How Walmart uses GenAI across shopping and merchandising with Sparky

Walmart has deployed generative AI across its shopping and merchandising operations through Sparky, a conversational assistant that supports both customer-facing commerce and internal workflows. The retailer began testing Sparky in beta form in early 2025, initially focusing on product discovery and customer service functions. By 2026, the technology has expanded to include merchandising support, supplier collaboration tools and enhanced search capabilities across Walmart's digital platforms.

What is Walmart's Sparky assistant?

Sparky serves as Walmart's generative AI-powered shopping assistant, designed to help customers find products through natural language conversations. The assistant can interpret broad queries such as 'outfit for a beach wedding' or 'back to school supplies for a teenager' and generate curated product selections from Walmart's catalogue. The tool integrates with Walmart's mobile app and website, allowing customers to refine searches through follow-up questions.

Beyond product discovery, Sparky handles customer service enquiries including order tracking, returns processing and store information. The assistant can access individual customer purchase history and preferences to provide personalised recommendations, though Walmart has not disclosed specific adoption rates or customer satisfaction metrics for the 2026 period.

How does Walmart apply AI to search and personalisation?

Walmart has implemented generative AI to improve search relevance across its e-commerce platform. The retailer uses large language models to better understand customer search intent, including misspellings, colloquial terms and contextual queries. This technology allows the search function to interpret requests like 'things for a picnic' or 'comfy work clothes' and surface relevant products even when customers do not use specific product names or categories.

AI-driven personalisation engines analyse browsing behaviour, purchase history and demographic data to customise product recommendations and homepage layouts for individual shoppers. The system adjusts in real time based on session activity, prioritising categories and brands that align with demonstrated customer preferences. Walmart has integrated these capabilities across its grocery and general merchandise offerings, though apparel and fashion categories represent a growing focus area.

What merchandising functions does Sparky support?

Walmart has extended Sparky's capabilities to internal merchandising teams, where the assistant helps analyse sales trends, manage inventory levels and identify supply chain issues. Merchandisers can query Sparky for insights on product performance, stock availability across distribution centres and recommended reorder quantities based on historical demand patterns and seasonal forecasts.

The AI system also supports supplier collaboration. Vendors can use Sparky-powered tools to update product information, submit new item listings and receive automated feedback on catalogue quality and completeness. This streamlines the onboarding process for new products and reduces manual data entry for both Walmart's merchandising teams and external suppliers.

Key merchandising applications include:

• Automated analysis of sales velocity and stock turn rates across product categories
• Predictive recommendations for markdown timing and promotional pricing
• Natural language queries for inventory positions and fulfilment capacity
• Supplier communication tools for product data management and compliance
• Trend identification from customer search patterns and browse behaviour

How does Walmart's approach compare to other retailers?

Major retailers including Amazon, Target and Home Depot have introduced similar generative AI shopping assistants during 2025 and 2026, reflecting industry-wide investment in conversational commerce. Walmart's integration across both customer-facing and back-end merchandising functions distinguishes its approach from competitors focused primarily on enhancing the shopping experience.

The fashion and apparel sector has seen particular interest in AI-powered styling and discovery tools. Retailers are using generative AI to bridge the gap between inspiration-driven browsing and transaction-focused product search, allowing customers to describe desired looks or occasions rather than specific items. Walmart's mass-market positioning and broad assortment provide advantages for training models on diverse customer preferences and use cases.

What challenges does Walmart face with AI deployment?

Despite progress, generative AI shopping assistants encounter accuracy and reliability issues. Customers have reported instances where AI recommendations miss the mark on style preferences, price points or product availability. Ensuring that large language models accurately reflect Walmart's current inventory and pricing across thousands of stores and online fulfilment centres remains an ongoing technical challenge.

Data privacy concerns around AI's use of customer purchase history and browsing behaviour have prompted scrutiny from regulators and consumer advocates. Walmart must balance personalisation capabilities with transparency about data collection and usage, particularly as generative AI systems require extensive training data to improve performance.

Operational challenges include training merchandising teams and suppliers to effectively use AI-powered tools, integrating Sparky with legacy inventory management systems and maintaining consistent performance as the platform scales. Walmart has not publicly disclosed the cost of its generative AI investments or specific return on investment metrics tied to Sparky deployment.

What does Walmart's AI strategy signal for fashion retail?

Walmart's dual focus on customer experience and operational efficiency through generative AI reflects broader patterns in retail technology adoption. Fashion retailers are increasingly prioritising AI tools that serve multiple functions across discovery, merchandising and supply chain management, seeking to justify investment through efficiency gains alongside revenue growth.

The shift towards conversational interfaces challenges traditional category-based navigation and search-filter paradigms that have dominated e-commerce for two decades. For fashion merchants, this requires rethinking product taxonomy, attribute tagging and content creation to ensure AI systems can accurately interpret and recommend items based on style, occasion and aesthetic preferences rather than functional specifications alone.

As generative AI capabilities mature, retailers will need to address questions about creative direction, brand differentiation and the role of human curation in shaping customer experiences. Walmart's approach demonstrates both the potential for AI to enhance scale and personalisation, and the complexity of deploying these technologies across diverse product categories and customer segments in live commercial environments.