How Generative AI Is Transforming the Retail Industry

Generative AI is rapidly disrupting retail, reshaping customer experiences, marketing, operations and more. Since e-commerce emerged in the 1990s, digital innovation has constantly changed retail. Now, generative AI presents the opportunity to take a monumental leap forward with its ability to produce detailed text, images, audio and video.

Generative AI has become more accessible in the last two years thanks to advancements in transformers, a type of machine learning that has allowed researchers to train larger models without having to label data, and large language models (LLMs), which put billions of parameters around that data. The results are nothing short of magical. We can now have authentic conversations with these LLMs, and they respond with knowledge and confidence. This holds even though they’re sometimes too confident, which is called a hallucination.

Recent Hostinger research highlights the explosive growth of generative AI adoption. About  78 percent of companies now regularly use these tools for at least one function. Their capabilities are so significant that 87 percent of retailers  have experimented with the technology, and a good number are likely to increase their AI investments, according to McKinsey.

Generative AI’s revenue potential is massive. It could add $240 to $390 billion annually in economic value for retailers, according to some estimates. On the buyer side, an Adobe survey found that 39 percent of shoppers have already used the technology for inspiration, and 53 percent more are interested in its potential. In the online retail segment, generative AI is expected to dramatically improve consumers’ experience. 

So, let’s explore the key generative AI applications that are revolutionizing retail.

More From Moudy ElbayadiPragmatic Root Causes of Data Breaches

Ways Generative AI Is Transforming Retail

Facilitating E-Commerce

While e-commerce has already transformed how people shop over the past couple decades, generative AI is pushing that evolution even further, making it easier than ever for users to find and buy what they need. For example, OpenAI has integrated product discovery and purchasing capabilities directly in ChatGPT, where the chatbot detects shopping intent, recommends items from retailers like Etsy, Shopify and Walmart, and completes transactions through a protocol do-developed with online payment processing tool Stripe. 

Although this implementation of AI in the retail industry is fairly new, it could have significant implications. Because of its potential to streamline the shopping experience and remove friction points, platforms like ChatGPT may significantly influence purchasing decisions and could push retailers to lean further into AI in the coming years.

Reimagining Customer Experiences

On-demand personalized experiences are crucial for customer satisfaction and loyalty. AI chatbots enable instant 24/7 customer support in any language for queries on product recommendations, order status, and more. With speed and accuracy, they handle common requests so service teams can focus on complex issues and optimize their workloads. It’s like having your most talented and knowledgeable staff available to all your customers at all times! This level of personalization prevents losing customers to competitors and provides a seamless and speedy response for demanding consumers.

Further, generative AI allows granular customer segmentation, which means you can offer them the right product or service at the right time. Retailers can define categories like “high-value customers,” “discount-driven shoppers,” “families with pets,” and. “expecting parents.” You can then develop unique product suggestions for each group and subgroups based on their interests, needs, and behavior. 

At Shutterfly, we have continued to experiment with new ways to interact with our customers and help them find and personalize the ultimate product. For example, we are testing a personal AI designer to help customers design anything from customized holiday cards to photo books. The AI guides them in choosing layouts, images, and text for a one-of-a-kind personalized product. It’s part of the conversational commerce capabilities we are building that allow customers to engage with the AI agent and craft alongside the bot. 

The personal AI designer will help our customers create Shutterfly products in less time, intending to continuously improve customer satisfaction. We’ve seen similar results with current AI offerings that reduce creation time, such as our 24-hour designer service for photo books. Additionally, having the AI designer feature available within our mobile app will increase downloads and utilization. Connecting with our customers on the app will allow us to deliver a more personalized and engaging experience with features such as push notifications.

Revamping Marketing Campaigns

When it comes to marketing, generative AI can create product descriptions, social media posts, and other materials faster than humans can. Retailers can maintain personalized messaging, communication and special offers while exponentially scaling campaigns. Once gen AI learns what your brand messaging is, it can stay on point while allowing you to try out different messages. For example, you can curate language for email campaigns that target different groups of people such as pet owners, parents or travel enthusiasts to deliver product suggestions that actually matter to your customers. You can also A/B test messages based on the tone you set and see which ones improve customer actions. For example, if you are running a campaign to re-activate old customers, you can provide metadata about them to generate custom messages that could activate them and get them to respond.

With the right prompt engineering, creative input, and ethical guidance, generative AI can generate numerous high-quality ads with images and captions tailored to a brand’s voice and purpose with a powerful call to action. The same output would take in-house creative teams much longer to produce.

You can also use generative AI for dynamic pricing campaigns and personalized promotions. It can analyze individual customer data and broader market trends to generate optimized pricing and custom discount offers, boosting conversion rates and profitability.

Supporting Customization at Scale

Consumers increasingly demand unique, customized products. Generative AI makes offering such personalization at scale possible. With the tech built into apps for easy product customization, consumers can create one-of-a-kind coffee mugs, t-shirts, or photo books. This satisfies the growing demand for self-expression in products. Customers feel engaged in the design process while retailers deliver a personalized final product. Brands embracing customization empower consumers to become co-creators. 

We have seen Nike allow their customers more creative control with offerings like Nike By You, which allows people to customize shoes for a one-of-a-kind style. In Shutterfly’s rich photo book creation experience, we also provide customers with an AI-generated 24-hour designer service that helps make a photo book within 24 hours and also gives them the power to create and design their own elements. Giving consumers the ability to make custom products easily delivers on the growing demand for personalization and increases customer retention and loyalty.

Reinventing Inventory and Demand Planning

Inventory issues cause massive overhead costs. But AI forecasting predicts demand more accurately, preventing overstocking or shortages. It analyzes past sales data, trends, seasonality, and external factors to forecast future needs.

During busy seasons, generative AI automatically increases reorder points to meet demand surges. For a Black Friday promotion, for instance, it may boost inventory volumes by 30 percent over regular months based on past spikes. This optimization reduces expenses and revenue losses.

Informing Product Development

Generative AI also assists in product development and design, generating innovative ideas informed by customer preferences, market trends, and past sales data.

Retailers can provide a simple prompt such as “2023 outdoor furniture designs targeting eco-conscious millennials.” The AI will then produce numerous high-quality images and descriptions of potential new products tailored to that customer segment. The fine-tuned model can also stay on brand to reflect the unique aspects of your firm while generating entirely new ideas that can then be further refined by designers. 

Generating Actionable Business Insights

Large language models analyze disparate data sources and communicate insights in plain language. This optimizes inventory positions, labor scheduling, supply chain issues, and other critical business decisions. Business leaders are able to uncover trends and patterns in behavior such as what time of day they typically shop, what product categories are their favorite, or when they start their holiday shopping that could help them create even more compelling offers for their customers. One compelling example is the ability to read online trends and using that data from social media to inform designs or content that a particular segment would react to.

More on Artificial IntelligenceExplore Built In’s AI Coverage

Prepare Your AI Strategy

As retailers adopt generative AI, responsible development is crucial including prioritizing ethics, security and privacy. Start by identifying high-impact areas like customer service bots or personalized recommendations. Next, pilot small experiments to guide your AI strategy.

You should also aim to capture company-specific data and use it to train generative AI models. This powers more accurate outputs tailored to your customers and products by better understanding behaviors, strengths and weaknesses.

The Future is Here

The retail potential of generative AI is vast, but it requires careful management. This technology performs best as an assistant to humans, not a replacement. With proper governance, generative AI unlocks immense opportunities to enhance customer engagement and drive sales.

The future is here. Generative AI enables revolutionary customer experiences while optimizing complex backend processes. Retailers who embrace it will gain a true competitive edge. Is your business ready?

Related ReadingAI In Retail and E-Commerce: 33 Examples to Know

Significant AI Advances in the Retail Industry

Over the past two decades, major retailers have leveraged artificial intelligence to personalize experiences, streamline purchases, and enable entirely new ways for customers to interact with products and services. These are some of the most significant AI advancements in retail:

Walmart Partners with OpenAI for AI-First Shopping Experience (October 2025)

Walmart announced a partnership with OpenAI to integrate ChatGPT’s Instant Checkout feature, allowing customers to shop directly through the chatbot. This collaboration aims to create an AI-first experience for Walmart shoppers, enabling users to plan meals, restock essentials or discover new products simply by chatting. The initiative reflects Walmart’s commitment to leveraging AI to enhance customer engagement and streamline the shopping process.  

OpenAI Introduces Agentic Commerce Protocol (September 2025)

OpenAI introduced the agentic commerce protocol, an open standard that facilitates conversations between buyers, their AI agents, and businesses to complete purchases. Co-developed with online payment processing tool Stripe, this protocol is designed to streamline the shopping process by enabling more seamless interactions across platforms, marking a significant step toward the future of online shopping.

Amazon Introduces ‘Rufus’ AI Shopping Assistant (February 2024)

Amazon launched Rufus, a generative AI-powered shopping assistant integrated into the Amazon Shopping app and website. Rufus assists customers by answering product-related questions, providing comparisons and offering personalized recommendations based on Amazon’s extensive product catalog, customer reviews and community Q&As. This initiative aims to streamline the shopping experience and improve customer satisfaction. 

Sephora Introduces Sephora Virtual Artist (February 2016)

Sephora launched the Sephora Virtual Artist, an application that uses augmented reality to allow customers to try on makeup virtually using their smartphones or in-store tablets. This innovative tool utilized facial recognition technology to provide personalized product recommendations, enabling customers to experiment with different looks without physically applying the products. By integrating AR with AI-driven personalization, Sephora enhanced the shopping experience, making it more interactive and tailored to individual preferences.

Macy’s Introduces Early Virtual Assistants (2009)

Macy’s experimented with early chatbots on their website to assist customers in navigating products and answering FAQs. These early virtual shopping assistants foreshadowed later, more sophisticated AI chatbots like Amazon’s Rufus.

Target Implements Predictive Analytics for Shopping (2002)

Target implemented predictive analytics to anticipate customer needs, using purchase histories to suggest products and promotions tailored to individual customers. This early AI application in retail highlighted the potential for data-driven personalization to boost sales and customer loyalty. 

Amazon Develops Item-to-Item Collaborative Filtering (2003)

Amazon introduced item-to-item collaborative filtering, a recommendation algorithm that identifies similarities between products based on customer purchasing patterns. Unlike traditional user-based models, this approach compares items directly, enabling more accurate and scalable recommendations. By analyzing which products are frequently bought together, Amazon can suggest relevant items to customers, enhancing the shopping experience and driving sales.

Frequently Asked Questions