Generative AI in Retail: Practical Use Cases and Implementation Strategies for 2025 and Beyond

Introduction

The retail industry is entering a new era, driven by the rapid evolution of generative artificial intelligence (AI). No longer just a buzzword, generative AI is already transforming how retailers operate, engage customers, and manage supply chains. As we look to 2025 and beyond, the question is not whether to adopt generative AI, but how to do so in a way that delivers real business value, responsibly and at scale.

This page explores the most impactful use cases for generative AI in retail, drawing on real-world examples from industry leaders like Walmart, Amazon, and eBay. We also provide actionable strategies for implementation, address the challenges and ethical considerations, and offer a roadmap for retailers to experiment, scale, and govern AI responsibly.

How Generative AI Is Transforming Retail

Generative AI refers to models that can create new content—text, images, code, and more—based on large datasets. In retail, this capability is unlocking new efficiencies and experiences across three core areas:

Key Use Cases for 2025 and Beyond

1. Conversational Commerce

Generative AI is redefining how customers interact with retailers online. Advanced chatbots and virtual shopping assistants, powered by large language models, can now handle complex queries, guide product discovery, and provide personalized recommendations in real time. For example:

2. Automated Content Creation

Retailers are using generative AI to automate the creation of product descriptions, marketing copy, and even personalized images. This not only accelerates time-to-market but also ensures consistency and relevance across channels. For instance:

3. Supply Chain Optimization

Generative AI is enhancing supply chain resilience by enabling more accurate demand forecasting, dynamic inventory allocation, and real-time decision support. Retailers can use AI-powered control towers to answer questions like “Where is my package?” or “Can this shipment be rerouted?” in natural language, reducing manual effort and errors. AI can also suggest optimal packing configurations and shipping label designs based on real-time constraints.

4. Personalized Marketing and Retail Media

Generative AI enables hyper-personalized marketing at scale. By analyzing customer data, AI can generate tailored offers, dynamic product bundles, and even custom product designs. Retailers are also leveraging AI to power retail media networks, delivering targeted ads and content to shoppers at the point of purchase. This not only drives incremental revenue but also deepens customer engagement.

Implementation Strategies: From Experimentation to Scale

1. Start with Data Strategy

Generative AI’s effectiveness depends on the quality and breadth of your data. Retailers should prioritize building robust, centralized customer data platforms (CDPs) that break down silos and enable real-time analytics. Investing in data governance, privacy, and consent management is essential to build trust and ensure compliance.

2. Pilot High-Impact Use Cases

Begin with use cases that offer clear ROI and are relatively low risk, such as conversational commerce or automated content creation. Identify where employees or customers spend the most time, or where there are frequent bottlenecks. Use A/B testing to measure impact and iterate quickly.

3. Build Cross-Functional Teams

Successful AI adoption requires collaboration across business, technology, and legal functions. Empower cross-functional teams to experiment, share learnings, and drive adoption. Ensure that teams have access to the right tools, data, and training.

4. Invest in Scalable, Flexible Architecture

Composable commerce and cloud-native platforms enable retailers to integrate best-of-breed AI solutions without being locked into monolithic systems. This flexibility allows for rapid experimentation and scaling of successful pilots across the organization.

5. Prioritize Responsible AI and Governance

As generative AI becomes more pervasive, retailers must address ethical considerations, including bias, transparency, and data privacy. Establish clear guidelines for responsible AI use, invest in AI literacy for employees, and create mechanisms for ongoing monitoring and governance. Leading retailers like Walmart have published responsible AI pledges, setting a standard for the industry.

Challenges and Ethical Considerations

While the potential of generative AI is immense, retailers must navigate several challenges:

Real-World Results: What Leading Retailers Are Achieving

Roadmap for Retailers: Experiment, Scale, and Govern

  1. Assess Readiness: Evaluate your data infrastructure, talent, and organizational culture for AI adoption.
  2. Identify Use Cases: Prioritize high-impact, low-risk pilots in areas like conversational commerce, content automation, or supply chain decision support.
  3. Build and Test: Develop prototypes, measure outcomes, and iterate based on feedback.
  4. Scale What Works: Expand successful pilots across channels and business units, leveraging composable architecture for agility.
  5. Govern Responsibly: Establish AI ethics guidelines, invest in ongoing monitoring, and ensure compliance with evolving regulations.

Looking Ahead: The Future of Generative AI in Retail

By 2025 and beyond, generative AI will be a foundational capability for leading retailers. Those who invest in data strategy, responsible experimentation, and scalable architecture will be best positioned to unlock new sources of value—delivering seamless, personalized experiences for customers, optimizing operations, and driving sustainable growth.

Publicis Sapient partners with retailers worldwide to design, implement, and govern generative AI solutions that deliver measurable business outcomes. To learn more or start your AI journey, connect with our retail and AI experts today.