ARTIFICIAL INTELLIGENCE IN RETAIL MARKET SIZE AND SHARE ANALYSIS - GROWTH TRENDS AND FORECASTS (2024-2031)

Artificial Intelligence in Retail Market Size and Trends

The Global Artificial Intelligence in Retail Market is estimated to be valued at USD 10.48 Bn in 2024 and is expected to reach USD 73.02 Bn by 2031, exhibiting a compound annual growth rate (CAGR) of 32% from 2024 to 2031.

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Artificial intelligence is helping retailers improve operations across key areas such as merchandising and supply chain planning. Techniques like machine learning and deep learning are enabling personalized product recommendations and predictive analytics.

Retailers are deploying AI-powered solutions such as computer vision, chatbots, and predictive analytics to enhance customer experience. AI technologies allow retailers to analyze shopping patterns and predict demand more accurately. They are also assisting in reducing inventory costs and improving supply chain efficiencies. The growing customer demand for personalized experiences is further driving retailers to adopt AI at scale across their operations.

Inventory Management and Supply Chain Optimization

One of the key drivers of artificial intelligence adoption in the retail industry is the potential it shows to optimize inventory management and supply chain processes. With AI, retailers can now analyze past sales data patterns and use predictive analytics to forecast consumer demand trends and purchase behaviour more accurately. This helps them in planning inventory levels according to anticipated sales and avoid situations of stockouts as well as overstocking. With precise demand forecasting, retailers save huge costs associated with holding excess inventory, disposing unsold items, and lost sales opportunities due to stockouts.

AI applications like computer vision and machine learning algorithms are also enabling retailers to optimize supply chain operations from sourcing to distribution. Tools like inventory tracking using image recognition and predictive analytics for replenishment automatically identify low stock items on shelves and replenish them before running out. This enhances on-shelf availability and improves customer satisfaction without needing manual checks. Similarly, demand forecasts combined with the optimization of transportation routes is reducing logistics costs for retailers significantly. Systems can now calculate the most efficient routes by consolidating deliveries and maximizing truck capacity utilization.

Market Concentration and Competitive Landscape

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Fraud Detection and Security

As online shopping has proliferated, problems of payment frauds and identity thefts have also increased exponentially. Traditional rule-based and manual methods of fraud detection are no longer effective against the evolving tactics of sophisticated fraudsters. This is a key challenge for the retail industry where even a single fraudulent transaction can dent customer confidence and profit margins. Advanced AI solutions deploying techniques such as machine learning, deep learning, and neural networks are emerging as a potent weapon against payment frauds. Systems can analyze a massive volume of transaction data, detect complex patterns, and spot even subtle anomalies that human analysts may miss.

Machine learning algorithms can consider a wide range of customer attributes as well as device parameters to compare a transaction against known risk profiles. This helps determine if an online purchase, return, or exchange request is legitimate or potentially fraudulent within real-time. AI tools are also capable of continuous learning from new legitimate and fraudulent data to improve detection accuracy over time. Integrated with appropriate security measures, AI significantly strengthens the frontline defense for retailers and payment gateways against financial and identity thefts in the digital era. This protects businesses as well as enhances safe shopping experience for the customers.

Key Takeaways from Analyst:

Major drivers include increasing demand for personalized customer experience and growth of digital retail channels. AI helps retailers gain insights into customer preferences to offer personalized recommendations and targeted promotions. This enhances customer loyalty and lifetime value. North America currently dominates the Artificial Intelligence in Retail Market owing to high technology adoption. However, Asia Pacific is expected to see the fastest growth with India and China emerging as lucrative markets.

While AI brings opportunities to understand customers better and automate tasks, retailers face challenges related to data privacy and potential job losses. Customer concerns around data security and privacy can restrain the adoption of AI-powered technologies. Retailers need to ensure responsible and transparent use of customer data. Integrating AI also requires substantial investments and expertise. Lack of skilled professionals to develop, deploy and maintain advanced AI systems poses a hurdle. Moreover, automating repetitive jobs through AI may reduce the need for certain human jobs in warehouses and stores.

However, AI is also expected to create new types of jobs requiring advanced technical and soft skills. By overcoming constraints around data privacy, investments and skills shortage, retailers can unlock the true potential of AI to digitalize operations, enhance customer service and boost revenues.

Market Challenge - Lack of Standardization and Interoperability

One of the major challenges currently faced in the global artificial intelligence in retail market is lack of standardization and interoperability. There are several AI platforms such as Microsoft Azure AI, Amazon SageMaker, IBM Watson, etc. and solutions available in the market by various vendors, however, they often use different algorithms, standards, integrations, data formats, and APIs, which makes it difficult for retailers to seamlessly adopt and integrate multiple AI solutions together. Retailers face significant challenges in exploring different AI vendors and solutions due to lack of common standards and integration points. This further limits the scale of adoption of AI-based applications and integration with other IT systems in the retail ecosystem. For the market to grow to its full potential, the development of universal standards for data integration and platform interoperability is highly needed. Vendors must work together to establish common protocols, data formats and interfaces that allow solutions to securely communicate and work in tandem with each other. Adoption of standardized APIs will enable wider application of AI by simplifying the integration process for retailers.

Opportunity - Integration with Internet of Things (IoT) and Big Data

One major opportunity for the global artificial intelligence in retail market lies in deeper integration of AI with Internet of Things (IoT) devices and big data analytics tools. Retailers are increasingly adopting IoT sensors to gather real-time customer insight and operational intelligence from physical store locations. AI has the capability to analyze huge volumes of data from these IoT deployments and customer transactions to generate valuable patterns. By fusing AI with IoT data streams and big data, retailers can gain unprecedented visibility into consumer behaviour, predict demand trends, optimize inventory, recommend personalized offers, and enhance overall shopping experience. AI combined with IoT also enables new areas like predictive maintenance of store equipment, advanced computer vision powered store operations, and drone-based warehouse management. The mergers of these technologies will be a key driver of innovation and growth in the AI retail market in the coming years.

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Insights By Technology - The Machine Learning Segment Dominates Due to the Rise of Personalized Customer Experiences

In terms of Technology, the Machine Learning segment is estimated to hold 48.7% share of the market in 2024 owing to its capability to analyze large amounts of customer data. Machine Learning algorithms can scan purchasing patterns, browsing history, product reviews, and more to gain deep customer insights. With these insights, machine learning powers personalized product recommendations and experiences. It understands customer preferences, priorities and what they are likely to purchase next. This level of personalized engagement has transformed the retail experience. Customers receive tailored suggestions for items they really want, rather than generic promotions. They feel known and valued by the brand. Machine learning also develops an understanding of customers over time, providing an even more customized experience on future visits. This constant improvement keeps customers engaged and loyal to retailers leveraging machine learning's recommendation abilities.

Insights By Application - The Personalized Recommendations Segment Leading the Market by Enhancing Inventory Visibility and Management

In terms of Application, the Personalized Recommendations segment is estimated to hold 31.5% share of the market in 2024. However, Inventory Management is growing rapidly due to its importance. Natural Language Processing (NLP) allows retailers to understand product specifications, attributes and relationships. This information provides critical inventory visibility when combined with sales data. NLP recognizes when stock is running low and automatically orders more through integration with supply chain systems. It identifies slow-moving items and recommends price adjustments or alternate purchasing options. Out of stock items can paralyze the customer experience and lose sales. NLP ensures retailers always have the right products in the right places to meet customer demands. The technology streamlines replenishment, reduces waste and keeps retailers responsive to shifts in consumer behavior.

Insights By End User - The E-Commerce Segment's Growth Driven by Embracing Digital Transformation

In terms of End User, the E-commerce segment is estimated to hold 57.8% share in 2024 owing to its fully digital business model. However, brick-and-mortar stores are increasingly leveraging AI just to survive in this new era. Computer vision installed in physical stores can detect inventory levels, perform real-time pricing and planogram compliance checks to keep shelves fully stocked with correctly priced items. It also provides timely alerts about spills, low levels, and misplaced products. Computer Vision gives brick-and-mortar retailers the same level of visibility that E-commerce giants enjoy through Machine Learning and NLP. Robotic Process Automation (RPA) executes repetitive administrative tasks to reduce costs. AI solutions allow retailers with physical footprints to streamline operations, enhance the in-store experience and compete effectively against their online competitors. Adopting emerging technologies has become critical for multi-channel retailers to engage customers both online and offline.

For instance, In January 2024, Google Cloud, a leading provider of cloud computing services, launched several new AI-powered technologies to help retailers personalize online shopping experiences, modernize operations, and transform in-store technology rollouts. As part of these innovations, Google Cloud enhanced its flagship search technology for retailers with large language model capabilities, enabling shoppers to more easily find and discover products. These new offerings aim to provide retailers with practical and powerful tools to fuel growth and evolve customer experiences in an increasingly competitive landscape.

Regional Insights

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North America has established itself as the dominant region in the global artificial intelligence in retail market with an estimated 38.9% share in 2024. This can be attributed to heavy investments being made by major tech companies like Microsoft, IBM, Nvidia, C3.ai, etc. as well as retailers based in the U.S. and Canada to integrate AI-based technologies across their operations. Moreover, the presence of several AI startup incubators and accelerators in the region has fostered innovation.

Furthermore, retailers in North America are among the early adopters of AI globally. Applications around predictive analytics, demand forecasting, customer service, and dynamic pricing are being widely used. The promotion of technology adoption through government initiatives has also propelled the artificial intelligence in retail market in the region. High disposable income levels provide retailers with abundant opportunities to experiment with personalized and customized shopping experiences powered by AI. This has significantly boosted demand.

On the other hand, the Asia Pacific region has emerged as the fastest growing market for artificial intelligence in retail. Rapid digitalization of the retail sector and growing penetration of internet and smartphones are driving regions growth. Countries like China, India, and Japan house a massive consumer base that is highly receptive to innovative AI-enabled technologies.

According to SAP SE's analysis from 2020, China secured a 23.4% share of AI investments in its commerce and retail industry. SAP SE, a global leader in enterprise application software, provides innovative solutions that help businesses transform their operations and leverage technology effectively.

E-commerce is booming in the region which has prompted retailers to deploy AI for applications such as product recommendations, process automation, and supply chain optimization. Domestic players are aggressively focusing on developing AI capabilities in-house to gain a competitive advantage in this digital era.

Market Report Scope

Key Developments

• In January 2024, Lenovo, a global technology leader, launched its AI-powered, end-to-end retail solutions designed to support smarter, safer, and more secure experiences for retailers and shoppers across any category. These solutions aim to help retailers reduce loss, grow business opportunities, and enhance customer experiences through intelligent transformation.
• In January 2024, Salesforce announced new AI-powered tools and data innovations to transform every shopping experience. Powered by the Einstein 1 Platform, these AI-infused tools for commerce and marketing help retailers and marketers optimize customer interactions by leveraging real-time insights into shopper preferences and behaviour. By integrating generative AI capabilities into Salesforce Commerce Cloud and Marketing Cloud, businesses can create more personalized experiences, increase customer loyalty, drive revenue growth, and boost employee productivity.
• In May 2023, Upliance.ai, a consumer hardware startup focused on smart appliances, integrated ChatGPT into its flagship product, DelishUp, a smart cooking assistant designed to simplify the cooking process through automation. This innovative integration allows users to receive personalized recipe suggestions and real-time assistance while cooking. Additionally, Upliance.ai plans to expand its product line into the home appliances category, establishing a strong presence in the artificial intelligence sector within the retail industry.
• In February 2023, Google, a leading technology company known for its innovative cloud solutions, partnered with Accenture, a global professional services firm, to launch new tools aimed at helping retailers innovate their businesses and leverage cloud technology. This collaboration involved integrating Accenture's ai. RETAIL platform with Google Cloud, building on their joint successes to provide retailers with enhanced capabilities for modernization and growth.
• In January 2023, EY (Ernst & Young), a global leader in assurance, consulting, strategy, transactions, and tax services, launched the EY Retail Intelligence solution built on Microsoft Cloud. This innovative solution is designed to provide clients with a time-saving and secure shopping experience by leveraging Microsoft Cloud for Retail and its advanced technologies, including artificial intelligence (AI), analytics, and image recognition, to deliver valuable insights across all customer touchpoints.

*Definition: The Global Artificial Intelligence in Retail Market refers to the use of artificial intelligence technologies in the retail industry around the world. It involves the implementation of AI-based solutions and services across various retail operations such as e-commerce websites, supply chain and logistics management, customer relationship management, inventory management, and brick-and-mortar stores. These AI technologies help retailers improve operational efficiency, enhance customer experience, promote personalized marketing & product recommendations, enable predictive analytics, optimize supply chain networks and facilitate inventory management.

Market Segmentation

• By Technology Insights (Revenue, USD Bn, 2019 - 2031)
• • Machine Learning
  • Natural Language Processing (NLP)
  • Computer Vision
  • Robotic Process Automation (RPA)
• By Application Insights (Revenue, USD Bn, 2019 - 2031)
• • Personalized Recommendations
  • Inventory Management
  • Customer Service Chatbots
  • Fraud Detection
  • Pricing Optimization
• By End User Insights (Revenue, USD Bn, 2019 - 2031)
• • E-commerce
  • Brick-and-Mortar Stores
  • Wholesalers
• By Regional Insights (Revenue, USD Bn, 2019 - 2031)
  • North America
    • U.S.
    • Canada
  • Latin America
    • Brazil
    • Argentina
    • Mexico
    • Rest of Latin America
  • Europe
    • Germany
    • U.K.
    • Spain
    • France
    • Italy
    • Russia
  • Rest of Europe
    • Asia Pacific
    • China
    • India
    • Japan
    • Australia
    • South Korea
    • ASEAN
    • Rest of Asia Pacific
  • Middle East
    • GCC Countries
    • Israel
    • Rest of Middle East
  • Africa
    • South Africa
    • North Africa
    • Central Africa
• Key Players Insights
• • Adobe
  • Alibaba Group
  • Amazon Web Services (AWS)
  • Apple
  • Appier
  • Ceconomy
  • Edeka
  • Foot Locker
  • Home Depot
  • IBM
  • Kroger
  • Lemon AI
  • Lowe's
  • Microsoft
  • NIKE