Augmented Retail: Walmart built internal AI platform serving 1.5M associates daily

Walmart has built Element, an internal AI foundry platform that now serves 1.5 million associates across its operations, handling 3 million daily queries from 900,000 weekly users. Rather than buying enterprise AI solutions, the retail giant created a manufacturing-style approach to AI development that treats applications like products rolling off an assembly line, fundamentally changing how large enterprises can deploy artificial intelligence at scale.

What you should know: Element’s foundry model eliminates traditional AI deployment cycles by standardizing the entire development process from conception to production.

• The platform is LLM-agnostic, allowing Walmart to select the most cost-effective model for each specific use case or query type.
• Development time has shifted from quarters to weeks, with new applications inheriting battle-tested components from previous builds.
• Five major applications already run on Element: AI task management, real-time translation across 44 languages, conversational AI, AR-powered inventory systems, and corporate document analysis.

The big picture: Walmart has industrialized AI development by treating it as a manufacturing capability rather than individual software projects.

• “We have built Element in a way where it makes it agnostic to different LLMs,” explained Parvez Musani, Walmart’s SVP of stores and online pickup and delivery technology. “For the use case or the query type that we are after, Element allows us to pick the best LLM out there in the most cost-effective manner.”
• The platform transforms operational complexity into competitive advantage, with each of Walmart’s 4,000+ U.S. stores generating unique data patterns that Element leverages rather than averages away.

Key performance metrics: Element’s real-world applications demonstrate significant operational improvements across Walmart’s workforce.

• Task management planning time reduced from 90 minutes to 30 minutes, saving managers 60 minutes daily.
• Conversational AI handles 30,000 daily queries with zero human escalation for routine tasks.
• Inventory accuracy improved from 85% to 99% using AR-powered VizPick technology that combines RFID tracking with computer vision.

How the foundry model works: Element operates like a lean manufacturing system, processing multiple development requests concurrently with minimal waste.

• Data scientists submit specifications while Element handles model selection, infrastructure provisioning, scaling, and deployment automatically.
• Unified data pipelines connect supply chain information directly to store floor operations, creating development fuel from operational data.
• Built-in feedback loops capture signals from 1.5 million user interactions to continuously improve applications before new launches.

What they’re saying: Walmart executives emphasize the collaborative approach driving Element’s success.

• “Our associates are constantly giving us feedback, which is allowing us to iterate and be agile in delivering those capabilities for them,” said Brooks Forrest, Walmart’s VP of Associate Tools.
• “At our scale, with over a million associates across 4000-plus stores, it’s really important to have that simplicity for associates and to provide them these tools,” Forrest continued.
• Musani noted the platform’s flexibility: “Element allows us to pick the best LLM out there in the most cost-effective manner, and also the one that is going to give us the best answer that we are looking for.”

Competitive implications: Element creates compounding advantages that challenge conventional enterprise AI deployment strategies.

• External platforms optimize for generalization across industries, while Element optimizes specifically for Walmart’s 2.1 million associates worldwide.
• Competitors face difficult choices between massive investment in similar capabilities, accepting vendor limitations, or falling further behind as Walmart’s foundry accelerates.
• The shift planning tool alone translates to millions in labor cost savings when multiplied across dozens of Element-built applications.

Why this matters: Walmart’s approach provides a blueprint for enterprise AI transformation that redefines deployment strategy beyond traditional buy-versus-build decisions.

• The foundry model treats AI development as a repeatable, scalable manufacturing process rather than unique project implementations.
• Success stems from building organizational capability to consistently turn AI potential into operational reality at scale.
• This represents a fundamental mindset shift from thinking of AI as software to install toward AI as a capability to manufacture internally.