Five Proven Ways Retailers Are Winning with AI
Practical and accessible AI moves you can already make today.
Saptarshi Nath
Retailers who still rely on legacy POS systems and basic loyalty programs are falling behind. These tools were built for another era—good for recording transactions, poor at acting on insights. In 2025, that simply isn’t enough. AI is the new retail operating system. It predicts demand, personalizes customer engagement, and fixes in-store inefficiencies before they escalate.
Smart retailers are not waiting for the next wave—they are using AI right now to win share and drive profit. The rest risk getting left behind by faster, more adaptive competitors. If your store is still running on rules and reports rather than algorithms and real-time data, it’s time to rethink your toolset. Here’s how you should be using AI to stay ahead of the curve.
If your team is still doing things this way, you may be up for a revival:
Relying on month-end sales reports to make store decisions
Sending the same promotions to every customer
Depending on brand/vendor-led discounts for your own offers
Using last year’s sales data to plan inventory and staffing
Making manual price changes across shelves
Running static floor plans that never adapt to shopper behavior
Relying on generic loyalty programs that don’t personalize rewards
Treating in-store and online data as separate worlds
These habits don't just slow you down—they leave you exposed.
AI-driven retailers move faster, adapt instantly, and serve customers with a precision old methods can't match. The gap is only widening.
But how do you get started?
Glad you asked: that’s what we’re going to talk about today.
Here are five specific, practical ways you should be adopting AI in your store—today. The opportunity is here for those willing to move now and leave old habits behind.
AI for Personalized Promotions
Today, most sales and discount offers in retail are pushed by vendors or brands, not tailored to individual shoppers. That means every store ends up offering the same deal to everyone—whether or not they actually need a discount to make a purchase.
You might be handing out margin for free to loyal customers who would have bought anyway. Worse, customers get swamped with irrelevant promotions and start tuning them out. AI can flip this script by helping you focus offers only where they make a difference, so shoppers aren’t overwhelmed, and your margins stay protected.
Offer discounts targeted at a key goal: winning back lost customers, increasing frequency of purchases, or getting loyal customers to spend more.
Here’s the good news: your POS and loyalty systems already hold the data you need for personalized promotions. The challenge? Most of these platforms aren’t built with AI in mind, so the data sits underused.
That’s why using purpose-built AI platforms, like Goodlight AI, can help you tap into the data from your POS and loyalty systems—so you understand your shopper segments and can send offers that matter to them.
Case-in-point
Kroger, one of the largest grocery chains in the U.S., uses AI to analyze each customer’s purchase history and demographics to deliver personalized promotions. For example, regular buyers of a particular brand receive targeted digital coupons, increasing both engagement and sales.
Rather than sending out the same discount to everyone, Kroger’s system ensures that shoppers get offers tied closely to their actual buying habits. This data-driven approach leads to better ROI—customers respond more positively to relevant offers, and the retailer protects its margins by avoiding unnecessary discounts.
AI for Dynamic Pricing
Dynamic pricing in stores has become more nuanced with AI. It’s not just about marking things down at the end of the week. Now, retailers can quietly adjust prices based on local demand, in-store traffic, even weather—and do it in real time. For instance, if grilling supplies start moving fast on a sunny weekend, prices can respond on the spot.
AI also lets you spot micro-patterns: maybe one section of your store needs a price tweak, or a particular product does better with a small discount at certain times. Some retailers are even testing different price points in parallel, learning which items drive traffic and which quietly add to margins.
AI doesn’t just follow the competition—it learns from your own data, noticing small shifts that might otherwise slip by. This leads to sharper margin management, less waste, and prices that actually reflect what’s happening in your store. Simply making your POS data available to an AI engine can change the way your business operates.
Case-in-point
Ralph Lauren uses AI to sharply reduce markdown rates by accurately predicting demand and optimizing inventory allocation. Its predictive AI buying program analyzes real-time sales, inventory trends, and customer demand to forecast which products and sizes will sell best. This allows the brand to “chase” popular items—quickly replenishing stock where demand surges—while avoiding excess inventory on slower-moving SKUs.
As a result, more product is sold at full price and fewer markdowns are needed. The AI also enables better planning by integrating with the company’s new ERP and supply chain systems, giving visibility into product flows and supporting smarter decisions.
After piloting the program in Asia and Europe, Ralph Lauren found efficiency gains and is now scaling AI-driven buying across more categories and regions. This tech-forward approach helps them keep shelves stocked, limit unsold goods, and sustain gross margins in a challenging global retail market.
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AI for Smart Inventory Forecasting
AI-powered inventory forecasting moves beyond guessing or relying on last year’s sales numbers. Today, systems factor in everything from changing local tastes to weather shifts, promotions, and even real-time events. If a cold snap hits or a big game is coming, inventory plans can adjust instantly: no more empty shelves or piles of unsold stock.
Smart forecasting tools also help spot subtle purchase trends, flagging products that are quietly picking up traction or starting to taper off. Retailers get alerts in advance, not after the fact. This means better shelf planning, fewer stockouts, and less waste.
Instead of long meetings and gut feel, you’re getting automated recommendations from data. Your team spends less time firefighting inventory issues and more time making sure what’s on your shelves matches what customers want right now.
When you tie offers to strong forecasting, you use sell-through rates and inventory days to discount products strategically and balance cash flow with revenue.
Case-in-point
Levi’s uses AI-driven forecasting to manage inventory with far more precision than traditional systems allow. Levi’s integrates diverse data streams—past sales, geographic patterns, promotions, and even external signals like social trends and weather—into its AI models. This allows their planners to fine-tune allocations at the SKU and store level, based not just on what sold last season, but on what is likely to sell in each unique context.
As a result, Levi’s improves on-shelf availability for top-performing items while cutting back on excess, reducing both markdown risk and inventory waste. This approach keeps their supply chain nimble and helps align production with real demand—essential in fast-moving apparel.
AI and ML for Personalized Product Recommendations
Studies by McKinsey highlight the power of personalization: 71% of customers expect personalization.
Progressive retailers now use AI and Machine Learning to deliver in-store product recommendations driven by real purchase histories and basket patterns unique to each store and shopper segment.
When you combine loyalty card data and time-of-day trends, you can spot missed purchases and prompt effective cross-promotions. When you deep dive into the products that go into each basket, you can estimate the shopper persona and provide personalized recommendations.
Digital displays, POS systems, and your in-store cashiers are able to deliver these tailored prompts at the right moment—nudging a relevant add-on based on data, not guesswork. This approach lets you quickly adapt suggestions as habits change, keeping recommendations timely and valuable for both customer and business.
Case-in-point
McKinsey highlights a large European grocery chain that uses AI-driven engines in-store to personalize offers and recommendations based on transaction data, time of day, weather, and location.
Their system routes discounts and product prompts to specific customer segments—such as users of their smartphone app as they pass by the store—instead of blanket promotions for everyone. For example, their engine does not offer discounts to regular shoppers who buy coffee or lunch at the store every day. This personalized approach optimizes promotions and increases both conversion and loyalty, showing strong business impact in a brick-and-mortar context.
AI and ML as an Early Warning System
Leading retail chains and grocers are now starting to use AI as an early warning system for a wide range of use cases: when a hot selling item might be running out of stock, or when a discount is not providing results. Machine Learning techniques that leverage your POS data to identify trends and alert you when something is about to go wrong can help retail chains avoid stock-outs, increase margins, and build a more sustainable business.
When algorithms spot unusual sales spikes, rapid slowdowns, or out-of-pattern combinations, they flag issues before they become larger problems—such as stockouts, emerging shrink risks, or supply chain disruptions. This allows store and category managers to respond quickly, adjust orders, or intervene with targeted promotions.
Case-in-Point
A central U.S. grocery chain used Goodlight AI to build an alerting system on their POS data, revealing that their top-selling item—ground beef—was discounted over 90% of the time. The system flagged this heavy, unnecessary discounting, uncovering $1.6 million in lost margin across $17 million of transactions. The alert showed that most discounts were not driving extra sales; core shoppers would have purchased at full price.
AI-driven retailers move faster, adapt instantly, and serve customers with a precision old methods can't match. The gap is only widening.
You Have to Start Now
If your retail chain has been around more than a year, you likely already have all the data you need to feed into an AI-powered data and personalization engine. The sooner you start using AI and machine learning to convert that data into insights, the faster you can improve your margins.