Generative AI in Retail: Practical Use Cases and Pitfalls for 2025

Introduction: From Hype to Real-World Impact

Generative AI (GenAI) is no longer just a buzzword in retail—it’s a transformative force reshaping how retailers operate, engage customers, and drive profitability. As we move into 2025, the conversation has shifted from “what could GenAI do?” to “what is GenAI doing right now?” Retailers are leveraging GenAI to streamline operations, personalize experiences, and unlock new revenue streams. Yet, as with any disruptive technology, the path to value is lined with both opportunities and pitfalls.

This page explores the most practical, high-impact use cases for GenAI in retail, drawing on real-world examples and measurable outcomes. We also address the risks, ethical considerations, and organizational challenges that retail leaders must navigate to ensure sustainable success.

Where GenAI Is Delivering Value in Retail

1. Customer Experience: Personalization, Search, and Service

• Conversational Commerce and Search
  GenAI is revolutionizing how shoppers find and buy products. Conversational product search, powered by large language models, enables customers to ask natural-language questions—"Show me everything I need for a summer BBQ"—and receive curated, relevant results. This reduces friction, increases conversion rates, and boosts average basket size.
  Retailers are A/B testing GenAI-powered search and recommendation engines, finding that these tools can significantly improve the quality and speed of product discovery.
• AI-Powered Chatbots and Virtual Assistants
  Advanced chatbots, such as Walmart’s “My Assistant,” are now supporting both customers and associates. These tools answer complex queries, summarize information, and even help with tasks like meeting notes or HR requests. For customers, GenAI chatbots provide 24/7 support, personalized recommendations, and seamless handoffs to human agents when needed.
• Review Summarization and Content Curation
  Amazon’s AI-generated review summaries distill thousands of customer reviews into concise, actionable insights, helping shoppers make faster, more confident decisions. This not only improves the customer experience but also reduces return rates by setting clearer expectations.

2. Content Creation and Merchandising

• Automated Product Descriptions and Listings
  eBay’s GenAI-powered product description generator enables third-party sellers to create high-quality, consistent listings from photos and minimal input. This reduces manual effort, accelerates time-to-market, and improves SEO performance.
  Retailers are also using GenAI to standardize product information across marketplaces, ensuring brand consistency and compliance.
• Personalized Visual Content
  Emerging GenAI tools can generate personalized product images and marketing assets tailored to individual customer segments or even specific shoppers. This capability is poised to drive higher engagement and conversion rates, especially as digital shopping journeys become more immersive.

3. Supply Chain and Operations

• Decision Support and Secondary Automation
  GenAI is being layered onto existing supply chain control towers to provide conversational decision support. For example, supply chain managers can ask, “Where is my package?” or “Can this shipment be rerouted?” and receive instant, context-aware answers.
  In logistics, GenAI can suggest optimal packing configurations or generate new shipping label layouts based on real-time constraints, reducing errors and improving efficiency.
• Returns Optimization
  By analyzing customer data and return patterns, GenAI can help retailers predict which products are likely to be returned, recommend sizing or fit adjustments, and personalize return policies. This reduces costs and improves customer satisfaction.

4. Associate Productivity and Internal Enablement

• AI Assistants for Employees
  Tools like Walmart’s “My Assistant” are streamlining internal workflows, from summarizing documents to answering HR questions. This frees up associates to focus on higher-value tasks and improves overall productivity.
  GenAI can also support training, onboarding, and knowledge management, making it easier for employees to access information and best practices.

Measurable ROI: What’s Working

• Content Optimization: Retailers using GenAI to optimize product titles and descriptions have reported traffic increases of up to 17% on major marketplaces.
• Operational Efficiency: Automated content generation and decision support tools are reducing manual workload and time-to-market for new products.
• Customer Engagement: AI-powered personalization and review summarization are driving higher conversion rates and reducing return rates.
• Employee Productivity: Internal AI assistants are saving hours per week per associate, translating into significant cost savings at scale.

Pitfalls and Risks: What Retailers Must Watch For

1. Data Quality and Governance

GenAI’s effectiveness is only as good as the data it’s trained on. Poor data quality can lead to inaccurate recommendations, biased outputs, or compliance risks. Retailers must invest in robust data governance, privacy controls, and ongoing model monitoring.

2. Brand Safety and Compliance

Automated content generation can introduce risks around brand voice, regulatory compliance, and misinformation. Retailers in regulated categories (e.g., health, beauty) must set clear guardrails and review processes to prevent errors or inappropriate content.

3. Ethical and Social Considerations

GenAI models can inadvertently perpetuate biases present in training data. Transparency, explainability, and ethical oversight are essential—especially as AI-generated content becomes indistinguishable from human-created material.

4. Organizational Readiness and Change Management

The biggest barrier to GenAI adoption is often organizational, not technical. Retailers must break down silos, upskill teams, and foster a culture of experimentation and cross-functional collaboration. Clear communication and stakeholder buy-in are critical for successful deployment.

5. Customer Trust and Transparency

Consumers are increasingly aware of—and sometimes wary about—AI-generated content. Retailers should be transparent about when and how AI is used, and provide easy ways for customers to escalate to human support when needed.

Getting Started: Recommendations for Retail Leaders

1. Start with High-Impact, Low-Risk Use Cases: Focus initial GenAI investments on areas like content generation, review summarization, and internal productivity tools, where ROI is clear and risks are manageable.
2. Invest in Data Strategy: Centralize and clean customer and product data to maximize GenAI effectiveness and minimize bias or error.
3. Establish Ethical and Compliance Guardrails: Develop clear policies for AI-generated content, including human-in-the-loop review for sensitive categories.
4. Upskill and Empower Teams: Provide training and resources to help associates, marketers, and supply chain leaders leverage GenAI tools effectively.
5. Measure, Learn, and Iterate: Track ROI, customer feedback, and operational metrics to refine GenAI deployments and scale what works.

The Road Ahead: GenAI as a Retail Differentiator

As generative AI matures, its role in retail will only expand—from powering hyper-personalized shopping journeys to enabling new business models and revenue streams. The winners in 2025 will be those who move beyond the hype, invest in data and talent, and deploy GenAI with a clear focus on value, ethics, and customer trust.

Publicis Sapient partners with leading retailers to design, implement, and scale GenAI solutions that drive measurable business outcomes. To learn more about how we can help your organization harness the power of generative AI, contact our retail transformation experts today.