What metrics or KPIs are used to measure the success of technology initiatives at Walmart?

Walmart Global Tech measures the success of its technology initiatives using key metrics and KPIs. These include performance metrics to track how well the technology works, operational metrics to check efficiency, data and analytics metrics to understand customer and business impact, and change management metrics to ensure improvements are effective. Together, these metrics help make sure technology initiatives deliver value and support business goals.

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How does Walmart's Element platform support the full lifecycle of intelligent workflows?


Walmart's Element platform supports the full lifecycle of intelligent workflows by enabling teams to develop, test, and deploy AI applications efficiently. It provides a unified framework for reusing models, preparing data, and ensuring compliance, while leveraging MLOps, multi-cloud deployment, and GPU-accelerated experimentation to build intelligent systems that can reason, remember, and act.
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What are some recent blog topics that illustrate Walmart’s focus on intelligent systems and AI (agentic capabilities)?

Walmart highlights its work with intelligent systems through blogs on topics like managing product catalogs with LLMs, reimagining the developer role with AI, and tackling challenges such as agent and RAG sprawl. These posts reflect how Walmart is using intelligent systems to drive efficiency, improve collaboration, and scale innovation across retail operations.

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Who is eligible to attend the Converge event hosted by Walmart?

Only registered professionals such as technologists, industry leaders, entrepreneurs, and influencers working in retail tech, product, data science, engineering, design or related areas are eligible to attend Converge. Final acceptance of Converge Registration is at the sole discretion of Walmart Global Tech India.

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What is the main business problem that Walmart is trying to solve with LLMs in its product catalog management?

Walmart faced a massive, messy product catalog with millions of listings with inconsistent details, missing attributes, and poor discoverability leading to customer frustration, inventory mismanagement, and reduced sales. By deploying large language models (LLMs), Walmart automated the cleanup and enrichment of over 850 million product catalog entries with far fewer resources. This helped standardize descriptions, fill gaps in data, and ensure accuracy at scale, vastly improving search relevancy, customer experience, and operational efficiency.

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