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10 Ways AI Is Changing Retail Experiences in 2024

AI has already left an indelible mark on the retail sector, from personalized product recommendations and inventory management to enhanced customer service and loyalty programs. 

As we move forward in 2024, AI will continue to affect retail operations to bring even more change to this quickly evolving sector. 

Join us as we explore 10 ways that AI is impacting customer experience and overall operations in the retail industry.

1. Personalized recommendations

In the years to come, successful retailers will continue to rely on AI to provide personalized product recommendations to customers based on their preferences and behavior. With online sales and keyword-driven advertising the norm, all areas of retail must be personalized, unique and inspiring to keep up with consumer expectations and maintain a competitive edge. This approach is crucial across all touchpoints, ensuring a seamless and memorable shopping journey.

Examples of how AI can be used in retail to enhance personalized shopping experiences include:

Tailored homepage experience

In the realm of mobile and digital interfaces, platforms are now adept at recognizing individual customers and personalizing the e-commerce journey based on their current situation, past purchases, and browsing habits. Through the continuous evolution of AI systems, users can expect hyper-relevant displays tailored to their preferences with each interaction.

Proactive consumer engagement

Employing advanced Customer Relationship Management (CRM) and marketing technologies, businesses are gaining insights into consumer behavior and preferences over multiple interactions. This accumulated knowledge is utilized to craft comprehensive shopper profiles, enabling the delivery of proactive and personalized outbound marketing. This may include tailored recommendations, rewards, or curated content.

According to one report by McKinsey & Company, effective personalization efforts lead to elevated client engagement and revenue in retail, resulting in a 20% increase in customer satisfaction, sales conversion rates, and employee engagement.

2. Inventory optimization

Efficient inventory management is vital for retailers to strike a balance between having adequate stock and avoiding excess inventory, which can result in increased management costs and markdowns. Today’s successful retailers use AI to optimize inventory levels and benefit from better management of stock and reduced instances of stockouts and overstocking.

AI in retail improves demand forecasting by analyzing data from the market, consumers, and competitors. AI business intelligence tools use these insights to predict changes in the industry and make proactive adjustments to a company’s marketing, merchandising, and business strategies. This also influences supply chain planning, pricing, and promotional strategies.

Walmart provides a notable example of leveraging AI technologies to enhance its inventory management. Their system of “inventory intelligence towers” is mounted on top of floor scrubbers and captures over 20 million photos of merchandise on shelves daily. AI algorithms analyze these images, accurately identifying individual brands on the shelves and determining inventory levels with over 95% precision. It is a winning example of how AI can be used to optimize the balance between stock levels and operational efficiency.

3. Pricing optimization

Moving forward, AI will continue to help retailers optimize their pricing strategies by analyzing market trends, customer behavior, and other factors to set the right prices for products.

Smart algorithms can analyze extensive data and explore numerous pricing possibilities, then recommend the most suitable price. These algorithmic models consider numerous hidden connections, like how changing the price of one product can impact the sales of other items in the retailer’s lineup. By considering these intricate relationships, the algorithms do work that would take thousands of hours if done by humans, and accurately suggest individual prices that optimize both revenue and overall sales.

And by complementing your AI methods with top of the line electronic shelf labels and digital signage, every person in the loop can be made aware of price changes in record time.

Using AI for price optimization in retail typically involves five steps:

  • Gather the required data.
  • Decide whether to hire a technology provider or develop an in-house solution.
  • Select the products for which AI will recommend prices.
  • Ensure that managers are educated about the system.
  • Maintain the system to keep it operational and effective.

4. Fraud detection

As fraud continues to cost retailers billions of dollars annually, AI-powered tools will increasingly be used to secure online transactions and protect customer data. AI helps to detect and prevent fraud in retail, especially the most common types of fraud:

  • Return Fraud: Return fraud, involving deceptive practices such as false returns or using fake receipts, poses a significant challenge for retailers, leading to financial losses through illegitimate reimbursements and potential inventory inaccuracies.
  • Credit Card Fraud: Credit Card Fraud, involving the unauthorized use of stolen credit card information for transactions or withdrawals, poses a significant problem for businesses, as they may be held responsible for chargebacks, leading to substantial financial losses.
  • Inventory shrinkage: Inventory Shrinkage, a prevalent issue in retail, refers to losses in inventory due to theft, damage, or poor management, with challenges in detection often arising from the complexity of maintaining large inventories and various factors like employee theft, shoplifting, and procedural errors contributing to the problem.
  • Employee Fraud: Employee fraud in retail involves harmful acts committed by an organization’s employees, exploiting their access to sensitive information and internal systems, encompassing activities such as theft, document fabrication, financial manipulation, and collaboration with external parties like suppliers or consumers, posing significant risks for merchants.
  • Gift Card Fraud: Theft of gift cards is a growing fraud trend in retail, with fraudsters exploiting the simplicity and anonymity of gift cards, often involving the use of stolen credit card information to purchase and sell gift cards for cash or create fake, indistinguishable gift cards, posing risks for both consumers and retailers.

Fraud detection techniques involving the use of AI can greatly reduce the risk of fraud in retail. The most common types of fraud detection that AI-powered tools can assist are:

  • Anomaly Detection: In anomaly detection, AI tools are used to identify data points that significantly deviate from the norm. In the context of fraud detection, unsupervised machine learning methods can be employed to distinguish unusual transactions by comparing them to a baseline model developed from a sizable dataset of typical transactions.
  • Predictive Modeling: Predictive modeling examines trends in past data, and finds traits linked to fraudulent transactions. It then refers to this information to analyze large datasets of transactional data and customer behavior to identify patterns that may indicate fraudulent activity.
  • Natural Language Processing: NLP approaches may be used to examine text data, such as product descriptions, customer reviews, and comments on social media, in the context of retail fraud detection to spot possible fraudulent behavior. NLP algorithms help in fraud detection by pulling out important words and phrases from customer reviews that mention a fraudulent experience, signaling the need for a closer look. Additionally, in chatbots and virtual assistants, NLP can spot potential fraud by analyzing language, and prompt further investigation by the retailer.

There are significant benefits to using AI-based tools for fraud detection, including their ability to analyze large amounts of data and reduce the instances of false positives. By implementing these tools effectively, retailers can boost consumer confidence and cut costs in the long run.

5. Customer service

AI-powered chatbots and virtual assistants will continue to be used to provide round-the-clock customer support. These AI tools answer queries and assist with purchases, adding a level of all-important interactivity to the retail experience and making it possible to provide exceptional customer service at any time of day or night.

Interactive chat programs “converse” with customers to guide users to helpful answers and outcomes, without the burden of heavily staffing a call center. While doing so, these bots also collect invaluable consumer data which can be used as a basis for later decision-making. 

In addition, retailers are elevating customer service by employing AI to offer special, personalized experiences, such as:

Customer sentiment analysis

This involves using AI algorithms to examine customer feedback, social media posts, and online reviews, enabling businesses to address complaints promptly, gauge customer sentiment and pinpoint areas for enhancement. AI not only aids in monitoring these accounts but can also provide suggested responses to complaints through generative AI and, if allowed, even respond to them. Retailers are likely to increasingly rely on these AI tools to handle customer relations and address issues promptly in the future, as better customer service always translates into higher customer satisfaction.


By leveraging IoT-enabled technologies in retail, it’s possible to obtain valuable insights into consumer behavior and preferences without direct interaction. For instance, tools like interactive tablets can boost engagement and success with customers by making it possible to analyze consumer data and behavior trends with optimum efficiency. As all elements of retail operations become increasingly interconnected, IoT is becoming the logical way to integrate the benefits of AI into everyday retail operations.

6. Supply chain management

AI will continue to be used to optimize supply chain operations, forecast demand, and identify potential disruptions, leading to more efficient and reliable supply chains.

As uncertain global events and labour shortages continue to affect the stability of retail operations, AI technology can provide retailers with the timely data and insights necessary to make fast adjustments and meet the next challenge. 

Efficient supply chain management affects every area of retail operations, and when events affect multiple retail locations, both online and in-store, digital technology can keep promotions on track. AI tools can assemble and analyze vast quantities of data quickly to present retailers with the information they need to make vital decisions related to supply chain management. 

To meet a diverse array of customer needs transitioning from mainstream to niche, retailers should reconsider their conventional supply chain in favor of adaptable and flexible ecosystems capable of swiftly responding to changes in consumer behavior. Overall, AI tools make it possible to strengthen agile logistics networks.

7. Enhanced shopping experiences

AI-powered technologies such as augmented reality (AR) and virtual reality (VR) will be used even more frequently to create immersive and interactive shopping experiences for customers. Here are a few ways in which AI is helping to create enhanced shopping experiences:

Guided discovery

To assist customers in gaining confidence for their purchase decisions, automated assistants can recommend products that align with shoppers’ needs, preferences and fit, helping to narrow down the selection.

Visual curation

Algorithmic engines transform real-world browsing habits into digital retail possibilities, enabling customers to explore new or related products through image-based search and analysis. Recommendations are curated based on aesthetics and similarity.

Personalized recommendations

Artificial intelligence in retail can be used to make suggestions based on past order history and user profiles. Online retailers leverage recommendation AI to enhance the customer experience and maximize product sales, replicating the cross-selling benefits of physical stores through intelligent upselling and suggesting complementary add-ons.

Adjustable in-store product displays

Smart retail environments identify customers and adjust in-store digital signage, product displays, prices, and services using biometric recognition to match customer profiles, loyalty accounts, or available rewards and promotions. This creates a tailored shopping experience for each visitor on a larger scale. Stores also leverage AI and sophisticated algorithms to predict customer interests based on factors like demographics, social media activity, and purchasing history, enhancing both online and in-store shopping experiences.

8. Predictive analytics

AI will be used to analyze data and predict customer behavior, market trends, and other factors, helping retailers make informed decisions and stay ahead of the competition. The primary functions of predictive analytics are demand forecasting and insight generation.

Demand forecasting

Knowing which customers want specific products and their preferred purchase locations is crucial for assisting retailers in handling the supply chain, optimizing inventory levels, and preventing markdowns. AI business intelligence tools analyze data from the marketplace, consumers, and competitors to predict shifts in demand, enabling proactive adjustments to a company’s marketing, merchandising, and business strategies.

As AI algorithms become more advanced, an increasing number of companies are expected to adopt the capabilities of demand forecasting using AI.

Generate insights from diverse data

Moving forward, more and more companies are likely to utilize AI tools to sift through vast amounts of data and gain insights into their customer base. Retailers are inundated with information spanning supply chains, stores, and consumer interactions. In today’s retail landscape, it’s crucial to sort data and discard clutter to convert vast masses of information into useful strategies centered around the needs of the consumer.

9. Visual search

AI-powered visual search tools will allow customers to search for products using images, making it easier to find and purchase items online.

This machine learning technology has been widely implemented in e-commerce, enabling consumers to find products by simply uploading images, improving the overall shopping experience. AI-driven visual search not only simplifies information retrieval but also opens up new possibilities for innovation across various industries, including retail.

10. Store operations

AI can be a powerful tool to implement more efficient and cost-effective retail operations. With its ability to analyze data and devise optimizations based on consumer behavior, AI can be used to improve various aspects of store operations, including staffing, layout, and energy management. Some examples include:

Operational optimization 

Logistics management systems, enhanced by AI, dynamically optimize a retailer’s inventory, staffing, distribution, and delivery processes in real-time. This ensures the creation of highly efficient supply and fulfillment chains that align with customers’ expectations for quality, immediate access, and support.

Synchronized online and offline retail

Digital and physical shopping channels often follow different strategies and approaches. However, treating these channels as separate business units creates friction for customers who want a seamless shopping experience, and results in operational inefficiencies. AI can help harmonize the relationship between multiple retail channels, making purchasing and marketing easier across the board.

Cashierless technology

Retailers are exploring cashierless technology like self-service registers to streamline the checkout process. In advanced cases of use, such as Amazon, AI and cameras are used together to track customers in-store, tally up selected items and automatically charge consumers as they leave – buyers no longer have to stop to pay.

Explore the latest in retail technology with JRTech

AI tools are here to stay. With incomparable data analysis capacities and a wide range of uses, AI applications are powerful allies for all retailers. Every business that needs to stay head of the competition by providing exceptional customer service and optimizing operations can rely on AI for help. 

In short, retail and AI go hand in hand. But AI can only function at its best with the right in-store technology to support it. JRTech is an established provider of digital technologies like electronic shelf labels, essential in-store communication tools that ensure your valuable AI-generated insights don’t go to waste.

Contact us today to acquire the tools you need to make the most of AI in retail. Act now to win over the retail shoppers of the future.