Generative AI in retail: How AI is revolutionizing retail

Generative AI (GenAI) for retail refers to the use of AI technologies that understand human language and respond with text or media to address common retail challenges. Unlike traditional AI, GenAI creates new content and processes large datasets to mimic human reasoning.

In retail and consumer packaged goods, GenAI is a game changer, promising to unlock $400 billion to $660 billion in economic value annually. Retailers use it to personalize marketing, generate content, summarize customer feedback for product innovation, and optimize supply chain decisions through accurate forecasting.

Key benefits of generative AI for retail

With GenAI, your retail business can achieve a wide range of operational and strategic benefits.

Enhanced customer experience

In a 2025 PwC survey, 52 percent of consumers reported they stopped using or buying from a brand after a bad experience with its products or services. Nearly a third cited poor customer experience as the reason they stopped buying from the brand.

73 percent of customers now rank the importance of the buying experience behind price and product quality, so retailers are turning to GenAI to inform their customer experience strategies beyond basic recommendations. Instead of relying on static algorithms, businesses use GenAI to analyze customers’

• Behaviors
• Preferences
• Past purchases
• Shopping activities
• Browsing patterns

Then, genAI interprets intent and context to deliver one-to-one suggestions that feel natural and relevant.

Consider how Amazon’s personalized recommendations use AI for e-commerce. The retail giant uses a product discovery AI Agent to help shoppers filter over 300 million products, find what they’re looking for faster, and discover relevant items that might interest them. What’s more, Amazon uses the technology to craft personalized product descriptions and promotional messages that match the shopper’s style and region. This kind of thoughtful, in-depth personalization strengthens brand loyalty and keeps customers engaged.

Automation of repetitive processes

Retailers are automating routine (yet essential) processes to save time and meet the rising demand for convenience and labor cost optimization. By 2034, the retail automation market is projected to reach $71.91 billion, with GenAI driving efficiency in several repetitive processes.

Inventory management

With GenAI, your retail business can analyze vast amounts of data, such as historical sales, real-time market trends, seasonal patterns, and external factors, for accurate demand predictions. The technology also lets you monitor stock levels and trigger replenishment when necessary to reduce overstocking and stockouts. According to a McKinsey report, organizations that introduce AI in their supply chains can

• Increase their revenue by up to 4 percent
• Reduce inventory by up to 20 percent
• Lower supply chain costs by up to 10 percent

Product design

GenAI makes it easier for retailers to incorporate customer preferences or trend data in product design. You can analyze customer feedback or social media trends and identify emerging trends before the competitors do, then generate prototypes. One of the good GenAI examples in product design is Nike’s use of artificial intelligence and athlete performance data to generate concept visuals and rapidly prototype new designs.

Customer support

Few retail AI tools have impacted the industry as broadly and deeply as customer service and support AI Agents. These types of GenAI allow retailers to efficiently manage an increasing number of digital customer interactions and improve the quality of communication.

With the right AI Agent, you can automatically handle routine inquiries and chat interactions, freeing your customer support staff to resolve more complex issues. In fact, a 2025 study from the Quarterly Journal of Economics says that AI-assisted customer support increased worker productivity by 15 percent.

Improved marketing and predictive analytics

From powering hyperpersonalization and automating marketing processes to altering the idea generation process, GenAI is rewriting how retailers do marketing. In fact, 85 percent of marketers report that they’re already using GenAI, with 90 percent seeing ROI. Instead of creating each marketing asset from scratch, you can prompt generative models to produce several variations of copy and visuals tailored to specific regions and communication channels. You’ll free your marketing team to focus on strategy instead of execution. 

But the true power of GenAI applications in marketing lies in combining its creativity with its predictive capabilities. The technology can interpret large datasets to forecast which campaigns or products are most likely to perform well. So, you can optimize ad spend and align promotions with real-time demand to drive stronger customer engagement. 

Cost-saving and efficiency gains

GenAI’s cost-saving capacity comes from its potential to automate routine tasks and optimize operations. McKinsey reports that GenAI can automate tasks that currently take 60 to 70 percent of employees’ time. Here are some ways the technology is cutting costs while improving efficiencies in retail.

Use cases of generative AI in retail

Practical use cases of GenAI in retail are almost limitless. But here are the five common ones your retail brand can integrate to engage customers more deeply, cut costs, drive revenue, and optimize operations.

AI-powered customer service and chatbots

You’ve most likely interacted with a customer service chatbot. Thanks to GenAI, retailers are now creating models that understand intent and respond conversationally, rather than only recognizing specific commands.

In fact, there has been a rise in virtual shopping assistants that help customers find products in online stores when they speak or type what they want. In some cases, users only need to share a picture, and the chatbot will locate a similar or exact match. This capability is crucial for large e-commerce stores, where navigation can be overwhelming. Customers don’t have to try to figure out product categories or struggle with endless search filters. They just ask for what they want.

A good example is the eBay ShopBot. It helps customers navigate eBay’s massive catalog to find the right deal. And it supports voice, text, and photo prompts.

Personalized shopping experiences and dynamic pricing

GenAI allows retailers to hyper-personalize marketing communications and promotions. With the technology, retail brands tailor experiences to each customer instead of a demographic group, as GenAI can rapidly create communications at scale. It can also analyze purchase history, browsing patterns, contextual factors, and preferences to create a custom-made experience.

Retailers are also using generative models for dynamic pricing, adjusting product prices in real time to reflect demand or other fluctuating factors. Amazon’s dynamic pricing engine, for example, changes prices millions of times per day to remain competitive while maximizing margins. Such adaptability helps customers always see relevant offers while retailers maintain profitability.

Virtual product creation

Retailers are using GenAI to shorten product design life cycles and spark innovation. The technology allows them to visualize product concepts in high fidelity much earlier in the design process, so they can gather more precise feedback to fine-tune every element of the user experience. McKinsey estimates that using GenAI in product research and design alone could unlock $60 billion in productivity.

Apparel brands, for instance, use AI to create virtual prototypes based on trend data and customer feedback to cut design cycles from months to weeks. Walmart has a Trend-to-Product genAI tool that analyzes emerging styles and generates mood boards to accelerate fashion turnover by 18 weeks. In home decor, retailers use GenAI to create realistic room visualizations to help customers see how furniture or paint colors will look in their home before buying. 

Supply chain optimization using predictive AI models

The retail industry’s success heavily relies on a supply chain’s reliability, and genAI gives retailers an edge. Many organizations today use digital twins — an AI tool that replicates the supply chain end to end — to plan scenarios, optimize operations, and inform decision-making. For example, GenAI in digital twins helps retailers predict which products will run out in a specific region and recommend adjustments before shortages occur.

Unilever’s AI-driven forecasting and replenishment model is a great example. It integrates forecast and actual sales data between the company and customers, synchronizing actual sales data with the sourcing materials for a more efficient supply chain. Similarly, Target uses predictive AI to monitor inventory levels across its stores and distribution centers to anticipate demand surges and optimize restocking schedules to avoid stockouts.

Fraud detection in online transactions

According to Juniper Research, e-commerce fraud is projected to cause global losses of $343 billion by 2027 due to payment fraud alone. GenAI is helping retailers mitigate these risks by synthesizing fraud patterns and enhancing anomaly detection. Unlike traditional fraud detection systems that rely on static rules, AI continuously learns from new transaction data to detect emerging fraud patterns. GenAI also generates synthetic examples of fraudulent behaviors to train itself faster and improve accuracy.

Retailers also use GenAI to flag suspicious orders and prevent account takeovers in real time. The proactive approach reduces financial losses and builds trust with online shoppers.

Challenges and limitations of generative AI in retail

GenAI in retail has massive potential. However, many retailers face steep financial and technical barriers that make the implementation of AI initiatives challenging. To help you maximize genAI’s value and realize its full potential, here are the common challenges you should address.

High implementation costs and required technical expertise

Building and deploying GenAI systems is costly. Beyond acquiring the technology, retailers must invest in

• Infrastructure such as cloud computing resources and GPU servers
• Data pipelines for collecting and integrating diverse data sources
• Skilled talent to train and maintain models
• Continuous model retraining so they remain accurate and relevant
• Compliance and security frameworks to protect sensitive business and customer data

For small retailers, these costs can be prohibitive. A no-code platform like Jotform helps lower the barrier by offering an AI-powered tool like a simple order AI Agent that doesn’t require technical expertise or heavy infrastructure.

Ethical concerns

While many organizations seek to adopt genAI, ethical concerns are a big challenge. If an AI tool uses incomplete or biased data, the results can reinforce stereotypes or produce negative recommendations. Similarly, a biased algorithm could favor misinterpreted sentiment in customer reviews. Adopt transparent AI governance practices and regularly audit training data to maintain ethical oversight and minimize reputational risks.

Dependence on quality data

Like any other subset of artificial intelligence, GenAI’s performance depends heavily on data quality and availability. Retailers often struggle to integrate data from multiple systems into a single, clean source of truth. Without quality data, even the most advanced AI model can produce unreliable outcomes that erode user trust rather than enhance it.

Potential for overreliance on automation at the expense of human touch

While automation boosts efficiency, excessive dependence can dilute your brand’s human touch. For instance, relying solely on a sales associate AI Agent may make the customer experience impersonal, especially when customers seek empathy or nuanced assistance. While the agent can answer product questions instantly, it can’t always sense frustration or build a rapport the way a human associate can. 

Future trends: Generative AI’s growing role in retail

GenAI continues to transform how retailers manage operations and engage with customers. The coming years look even bigger in terms of advances made in AI models and their practical application in commerce. Here are some trends to watch out for.

Shaping sustainable retail practices

Over two-thirds of Americans believe that sustainability should be a standard business practice, not an optional add-on. In fact, 44 percent think companies should eliminate unsustainable products from the market altogether. GenAI is helping retailers make sustainability achievable.

The technology analyzes production data to

• Identify waste
• Predict excess inventory
• Recommend eco-friendly supply options
• Design virtual samples before production to reduce waste
• Optimize distribution routes

As more people become more eco conscious, GenAI will help retailers meet environmental targets while maintaining profitability.

Emerging technologies: Digital twins and immersive shopping

Generative AI is increasingly merging with other technologies like digital twins, augmented reality (AR), and virtual reality (VR) to create an immersive retail environment. A digital twin allows retailers to test promotions and forecast outcomes before employing them in the real world.

Meanwhile, AR and VR, powered by genAI, allow shoppers to visualize furniture in their living rooms or try on an outfit virtually before purchase. Retailers like IKEA and Nike already use such a system to bridge the gap between online and in-store experiences to increase customer experience and confidence.

AI-driven customer experience innovations

In the near future, GenAI will become a copilot in retail decision-making and customer engagement. Imagine AI systems that remember a customer’s past interaction across all touchpoints and proactively generate new ideas before the shopper asks.

We’ll also see pre-order AI collaborating with customers and generating custom offers based on voice or visual cues. As these technologies mature, retailers that blend GenAI with authentic human service will set the new benchmark for customer experience.

Transform your retail operation with Jotform AI Agents

GenAI is already changing how retailers connect with customers, and the Jotform AI Agents simplify the transformation with a no-code approach. With the agents, you can transform your forms into dynamic, conversational experiences that understand context and engage customers without a single line of code. You’ll train the agents with the data you provide.

If you run a modern retail business seeking to enhance customer interactions and data collection processes, you’ll appreciate that our intelligent forms will adapt to your unique business needs. Whether you want to streamline product inquiries and inventory management or handle order management and customer support, the agents will create a dynamic, conversation-like experience. Get started today by uploading your product catalogs, FAQs, or service documentation to create an AI assistant that enhances customer engagement and simplifies data collection.