficiency in retail sector, and government investments and initiatives to bolster market 180

    • 10.4.4 INDIA 180

      • 10.4.4.1 Rapid eCommerce growth, increasing smartphone penetration, and demand for personalized customer experiences to augment market growth 180

    • 10.4.5 AUSTRALIA & NEW ZEALAND 185

      • 10.4.5.1 Increasing eCommerce activity and need for enhanced customer experience to propel market 185

    • 10.4.6 SOUTH KOREA 186

      • 10.4.6.1 Advanced technological infrastructure, high internet penetration, and implementation of AI National Strategy to accelerate market 186

    • 10.4.7 ASEAN COUNTRIES 186

    • 10.4.8 REST OF ASIA PACIFIC 186

  • 10.5 MIDDLE EAST & AFRICA 187

    • 10.5.1 MIDDLE EAST & AFRICA: MACROECONOMIC OUTLOOK 188

      • 10.5.1.1 UAE 194
        • 10.5.1.1.1 Investments and collaborations aimed at enhancing retail experiences through AI technologies to drive market 194
      • 10.5.1.2 KSA 194
        • 10.5.1.2.1 Substantial investments in AI and establishment of Vision 2030 to foster market growth 194
      • 10.5.1.3 Kuwait 199
        • 10.5.1.3.1 Rapid development of Kuwait Vision 2035 to fuel demand for AI in retail market 199
      • 10.5.1.4 Bahrain 200
        • 10.5.1.4.1 Strategic location, supportive government policies, and growing eCommerce industry to drive market 200
      • 10.5.1.5 South Africa 200
        • 10.5.1.5.1 Rise of AI and related technologies during COVID-19 to fuel market growth 200
      • 10.5.1.6 Rest of Middle East & Africa 200

  • 10.6 LATIN AMERICA 201

    • 10.6.1 LATIN AMERICA: MACROECONOMIC OUTLOOK 201

    • 10.6.2 BRAZIL 206

      • 10.6.2.1 Influx of foreign eCommerce platforms to boost demand for AI in retail market 206

    • 10.6.3 MEXICO 211

      • 10.6.3.1 Embracing emerging technologies with notable funding from both domestic and international investors to bolster market growth 211

    • 10.6.4 ARGENTINA 211

      • 10.6.4.1 Focus on advancing digital infrastructure to drive market 211

    • 10.6.5 REST OF LATIN AMERICA 211

• 11 COMPETITIVE LANDSCAPE 212

  • 11.1 INTRODUCTION 212

  • 11.2 KEY PLAYER STRATEGIES/RIGHT TO WIN 212

    • 11.2.1 OVERVIEW OF STRATEGIES ADOPTED BY KEY ARTIFICIAL INTELLIGENCE IN RETAIL MARKET VENDORS 212

  • 11.3 REVENUE ANALYSIS 213

  • 11.4 MARKET SHARE ANALYSIS 214

    • 11.4.1 MARKET RANKING ANALYSIS 215

  • 11.5 COMPANY EVALUATION MATRIX: KEY PLAYERS, 2023 215

    • 11.5.1 STARS 215

    • 11.5.2 EMERGING LEADERS 215

    • 11.5.3 PERVASIVE PLAYERS 215

    • 11.5.4 PARTICIPANTS 215

    • 11.5.5 COMPANY FOOTPRINT: KEY PLAYERS, 2023 217

      • 11.5.5.1 Company footprint 217
      • 11.5.5.2 Type footprint 217
      • 11.5.5.3 Offering footprint 218
      • 11.5.5.4 Regional footprint 219

  • 11.6 COMPANY EVALUATION MATRIX: START-UPS/SMES, 2023 220

    • 11.6.1 PROGRESSIVE COMPANIES 220

    • 11.6.2 RESPONSIVE COMPANIES 220

    • 11.6.3 DYNAMIC COMPANIES 220

    • 11.6.4 STARTING BLOCKS 220

    • 11.6.5 COMPETITIVE BENCHMARKING: START-UPS/SMES, 2023 222

      • 11.6.5.1 Key start-ups/SMEs 222
      • 11.6.5.2 Competitive benchmarking of key start-ups/SMEs 223

  • 11.7 COMPETITIVE SCENARIOS AND TRENDS 224

    • 11.7.1 PRODUCT LAUNCHES & ENHANCEMENTS 224

    • 11.7.2 DEALS 226

  • 11.8 BRAND/PRODUCT COMPARISON 229

  • 11.9 COMPANY VALUATION AND FINANCIAL METRICS 230

• 12 COMPANY PROFILES 231

  • 12.1 KEY PLAYERS 231

    • 12.1.1 IBM 231

      • 12.1.1.1 Business overview 231
      • 12.1.1.2 Products/Solutions/Services offered 232
      • 12.1.1.3 Recent developments 234
        • 12.1.1.3.1 Product enhancements 234
        • 12.1.1.3.2 Deals 234
      • 12.1.1.4 MnM view 235
        • 12.1.1.4.1 Right to win 235
        • 12.1.1.4.2 Strategic choices 235
        • 12.1.1.4.3 Weaknesses and competitive threats 235

    • 12.1.2 AMAZON 236

      • 12.1.2.1 Business overview 236
      • 12.1.2.2 Products/Solutions/Services offered 237
        • 12.1.2.2.1 Deals 238
        • 12.1.2.2.2 Other deals/developments 239
      • 12.1.2.3 MnM view 239
        • 12.1.2.3.1 Right to win 239
        • 12.1.2.3.2 Strategic choices 239
        • 12.1.2.3.3 Weaknesses and competitive threats 239

    • 12.1.3 SALESFORCE, INC 240

      • 12.1.3.1 Business overview 240
      • 12.1.3.2 Products/Solutions/Services offered 241
      • 12.1.3.3 Recent developments 243
        • 12.1.3.3.1 Product launches and enhancements 243
        • 12.1.3.3.2 Deals 244

    • 12.1.4 ORACLE 245

      • 12.1.4.1 Business overview 245
      • 12.1.4.2 Products/Solutions/Services offered 246
      • 12.1.4.3 Recent developments 247
        • 12.1.4.3.1 Deals 247

    • 12.1.5 MICROSOFT 248

      • 12.1.5.1 Business overview 248
      • 12.1.5.2 Products/Solutions/Services offered 249
      • 12.1.5.3 Recent developments 250
        • 12.1.5.3.1 Deals 250
      • 12.1.5.4 MnM view 250
        • 12.1.5.4.1 Right to win 250
        • 12.1.5.4.2 Strategic choices 251
        • 12.1.5.4.3 Weaknesses and competitive threats 251

    • 12.1.6 GOOGLE 252

      • 12.1.6.1 Business overview 252
      • 12.1.6.2 Products/Solutions/Services offered 253
      • 12.1.6.3 Recent developments 254
        • 12.1.6.3.1 Product enhancements 254
        • 12.1.6.3.2 Deals 255
      • 12.1.6.4 MnM view 256
        • 12.1.6.4.1 Right to win 256
        • 12.1.6.4.2 Strategic choices 256
        • 12.1.6.4.3 Weaknesses and competitive threats 256

    • 12.1.7 NVIDIA 257

      • 12.1.7.1 Business overview 257
      • 12.1.7.2 Products/Solutions/Services offered 258
      • 12.1.7.3 Recent developments 259
        • 12.1.7.3.1 Product enhancements 259
        • 12.1.7.3.2 Deals 259
      • 12.1.7.4 MnM view 260
        • 12.1.7.4.1 Right to win 260
        • 12.1.7.4.2 Strategic choices 260
        • 12.1.7.4.3 Weaknesses and competitive threats 260

    • 12.1.8 ACCENTURE 261

      • 12.1.8.1 Business overview 261
      • 12.1.8.2 Products/Solutions/Services offered 262
      • 12.1.8.3 Recent developments 263
        • 12.1.8.3.1 Deals 263

    • 12.1.9 SAP SE 264

      • 12.1.9.1 Business overview 264
      • 12.1.9.2 Products/Solutions/Services offered 265
      • 12.1.9.3 Recent developments 266
        • 12.1.9.3.1 Deals 266

    • 12.1.10 SERVICENOW 267

      • 12.1.10.1 Business overview 267
      • 12.1.10.2 Products/Solutions/Services offered 268
      • 12.1.10.3 Recent developments 269
        • 12.1.10.3.1 Product enhancements 269
        • 12.1.10.3.2 Deals 269

    • 12.1.11 INFOSYS 270

      • 12.1.11.1 Business overview 270
      • 12.1.11.2 Products/Solutions/Services offered 271
      • 12.1.11.3 Recent developments 272
        • 12.1.11.3.1 Deals 272

    • 12.1.12 INTEL CORPORATION 273

      • 12.1.12.1 Business overview 273
      • 12.1.12.2 Products/Solutions/Services offered 274
      • 12.1.12.3 Recent developments 275
        • 12.1.12.3.1 Product launches 275
        • 12.1.12.3.2 Deals 275

    • 12.1.13 AMD 276

      • 12.1.13.1 Business overview 276
      • 12.1.13.2 Products/Solutions/Services offered 277
      • 12.1.13.3 Recent developments 278
        • 12.1.13.3.1 Product enhancements 278
        • 12.1.13.3.2 Deals 278

    • 12.1.14 HUAWEI 279

      • 12.1.14.1 Business overview 279
      • 12.1.14.2 Products/Solutions/Services offered 279
      • 12.1.14.3 Recent developments 280
        • 12.1.14.3.1 Product launches 280

    • 12.1.15 ALIBABA 282

    • 12.1.16 FUJITSU 283

    • 12.1.17 CAPGEMINI 284

    • 12.1.18 TCS 285

    • 12.1.19 TALKDESK 286

    • 12.1.20 SYMPHONY AI 287

    • 12.1.21 BLOOMREACH 288

    • 12.1.22 C3.AI 289

  • 12.2 START-UPS/SMES 290

    • 12.2.1 VISENZE 290

    • 12.2.2 PATHR.AI 291

    • 12.2.3 VUE.AI 292

    • 12.2.4 NEXTAIL 293

    • 12.2.5 DAISY INTELLIGENCE 294

    • 12.2.6 CRESTA 295

    • 12.2.7 MASON 296

    • 12.2.8 SYTE 297

    • 12.2.9 TRAX RETAIL 298

    • 12.2.10 FEEDZAI 299

    • 12.2.11 SHOPIC 300

    • 12.2.12 ITREX 300

    • 12.2.13 H2O.AI 301

    • 12.2.14 RETAILMETRIX 302

• 13 ADJACENT/RELATED MARKETS 303

  • 13.1 INTRODUCTION 303

  • 13.2 ARTIFICIAL INTELLIGENCE MARKET - GLOBAL FORECAST TO 2030 303

    • 13.2.1 MARKET DEFINITION 303

    • 13.2.2 MARKET OVERVIEW 303

      • 13.2.2.1 Artificial intelligence market, by offering 303
      • 13.2.2.2 Artificial intelligence market, by technology 304
      • 13.2.2.3 Artificial intelligence market, by business function 305
      • 13.2.2.4 Artificial intelligence market, by vertical 306
      • 13.2.2.5 Artificial intelligence market, by region 308

  • 13.3 RETAIL ANALYTICS MARKET - GLOBAL FORECAST TO 2029 309

    • 13.3.1 MARKET DEFINITION 309

    • 13.3.2 MARKET OVERVIEW 309

      • 13.3.2.1 Retail analytics market, by offering 309
      • 13.3.2.2 Retail analytics market, by business function 310
      • 13.3.2.3 Retail analytics market, by application 310
      • 13.3.2.4 Retail analytics market, by end user 311
      • 13.3.2.5 Retail analytics market, by region 312

• 14 APPENDIX 313

  • 14.1 DISCUSSION GUIDE 313

  • 14.2 KNOWLEDGESTORE: MARKETSANDMARKETS’ SUBSCRIPTION PORTAL 316

  • 14.3 CUSTOMIZATION OPTIONS 318

  • 14.4 RELATED REPORTS 318

  • 14.5 AUTHOR DETAILS 319