Walmart builds "orchestrator" AIs that act as project managers for other task-based agents (e.g., writing user stories). This system automates the product development lifecycle, from discovery to developer handoff, only alerting the human PM for key decisions or anomalies, dramatically boosting efficiency.
Walmart replaced a $25 million/year translation process with an AI platform that costs 1% of the original. The system uses orchestrated AI and human experts to translate the *intent* and cultural nuance behind words—not just literal text—processing millions of items in milliseconds and boosting customer trust.
Poor translation isn't just a content error; it's a fundamental breach of trust. Walmart's CPO states that data shows 71% of customers lose faith in an entire website or app if the language is incorrect, highlighting localization as a critical component of brand credibility, not just a line item.
To manage its 18 international markets, Walmart is moving from bespoke tech stacks to a single, multi-tenant global platform. The key to this strategy is allowing local markets to build custom extensions on top of the core platform, balancing global efficiency with the need for hyper-local innovation.
Walmart leverages agentic AI to learn from its vast complexity across languages, brands, and markets. Instead of slowing them down, this complexity serves as a massive training dataset, making their AI systems smarter and more resilient, creating a unique competitive edge that is difficult for others to replicate.
Walmart measures the ROI of its internal AI tools for product managers using a three-part framework. They track user adoption (3,100 PMs), output accuracy (88% of AI-generated user stories are accepted on the first pass), and efficiency gains (a 75% reduction in time spent on the task).