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How Levi Strauss is using AI for its DTC-first business model

Simon Osuji by Simon Osuji
November 17, 2025
in Artificial Intelligence
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How Levi Strauss is using AI for its DTC-first business model
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In its pursuit of a direct-to-consumer (DTC) first business model, Levi Strauss is weaving AI and cloud platforms into its core operations.

The nearly 175-year-old apparel company is leveraging Microsoft technologies to modernise its consumer experiences and improve internal productivity. Levi Strauss’ approach provides a case study for other enterprises in using a unified technology stack to address a specific commercial objective.

AI ‘superagent’: A unified front-end for operations at Levi Strauss

A central component of this initiative is the development of agentic AI solutions. Levi Strauss is deploying an Azure-native “orchestrator agent” embedded within Microsoft Teams, which functions as a “superagent”.

This agent serves as a single conversational portal for employees across corporate, retail, and warehouse environments. Operationally, it fields employee questions and routes them to specialist behind-the-scenes subagents, some of which are already deployed. This points toward a consolidation of employee-facing tools; instead of training staff on multiple applications, the agent provides a single interface to streamline workflows.

This AI-centric business model is part of a wider goal outlined by Jason Gowans, Chief Digital and Technology Officer at Levi Strauss & Co.

“We’re rewiring Levi Strauss & Co to be a DTC-first, fan-obsessed retailer making every interaction faster, smarter, and more personal,” said Gowans. “AI sits at the center of that pivot—fueling innovation, elevating employee creativity, unlocking productivity, and helping us deliver the connected, memorable experiences that keep our fans returning again and again.”

This focus on productivity extends to the developer side. Teams are using GitHub Copilot for key projects involving quality engineering and release management. At the same time, other employees are being equipped with Microsoft Surface Copilot+ PCs. Employee feedback indicates these devices have led to improvements in speed and data handling, with features like the Copilot key reducing time spent searching for information.

The foundational cloud and security posture

Levi Strauss’ AI implementation rests on prerequisite infrastructure work. For business leaders, this highlights that advanced AI models require a consolidated cloud environment.

As part of its broader digital efforts, Levi Strauss is relying on Microsoft Azure, having moved application workloads from its on-premises data centres. The company used Azure Migrate and GitHub Copilot to plan and execute the consolidation of its private data centre environment.

This cloud foundation is also central to the company’s security posture. Levi Strauss is leveraging Azure AI Foundry and Semantic Kernel to build intelligent automation capabilities.

Security is being integrated into the AI framework itself and the tools are being used to power security agents and policy orchestration; a method that allows Levi Strauss to maintain a zero-trust security model while continuing to scale its AI-driven initiatives across global operations.

To manage the new hardware endpoints, the company is also using Microsoft Intune for zero-touch device onboarding and application deployment.

Leveraging the full AI ecosystem to pivot business model

Levi Strauss’ initiative demonstrates an ecosystem-based approach to adopting AI. Rather than a piecemeal rollout of individual tools, the company is integrating AI agents, developer tools, and new hardware on top of a common cloud platform.

Keith Mercier, VP of Worldwide Retail and Consumer Goods Industry at Microsoft, noted that Levi Strauss “exemplifies how iconic brands can reinvent themselves with cloud and AI technologies.”

For other enterprises, the Levi Strauss case study serves as a blueprint for linking AI and foundational cloud migration directly to high-value outcomes like the pivot to a DTC business model.

See also: IBM: Data silos are holding back enterprise AI

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