ReverseLogix CEO: AI's Biggest Impacts on Retail are Behind the Scenes

Point of View, Retail
Gaurav Saran headshot

This article was originally published here in Retailist Magazine.

When you hear the term “artificial intelligence” or “AI,” it’s often in a click-bait headline like, “Is AI now smarter than us?” or “Is AI taking over the world?”

I’d like to bring the conversation back down to earth because the use of AI in retail is multi-faceted and fascinating, but often behind the scenes. AI-led transformation isn’t happening where the average consumer can obviously spot it, but it’s still having a big impact on their experience and the brands they love. 

AI: The Basics of a Consumer Experience

The term “AI” gets thrown around a lot, so let’s first define what I mean by it. I like the straightforward definition from Forbes: “Traditional AI systems are primarily used to analyze data and make predictions.” 

At its earliest stages and most basic levels, AI in retail has enabled better cost management and more engaging product interactions: It’s the upselling on a checkout page based on what’s in a customer cart; it’s more accurate price forecasting based on a wealth of business and economic data. 

On the consumer-facing side, AI is transforming the speed, ease, and enjoyment of shopping. Virtual dressing rooms help shoppers preview clothes to find the right fit and style (and lower the likelihood of a return). Augmented reality allows you to upload a photo of a room in your home and preview what a paint color or couch style looks like in it. In these cases, it’s not just about AI selling more products – it’s about using AI to sell the right product.

Man in Suit Uses Computer to Configure Reverse Logistics Process

AI Behind the Scenes: Enhanced Inventory Management

As AI in retail matures, it’s less consumer-facing. It’s moving behind the scenes to automate work that used to require human intelligence. Though not readily apparent to the public, AI has big impacts throughout retailers’ operations, ultimately trickling down to the customer experience. 

Inventory accuracy

Inventory management and demand forecasting can be excruciatingly expensive if not done well. AI can improve accuracy by analyzing data from past sales trends, seasonality, and economic factors in order to predict when customers will buy specific products and at what volume. 

The result has enabled retailers to buy, price, and stock inventory more accurately and profitably. By improving their ability to predict and meet customers’ needs and align supply chains, they’re building better margins and lowering rates of returns.  

Replenishment speeds and product returns

For fast-moving items, AI can leverage product returns as a source of inventory. Let’s say AI knows you have 10 orders for a certain Bluetooth speaker. AI also sees three online customers initiate a return for the same speaker because they changed their minds (i.e., it’s not defective). AI informs the retailer’s returns management system to direct the online customers to return their speakers directly to the waiting customer’s nearest store. This way, the store employee can more quickly inspect the item and ship it out (or incentivize the buyer to pick it up for even more cost savings). 

This real-time orchestration of returns and inventory improves replenishment speeds by bypassing the extra steps of receiving the item at the warehouse, inspecting it, stocking it, and waiting for a customer to purchase it. 

Price optimization

Product pricing depends on a lot of factors, which makes AI an especially valuable tool for it. AI can help set dynamic pricing strategies based on demand, competition, seasonality, and other factors important to the retailer. 

If a product is returned and can be resold, a returns management system with AI can automatically set pricing based on the factors mentioned above. For instance, if a Christmas tree is returned between Dec. 20 and Dec. 24, mark it down 30%. But if it’s returned after Dec. 25, mark it down 75%. 

eCommerce online shopping concept with shipping boxes and a computer

Enhanced fraud prevention 

Some of the most exciting and interesting AI applications in retail are around fraud prevention. The processing power of AI and its ability to recognize patterns is being applied to stop fraudulent product returns. According to Gartner, fraudulent returns have grown 385% between 2018 and 2022, forcing retailers to figure out how to catch suspicious returns without inconveniencing (or insulting) honest customers. 

When a return is made, AI can alert the warehouse or store employee if the customer has made suspicious returns in the past. Cross-department information can be mined to see whether a stolen credit card was used to make the purchase. The returning individual’s contact information can be cross-referenced with the original order data to ensure the person who bought it and returning it is the same.  

Buying an item online, initiating a chargeback, and then quickly returning an item to a brick-and-mortar store for an extra refund is one-way criminals are scamming omnichannel retailers. But if given visibility across systems and data, AI can detect those actions and catch chargebacks that haven’t been filed yet. 

According to the National Retail Federation, organized retail crime is up 65% since 2015, often in the form of criminals stealing physical items from a store and selling them online. Though controversial, many retailers are installing AI-driven surveillance that compares faces on security systems with known criminals and alerts store security of potential threats. 

In-store experience enhancement

In-store AI tools enhance the customer experience, though neither store employees nor customers may even realize it’s AI. On-the-floor team members can use a mobile POS device to make personalized buying suggestions based on the shopper’s profile. Some retailers are experimenting with cashier-less stores that automatically scan products and charge a customer as they walk out the door. Retailers with small footprints (and small average cart sizes) may take a different approach by implementing self-checkouts while keeping one or two cashiers stationed nearby. 

Advanced marketing strategies

One of the biggest benefits of AI is precision. Leveraging precision for marketing will truly make brands stand apart. Whether it’s better customer segmentation or using predictive analytics to get ahead of emerging trends, AI is giving marketers more insight and faster response times to consumer desires. 

By analyzing things like feedback, ratings, rankings, and social media posts, AI is delivering data that enables brands to get a more accurate reading of their reputation and consumer sentiment. It’s suggesting responses or, in some situations, responding on its own to online inquiries and comments. 

Size and fit are the top reasons for an online apparel return. AI can give a shopper a size suggestion based on their personal return patterns, past purchases, and other consumers’ feedback on the sizing of a particular item (“runs small,” “runs large,” etc.). This helpfully directs the customer to the right size and fit the first time, lessening the chance that the customer will bracket their purchases (buy the same item in many different sizes and colors with the intent to return most of the items). 

A woman returning an item she bought at a store

Sustainability and AI in Retail

AI’s role in making the supply chain, marketing, and inventory more efficient also has significant benefits for a retailer’s sustainability efforts: Helping customers make better decisions and reducing return volumes lowers emissions and waste. More precise demand forecasting and smarter pricing streamline supply chains and ensure less goes to a landfill. 

For brick-and-mortar stores, AI in building systems can project a building’s energy usage and adjust ambient systems for efficiency. Optimal routing can reduce transport miles and emissions, perhaps even incentivizing and rewarding customers who choose a greener shipping option, like consolidating boxes or waiting a few extra days for their purchase to arrive. 

Measuring and reporting on sustainability efforts is notoriously difficult because of the many inputs and factors involved – which makes it a great candidate for AI technology. Tying together the huge range of data and outputs and predicting future success can help forward-thinking retailers set and achieve their goals. 

The Future of AI and Retail: What Lies Ahead?

AI holds a lot of promise, but retailers are rightfully cautious about some of its applications. Implementing it has been a “crawl, walk, run” process, and I think we’re somewhere between the crawl and walk stages. Concerns about privacy, security, and profiling are real and must be navigated carefully. 

As retail systems continue to integrate and share data, the power of AI will become even more apparent and useful, especially in complex areas like sustainability. Retail environments are evolving to include more automation and tightly integrated supply chain systems, which is the perfect setting to unleash the power of AI and transition to a full-on sprint with this transformative technology.