Generative AI is rapidly transforming the retail sector, ushering in a new era of customer experience, operational efficiency, and measurable business value. Yet, for many retailers, the journey from experimentation to enterprise-scale impact is anything but straightforward. The promise of AI-powered content creation, conversational shopping assistants, dynamic pricing, and hyper-personalization is real—but so are the challenges around data quality, integration, and ROI. Here, we explore how generative AI is reshaping retail, the foundational steps required for success, and the strategies that leading organizations are using to unlock value at scale.
Retailers today face razor-thin margins, shifting consumer expectations, and fierce competition from both digital natives and global marketplaces. Generative AI offers a powerful toolkit to address these pressures:
However, the leap from pilot projects to scalable, ROI-driven transformation is not automatic. According to recent industry research, only 11% of retail leaders are developing custom generative AI solutions tailored to their enterprise needs, while the majority still rely on public tools and pre-built models. The main barrier? Data.
The effectiveness of generative AI in retail hinges on the quality, structure, and accessibility of customer data. Fragmented, unstructured, or siloed data can undermine even the most sophisticated AI models. In fact, 93% of C-suite executives in retail cite data quality and integration challenges as significant barriers to generative AI adoption.
To move from experimentation to impact, retailers must:
Retailers that prioritize these foundational steps are better positioned to scale AI initiatives and realize measurable ROI.
Large language models (LLMs) can generate product descriptions, promotional assets, and personalized marketing at scale. Yet, quality and brand consistency remain challenges. The key is to automate customer data collection and integrate it into the content supply chain, enabling predictive shopping and real-time recommendations. Retailers who master this can increase conversion rates and customer loyalty, but only if their data is clean, structured, and actionable.
With the rise of AI-powered search interfaces, consumers are increasingly turning to chatbots and virtual assistants for product discovery. Retailers can deploy their own conversational shopping assistants—on both owned platforms and third-party marketplaces—to guide customers through the entire journey, from search to purchase. In grocery, for example, AI assistants can suggest recipes, build shopping lists, and recommend products based on dietary preferences and purchase history, creating a more engaging and valuable experience.
Dynamic pricing, powered by machine learning, enables retailers to adjust prices in real time based on demand, inventory, and competitive benchmarks. For convenience and grocery retailers, this means staying competitive while managing margins and reducing waste (e.g., through electronic shelf labels that automatically discount expiring products). The success of these initiatives depends on high-quality, real-time data and the ability to test and refine pricing strategies through micro-experiments.
Generative AI is also transforming B2B retail by enabling virtual knowledge assistants that help employees access internal sales knowledge, answer customer queries, and streamline complex transactions. These tools, trained on proprietary company data, can provide contextual, expert-level support—improving both customer satisfaction and operational efficiency.
Retailers are advised to start with focused micro-experiments—small, targeted AI projects that can be tested, measured, and scaled. This approach allows organizations to:
A balanced portfolio of innovation projects, rather than a single flagship initiative, helps retailers manage risk, control costs, and maximize learning. Empowering domain experts and connecting business and technology teams is essential for sustained success.
Despite the clear opportunities, many retailers are still in the early stages of AI maturity. Only 32% report being “very mature” in data management and predictive analytics, and just 11% have a mature enterprise customer data strategy. To move forward, organizations must:
The retailers who will win in the generative AI era are those who can harness their proprietary data to create differentiated, high-value experiences. Integrating first-party data into AI models enables more accurate personalization, better product recommendations, and more effective dynamic pricing. This not only drives top-line growth but also creates a defensible competitive advantage that public models cannot easily replicate.
Publicis Sapient partners with leading retailers to bridge the gap between AI experimentation and enterprise-scale transformation. Our approach combines:
By focusing on both foundational data strategy and innovative AI use cases, we help retailers unlock measurable ROI and reimagine the customer journey for the future.
Ready to accelerate your generative AI journey in retail? Connect with Publicis Sapient to discover how we can help you build a robust data foundation, scale AI-powered experiences, and achieve sustainable business value.