Building AI-first retail operations: real-world examples from Shopify, L'Oréal, and Lotte Homeshopping

Moving beyond isolated pilots into building AI-native operations requires reimagining entire workflows around AI capabilities rather than implementing point solutions. Organizations that achieve breakthrough results in one function use those wins to catalyze adoption across the enterprise.

The examples below showcase how retail and e-commerce organizations use Claude to transform operations across multiple functions simultaneously.

Merchant enablement and platform productivity: from setup to scale

Retail organizations achieve dramatic gains when they reimagine core workflows around AI capabilities. An e-commerce platform, a global beauty company, and a home shopping operator demonstrate how AI transforms operations.

Empowering millions of merchants worldwide: Shopify

Shopify (opens in new tab), the global e-commerce platform powering millions of merchants, deployed Claude to transform how businesses build and grow online stores, democratizing expert guidance that was previously available only to enterprises with dedicated teams.

The challenge: helping every merchant succeed, regardless of experience

Running an online business requires expertise across marketing, analytics, inventory management, and customer engagement. Small merchants and first-time entrepreneurs often lack the resources for dedicated specialists, creating barriers to their first sale and ongoing growth. Meanwhile, Shopify's internal teams needed faster ways to build tools without bottlenecking on engineering resources.

The solution: AI-powered merchant assistance and internal productivity

After evaluating multiple AI providers, Shopify selected Claude for its reasoning capabilities and the balance between latency and quality critical for real-time merchant interactions. Claude powers Sidekick, Shopify's AI assistant that provides conversational commerce guidance to merchants.

When a merchant asks a question in natural language, Claude translates complex requests into actionable insights, including converting questions into ShopifyQL queries that previously required technical expertise. The assistant guides new merchants through setup, helps optimize listings, and surfaces growth opportunities from business data.

Internally, Claude enables teams across Shopify to build their own tools without waiting for engineering support, democratizing development capabilities across the organization.

Results and impact:

• Merchants reach their first sales in days rather than weeks
• Analytics insights now accessible without technical expertise
• Employees building sophisticated internal applications in minutes across departments

Enterprise analytics transformation: L'Oréal

L'Oréal (opens in new tab), the world's largest cosmetics and beauty company operating in over 150 countries with 37+ international brands, deployed Claude to transform how 44,000 employees access and analyze business data, positioning the company as a Beauty Tech leader.

The challenge: democratizing data access across a global workforce

With operations spanning skincare, haircare, makeup, and fragrance across global markets, L'Oréal's teams needed sophisticated analysis for complex financial and analytical tasks. Traditional approaches required building custom dashboards for each ad-hoc question, creating bottlenecks that slowed decision-making. The company needed AI capable of math, coding, and SQL generation while maintaining accuracy critical for building user trust.

The solution: multi-agent orchestration with Claude at the core

L'Oréal selected Claude through rigorous auto-evaluation testing that demonstrated superiority across multiple use cases, particularly for complex analytical tasks. Claude serves as the main orchestrator of 15+ specialized agents that work together to transform user questions into insights and visualizations.

When an employee asks a question in natural language, Claude coordinates with semantic API agents, data retrieval systems, and specialized agents for calculations, product master data, and geography master data. This architecture mitigates accuracy risks by routing specific query types to purpose-built agents while Claude manages the overall workflow and synthesizes results.

The system queries L'Oréal's Beauty Tech Data Platform in natural language while managing user identity and access controls.

Results and impact:

• 99.9% accuracy on conversational analytics applications (up from 90%)
• 44,000 monthly users generating 2.5 million messages per month
• 15,000 daily unique users across the internal AI platform

Streamlining retail operations and partner relationships: Lotte Homeshopping

Lotte Homeshopping (opens in new tab), a major Korean home shopping operator, deployed Claude to transform quality assurance processes and partner support, demonstrating how AI accelerates product launches while improving relationships across the supply chain.

The challenge: communication bottlenecks delaying product launches

Home shopping operations require rigorous quality assurance across thousands of products from diverse partner suppliers. Communication bottlenecks between QA teams and product partners delayed launch timelines, while time-consuming verification of test reports, documentation, and regulatory compliance requirements (including KC certification for the Korean market) created friction that slowed time-to-market.

The solution: 24/7 AI-powered partner support

Lotte Homeshopping deployed Moni, an AI assistant powered by Claude through Sendbird, to provide around-the-clock support for partner suppliers. The system handles QA inquiries, validates test documentation, guides partners through regulatory requirements, and provides consistent responses across all interactions.

Rather than waiting for business hours or navigating complex internal processes, partners receive immediate assistance on compliance questions and documentation requirements. The system's accuracy and judgment meet the rigorous standards QA demands while making enterprise-grade guidance accessible to partners of all sizes.

Results and impact:

• 30-40% reduction in QA delays
• Reduced product launch timelines across categories
• 24/7 partner support with increased satisfaction scores