Generative AI in Retail: Unlocking Efficiency and Personalization

When Amazon first rolled out Prime two-day shipping nearly two decades ago, it reset consumer expectations forever. In 2019, the company pushed the standard to one-day delivery, and now it is leaning on generative AI to make same-day delivery the new normal. 

Inside Amazon’s vast fulfillment centers, generative AI is teaching robots how to weave around each other more intelligently, predicting which items customers will order before they click “buy,” and even generating product descriptions and review summaries in real time. At scale, these seemingly small shifts translate into faster delivery, more efficient operations, and a customer experience that feels almost effortless.

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What we’re seeing is more than a logistics upgrade. It’s a strategic question for every retailer: how is Gen AI used in retail? The impact lies in what generative AI can create. Beyond analyzing data, it can generate scenarios, insights, and interactions that reshape the way retailers forecast demand, personalize experiences, and automate both checkout and fulfillment. The result is greater efficiency, stronger margins, and a more seamless customer journey. 

Retailers are still in the early stages of experimentation, yet the potential is already clear. Industry analysts estimate that generative AI could unlock between $400 and $660 billion of annual value for retail. 

In this article, we’ll explore how retailers are beginning to realize that promise. We’ll look at case studies that showcase how generative AI is shaping the future of supply chains, customer experience, and marketing, and why those who act quickly will be best positioned to capture the gains.

‍Generative AI in Retail: Meeting the New Consumer Standard

The baseline for AI in retail has shifted. Consumers are no longer impressed by digital convenience; they expect it. In the United States and China, more than 90% of shoppers report making a purchase from an online-only retailer in the past month. Even in Germany and the United Kingdom, markets that have traditionally moved more slowly into e-commerce, more than 80% of consumers say the same. Convenience is now the default, and expectations for speed, personalization, and flexibility keep climbing.

Yet delivering on those expectations remains a challenge. Shoppers move fluidly between websites, apps, and physical stores, but the experience often feels disjointed. Research shows that 71% of consumers expect brands to deliver personalized interactions, while 76% become frustrated when this does not happen. Customer loyalty is fragile, and switching to a competitor requires only a click.

Behind the scenes, the root causes are pretty straightforward. Many retailers still rely on disconnected systems, and about 75% admit they struggle to integrate new technology with existing infrastructure. The result is siloed data, repetitive marketing, and customer experiences that fall short of seamless.

Generative AI offers a way forward. Instead of only analyzing and predicting, it can create: real-time offers, tailored content, and demand scenarios that anticipate supply needs. Like Amazon’s use of generative AI in its fulfillment centers, much of this happens behind the scenes, yet it’s what makes the customer experience feel more seamless and streamlined.

In addition to behind-the-scenes uses, generative AI in retail is also being used directly by consumers. Nearly 60% of Americans say they turn to these tools when shopping online, whether to discover new products, compare options, or try items virtually. 

Three Generative AI Strategies That Are Already Delivering Results

Retailers aren’t just exploring generative AI in retail anymore; they are embedding it into supply chains, customer service, and operations. A 2024 survey of Fortune 500 retail executives found that 90% have already begun experimenting with generative AI solutions, and many are moving beyond pilots to scale priority use cases. 

Here are three generative AI strategies in retail that are already proving effective:

• Smarter AI demand forecasting and supplier optimization: Generative AI can analyze patterns across sales, inventory, and supplier data to improve demand forecasting and even automate negotiations. This role reduces costs, increases accuracy, and strengthens supply chains.
• Personalization at scale through unified data: With a single view of the customer, generative AI in retail enables personalized recommendations and consistent experiences across every channel. Retail AI solutions help employees and systems act on insights instantly, turning data into stronger loyalty and higher margins.
• Faster, cost-efficient content creation and engagement: Generative AI in retail can generate visuals, campaigns, and copy in days rather than weeks, helping brands stay on trend while lowering costs. This is one of the clearest examples of how artificial intelligence in retail is reshaping marketing and customer engagement.

Next, we’ll walk through case studies that highlight how enterprises are successfully putting these retail AI strategies into practice.

How Walmart Uses Generative AI in Retail to Streamline Supplier Deals

Walmart is a great example of how generative AI can tackle problems that once seemed too complex to scale. Its supplier negotiations are a perfect case in point. With thousands of vendors and countless contracts to manage, the process was often slow, required significant manpower, and produced uneven results depending on who was at the table.

Looking for a better way to forecast needs and streamline supplier relationships, Walmart turned to generative AI. The company piloted a chatbot that could negotiate directly with vendors, holding natural conversations about pricing, payment terms, and other contract details. In its first trial, the tool engaged with 89 suppliers on items like shopping carts and store equipment.

The pilot delivered impressive results. The chatbot successfully closed nearly two-thirds of deals, cut costs by an average of 1.5%, and extended payment terms by more than a month, boosting cash flow. Perhaps most surprising, 83% of suppliers said they actually liked negotiating with the AI chatbot, describing the process as simple and efficient.

By reimagining supplier negotiations with generative AI, Walmart showed that automation can create value on both sides of the table - helping the company save money while strengthening vendor relationships.

LVMH Harnesses Generative AI in Retail for Client-Centered Luxury

LVMH, the French luxury group behind Louis Vuitton, Sephora, and Dom Pérignon, oversees 75 maisons, each with its own identity and clientele. The strength of these brands has always come from personal service, yet behind the scenes, the group faced a familiar challenge. 

With data scattered across maisons and markets, it was difficult to build a complete view of each client. Off-the-shelf retail AI tools designed for mass personalization simply did not fit the luxury sector, where relationships are built on exclusivity, discretion, and human connection.

To solve this, LVMH partnered with Google Cloud to build a data platform designed specifically for generative AI. The system gives client advisors conversational access to insights such as product affinities, preferences, and recommendations, helping them serve customers with the same level of care in digital channels as they would in-store. 

“We layer on an AI agent, and the advisor can chat with this data to find what they need even faster and be better informed, and it can even make new connections for them, new opportunities for additional items or experiences. And this leaves more time for the advisor to focus on the relationship with the customer.”

Franck Le Moal, Chief Information Officer at LVMH.

Generative AI also supports more practical tasks like drafting product descriptions, translating content, and creating tailored marketing, all under LVMH’s “quiet tech” approach that keeps AI invisible to the client but impactful for the business.

The results have been significant. Today, more than 40,000 employees across maisons use the platform, generating over 1.5 million AI queries each month. Advisors deliver faster, more personalized suggestions, while marketing and operations teams work more efficiently across languages and markets. By unifying data and using generative AI to act on it, LVMH has preserved the human touch at the core of luxury while gaining the scale and agility of advanced technology.

Artificial Intelligence in Retail Marketing: Lessons from Zalando

Zalando, one of Europe’s largest online fashion retailers, faced a major marketing challenge. Traditional photoshoots took weeks of planning, model bookings, and editing. By the time campaigns were ready, fast-moving fashion trends had often shifted, leaving teams with high costs and content that risked missing the moment.

To stay agile, Zalando embraced generative AI. Today, about 70% of its editorial visuals are AI-generated, including digital twins of models that replace many conventional shoots. The aim is not pixel-perfect imagery but timely, trend-aware content that mirrors the speed of social media. Zalando also applies AI to address practical customer pain points, such as size and fit, while being transparent about when and how the technology is used.

The payoff has been amazing. Campaign production timelines dropped from up to eight weeks to just three or four days, while costs fell by as much as 90%. By treating generative AI as a creative partner, Zalando has turned marketing into a faster, more cost-efficient engine for customer engagement.

Why Now Is the Moment for Generative AI in Retail

Just like how Amazon has quietly been using generative AI to make same-day delivery feel effortless, other retailers are proving that the technology can redefine what customers expect. Walmart is showing how deals get done faster, LVMH is proving that personalization can scale without losing its human touch, and Zalando is turning marketing into a real-time engine for engagement.

These stories show that generative AI in retail is no longer a side experiment. It is becoming the foundation for how the industry will compete - by blending efficiency with creativity and speed with personalization. The retailers who embrace this shift now will be the ones shaping the next chapter of customer loyalty.