In 2025, more than 15,000 U.S. retail stores are expected to close, double the number in 2024 (CNN). The reasons are vast, from shifting customer expectations, rising operating costs, to the widening gap between digital innovation and physical retail experiences. 

Large language models (LLMs) offer a practical solution. These advanced AI systems are already influencing purchase decisions and are part of how retailers plan to transform how they engage customers, manage operations, and drive growth.

This article explains how LLMs apply to the retail industry, the benefits and risks they bring, and how leaders can utilize them to deliver measurable results. From automating product content to enabling conversational shopping and improving procurement, LLMs are reshaping retail strategy at the enterprise level.

What is an LLM in retail? 

Large language models (LLMs) are advanced artificial intelligence (AI) systems designed to understand, generate, and interact using human-like language. Trained on massive datasets, they utilize deep learning, an AI technique that simulates the human brain, and natural language processing (NLP), which enables machines to understand and produce language, to grasp context, predict trends, and generate human-like responses.

In retail, LLMs are fine-tuned on domain-specific data, including product catalogs, customer reviews, and sales transactions. This enables them to deliver highly relevant insights tailored to retail operations without losing context, ideal for the complexity of retail environments.

How can LLMs support the retail industry?  

The persistent challenges facing retailers, as referenced in ‘The Five Roadblocks Stunting Retail Growth’, include: 

  • Retail shrinkage due to theft and fraud
  • High employee turnover and ineffective training
  • Low customer satisfaction and conversion rates
  • Lack of integration between digital and physical retail
  • Inefficient internal communication and collaboration

These problems cost businesses billions in lost revenue, waste, and missed opportunities.

LLMs can address these problems directly. 

They can detect fraud patterns in transaction data and customer conversations to reduce shrinkage and tie together in-store and online operations. The right hybrid architecture enables real-time content generation and retrieval, even for long, multimodal datasets — a crucial advantage when synchronizing online and physical retail environments. 

For training, LLMs deliver consistent, on-demand knowledge through chat-based assistants, improving customer service by powering instant, accurate responses across various channels.

Used effectively, LLMs reduce waste, expedite operations, and enhance customer and employee outcomes.

Key growth areas for LLM in retail 

LLMs are creating new opportunities for retailers to deliver more personalized, efficient, and engaging customer experiences. Key areas of growth include the following: 

Visual search and multimodal LLMs

Multimodal large language models (LLMs) are AI systems that can process and understand both text and images simultaneously. In retail, they can unlock new ways for customers to discover products by allowing searches to be made through photos instead of typed queries.

For example, a customer can upload a photo of a jacket they like, and the model will recommend visually similar products, combining image recognition with any text the customer provides. This approach makes product discovery more intuitive and engaging, reducing friction during the shopping journey.

Conversational shopping

Conversational shopping allows customers to browse, receive recommendations, and complete purchases through natural dialogue with AI-powered assistants. Rather than navigating complex menus, customers interact conversationally as they shop.

An LLM-powered assistant can remind a customer of a previously viewed product and offer a personalized discount, creating seamless omnichannel engagement.

Hyper-personalization 

Hyper-personalization refers to the dynamic tailoring of content, offers, and interactions based on real-time customer data, including browsing behavior, purchase history, location, and sentiment. Unlike traditional segmentation, this approach adapts to individual users continuously across channels.

LLMs enhance this capability by interpreting large amounts of unstructured data at scale. For example, an AI assistant powered by an LLM can recall a customer’s previous conversation, adjust its communication style, and recommend items in the right size and color — all in one session. Models with memory persistence and multilingual output also support global retail strategies without requiring manual configuration per region.

LLM applications for retail 

LLMs drive faster decision-making, enhance content, and engage customers. Here are a few examples of LLM applications for retail in action.

LLM applications for retail

Marketing and content creation

LLMs enable retailers to automate marketing content production while maintaining brand voice.
For instance, a retailer could replace their human copywriting processes by using an LLM to generate hundreds of optimized product descriptions, localized ads, and blog posts. This shift allows creative teams to focus on strategy rather than repetitive writing tasks.

24/7 Personalized customer experiences 

LLMs power AI assistants that provide support across websites, mobile apps, and voice platforms. An AI assistant can greet returning shoppers by name, recommend items based on past purchases, and instantly resolve order status queries at any hour. 

Future hyper-personalized assistants are expected to further enhance this by remembering previous conversations, dynamically adjusting pricing, and tailoring support based on browsing behavior and real-time sentiment.

Sentiment analysis and customer feedback interpretation

LLMs analyze customer feedback across reviews, social media, and support channels to identify patterns or emerging trends/ issues. 

For instance, a retailer could use sentiment analysis to detect growing dissatisfaction with a particular product line and quickly trigger updates to marketing messaging or adjust inventory planning. 

Procurement and distribution

62.5% of retailers and consumer organisations believe that achieving ‘more efficiency in the supply chain’ presents the biggest opportunity for them. 

LLM-powered procurement enables faster and smarter purchasing decisions by transforming unstructured supplier communication into actionable data. Instead of reviewing emails and PDFs, a model can handle long, complex documents, making it highly suitable for extracting structured data from supplier emails, contracts, and shipment updates.

This streamlines reordering processes, reduces admin time, and ensures procurement teams can act before stock levels become critical. For example, when a shipment update arrives, the LLM can instantly surface any issues across SKUs, assess current stock positions, and trigger timely reorder suggestions, all without manual input.

Enhanced in-store operations

LLMs improve in-store operations by powering smart kiosks, automating customer queries, and optimizing product placement based on real-time analytics. AI-driven assistants guide customers to products, suggest complementary items, and speed up checkout processes. These innovations create a smoother, more personalized in-store experience, increase operational efficiency, and help bridge digital and physical shopping environments.

Conversational search and product discovery

LLMs transform product discovery by enabling conversational search, allowing customers to find products using natural language questions. AI-powered systems interpret context, correct errors, and predict user needs, making shopping faster and more intuitive. Retailers using conversational search improve engagement, shorten buying journeys, and increase conversion rates by offering smarter, more human-like digital interactions.

Real-world examples of LLM in retail

Case studies continue to reveal real-world examples of how companies apply LLMs within their businesses. 

Walmart 

Walmart developed Wallaby, a series of LLMs trained on decades of internal data to power customer-facing AI assistants in stores. Wallaby works with other models to deliver highly personalized shopping experiences. Walmart also introduced an AI-powered Content Decision Platform, predicting customer interests to create customized homepages that improve product discovery and engagement. 

Alibaba

Alibaba has launched Macro MT, a proprietary large language model focused on translation, delivering faster, more accurate translations for product listings and customer communications after noticing that “existing translation tools fall short in navigating the intricacies of culturally nuanced and idiom-laden expressions.” improving accessibility and enhancing the global shopping experience for millions of users across many of their sites. 

Amazon  

Amazon introduced “Interests,” an LLM-powered feature that translates everyday language into structured search queries and product recommendations. For example, a query for “travel-friendly skincare products from premium brands” is intelligently converted into specific product results. This innovation enables shoppers to find relevant products more quickly and enhances personalization across Amazon’s marketplace.

Best practice for deploying LLMs in retail  

LLMs only drive results when paired with the right tools, data, and oversight. Here are some key considerations to keep in mind before deploying an LLM model. 

Define clear objectives

Retailers must set specific goals to ensure LLM initiatives deliver measurable results.

For example, a retailer might aim to increase online conversion rates by 10% through hyper-personalized product recommendations within six months.

Objectives can include enhancing customer service, optimizing inventory management, automating content creation, improving customer interactions, streamlining decision-making processes, boosting operational efficiency, predicting market trends, or enhancing product demand forecasting.

Select the right LLM model

Not all  LLMs are created equal.  Selecting the right LLM necessitates a thorough evaluation of its capabilities, integration requirements, and long-term scalability.

For instance, a retailer could prioritize models that easily integrate with existing CRM and e-commerce platforms, ensuring flexibility to adapt as AI requirements evolve. It’s also wise to consider open-weight models (leaders include Jamba Mini and Jamba Large), which can be privately deployed within a retailer’s own infrastructure, thereby removing concerns about vendor lock-in and enabling fine-tuning with proprietary data.

Scalability and security 

Maintaining strong security is essential for protecting customer data. A private deployment enables retailers to use sensitive behavioral and transactional data securely for hyper-personalization, while avoiding cloud leakage and maintaining strict compliance with data privacy regulations​. This may be one option to consider. 

Security best practices include implementing data anonymization techniques and exploring technologies such as federated learning, which enables models to be trained without direct access to sensitive data. Additionally, these practices encompass processes involving encryption, multi-layered access controls, and regular security audits.

The future of retail with LLM 

Large language models offer more than incremental improvements, they enable fundamental change. From personalized experiences to automated operations, LLMs help retailers make faster, smarter decisions.

But technology alone does not drive success. Leaders must define clear objectives, ensure secure deployments, and maintain strong governance over data and outputs. Without these foundations, even the most powerful models can underdeliver.

Retailers that act now will set the pace of innovation and shape what the next decade of commerce looks like.

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