Different for a Reason: Customizing Retail AI Use Cases to Your Needs

While AI—particularly Generative AI—is still in its infancy within the Retail industry, a clear convergence is emerging in how it is being deployed, with notable similarities in pilots and subsequent rollouts. However, critical differences arise in how Retailers prioritize these pilots and use cases, creating opportunities for differentiation.

Our takeaway? Learn from others but ensure AI solutions meet the Retailer’s distinctive brand promise, customer experience strategy, and associate needs. Contact us to learn more about our Gen AI Transformation Immersion, designed to help your team create a clear and customized AI action plan for real results.

AI Use Cases in Retail 

We have seen four consistent use cases in Retail: Associate (employee) enablement, Product content creation, Marketing content creation, and Customer service. We provide our analysis of how each solution improves efficiency (bottom-line productivity), efficacy (top-line revenue and/or margin growth), or both.

1. Associate Enablement: Solutions that help Retail employees conduct their job more efficiently and effectively

Retailers are launching AI-enabled Associate platforms; however, the focus of these platforms varies, with some targeting HQ employees while others are designed specifically for store associates.

While all associate enablement platforms can extend and enrich a Retailer’s associate capabilities (efficiency play), those platforms that support and reinforce key strategies and Customer Experience (CX) parameters provide strategic differentiation and provide opportunities for revenue and margin growth.

• Walmart initiated solution(s) for HQ-based employees in 2023, rolling out to store associates beginning in 2024. At the recent Consumer Electronics Show, Walmart CEO Doug McMillon addressed the balance between technology and people, focusing on an approach that focuses on “technology to serve people, and not the other way around”. Beginning a pilot with HQ-based personnel enables structured experimentation, since we know most knowledge-workers are experimenting with AI – whether sanctioned or not.
• Target took a different approach, solely focused on the store associate, piloting the application first with a subsequent rapid rollout across their chain. It’s “Store Companion” chatbot can answer on-the-job process and procedure questions, address specific customer questions, and more. What is notable about this solution is the relatively rapid development and deployment of this solution – developed and rolled out chainwide in roughly six months.
• Tractor Supply’s associate application “Ask GURA” is tightly linked to their overall customer engagement strategy, reinforcing their existing GURA customer experience strategy (Greet, Uncover, Recommend, and Ask) with tools to support each component. For example, the Uncover component, critical to the customer experience, is enhanced through digitized tools: a) personalized product recommendations; b) insights into the product database; and c) in-store product locator. This innovative approach earned Tractor Supply a 2024 CIO 100 Award for its alignment with their strategic goals and impact on associate performance.

2. Product Content Creation: Solutions that create and/or extend the information about the product, inclusive of text, photos, and video

We have seen large, marketplace-oriented Retailers achieve significant results through AI-enabled product content creation. For those Retailers with a deep catalog, automation or automated extension of product information is proving to be impactful in the product discovery and sales process.

• Walmart acknowledged that it has used multiple LLM (large language models) to create or improve more than 350 million pieces of data in its product catalog, stating that manual efforts to do the same would have required 100 times the existing headcount. They have also stated they plan to extend this technology to assist in shopping discovery.
• Target has taken a similar approach, leveraging GenAI to make enhancements to its product detail pages (PDP) as well as assist in more intelligent, guided search. The supporting technology partner has claimed Target is now able to automate 96% of product inspections with up to 99% accuracy.
• eBay’s Magical Listing Tool uses AI to extrapolate details about product listings from a small amount of seller-supplied information. The first iteration of this tool was implemented in 2023 and auto-generated product content from a seller-supplied product description (text from text). The second iteration of this platform auto-generates product content from a seller-supplied product photograph uploaded through the eBay app. This solution works well for eBay’s business model where thousands of individual sellers struggle with a “cold start” product listing.

3. Marketing Content Creation and Personalization: Solutions that create content used to support individual product discovery (1:1 marketing) as well as more conventional marketing efforts (1:many marketing)

Marketing content creation and personalization has created the clearest and quickest ROI of all the use cases we are profiling – making it a “no brainer” for piloting. That said, these solutions require both clean data sets and integration with brand standards and guidelines. Without both components, marketing content may be quickly created but personalization and brand positioning will not meet marketing standards and objectives.

• Amazon has pioneered this effort with personalized product recommendations and customization of the home page for each customer using data collected from stated preferences, past purchases, and active wish lists and cart components. Amazon also recently introduced Rufus, an “expert shopping assistant” trained on both Amazon and external data to answer customer’s product questions and make product recommendations.

Although Amazon remains the leader in this space, we are now seeing this use case being piloted and implemented at the smallest of Retailers with limited investment costs and clear payback. Additional evidence of this use case becoming “mainstream” is indicated by multiple universities creating classes directed at creating AI Marketing use cases.

4. Customer Service: Solutions that facilitate and enhance customer service before, during, and after the sale

We have seen many large Retailers pilot AI in Customer Service, with significant savings reported to their financial stakeholders. While most success stories have profiled post-purchase activities, we see the greatest potential achieved when a comprehensive solution encompassing the entire customer journey – pre-purchase, during-purchase, and post-purchase.

• Walmart has been using both Natural Language Understanding (NLU) and text-enabled Chatbots, enabling their customers to connect in the manner they choose. They have deployed these solutions across the entire journey, from product discovery to reordering to scheduling pickup or delivery. Walmart has also deployed AI chatbots to immediately address the most common customer service inquiries for order status, returns, and more across the US, Mexico, Canada, Chile, and India.

• H&M has effectively deployed AI Customer Service Chatbots in pre-purchase activities, including assistance in product information, availability and sizing. Their solution is specifically designed to mimic a human experience, engaging in friendly and conversational tones. Given the high cost of returns in the Specialty Apparel sector, investments made in ensuring the right selection may be more cost effective than post-purchase endeavors.

2025 is the year to act and realize results from Generative AI, as advancements in technology and competitive pressures make adoption critical for staying ahead. At McMillanDoolittle, we specialize in guiding retailers through transformation and ensure AI solutions align with their brand promises and strategic goals. Reach out to discuss.

Articles Referenced: RetailBrew.com, Target.com, TractorSupply.com, Amazon.com,  Invoca.com, ModernRetail.com, Target.com, Super.ai, Ebayinc.com, Sitegpt.ai, Walmart.com, Forbes