Walmart’s AI Foundry Element Powers Rapid Deployment and Continuous Improvement for 1.5 Million Associates

Walmart has revolutionized its approach to enterprise AI by developing an internal platform called Element, which has transformed traditional software development into a streamlined, manufacturing-like process. Launched by Walmart’s AI foundry, Element now powers multiple AI applications across the company, supporting 1.5 million associates and handling 3 million daily queries from 900,000 weekly users. This innovative platform addresses the build-versus-buy dilemma by offering a solution that is both flexible and efficient. Element's design is centered around scalability and adaptability. It operates as an agnostic platform, capable of integrating various large language models (LLMs) to select the best one for specific use cases at the lowest cost. This approach allows Walmart to rapidly deploy new applications without being locked into any single vendor, maintaining a competitive edge in the fast-moving AI landscape. Parvez Musani, SVP of Stores and Online Pickup and Delivery Technology, highlighted this flexibility, noting that Element picks the best LLM for each query type automatically. One of Element’s key strengths is its ability to unify data access from diverse sources, including supply chain systems, customer analytics, and operational data. This integration ensures that AI applications can leverage comprehensive and real-time information, making them more effective and responsive to changing business needs. For example, the platform's real-time translation tool supports 44 languages, reducing shift planning time from 90 minutes to 30 minutes. The AR-powered inventory system and conversational AI chatbot are other notable applications built on Element. Walmart’s foundry model treats each AI application as a product on an assembly line. Standardized infrastructure, data pipelines, and deployment patterns enable rapid development and deployment cycles, cutting traditional timelines from quarters to weeks. Brooks Forrest, VP of Associate Tools, emphasized the importance of associate feedback in the development process, which is continually incorporated to enhance application performance and usability. Each new application built on Element inherits robust components from previous builds, reducing development friction and accelerating innovation. This iterative and agile approach ensures that the platform evolves based on real-world usage, improving with each user interaction. The shift planning tool, for instance, saves managers 60 minutes per day, and the conversational AI handles 30,000 daily queries, providing valuable insights and enhancing efficiency. Walmart’s LLM-agnostic architecture and continuous cost-performance optimization further solidify its competitive position. The company can seamlessly switch between models and providers, ensuring that it always leverages the most cost-effective and accurate solutions. This flexibility has been particularly beneficial for the translation tool, which handles different language pairs with varying model requirements. The foundry model also integrates real-time feedback loops, allowing Walmart to capture and utilize data from user interactions. This feedback not only improves existing applications but also informs the development of new ones. According to Musani, this continuous improvement is a cornerstone of ELEMENT’s success. Industry insiders recognize the significance of Walmart’s approach. They point out that the retail giant has created a blueprint for how enterprises can transform their AI capabilities. By treating AI development as a manufacturing process—standardizing procedures, modularizing components, and continuously refining—the company has set a new standard for scale and efficiency. Building similar capabilities internally, as Walmart has done, requires a substantial investment and technical expertise. However, the benefits are clear: greater control, faster innovation cycles, and significant cost savings. Rival retailers and enterprises face a challenging choice: invest heavily in internal foundries, accept vendor limitations with slower innovation, or risk falling behind in the AI race. Walmart’s strategy not only saves time and money but also enhances operational efficiency and employee satisfaction. With thin margins and intense competition, the financial impact of these improvements is strategic. For enterprise leaders struggling to scale AI projects, Walmart’s Element Foundry provides a compelling model. The true lesson is that AI’s success lies in building the organizational capability to turn potential into tangible operational gains at scale. Businesses that adopt this mindset will likely lead the next decade of AI-driven transformation.