How leading retailers are turning AI pilots into enterprise-wide transformation

Most enterprise AI initiatives start strong and stall fast. Our new guide documents what the organizations pulling ahead are doing differently, drawing from our work alongside retail organizations seeing measurable ROI.

What We're Seeing

Retailers face a familiar squeeze: margins are thin, customer expectations keep rising, and the pressure to automate runs headlong into the need to preserve service quality. PwC reports 88% of executives plan to increase AI investment this year.

But investment alone isn't the bottleneck. Technology stacks are fragmented across e-commerce, POS, inventory, and CRM systems that weren't designed to work together. Seasonal demand cycles make ROI hard to model. And teams that understand both retail operations and AI capabilities are hard to find and harder to keep.

The organizations making progress have stopped treating these as reasons to wait.

The Three-Phase Framework

Successful retail AI transformations follow a consistent pattern across three distinct phases, each building on the previous while managing risk appropriately.

Phase 1: Laying Your Foundation

Before deploying any AI technology, leading retailers establish the organizational groundwork:

Stakeholder Alignment: Executive sponsorship matters, but it's not enough. Successful implementations involve frontline managers, IT teams, compliance officers, and crucially, the employees who will work alongside AI daily. Early involvement reduces resistance and surfaces practical concerns before they become blockers.

Governance Framework: Clear policies around data usage, AI decision-making authority, and human oversight. This includes defining which decisions AI can make autonomously versus which require human approval, establishing audit procedures, and creating escalation paths for edge cases.

Infrastructure Assessment: Honest evaluation of current systems. Do you have clean, accessible data? Can your infrastructure handle additional compute load? Are APIs available for the integrations you'll need? Many retailers discover they need to address technical debt before AI deployment becomes practical.

Success Metrics Definition: What does success look like? Leading retailers define specific, measurable outcomes before starting pilots. Not vague goals like "improve customer experience" but concrete targets: "reduce customer service response time by 30%" or "decrease inventory waste by 15%.")

Phase 2: Launching Carefully Selected Pilots

With foundations in place, successful retailers start with focused, lower-risk applications:

Supplier Support Automation: Several retailers begin here because it's high-impact but lower-risk than customer-facing applications. AI assistants handle routine supplier inquiries about order status, documentation requirements, and compliance questions. This reduces support team workload while improving supplier experience.

Lotte Homeshopping deployed an AI assistant providing 24/7 support for partner suppliers, handling QA inquiries, validating documentation, and guiding partners through regulatory requirements. The result: supplier satisfaction increased while support costs decreased.

Internal Knowledge Management: Another common starting point. Retail organizations accumulate vast amounts of internal documentation, procedures, and institutional knowledge. AI assistants help employees quickly find information, understand policies, and learn new systems.

Employees can ask natural language questions like "What's our return policy for online orders shipped to Alaska?" and receive instant, accurate answers with source citations. This is particularly valuable during seasonal hiring surges when you're onboarding hundreds of temporary workers quickly.

Inventory Optimization: For retailers with complex inventory across multiple channels and locations, AI can analyze patterns, predict demand, and recommend optimal stock levels. Starting with non-perishables or items with longer lead times reduces risk while proving value.

Merchant Tools: Platforms like Shopify have integrated Claude to power assistants that translate complex merchant requests into actionable insights. When a merchant asks a question in natural language, Claude converts it into technical queries (like ShopifyQL) that previously required specialized expertise. This democratizes access to business intelligence across the merchant base.

Phase 3: Scaling What Works

Once pilots demonstrate clear value, leading retailers scale systematically:

Staged Rollout: Expand successful pilots gradually. Go from 10 customer service agents using AI to 50, then 200. This allows you to refine processes, train additional staff, and address issues before they affect the entire operation.

Cross-Functional Expansion: Apply learnings from successful pilots to adjacent use cases. The infrastructure and processes built for supplier support can often be adapted for customer service, HR support, or internal IT help desk functions.

Organizational Capability Building: As AI becomes embedded in operations, develop internal expertise. Train existing employees to work effectively alongside AI, establish centers of excellence that share best practices across business units, and build internal teams that can customize and extend AI capabilities for your specific needs.

Continuous Optimization: Successful deployments aren't "set and forget." Leading retailers continuously monitor AI performance, gather user feedback, and iterate on implementations. They treat AI deployment as an ongoing capability development, not a one-time project.

What This Looks Like in Practice

Shopify: Democratizing Business Intelligence

Shopify deployed Claude to power Sidekick, an AI assistant that helps merchants understand their business data. Previously, merchants needed to learn ShopifyQL (Shopify's query language) to answer complex questions about their sales, inventory, or customer data.

Now, merchants can ask questions in natural language: "Show me my best-selling products in Canada last month" or "Which customers haven't ordered in 90 days?" Claude translates these into proper ShopifyQL queries, executes them, and presents results in an understandable format.

The impact: Merchants who previously couldn't access their own business intelligence now make data-driven decisions daily. This is particularly transformative for small merchants who don't have dedicated analytics teams.

L'Oréal: Multi-Agent Enterprise Intelligence

L'Oréal built a sophisticated multi-agent system with Claude at the core, orchestrating 15+ specialized agents that work together to transform user questions into insights and visualizations. This serves 44,000 employees across 150 countries.

The system handles inquiries spanning marketing analytics, supply chain optimization, product development data, and competitive intelligence. Rather than employees navigating multiple systems and running separate queries, they ask questions in natural language and the AI system orchestrates the necessary data gathering, analysis, and visualization.

The scale of impact: Thousands of employees who previously relied on dedicated analytics teams for insights can now self-serve, dramatically reducing turnaround time for business-critical questions.

Lotte Homeshopping: 24/7 Supplier Support

Lotte Homeshopping, a major South Korean retailer, implemented an AI assistant for their supplier ecosystem. The system handles documentation validation, compliance questions, quality assurance inquiries, and general support—all available 24/7 in multiple languages.

For suppliers, this means instant answers to questions that previously required waiting for business hours and human support staff. For Lotte, it means their support team focuses on complex, high-value interactions rather than routine inquiries.

The business impact: Improved supplier relationships, reduced support costs, and faster onboarding of new suppliers into the platform.

Common Challenges and Solutions

Challenge: Data Quality and Accessibility

Problem: AI needs clean, structured, accessible data. Many retailers have data trapped in siloed systems, inconsistent formats, or incomplete records.

Solution: Don't wait for perfect data. Start with the best data you have, implement AI where data quality is sufficient, and use AI insights to identify and prioritize data quality improvements. Many retailers find that AI deployment creates the business case for data infrastructure investments that were previously hard to justify.

Challenge: Change Management

Problem: Employees worry AI will replace them. Managers doubt AI can handle the complexity of retail operations.

Solution: Position AI as a tool that augments human capabilities rather than replacing them. Involve employees in pilot design and rollout. Share success stories where AI has reduced tedious work and allowed staff to focus on more interesting, higher-value activities. Provide clear career development paths that incorporate AI skills.

Challenge: ROI Measurement

Problem: Retail has thin margins and seasonal patterns that make it hard to isolate AI impact from other variables.

Solution: Define clear, measurable success criteria before pilots. Use control groups where possible. Track both quantitative metrics (cost savings, time reduction, error rates) and qualitative outcomes (employee satisfaction, customer feedback). Be realistic about timelines—some benefits emerge quickly while others take quarters to materialize.

Challenge: Integration Complexity

Problem: Retail technology stacks are complex, with legacy systems that weren't designed for modern integration.

Solution: Start with use cases that require minimal integration. As you demonstrate value, the business case for deeper integration becomes clear. Consider using AI itself to help with integration challenges—Claude can write integration code, debug API issues, and help navigate complex system architectures.

The Path Forward

For retail organizations planning their 2026 AI strategy, our guide provides detailed frameworks for each phase:

Foundation Phase: Stakeholder alignment checklists, governance template, infrastructure assessment frameworks, and success metrics examples

Pilot Phase: Use case selection criteria, pilot team composition, success measurement approaches, and go/no-go decision frameworks

Scale Phase: Rollout planning, capability building programs, continuous improvement processes, and advanced use case ideas

The retail organizations pulling ahead aren't waiting for perfect conditions. They're starting with focused pilots, learning quickly, and scaling what works. The question isn't whether retail will be transformed by AI—it's whether your organization will lead or follow.

Read the full Enterprise AI Transformation Guide for Retail here.