Summary

The Artificial intelligence in retail market is estimated to be USD 31.12 billion in 2024 to USD 164.74 billion in 2030 at a CAGR of 32.0% from 2024 to 2030. One of the primary drivers for AI adoption in retail is the growing consumer demand for personalized shopping experiences. AI technologies such as machine learning and natural language processing enable retailers to analyze large volumes of consumer data to understand preferences and behavior patterns. This data-driven insight allows retailers to offer personalized recommendations, targeted promotions, and tailor-made marketing strategies. AI solutions are becoming essential for businesses seeking to enhance customer engagement and satisfaction due to hyper-personalization in the retail market.
“During the forecast period, the marketing and sales business function contributed the largest market share in the artificial intelligence in the retail market.”
Al in retail is changing the marketing and sales business functions by offering superior tools and business insights that enhance customer engagement, personalize marketing efforts, and optimize sales processes. AI Chatbots and AI virtual assistants can help to improve customers’ experience by offering prompt support and helping them navigate throughout the buying process. Al revolutionizes marketing by enabling hyper-personalized campaigns and product recommendations; companies such as Amazon and eBay use Al to analyze customer data and preferences, helping them deliver personalized ads, product suggestions, and promotions. Another application of Al is dynamic pricing, where the prices can change as frequently as in real life depending on the demand, competition, and customers’ behavior. Al also assists in managing customer loyalty programs by targeting relevant customers with targeted messages. Generative Al is used to automate content creation for marketing, including emails and advertisements. Some key players at the forefront of using Al in marketing and sales include Alibaba, H&M, and Nike.
 
“The visual search solution is projected to register the highest CAGR during the forecast period.”
Visual search employs Al to make it easier for customers to search for products by uploading images and getting similar products, changing the shopping experience. This technology showcased higher usage in the fashion industry and in home decor. Al-driven visual search tracks customer’s search history and needs to provide customized solutions. Visual search technology makes shopping online and offline identical by interconnecting them. Consumers can snap images of the goods they are interested in and conduct a visual search to get their details online. This makes it easier for the customer to buy the needed products, thus increasing convenience. Retailers can also use visual search to manage their stock since they get to keep an eye on their current stock and know when certain products need to be renewed. E-commerce giants such as ASOS have implemented visual search technology to enhance the shopping experience among consumers.


"Middle East & Africa will register the highest growth rate during the forecast period.”
Middle Eastern retail market is estimated to grow at a higher growth due to several key factors, such as governments promoting AI adoption and businesses heavily investing in UAE and KSA. The e-commerce sector also compels retailers to explore AI solutions to understand online consumer behavior better and optimize their digital marketing strategies. Additionally, retailers leverage data analytics to improve in-store layouts and visual merchandising, enhancing the overall shopping experience. Presight's strategic alliance with Intel aims to foster advanced AI solutions across the Middle East, indicating a strong trend toward harnessing AI for improved customer insights and enhanced in-store shopping experiences. Developing nations such as South Africa and the UAE are anticipated to see notable growth, driven by e-commerce advancements encouraging retailers to adopt AI-driven strategies.

Breakdown of primaries
The study contains insights from various industry experts, from solution vendors to Tier 1 companies. The break-up of the primaries is as follows:

• By Company Type: Tier 1 – 62%, Tier 2 – 23%, and Tier 3 – 15%
• By Designation: C-level –50%, D-level – 30%, and Managers – 20%
• By Region: North America – 38%, Europe – 15%, Asia Pacific – 35%, Middle East & Africa- 7%, and Latin America- 5%.
The major players in the Artificial intelligence in retail market are Microsoft (US), IBM (US), Google (US), Amazon (US), Oracle (US), Salesforce (US), NVIDIA (US), SAP (Germany), Servicenow (US), Accenture (Ireland), Infosys (India), Alibaba (China), Intel (US), AMD (US), Fujitsu (Japan), Capgemini (France), TCS (India), Talkdesk (US), Symphony AI (US), Bloomreach (US), C3.AI (US), Visenze (Singapore), Pathr.ai (US), Vue.AI (US), Nextail (Spain), Daisy Intelligence (Canada), Cresta (US), Mason (US), Syte(Israel), Trax(Singapore), Feedzai(US) and Shopic(Israel). These players have adopted various growth strategies, such as partnerships, agreements and collaborations, new product launches, enhancements, and acquisitions to expand their artificial intelligence in retail footprint.

Research Coverage
The market study covers the artificial intelligence in retail market size across different segments. It aims to estimate the market size and the growth potential across various segments, including offering, infrastructure platform, application performance platform, security platform, digital experience platform, workforce operations platform, vertical, and region. The study includes an in-depth competitive analysis of the leading market players, their company profiles, key observations related to product and business offerings, recent developments, and market strategies.
Key Benefits of Buying the Report
The report will help market leaders and new entrants with information on the closest approximations of the global artificial intelligence in retail market's revenue numbers and subsegments. It will also help stakeholders understand the competitive landscape and gain more insights to position their businesses better and plan suitable go-to-market strategies. Moreover, the report will provide insights for stakeholders to understand the market's pulse and provide them with information on key market drivers, restraints, challenges, and opportunities.

The report provides insights on the following pointers:

Analysis of key drivers (increasing adoption of conversational AI in retail for advice and recommendations, evolving consumer expectations and social commerce integration, enhancing checkout experiences with AI-powered automation, data-driven decision making), restraints (high implementation costs, data privacy and security), opportunities (AI-powered customer engagement, enhanced decision-making with predictive analytics, AI in supply chain optimization) and challenges (addressing rising theft and fraud issues, integration with legacy systems, ethical concerns in AI) influencing the growth of the artificial intelligence in retail market.
Product Development/Innovation: Detailed insights on upcoming technologies, research and development activities, and new product and service launches in the artificial intelligence in retail market. Market Development: Comprehensive information about lucrative markets – the report analyses various regions' artificial intelligence in retail markets. Market Diversification: Exhaustive information about new products and services, untapped geographies, recent developments, and investments in the artificial intelligence in retail market. Competitive Assessment: In-depth assessment of market shares, growth strategies, and service offerings of leading players such as Microsoft (US), IBM (US), Google (US), Amazon (US), Oracle (US), Salesforce (US), NVIDIA (US), SAP (Germany), Servicenow (US), Accenture (Ireland), Infosys (India), Alibaba (China), Intel (US), AMD (US), Fujitsu (Japan), Capgemini (France), TCS (India), Talkdesk (US), Symphony AI (US), Bloomreach (US), C3.AI (US), Visenze (Singapore), Pathr.ai (US), Vue.AI (US), Nextail (Spain), Daisy Intelligence (Canada), Cresta (US), Mason (US), Syte (Israel), Trax (Singapore), Feedzai (US) and Shopic (Israel).

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Table of Contents

1 INTRODUCTION 34
1.1 STUDY OBJECTIVES 34
1.2 MARKET DEFINITION 34
1.3 STUDY SCOPE 35
1.3.1 MARKET SEGMENTATION 35
1.3.2 INCLUSIONS AND EXCLUSIONS 36
1.4 YEARS CONSIDERED 37
1.5 CURRENCY CONSIDERED 37
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 60
5.5 SUPPLY CHAIN ANALYSIS 61
5.6 ECOSYSTEM 62
5.7 TECHNOLOGY ANALYSIS 64
5.7.1 KEY TECHNOLOGIES 64
5.7.1.1 Conversational AI 64
5.7.1.2 Autonomous AI & autonomous agent 64
5.7.1.3 AutoML 65
5.7.2 COMPLEMENTARY TECHNOLOGIES 65
5.7.2.1 Edge computing 65
5.7.2.2 Big data analytics 65
5.7.2.3 Cloud computing 65
5.7.3 ADJACENT TECHNOLOGIES 65
5.7.3.1 Blockchain 65
5.7.3.2 Cybersecurity solutions 66
 
5.8 PATENT ANALYSIS 66
5.8.1 LIST OF MAJOR PATENTS 67
5.9 TRADE ANALYSIS 68
5.9.1 EXPORT SCENARIO OF PROCESSORS AND CONTROLLERS 68
5.9.2 IMPORT SCENARIO OF PROCESSORS AND CONTROLLERS 70
5.10 KEY CONFERENCES AND EVENTS, 2024–2026 71
5.11 TARIFF AND REGULATORY LANDSCAPE 72
5.11.1 TARIFF DATA (HSN: 854231) - PROCESSORS AND CONTROLLERS 72
5.11.2 REGULATORY BODIES, GOVERNMENT AGENCIES, AND OTHER ORGANIZATIONS 74
5.11.3 KEY REGULATIONS 77
5.11.3.1 North America 77
5.11.3.1.1 SCR 17: Artificial Intelligence Bill (California) 77
5.11.3.1.2 S1103: Artificial Intelligence Automated Decision Bill (Connecticut) 77
5.11.3.1.3 National Artificial Intelligence Initiative Act (NAIIA) 78
5.11.3.1.4 The Artificial Intelligence and Data Act (AIDA) - Canada 78
5.11.3.2 Europe 78
5.11.3.2.1 The European Union (EU) - Artificial Intelligence Act (AIA) 78
5.11.3.2.2 General Data Protection Regulation (Europe) 79
5.11.3.3 Asia Pacific 79
5.11.3.3.1 Interim Administrative Measures for Generative Artificial Intelligence Services (China) 79
5.11.3.3.2 The National AI Strategy (Singapore) 80
5.11.3.3.3 The Hiroshima AI Process Comprehensive Policy Framework (Japan) 80
5.11.3.4 Middle East & Africa 81
5.11.3.4.1 The National Strategy for Artificial Intelligence (UAE) 81
5.11.3.4.2 The National Artificial Intelligence Strategy (Qatar) 81
5.11.3.4.3 The AI Ethics Principles and Guidelines (Dubai) 82
5.11.3.5 Latin America 82
5.11.3.5.1 The Santiago Declaration (Chile) 82
5.11.3.5.2 The Brazilian Artificial Intelligence Strategy (EBIA) 82
5.12 PORTER’S FIVE FORCES’ ANALYSIS 83
5.12.1.1 Threat of new entrants 84
5.12.1.2 Threat of substitutes 84
5.12.1.3 Bargaining power of buyers 84
5.12.1.4 Bargaining power of suppliers 85
5.12.1.5 Intensity of competitive rivalry 85
5.13 KEY STAKEHOLDERS AND BUYING CRITERIA 85
5.13.1 KEY STAKEHOLDERS IN BUYING PROCESS 85
5.13.2 BUYING CRITERIA 86
5.14 EVOLUTION OF ARTIFICIAL INTELLIGENCE IN RETAIL 87
5.15 CASE STUDY ANALYSIS 88
5.15.1 TARGET LEVERAGED GOOGLE CLOUD TO ENHANCE CUSTOMER EXPERIENCES AND ACHIEVE SIGNIFICANT REVENUE GROWTH 88
5.15.2 PRADA GROUP IMPROVED CUSTOMER EXPERIENCE USING ORACLE'S CLOUD SOLUTIONS FOR PERSONALIZED RETAIL STRATEGIES 88
5.15.3 PEPE JEANS INDIA AUGMENTED ONLINE SHOPPING WITH SALESFORCE BY FOCUSING ON DIRECT CONSUMER ENGAGEMENT AND PERSONALIZATION 89
5.15.4 WALMART ENHANCED DIGITAL SHOPPING WITH MICROSOFT’S GENERATIVE AI FOR PERSONALIZED SEARCH AND IMPROVED CX 89
5.15.5 AI-POWERED CHECKOUT-FREE SHOPPING SOLUTION TRANSFORMED RETAIL OPERATIONS OF ITREX GROUP 90
6 ARTIFICIAL INTELLIGENCE IN RETAIL MARKET, BY OFFERING 91
6.1 INTRODUCTION 92
6.1.1 OFFERINGS: ARTIFICIAL INTELLIGENCE IN RETAIL MARKET DRIVERS 92
6.2 SOLUTIONS 93
6.2.1 PERSONALIZED PRODUCT RECOMMENDATIONS 95
6.2.1.1 AI to help tailor product suggestions based on customer behavior and drive engagement and sales in retail 95
6.2.2 CUSTOMER RELATIONSHIP MANAGEMENT 96
6.2.2.1 AI-driven CRM to automate personalized marketing and customer segmentation and churn prevention strategies 96
6.2.3 VISUAL SEARCH 97
6.2.3.1 Visual search to enable customers find products using images and enhance discovery and shopping experiences 97
6.2.4 VIRTUAL CUSTOMER ASSISTANT 98
6.2.4.1 AI-powered virtual assistants to offer real-time customer support, improving response times and personalization 98
6.2.5 PRICE OPTIMIZATION 99
6.2.5.1 AI-powered price optimization to help retailers adjust prices dynamically based on competition, demand, and market conditions 99
6.2.6 SUPPLY CHAIN MANAGEMENT & DEMAND PLANNING 100
6.2.6.1 AI to optimize retail supply chains by predicting demand and streamlining inventory management 100
6.2.7 VIRTUAL STORES 101
6.2.7.1 AI to offer immersive shopping experiences with AR and VR technologies 101
6.2.8 SMART CHECKOUT 102
6.2.8.1 AI to eliminate wait times and enable frictionless shopping experiences 102
6.2.9 OTHER SOLUTIONS 103
6.3 SERVICES 104
6.3.1 PROFESSIONAL SERVICES 105
6.3.1.1 Professional services in AI for retail to help businesses effectively integrate advanced AI technologies into their operations 105
6.3.1.2 Training & consulting 107
6.3.1.2.1 Optimizing IT operations for improved business performance to propel market 107
6.3.1.3 System integration & deployment 107
6.3.1.3.1 System integration & deployment services to help retailers seamlessly incorporate AI solutions into their existing infrastructure 107
6.3.1.4 Support & maintenance 108
6.3.1.4.1 Support & maintenance services in AI for retail to ensure that AI systems function optimally post-deployment 108
6.3.2 MANAGED SERVICES 108
6.3.2.1 Managed services in AI to provide continuous monitoring and management of AI systems for scalability and efficiency 108
7 ARTIFICIAL INTELLIGENCE IN RETAIL MARKET, BY TYPE 110
7.1 INTRODUCTION 111
7.1.1 TYPES: ARTIFICIAL INTELLIGENCE IN RETAIL MARKET DRIVERS 111
7.2 GENERATIVE AI 112
7.3 OTHER AI 113
7.3.1 DISCRIMINATIVE MACHINE LEARNING 114
7.3.1.1 ML to optimize retail with personalized recommendations, dynamic pricing, and efficient demand forecasting 114
7.3.2 NATURAL LANGUAGE PROCESSING 114
7.3.2.1 NLP to enhance customer service with AI chatbots and sentiment analysis for personalized, real-time engagement 114
7.3.3 COMPUTER VISION 114
7.3.3.1 Computer vision to revolutionize retail with smart checkouts, visual search, and in-store analytics to boost efficiency 114
7.3.4 PREDICTIVE ANALYTICS 115
7.3.4.1 Predictive analytics to improve demand forecasting, price optimization, and customer targeting in retail operations 115
8 ARTIFICIAL INTELLIGENCE IN RETAIL MARKET, BY BUSINESS FUNCTION 116
8.1 INTRODUCTION 117
8.1.1 BUSINESS FUNCTIONS: ARTIFICIAL INTELLIGENCE IN RETAIL MARKET DRIVERS 117
8.2 MARKETING & SALES 118
8.2.1 AI TO IMPROVE PERSONALIZED CAMPAIGNS, PRODUCT RECOMMENDATIONS, AND DYNAMIC PRICING IN RETAIL 118
8.3 HUMAN RESOURCES 119
8.3.1 AI TO AUTOMATE RECRUITMENT, WORKFORCE OPTIMIZATION, AND PERSONALIZED TRAINING IN RETAIL HR 119
8.4 FINANCE & ACCOUNTING 120
8.4.1 AI TO SYSTEMATIZE FINANCIAL PROCESSES, SUCH AS BILLING, FORECASTING, AND FRAUD DETECTION IN RETAIL 120
 
8.5 OPERATIONS 121
8.5.1 AI TO ENHANCE SUPPLY CHAIN OPTIMIZATION, INVENTORY MANAGEMENT, AND LOGISTICS IN RETAIL OPERATIONS 121
8.6 CYBERSECURITY 122
8.6.1 AI TO STRENGTHEN FRAUD DETECTION, DATA SECURITY, AND BIOMETRIC AUTHENTICATION IN RETAIL CYBERSECURITY 122
9 ARTIFICIAL INTELLIGENCE IN RETAIL MARKET, BY END USER 124
9.1 INTRODUCTION 125
9.1.1 END USERS: ARTIFICIAL INTELLIGENCE IN RETAIL MARKET DRIVERS 125
9.2 ONLINE 126
9.2.1 AI TO REVOLUTIONIZE ONLINE RETAIL BY IMPROVING SHOPPING EXPERIENCE THROUGH PERSONALIZATION, INVENTORY MANAGEMENT, AND REAL-TIME CUSTOMER SUPPORT 126
9.3 OFFLINE 127
9.3.1 ESSENTIAL SECURITY TOOLS TO MONITOR NETWORK TRAFFIC FOR THREATS 127
9.3.2 SUPERMARKETS & HYPERMARKETS 129
9.3.2.1 AI to improve inventory management, customer experience, and operational efficiency with smart checkout and predictive analytics 129
9.3.3 SPECIALTY STORES 130
9.3.3.1 AI to personalize shopping experiences and optimize inventory management in specialty stores 130
9.3.4 CONVENIENCE STORES 131
9.3.4.1 Smart checkout, dynamic pricing, and improved inventory management to ensure operational efficiency and quick service in convenience stores 131
9.3.5 OTHER OFFLINE STORES 132
10 ARTIFICIAL INTELLIGENCE IN RETAIL MARKET, BY REGION 133
10.1 INTRODUCTION 134
10.2 NORTH AMERICA 135
10.2.1 NORTH AMERICA: MACROECONOMIC OUTLOOK 135
10.2.2 US 141
10.2.2.1 Technological advancements and strategic partnerships to propel market 141
10.2.3 CANADA 146
10.2.3.1 Need for predicting product demand, optimizing inventory, and enhancing personalized customer experiences to drive market 146
10.3 EUROPE 146
10.3.1 EUROPE: MACROECONOMIC OUTLOOK 146
10.3.2 UK 152
10.3.2.1 Need to enhance customer experiences, streamline operations, and optimize inventory management to accelerate market growth 152
10.3.3 ITALY 157
10.3.3.1 Increasing demand for enhanced customer experiences, operational efficiency, and data-driven decision-making to fuel market growth 157
10.3.4 GERMANY 162
10.3.4.1 Need to enhance operational efficiency, customer engagement, and government initiatives to enhance market growth 162
10.3.5 FRANCE 162
10.3.5.1 Integration of AI to enhance customer experiences through personalized recommendations, dynamic pricing, and improved inventory management 162
10.3.6 SPAIN 163
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 163
10.3.7 NORDIC COUNTRIES 163
10.3.7.1 Increasing consumer expectations for personalized experiences and operational efficiency to foster market growth 163
10.3.8 REST OF EUROPE 163
10.4 ASIA PACIFIC 164
10.4.1 ASIA PACIFIC: MACROECONOMIC OUTLOOK 164
10.4.2 CHINA 170
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 170
10.4.3 JAPAN 175
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 175
10.4.4 INDIA 175
10.4.4.1 Rapid eCommerce growth, increasing smartphone penetration, and demand for personalized customer experiences to augment market growth 175
10.4.5 AUSTRALIA & NEW ZEALAND 180
10.4.5.1 Increasing eCommerce activity and need for enhanced customer experience to propel market 180
10.4.6 SOUTH KOREA 181
10.4.6.1 Advanced technological infrastructure, high internet penetration, and implementation of AI National Strategy to accelerate market 181
10.4.7 ASEAN COUNTRIES 181
10.4.8 REST OF ASIA PACIFIC 181
10.5 MIDDLE EAST & AFRICA 182
10.5.1 MIDDLE EAST & AFRICA: MACROECONOMIC OUTLOOK 183
10.5.1.1 UAE 189
10.5.1.1.1 Investments and collaborations aimed at enhancing retail experiences through AI technologies to drive market 189
10.5.1.2 KSA 189
10.5.1.2.1 Substantial investments in AI and establishment of Vision 2030 to foster market growth 189
 
10.5.1.3 Kuwait 194
10.5.1.3.1 Rapid development of Kuwait Vision 2035 to fuel demand for AI in retail market 194
10.5.1.4 Bahrain 195
10.5.1.4.1 Strategic location, supportive government policies, and growing eCommerce industry to drive market 195
10.5.2 SOUTH AFRICA 195
10.5.2.1 Rise of AI and related technologies during COVID-19 to fuel market growth 195
10.5.3 REST OF MIDDLE EAST & AFRICA 195
10.6 LATIN AMERICA 196
10.6.1 LATIN AMERICA: MACROECONOMIC OUTLOOK 196
10.6.2 BRAZIL 201
10.6.2.1 Influx of foreign eCommerce platforms to boost demand for AI in retail market 201
10.6.3 MEXICO 206
10.6.3.1 Embracing emerging technologies with notable funding from both domestic and international investors to bolster market growth 206
10.6.4 ARGENTINA 206
10.6.4.1 Focus on advancing digital infrastructure to drive market 206
10.6.5 REST OF LATIN AMERICA 206
11 COMPETITIVE LANDSCAPE 207
11.1 INTRODUCTION 207
11.2 KEY PLAYER STRATEGIES/RIGHT TO WIN 207
11.2.1 OVERVIEW OF STRATEGIES ADOPTED BY KEY ARTIFICIAL INTELLIGENCE IN RETAIL MARKET VENDORS 207
11.3 REVENUE ANALYSIS 208
11.4 MARKET SHARE ANALYSIS 209
11.4.1 MARKET RANKING ANALYSIS 210
11.5 COMPANY EVALUATION MATRIX: KEY PLAYERS, 2023 210
11.5.1 STARS 210
11.5.2 EMERGING LEADERS 210
11.5.3 PERVASIVE PLAYERS 210
11.5.4 PARTICIPANTS 210
11.5.5 COMPANY FOOTPRINT: KEY PLAYERS, 2023 212
11.5.5.1 Company footprint 212
11.5.5.2 Type footprint 212
11.5.5.3 Offering footprint 213
11.5.5.4 Regional footprint 214
 
11.6 COMPANY EVALUATION MATRIX: START-UPS/SMES, 2023 215
11.6.1 PROGRESSIVE COMPANIES 215
11.6.2 RESPONSIVE COMPANIES 215
11.6.3 DYNAMIC COMPANIES 215
11.6.4 STARTING BLOCKS 215
11.6.5 COMPETITIVE BENCHMARKING: START-UPS/SMES, 2023 216
11.6.5.1 Key start-ups/SMEs 216
11.6.5.2 Competitive benchmarking of key start-ups/SMEs 217
11.7 COMPETITIVE SCENARIOS AND TRENDS 218
11.7.1 PRODUCT LAUNCHES & ENHANCEMENTS 218
11.7.2 DEALS 219
11.8 BRAND/PRODUCT COMPARISON 221
11.9 COMPANY VALUATION AND FINANCIAL METRICS 222
12 COMPANY PROFILES 223
12.1 KEY PLAYERS 223
12.1.1 IBM 223
12.1.1.1 Business overview 223
12.1.1.2 Products/Solutions/Services offered 224
12.1.1.3 Recent developments 226
12.1.1.3.1 Product enhancements 226
12.1.1.3.2 Deals 226
12.1.1.4 MnM view 227
12.1.1.4.1 Right to win 227
12.1.1.4.2 Strategic choices 227
12.1.1.4.3 Weaknesses and competitive threats 227
12.1.2 AMAZON 228
12.1.2.1 Business overview 228
12.1.2.2 Products/Solutions/Services offered 229
12.1.2.2.1 Deals 230
12.1.2.2.2 Other deals/developments 231
12.1.2.3 MnM view 231
12.1.2.3.1 Right to win 231
12.1.2.3.2 Strategic choices 231
12.1.2.3.3 Weaknesses and competitive threats 231
12.1.3 SALESFORCE, INC. 232
12.1.3.1 Business overview 232
12.1.3.2 Products/Solutions/Services offered 233
12.1.3.3 Recent developments 235
12.1.3.3.1 Product launches and enhancements 235
12.1.3.3.2 Deals 236
 
12.1.4 ORACLE 237
12.1.4.1 Business overview 237
12.1.4.2 Products/Solutions/Services offered 238
12.1.4.3 Recent developments 239
12.1.4.3.1 Deals 239
12.1.5 MICROSOFT 240
12.1.5.1 Business overview 240
12.1.5.2 Products/Solutions/Services offered 241
12.1.5.3 Recent developments 242
12.1.5.3.1 Deals 242
12.1.5.4 MnM view 242
12.1.5.4.1 Right to win 242
12.1.5.4.2 Strategic choices 243
12.1.5.4.3 Weaknesses and competitive threats 243
12.1.6 GOOGLE 244
12.1.6.1 Business overview 244
12.1.6.2 Products/Solutions/Services offered 245
12.1.6.3 Recent developments 246
12.1.6.3.1 Product enhancements 246
12.1.6.3.2 Deals 246
12.1.6.4 MnM view 247
12.1.6.4.1 Right to win 247
12.1.6.4.2 Strategic choices 247
12.1.6.4.3 Weaknesses and competitive threats 247
12.1.7 NVIDIA 248
12.1.7.1 Business overview 248
12.1.7.2 Products/Solutions/Services offered 249
12.1.7.3 Recent developments 250
12.1.7.3.1 Deals 250
12.1.7.4 MnM view 250
12.1.7.4.1 Right to win 250
12.1.7.4.2 Strategic choices 250
12.1.7.4.3 Weaknesses and competitive threats 250
12.1.8 ACCENTURE 251
12.1.8.1 Business overview 251
12.1.8.2 Products/Solutions/Services offered 252
12.1.8.3 Recent developments 253
12.1.8.3.1 Deals 253
12.1.9 SAP SE 254
12.1.9.1 Business overview 254
12.1.9.2 Products/Solutions/Services offered 255
12.1.9.3 Recent developments 256
12.1.9.3.1 Deals 256
12.1.10 SERVICENOW 257
12.1.10.1 Business overview 257
12.1.10.2 Products/Solutions/Services offered 258
12.1.10.3 Recent developments 259
12.1.10.3.1 Product enhancements 259
12.1.10.3.2 Deals 259
12.1.11 INFOSYS 260
12.1.11.1 Business overview 260
12.1.11.2 Products/Solutions/Services offered 261
12.1.11.3 Recent developments 262
12.1.11.3.1 Deals 262
12.1.12 INTEL CORPORATION 263
12.1.12.1 Business overview 263
12.1.12.2 Products/Solutions/Services offered 264
12.1.12.3 Recent developments 265
12.1.12.3.1 Product launches 265
12.1.12.3.2 Deals 265
12.1.13 AMD 266
12.1.13.1 Business overview 266
12.1.13.2 Products/Solutions/Services offered 267
12.1.13.3 Recent developments 268
12.1.13.3.1 Product enhancements 268
12.1.13.3.2 Deals 268
12.1.14 HUAWEI 269
12.1.14.1 Business overview 269
12.1.14.2 Products/Solutions/Services offered 269
12.1.14.3 Recent developments 270
12.1.14.3.1 Product launches 270
12.1.15 ALIBABA 271
12.1.16 FUJITSU 272
12.1.17 CAPGEMINI 273
12.1.18 TCS 274
12.1.19 TALKDESK 275
12.1.20 SYMPHONY AI 276
12.1.21 BLOOMREACH 277
12.1.22 C3.AI 278
12.2 START-UPS/SMES 279
12.2.1 VISENZE 279
12.2.2 PATHR.AI 280
12.2.3 VUE.AI 281
12.2.4 NEXTAIL 282
12.2.5 DAISY INTELLIGENCE 283
12.2.6 CRESTA 284
12.2.7 MASON 285
12.2.8 SYTE 286
12.2.9 TRAX RETAIL 287
12.2.10 FEEDZAI 288
12.2.11 SHOPIC 289
13 ADJACENT/RELATED MARKETS 290
13.1 INTRODUCTION 290
13.2 ARTIFICIAL INTELLIGENCE MARKET – GLOBAL FORECAST TO 2030 290
13.2.1 MARKET DEFINITION 290
13.2.2 MARKET OVERVIEW 290
13.2.2.1 Artificial intelligence market, by offering 290
13.2.2.2 Artificial intelligence market, by technology 291
13.2.2.3 Artificial intelligence market, by business function 292
13.2.2.4 Artificial intelligence market, by vertical 293
13.2.2.5 Artificial intelligence market, by region 295
13.3 RETAIL ANALYTICS MARKET – GLOBAL FORECAST TO 2029 296
13.3.1 MARKET DEFINITION 296
13.3.2 MARKET OVERVIEW 296
13.3.2.1 Retail analytics market, by offering 296
13.3.2.2 Retail analytics market, by business function 297
13.3.2.3 Retail analytics market, by application 297
13.3.2.4 Retail analytics market, by end user 298
13.3.2.5 Retail analytics market, by region 299
14 APPENDIX 300
14.1 DISCUSSION GUIDE 300
14.2 KNOWLEDGESTORE: MARKETSANDMARKETS’ SUBSCRIPTION PORTAL 303
14.3 CUSTOMIZATION OPTIONS 305
14.4 RELATED REPORTS 305
14.5 AUTHOR DETAILS 306