GenAI in Retail: Enhancing, Not Replacing, Shopping Experience

Retail is at a turning point. Generative artificial intelligence has made a strong impact across various industries, and the retail sector is no exception. Lately, retailers have integrated AI-powered tools such as conversational shopping assistants, advanced chatbots, and automated product review summaries. However, consumers are still in the process of adapting to these new technologies.

Our recent study at Bain & Company revealed that despite 71% of surveyed shoppers in the United States being unaware of using generative AI tools, half of them believe these tools have made a significant or transformative impact on their shopping experience. This highlights the importance of retailers deploying these tools in an effective and customer-centric manner. The question that arises is, what are the best practices for retailers to achieve this goal?

One of the most relevant findings from the research is that consumers are not looking for a revolution in their shopping habits, but rather improvements that complement their existing flows. The familiarity and efficiency of traditional methods, such as direct online searches, remain essential for shoppers. Therefore, retailers must implement generative AI as a complement to these well-established habits, rather than attempting to replace them.

For example, conversational shopping assistants must align with consumer expectations. If a user is accustomed to receiving recommendations based on past purchases, a new tool should replicate and enhance this functionality rather than eliminate it. Otherwise, it risks causing confusion and resistance to change.

While chatbots have been one of the most visible applications of AI in retail, their potential goes far beyond that. Bain & Company identified three types of interactions where generative AI can play a key role: reactive, passive, and proactive. Each one addresses different consumer needs throughout the shopping journey.

• Reactive interactions: Respond to specific queries, such as virtual assistants providing detailed product information.

• Passive interactions: Enhance the experience without user intervention, such as automated review summaries that simplify decision-making.

• Proactive interactions: Anticipate needs, such as tools that suggest products based on preferences and shopping behavior.

A clear example of a well-received application is the automatic generation of review summaries. This feature saves shoppers time by extracting the most relevant comments without requiring them to browse through hundreds of opinions. Similarly, AI-generated expert responses can provide detailed product insights, increasing consumer confidence and conversion rates.

One of the most notable shifts brought by generative AI is the evolution of the relationship between consumers and their personal data. In the past, shoppers were more reluctant to share information, but Bain’s study shows that many are willing to do so if they perceive a tangible benefit, such as more personalized recommendations.

This means that retailers must rethink how they approach personalization. It is not just about suggesting products based on past purchases, but about offering more relevant and immersive experiences. For example, an online store could highlight reviews from other first-time parents for a consumer searching for a baby car seat, making them feel understood and facilitating their purchasing decision.

Transparency is essential. Customers want to understand how their data is being used and expect a degree of control over it. Companies that earn their trust in this area will gain a strong competitive edge as AI adoption grows. Despite the enthusiasm surrounding generative AI, challenges remain. Many consumers are still skeptical, often due to past frustrations with limited chatbot capabilities or inaccurate automated responses. Bain’s research shows that over half of respondents believe AI-generated errors negatively affect their experience.

To address these concerns, businesses must focus on clarity and reliability. AI-driven recommendations should clearly indicate their sources, and systems should incorporate feedback loops that let users report errors and fine-tune their preferences.

Beyond the purchasing phase, generative AI is also reshaping post-sale support. Advanced tools can streamline returns, answer product-related questions, and assist with warranties — enhancing customer satisfaction while cutting operational costs and freeing up human teams for more complex issues.

Ultimately, AI in retail is not about replacing human interactions but elevating the shopping experience with smarter, more personalized, and efficient solutions. Its success will hinge on how seamlessly retailers integrate these tools into consumers’ daily habits.

To succeed in the upcoming era of retail, industry leaders should focus on customer-centric strategies that prioritize transparency, trust, and genuine utility. Those who manage to strike a balance between these elements will be best placed to leverage the full potential of generative AI.