AI in retail: top use cases, benefits & implementation best practices

Retailers are under increasing pressure to meet rising consumer expectations while remaining agile and effective in changing market conditions. The rapid growth of ecommerce and the shift to omnichannel shopping have made it imperative for retailers to coordinate and analyze vast amounts of information in real time across sales, marketing, supply chain, inventory, and customer service channels to be able to respond quickly to market changes. To address these demands, many companies are turning to technologies like AI to automate operations while improving customer experience and satisfaction.

Over the next five years, the global AI in retail market is projected to exceed $160 billion, increasing fivefold in value. This forecast appears highly realistic, as many retailers are already implementing AI tools to transform their marketing, inventory management, and customer service operations. Moreover, as the use of AI expands, retailers increasingly collaborate with AI consulting firms to deploy enterprise-grade solutions and build models tailored to their use cases that drive measurable business outcomes.

Benefits of artificial intelligence in retail

The implementation of artificial intelligence tools can bring a wide range of benefits to the retail industry, enabling retail businesses to manage operations more efficiently, increase their profits, personalize customer engagement, and strengthen brand loyalty.

Improved customer experience

AI solutions help retailers better anticipate customer needs, provide them with instant support across different service channels, and personalize customer communication at scale. This leads to faster issue resolution, more relevant product recommendations, and more consistent customer support across all company touchpoints. Over time, such a tailored communication and service approach drives higher customer satisfaction and contributes to customer loyalty and improved lifetime value.

Better decision-making

By using AI in retail analytics, companies can extract actionable insights from large volumes of complex and unstructured data to foresee shifts in demand, supply availability, or market trends in advance. AI-powered retail analytics also plays a key role in uncovering patterns that are otherwise difficult to detect, supporting more informed strategic planning while reducing reliance on guesswork.

Increased operational efficiency

By enabling intelligent process automation, AI reduces the time and resources spent on tasks of varying complexity, like inventory tracking, customer interaction, and reporting, enabling staff to focus on higher-value activities that require active human involvement.

Reduced operational costs

AI tools help retailers reduce their operational costs by enabling smarter resource allocation, reducing overhead, and cutting inefficiencies across retail operations. This allows retailers to reinvest savings into growth initiatives such as product innovation, customer experience enhancements, and market expansion.

Greater business resilience

By processing vast datasets in real time and spotting subtle patterns in data, AI-driven solutions enable retailers to better prepare for previously unforeseen events, including supply chain disruptions, regulatory changes, and market volatility. Besides, AI-based analytics tools help simulate different outcomes, allowing retailers to explore various possibilities and develop contingency plans to minimize business downtime and remain stable even under pressure.

Key use cases of artificial intelligence in the retail industry

Inventory management

AI helps retailers maintain accurate stock levels across locations and avoid stockouts or overstocking. Here are the tasks AI software can perform to streamline inventory management and planning workflows:

• Tracking stock levels in real time across the shop floor and warehouses using sensor data and camera input
• Analyzing turnover rates to identify underperforming products for informed merchandising decisions, such as markdowns or product discontinuation
• Forecasting product replenishment needs based on sales velocity, current stock, and supplier delivery timelines
• Recommending supplier order timing and product quantities to balance warehouse space and customer demand
• Alerting store staff about projected stock shortages before they occur based on current demand, remaining stock levels, sales velocity, and historical replenishment data

Demand forecasting

Retailers use AI tools to generate more accurate demand forecasts based on historical and real-time sales data and a range of other factors. Here is how retail specialists can employ AI for this task:

• Processing historical sales data, seasonal trends, promotional patterns, and other variables to forecast demand across product types, SKUs, and store locations
• Detecting shifts in consumer purchasing intent and preferences based on customer behavior on their social media, feedback left on the company’s channels and review websites, local events, and economic changes
• Updating forecasts automatically as new relevant data becomes available

Price optimization

AI enables retailers to develop dynamic pricing strategies aligned with changes in demand, competitor activity, and inventory levels. Here’s how pricing engines and price tracking software equipped with AI capabilities help retailers:

• Monitoring competitor prices and market activity to detect undercutting and overpricing
• Calculating optimal prices and generating pricing recommendations based on stock levels, sales velocity, and customer insights
• Predicting how price changes will impact profit margins and sales volumes

Loss prevention & fraud detection

Retailers use AI to detect anomalous patterns across their transactions and in-store activity and perform the following risk-detection and loss-prevention activities:

• Flagging unusual transaction patterns like repeated returns, inconsistent location data, or mismatched payment and account information
• Analyzing in-store video feeds to detect theft-related behavior
• Comparing sales data with inventory logs to spot product loss or scanning errors
• Identifying manipulation attempts at self-checkouts using image recognition and behavioral pattern tracking

Supply chain optimization

AI helps streamline logistics operations by analyzing a wide range of real-time and historical data to help retail businesses plan more efficient delivery routes, anticipate shipping disruptions, and optimize the allocation of warehouse space, delivery vehicles, staffing resources, and inventory. Here’s how AI algorithms for supply chain management support retail efficiency:

• Predicting potential shipping delays by combining supplier performance history with live data, such as weather forecasts and traffic congestion data
• Adjusting delivery schedules and routes based on real-time data
• Analyzing supply chain data to uncover recurring delivery failures and recommending process improvements to prevent future disruptions

In-store experience enhancement

Retailers apply AI to monitor in-store activity and adapt store layout, inventory, and customer service based on shopper behavior and store performance data. These are the optimization tasks AI systems perform:

• Mapping customer movement through aisles to help store staff optimize product placement and reduce congestion
• Using computer vision and facial recognition to identify returning shoppers for more personalized in-store assistance
• Analyzing shopper interaction with displays or products to identify items consumers are most interested in
• Streamlining self-checkout  by verifying products purchased, preventing mis-scans, and reducing checkout delays with the help of computer vision techniques like image classification and object recognition

Workforce management

AI helps retail managers align staffing levels with the projected number of customers and other operational needs. Here’s how AI systems support smarter workforce management and recruiting:

• Estimating staffing needs based on forecasted workloads, sales targets, and seasonal trends
• Creating working schedules that take into account employee availability and labor regulations
• Analyzing attendance data and employee KPIs to detect hidden patterns and correlations that reveal productivity drops, signs of work dissatisfaction, and risk of workforce attrition
• Suggesting how to efficiently reallocate staff during peak hours or in cases of understaffing or unplanned events

Marketing optimization

AI enables marketers to automate marketing campaign execution and fine-tune promotional messages to achieve better customer engagement. These are the marketing functions handled by AI tools:

• Segmenting audiences based on their purchase history, browsing behavior, and engagement patterns
• Delivering personalized recommendations and messages to the target audience through emails, push notifications, or social media using rule-based targeting and predictive models
• Predicting customers’ or audience segments’ response to different campaigns and suggesting the optimal timing, season, and offers for launching them
• Generating marketing texts and visuals aligned with the company’s tone of voice and style
• Testing content variations automatically to identify the highest-performing materials and formats

Real-life examples of AI in the retail industry

Leading retailers are increasingly implementing AI algorithms and solutions in their stores to extract meaningful insights from large volumes of customer data and streamline various store and administrative operations. Here are examples of renowned retailers actively applying AI solutions for their offline and online operations.

Walmart

Walmart has various AI solutions implemented across its retail operations to enhance both employee productivity and customer experience. For instance, its AI-driven task management tool helps store associates prioritize workloads in real time, cutting shift-planning time by up to two-thirds.

Another solution, a proprietary GenAI-powered assistant, handles over 3 million queries from associates per day, providing them with step-by-step guidance for procedures such as processing returns. Walmart also offers an AI-powered translation tool that supports 44 languages, enabling smooth conversations between associates and customers.

For customers, Walmart has implemented chatbot-driven support tools powered by retail-specific large language models to provide a more personalized shopping experience on its ecommerce website. The company also offers an AI-powered AR platform, Retina, that allows customers to view products in 3D before purchasing.

Sephora

Sephora applies AI across various operations, from inventory planning to personalizing product recommendations, to create an omnichannel customer experience, optimize resource usage, and align stock with customer demand. For example, the company automates replenishment and forecasts demand with the help of dedicated AI tools.

In physical stores, AI tools like Color IQ and Lip IQ are used to analyze customer facial features to suggest makeup shades using a skin tone database with over 10,000 colors. Additionally, Sephora’s virtual assistant helps consumers choose products and reserve them for in-store pickup.

Nike

Nike employs AI-powered tools to augment employees’ capabilities when designing new products. For example, Nike uses AI as part of its A.I.R. (Athlete Imagined Revolution) initiative to co-create product concepts with elite athletes. Designers start with in-depth interview sessions, exploring each athlete’s footwear needs, playing style, and design preferences, and then feed these insights into generative AI solutions to produce shoe design concepts.

To reduce product returns and improve customers’ confidence, Nike introduced Nike Fit, an AI-powered feature that uses computer vision to scan shoppers’ feet and recommend the correct shoe size, with measurements further stored in their NikePlus profile. In addition to this, Nike created a solution for motion capture to let shoppers see how its Dri-FIT ADV football kits cool and dry in real time.

Best practices for implementing AI in retail

For AI solutions to deliver the expected value, retailers should follow certain best practices related to creating a robust data ecosystem, nurturing technology expertise, and enforcing AI governance and compliance. Below are key areas that retailers implementing AI into their workflows should focus on.

Ensure high data quality

The accuracy of AI systems depends on high-quality training datasets and the data they utilize after deployment. To achieve high data quality, retail teams can set up automated  ELT (Extract, Load, Transform) pipelines to combine, clean, and organize data from multiple sources into a single, consistent dataset. In addition, using dedicated data management platforms allows teams to centralize datasets, apply standardized formatting, and manage version control, reducing inconsistencies between systems. Finally, companies still need to conduct selective manual labeling and review for selected new datasets to ensure that key items, attributes, and customer preferences are represented accurately.

Select AI providers with retail expertise

AI software vendors and service providers with retail experience are better equipped to address real-world challenges that store owners typically encounter. Having worked with retailers, these professionals can anticipate client hidden needs and design granular development and implementation plans tailored to the company’s data ecosystem, technology stack, regulatory requirements, and operational processes.

In addition to building tailored AI models and tools, retail-focused developers can deliver other services relevant to retailers, like integrating solutions with existing POS and inventory systems, ensuring compliance with retail data privacy regulations, or optimizing algorithms for seasonal demand fluctuations. Thus, when evaluating potential AI implementation partners, retailers should examine their relevant case studies and especially projects of scope and complexity similar to theirs.

Establish AI governance & accountability frameworks

Having adopted AI tools, retailers need to ensure these systems operate reliably, comply with regulations, and deliver the expected business value. To do this, business owners should implement clear AI governance frameworks. They include establishing clear accountability across teams, implementing real-time monitoring dashboards, and deploying automated tools for detecting bias, drift, and anomalies in model outputs.

In addition, by adopting frameworks like ModelOps, retail companies can streamline model deployment, updates, and versioning. Finally, equally important is maintaining transparent documentation, such as decision logs, model test results, and audit reports, to ensure AI solution traceability.

Maintain data privacy & regulatory compliance

Retail AI systems often process sensitive information, from personally identifiable data to browsing and purchase histories, which creates an increased risk of cyberattacks. To protect their information and comply with data management laws like GDPR, retailers should apply safeguards at every stage of the AI system lifecycle, from collecting and storing customer data to training, deploying, and updating AI models.

The first step is to conduct privacy risk assessments during AI system design and before its launch to identify potential vulnerabilities, such as model memorization of personal details. Then, it’s essential to apply data minimization and retention limits, collecting only what is necessary for the intended purpose and deleting it as soon as possible. Other important strategies include encrypting data in transit and at rest, establishing role-based AI software access controls, implementing data anonymization options, and regularly testing these safeguards in AI tools.

Educate & support your workforce

To effectively work with AI tools, retail teams need to understand their capabilities and limitations and know how to interpret their outputs. To build employee trust and prevent software misuse, managers should invest in educational materials, such as books, courses, and training sessions conducted by dedicated IT specialists who can provide guidance on AI tool utilization. Companies can also organize peer-learning groups where employees with deeper AI expertise share practical knowledge and tips with the rest of the staff.

To wrap up

AI continues to reshape the retail landscape, helping businesses improve their profitability, streamline various workflows, and strengthen customer relationships. Its applications range from assisting customers with online shopping to AI-powered solutions for store associates like task management systems.

The best part is that AI is proven to benefit both small businesses and global players like Amazon, and its potential extends beyond retail to industries such as gaming, manufacturing, insurance, and agriculture. If you’re interested in adopting AI, Iflexion is an experienced provider of AI and ecommerce development services that helps retailers implement tailored solutions to support data-driven decision-making and enhance their overall efficiency.