AI in retail

Artificial intelligence (AI) in retail encompasses the use of AI technologies to enhance various aspects of the retail industry, including customer experience, business operations and decision-making. AI-driven systems in retail analyze data, automate processes and enable more personalized and efficient experiences for both customers and retailers.

AI-powered retail technologies are applied across online and physical stores, impacting everything from product recommendations and pricing to inventory management and customer service. In recent years, advancements in generative AI technologies have steadily altered the retail sector, offering new opportunities for content generation and real-time customer engagement in natural language.

For retail brands both large and small, AI tools can have a significant business impact, though organizations still sometimes struggle to deploy the technology in a large-scale and cost-effective way. By some estimates, generative AI alone is forecast to create between USD 240 billion and USD 390 billion in economic value for retailers.¹ But many executives, according to the management consultancy McKinsey, still struggle to successfully implement these technologies across their organizations.

Still, these technologies can be of great value to customers: According to a report from the IBM Institute for Business Value, roughly four in five consumers who haven’t tried AI for shopping would like to use. Customers are interested in using it to research products, look for deals, and resolve issues. And AI has already played a major role in the seamless integration of online and offline shopping, with automated checkout and instant omnichannel personalization becoming standard across large retail corporations. 

AI has the capacity to streamline the retail experience from end to end, optimizing back-of-office processes and surfacing hyperpersonalized content to individual shoppers. Some common AI use cases for AI include:

• Personalized shopping experiences
• Customer experience and virtual agents
• Demand forecasting
• Supply chain management
• Fraud detection, loss prevention and security

One of the most visible impacts of AI in retail is its ability to personalize consumer experiences. AI algorithms analyze customer behavior, preferences and past purchases to provide personalized recommendations and targeted marketing. This creates a more engaging and relevant shopping experience, increasing customer loyalty and conversion rates. Some businesses might use recommendation engines to suggest products based on a users’ browsing and purchase history—a tactic that’s become standard for streaming platforms like Netflix and large retailers like Amazon. Others personalize cost by using dynamic pricing, which adjusts prices in real-time based on demand, competitor pricing and customer preferences, allowing retailers to offer deals to customers during low-traffic periods and optimize revenue.

Increasingly, retail AI is moving toward hyperpersonalization, where nearly every aspect of the omnichannel shopping experience is tailored to the individual user. While personalization already plays a major role in retail, more advanced AI technologies can integrate even more granular data points, including real-time behaviors, preferences and environmental factors. This enables retailers to offer more precise and dynamic customer experiences. These customer experiences might include predictive customer insights prefiguring consumer needs, or completely personalized communications such as website landing pages or marketing emails. 

AI-powered virtual assistants and chatbots provide instant support to customers, answering queries, streamlining the ordering process and resolving issues. These tools are becoming more sophisticated with natural language processing (NLP), enabling human-like conversations. Retailers might use chatbots on websites or apps to help customers navigate product offerings, check an order status or troubleshoot issues. Virtual shopping assistants guide customers through their ecommerce experience, offering product suggestions and nurturing leads through a sales funnel. With the increasing power of generative AI, chatbots and virtual assistants have become more capable of automating complex customer experiences.

With AI-assisted search and augmented reality, customers have new ways to search for and research products before they buy. For instance, AI can analyze images uploaded by users and suggest visually similar products. This has become popular in fashion and home decor, where a consumer might be searching for visually similar products. Similarly, AI-enhanced AR allows customers to “try on” products before making a purchase. Fashion and beauty brands such as Sephora have had significant initial success with tools allowing customers to see how clothing or makeup might look before committing to a product. 

Demand forecasting uses advanced data analytics and machine learning models to predict future customer demand for products. Using a combination of sales data, customer data and third-party data like market trends these tools help organizations plan more effectively. As AI models can analyze vast amounts of data and detect patterns traditional methods might miss, these technologies tend to be more accurate than previous forecasting tools.

By predicting demand more precisely, retailers can better manage inventory and optimize logistics. These models also help organizations adapt quickly to unforeseen conditions or market changes by providing data-driven intelligence on future events. Demand forecasting powered by machine learning has had a major impact on the grocery industry. For example, as some brands have automated daily ordering for fresh-food departments to increase product availability and reduce waste.²

AI can play a critical role in managing the backend operations of a retail business, optimizing inventory and supply chain management. By integrating AI technologies into various supply chain functions like supplier management and transportation logistics, organizations can optimize inventory, increase visibility, lower costs and reduce errors. In the retail sector, AI algorithms optimize transportation routes, reducing delivery times and adjusting schedules to meet specific criteria such as carbon emissions thresholds. They’re also used to automate select aspects of the inventory management and supplier management process, automatically replenishing low-stock items or reducing the amount of manual effort required to place orders.

These tools can help an organization speed up operations, maintain ideal inventory levels and reduce human error. For example, the retail giant Wal-Mart uses AI to optimize delivery vehicles, routing them through more efficient paths and analyzing weather patterns to help ensure that goods arrive on time.³ 

AI is increasingly used to protect both retailers and customers from loss prevention and fraud. AI systems can analyze transaction patterns and detect anomalies that can indicate fraudulent activities, helping retailers prevent losses. AI tools can also increase cybersecurity in online payments, helping to monitor online transactions and customer accounts for potential data breaches, enhancing the security of ecommerce platforms.

Many financial institutions and large online platforms like eBay use automated fraud detection software to flag potential issues. In recent years, some retailers have implemented AI-assisted loss prevention technologies. These are used to analyze in-store data and respond to potential theft.⁴ 

AI for retail uses various technologies and data ecosystems. These tools are used to enhance operations, customer experience and business-wide decision making. Multiple technologies are often used in tandem depending on a particular retailer’s needs. Some of the most common AI technologies used for retail operations include:

• Data management systems
• Big data and predictive analytics
• Machine learning (ML)
• Natural language processing
• Computer vision
• RPA
• IoT

Data management systems are the backbone of AI in retail. These systems allow for the collection, storage and management of vast amounts of data from multiple sources, providing the foundation for data analytics and machine learning algorithms. These systems help ensure the quality, consistence and accessibility of data used to train an A. Such data management systems include:

• Data warehouses, data lakes and data lakehouses, centralized repositories that store data collected from various sources including sales, customer interactions and supply chain operations

• Data integration tools, which extract, transform and load data from various sources into a data storage repositories, ensuring data quality and consistency for AI applications 

• Cloud data platforms, which provide the infrastructure for storing, processing and analyzing large datasets, enabling scalable AI applications capable of analyzing data as it’s generated 

AI systems in retail depend on massive amounts of data to make accurate predictions and decisions. Big data tools and predictive analytics interpret this data, extracting meaningful insights. For example, these tools are used to perform:

• Customer behavior analysis, in which AI analyzes customer purchase patterns, preferences and interactions across channels to provide personalized experiences and targeted marketing

• Sales forecasting, in which predictive analytics helps retailers anticipate trends and align inventory with expected demand 

Automation processes and software perform routine tasks, reducing human error and expanding a retail organization’s capacity. These technologies can be used in multiple areas of a business, from inventory management and automated catalog updates to wide-scale business process automation.

Basic automations in retail might automatically display pricing based on a centralized repository, instantly provide a customer with delivery updates or generate invoices without human intervention. Intelligent automation, a more advanced form combing automation and AI, might involve a virtual assistant understanding a customer query in natural language and processing an order based on their request. Other types of automation frequently used in retail include:

• Robotic Process Automation (RPA), which automates more complex multistep tasks and workflows. RPA can be used in areas like inventory management and supply chain automation to track and update inventory levels or manage the fulfillment process.

• Voice automation, or voice recognition, which allows machines to understand and respond to human voices. These technologies are increasingly used for voice assistants like Alexa, which have become more popular as a retail platform.  

Machine learning is one of the backbones of AI applications in retail. ML algorithms enable systems to learn from data and improve their performance over time without being specifically programmed for a task. Machine learning powers:

• Recommendation engines, which suggest personalized products to customers based on their browsing behavior and purchase history.

• Demand forecasting, which analyzes historical sales data, market trends and other factors to predict future demand and optimize product stock levels

• Dynamic pricing, which adjusts prices in real-time based on demand, competition and other factors 

Natural language processing (NLP) enables AI systems to “understand” and generate human language. Computer vision allows those systems to understand and interpret visual data from the physical world, making it a useful technology for enhancing in-store and online shopping experiences. Often, NLP and computer vision technology are used in retail for:

• Chatbots and virtual assistants, which use NLP to engage with customers, answer queries and guide users through a shopping experience

• Sentiment analysis, a process through which NLP tools analyze customer reviews, chatbot communications and social media posts to measure customer sentiment and provide insights into consumer preferences

• Visual search, which allows customers to search for products by uploading images and finding similar items based on visual characteristics

• Automated inventory management, a process through which cameras and sensors detect stock levels in a store or warehouse, alerting staff when products need to be replenished

IoT devices generate real-time data from sensors, cameras and smart devices, which AI systems can analyze to optimize retail operations on the customer experience. These systems can be used to power:

• In-store analytics, in which IoT sensors track foot traffic and customer engagement levels to optimize store layouts and marketing strategies

• Ecommerce logistics, in which sensors track delivery trucks of other logistics vehicles, providing real-time updates to customers or businesses 

The integration of AI and associated technologies into the retail sector has the capacity to enhance both the customer experience and business operations. While there is a wide array of AI applications for retail businesses—from inventory management to marketing campaigns—some common benefits of the technology include:

• Increased efficiency
• Enhanced customer experience
• Cost reduction
• Data-driven decision making
• Advanced demand forecasting
• Price optimization
• Enhanced customer behavior analysis 

Increased efficiency: By automating routine tasks like inventory management, customer support, the generation of marketing materials, and fraud detection, AI allows retailers to focus on more strategic and creative activities.

Enhanced customer experience: Dynamic, data-driven personalization and instant support foster a better relationship between brands and customers, leading to higher satisfaction and loyalty.

Cost reduction: AI optimization processes such as supply chain management or automated delivery planning can reduce waste, improve accuracy, and lower operational costs.

Data-driven decision making: Using AI tools, retailers have access to real-time data and actionable insights, enabling more informed decisions around pricing, inventory, marketing and product development.

Advanced demand forecasting: By gleaning more accurate information about future demand, retail organizations that use AI can adapt more quickly to changing conditions and reduce the chances of costly waste

Price optimization: Using dynamic tools to determine the most effective pricing strategies, AI-assisted organizations can maximize revenue and remain competitive in crowded markets

Enhanced customer behavior analysis: By analyzing customer sentiment and behavior more quickly and accurately than before, retail brands stand to glean valuable insights about their strategies, pain points and potential avenues for increasing customer value.   

Incorporating AI into a retail business requires more than deploying new technology. Successful implementation demands a thoughtful strategic approach that is both comprehensive and adaptable based on company needs. Some common best practices include:

• Starting with a clear strategy
• Using high-quality data
• Choosing the right AI tools and partners
• Prioritizing customer experience
• Continuously monitoring and maintaining AI models
• Practicing good data governance

Retail businesses just beginning an AI initiative might start with a pilot focusing on a high-impact area, such as personalized marketing or automated inventory management. By prioritizing use cases offering the highest return on investment, an organization is more likely to see tangible results. When crafting this strategy, businesses often identify key metrics for success such as increased sales or improved customer satisfaction to track the progression of an AI initiative.

Clean, accurate and relevant data is critical for an effective AI. An organization embarking on an AI initiative typically vets and organizes its data extensively, and regularly validates datasets to maintain quality. A business might also procure high-quality third-party data to augment its internal data. 

Some AI software is general-purpose; other AI models are trained to be task- or industry specific. Typically, an organization will research which AI tools are most effective for a particular application, potentially collaborating with vendors or consultants with experience in the retail sector. Carefully selecting these tools and partners can help create AI initiatives that are scalable and mitigate risk. 

While automation can increase efficiency, it’s critical for a business to avoid over-automation or solutions that don’t center the customer. An organization might include options for human interaction when a customer prefers it or focus solely on AI solutions with tangible benefits to the customer, such as intuitive chatbots or seamless product recommendations. Periodically soliciting customer feedback can also be a useful strategy for prioritizing consumer needs.

To help ensure consistency and avoid errors, organizations typically perform regular audits to help ensure that AI models are working as expected and not deviating from their intended outcomes. This might mean updating models with new data, as well as continuously tuning and validating a model. Also, organizations that use AI monitor the performance of their tools to help ensure they are meeting key business objectives. 

Good AI models are explainable, trusted and transparent. To help ensure the responsible use of AI, an organization might invest in advanced data infrastructure to maintain security and compliance, as well as maintain assiduous technical documentation. These measures protect sensitive customer data, maintain trust, and reduce the chances of bias in AI.