The retail industry is in the middle of an AI gold rush. Over 90 percent of retailers are using or are planning to use AI for real-time product recommendations in the near term, and about three-quarters are already using AI to tailor customer experience. The excitement makes sense: generative AI (genAI) promises to deliver unprecedented levels of personalization, boost marketing efficiency, and unlock new ways to engage customers across channels.
But while the potential is huge, so is the hype. AI vendors often paint a picture of flawless, one-to-one customer personalization at scale. The reality is more complicated, especially for retailers managing hundreds of thousands — or millions — of customer profiles. Personalizing collateral for every individual isn’t just expensive; it’s technically impractical today. The good news? You don’t need hyper-individualization to win with AI.
Why retailers should be AI realists
Let’s do the math. If you have one million customers and want to deliver one-to-one personalization through a large language model (LLM), you’re looking at one million AI-generated assets, prompts, or content variations — each requiring a separate call to an LLM. That’s not only cost-prohibitive but risky, too. Without human oversight, it’s all too easy to flood your audience with inaccurate, off-brand, or poorly targeted content.
The smarter play is AI-enabled segmentation. Rather than aiming for infinite personalization, marketers should focus on maximizing value from their best customers and identifying current or potential customers who share traits with them. This approach allows you to right-size your AI initiatives while still delivering meaningful, personalized experiences.
Right-sizing AI is also a matter of brand safety. AI-generated campaigns need human oversight to ensure messaging is accurate, appropriate, and aligned with brand voice. No retailer wants to be the headline story about an AI campaign gone rogue, nor do they want to slowly lose customers due to off-target marketing collateral.
What good AI experiments look like
AI can absolutely deliver big wins in retail — if you start with the right goals and scope. Take for example the story of a regional home goods retailer: Historically, they built one monolithic holiday gift guide for their entire audience. Using customer data to segment by product category preferences and price sensitivity, they turned that single asset into 24 tailored guides. Each version spoke more directly to its audience segment without ballooning production costs or risking one-to-one personalization pitfalls.
Here are a few other right-sized experiments retailers could test:
Post-Holiday Campaign Optimization: After the holiday season, segment your top-tier “Platinum” customers based on their January–February purchasing habits. Use an LLM to identify which product categories performed best and which channels drove the most engagement. Then, launch a highly targeted email campaign focusing on those products, sent through your audience’s preferred channels.
Regional Product Push: Use AI to analyze regional sales trends and customer preferences. Rather than creating one nationwide campaign, develop a handful of localized messages promoting regionally popular products — scaling personalization without overextending your resources.
Dynamic Offers for Loyalty Members: Leverage genAI to generate exclusive offers tailored to your highest-value loyalty members based on their recent behaviors and purchase history. Instead of thousands of bespoke promotions, create a set of five to ten dynamic templates that can be quickly customized for different audience segments.
Why the AI opportunity is still worth getting excited about
The promise of AI isn’t about doing everything, everywhere, all at once — it’s about making smarter, faster decisions that scale. New AI tools are giving marketing teams more control over data-driven experimentation, reducing reliance on IT and data science teams. With a strong customer data foundation and the right AI workflows, marketers can run more experiments, iterate faster, and uncover new growth opportunities.
Platforms like Amperity help retailers unify and clean customer data, giving AI workflows a trusted foundation to build on. That means better segmentation, more relevant campaigns, and measurable improvements in customer lifetime value (CLV).
AI can absolutely transform how retailers engage customers — but it’s not a magic wand. The brands seeing the most success aren’t chasing one-to-one personalization at scale. They’re using AI to expand their number of meaningful audience segments, test creative ideas more efficiently, and uncover insights faster than ever before.
By right-sizing your AI ambitions and focusing on achievable, data-backed experiments, you’ll avoid the costly risks of over-personalization while still capturing the competitive advantages AI makes possible.
Want to learn more? Download our new guide, “New Opportunities, High Stakes: Maximizing the Value of AI and Customer Data in the Retail Industry,” for a deeper dive into AI strategies, real-world use cases, and the critical role clean customer data plays in successful AI adoption.