8 Biggest Benefits of AI in Retail
Michael Hickins | Content Strategist | July 15, 2024
Usually conservative with technology spending given their narrow profit margins, retailers are starting to get aggressive in adopting the hottest and possibly most important technological advance in recent history: artificial intelligence.
Retail industry spending on AI will reach $9 billion in 2024 and $85 billion by 2032, a compound annual growth rate of 32%, according to Fortune Business Insights, as retailers deploy the technology to help increase revenues and customer satisfaction and to reduce waste and costs.
What Is Artificial Intelligence (AI)?
Artificial intelligence is a technology that uses algorithms to mimic human reasoning. Subsets of AI include machine learning, deep learning, and generative AI. For example, a GenAI system can ingest huge amounts of data to respond to plain-language instructions or questions with text or images. Machine learning systems can learn from the huge amounts of data they consume to improve predictions (and the decisions based on those predictions), make personalized recommendations, automate complex processes and tasks (as with robotic assistants in manufacturing plants), root out fraud, and power a variety of other use cases.
How Can AI Help the Retail Industry?
Retailers can use AI for any number of reasons. They can use it to help create marketing campaigns targeted at individuals, as opposed to demographic slices, by quickly poring over customer purchases, browsing histories, and chat transcripts to identify areas of interest. AI can also help retailers improve how they display products on their physical shelves or ecommerce sites by creating compelling shelf labels and online content and by suggesting ideal merchandise layouts in physical stores. Another retail application of AI is in informing more accurate demand forecasts to help avoid markdowns and optimize available storage space.
It’s also used to choose more efficient delivery routes by analyzing data on traffic patterns, weather conditions, construction roadblocks, and other variables. Retailers can also use AI to improve many aspects of customer service, including prompts to help salespeople increase cross-selling and upselling and suggestions to help service agents provide relevant after-sales guidance.
8 Benefits of AI in Retail for Businesses
Retailers are adopting AI across their businesses, including to help automate repetitive back-office tasks and to help boost revenues by providing store associates with sales prompts in real time. Here are some examples.
1. Task automation
Retailers can use AI to help automate repetitive tasks that are susceptible to errors, such as the process of collecting and analyzing data from multiple internal and external sources in order to calculate demand forecasts that inform which product assortments to purchase, in what quantities, and to which stores they should be allocated. Retailers can also use AI to help automate the process of collecting and analyzing data related to pricing—including internal costs and competitive prices—and combine that with demand forecasts to set prices and even threshholds for markdowns in the event that they need to clear out excess inventory. They can also use AI to help automate responses to some types of customer queries, allowing humans to intervene in less of them.
2. Employee empowerment
One retailer uses GenAI to pull together all manner of information for store associates, such as how to reboot a cash register that has crashed and how to help a customer sign up for membership in the retailer’s loyalty program. The technology uses an AI chatbot to let employees ask questions and then pulls from the relevant data store to provide the right answer. Such detailed “cheat sheets” are all the more important in retail given the industry’s high employee turnover rates.
3. Loss prevention
Retailers are in a constant battle against losses stemming from shrinkage and from being overcharged by suppliers. Retailers are starting to turn to AI to help mitigate these losses. For example, they’re using AI to match orders against invoices for each supplier to help ensure they’re charged only for goods that were actually delivered. AI is also becoming an important part of the arsenal employed by retailers to identify fraudulent transactions initiated by cashiers at the point of sale, by analyzing in-store video and transaction logs for each individual cashier. Retailers can also use AI to analyze video from multiple store locations and provide alerts when it detects unusual behavior or activities, including in the back of the store, storerooms, aisles, and checkouts. And it can be used with traditional analytics to further correlate RFID data and other sensor data to explain how items left the store.
4. Waste minimization
Increased consumer awareness of—and emphasis on—sustainability is yet one more reason for retailers to focus on reducing waste. Retailers use AI to identify types of merchandise that have a tendency to spoil and to recommend discounting (or donating) them before they reach their expiry dates. Grocers that package their own prepared foods use AI to help ensure items are cut or selected as efficiently as possible—by, for example, calculating how much meat to fit into a standard package.
5. Supply chain optimization
Retailers and their suppliers use AI to optimize delivery routes, based on its analysis of real-time and historical data for a variety of factors, such as weather conditions, traffic patterns, construction rerouting, and extraordinary events that block streets, ports, or sea lanes. Although conventional data analytics has been used for some of this work, AI is uniquely capable of tying together all these elements and providing tangible recommendations. Retailers also use AI to help load trucks based on the delivery schedule so that goods can be offloaded easily and trucks can quickly move on to the next delivery location.
6. Customer satisfaction
AI can help retailers achieve higher customer satisfaction levels simply by creating offers that look to customers as if the retailer’s assortment was created for them alone, rather than for a giant demographic slice of people sort of like them. Retailers do this in a variety of ways, including using GenAI to create customized offer emails and using AI to render a personalized version of their commerce sites whenever a frequent shopper logs in, based on each customer’s purchase histories. Retailers can also use AI to help determine which incentives (price, product assortments, personalized assistance) will likely influence specific customers. At a more basic level, retailers use AI-imbued chatbots to quickly answer simple questions from customers about products, pricing, store layout, and other things.
7. Error reduction
Retailers can use AI to automate data collection, thereby helping reduce error rates in manual or repetitive tasks. This is particularly relevant given that many retailers still collate reports manually from spreadsheets, possibly introducing errors that can result in faulty sales and demand forecasts, which in turn can lead to missed opportunities to sell more items or to having too many items in stock.
8. Cost reduction
AI can help retailers meet demand fluctuations more effectively than earlier generations of analytic applications, leading to opportunities to reduce costs practically everywhere in the business. For example, by helping retailers more accurately forecast sales by store, AI analyses can help lower inventory carrying costs, the labor costs associated with inefficient replenishment, and the cost of labor at times when fewer store associates are needed. Retailers can also use sales forecasts based on data analytics and augmented by AI to help them order enough quantities of popular merchandise, allowing them to negotiate higher volume-based rebates from suppliers. AI can also help lower labor costs by reducing the number of hours spent by customer service agents on low-level inquiries, reducing shrinkage and waste (see above), reducing errors (see above), and reducing power consumption by suggesting changes in operating hours based on time-based analyses of sales volume.
8 Examples of AI in Retail
Retailers worldwide use AI in many ways, including helping store associates increase customer basket sizes, giving service agents relevant information so they can better serve returning customers, and helping back-office staff make the right decisions on staffing levels, inventory allocation, merchandising, and purchasing. Here are eight real-world examples.
1. In-store navigation
One large US department store uses an AI-powered chatbot to help customers find their way around its various outlets. Customers open an app on their smartphones to query the chatbot for directions to specific items on store shelves or to ask if desired items are in stock. The bot can even detect if customers get frustrated by analyzing the language they use and alert a human store associate to come to the rescue.
2. Smart stores
Apparel retailers use AI to help customers find the right fits for their clothing. One uses AI to run touchscreen mirrors, which let customers browse through clothing items and see how they fit on their bodies right there in the dressing room—without the hassle of having to strip down and try on a bunch of different items. An AI-powered app developed by another apparel retailer lets customers notify store associates when they need a different size item delivered to their dressing room and provides recommendations to store associates about additional items customers might like based on what they’ve already tried on.
3. Smart shelves
Using sensors on store shelves combined with AI built into its app, one US grocer alerts shoppers to items they might want to buy—such as gluten-free products for people with dietary restrictions—based on a real-time analysis of data it has already collected on each customer.
4. Merging digital and physical experiences
One furniture and home decor retailer uses AI to make in-store product recommendations based on customers’ design sensibilities, as defined by what they pin on their Pinterest boards, helping it convert browsers into buyers.
5. Cognitive computing
One retailer of outdoor apparel and footwear has rolled out an app based on cognitive computing (which seeks to mimic how humans think) that queries customers on where and when they intend to use a particular item and makes recommendations that help them find the right outfit for their activities.
6. Computer vision
A luxury department store uses AI to search store inventory for goods that match photographs of items snapped by customers. If the exact item isn’t in stock, or even carried by the brand, the store’s app recommends similar matches that could entice the customer.
7. Cashierless shopping
One warehouse shopping club uses an AI-powered app to help customers map the most efficient route through the store to find everything on their shopping list and then allows them to pay through the app and walk out of the store without having to wait in line.
8. Inventory management
One global fast-fashion retailer uses AI to analyze store receipts and returns to evaluate assortments for each store. Its algorithm helps the store know which items to promote, where to stock larger quantities, and even determine if a certain fad is fading faster than expected so it can cut back its purchasing of those items, which helps the retailer reduce markdowns and waste.
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Benefits of AI in Retail FAQs
How can AI help the retail industry?
Retailers can use AI to automate and help reduce repetitive tasks, allowing them to redeploy resources to more strategic ends, as well as to reduce errors and improve demand forecasts, helping lead to higher margins.
What are the benefits of GenAI in the retail industry?
One major retail application of GenAI is to create highly personalized email marketing copy, including limitless iterations of the same messages in different combinations to test which copy produces better results.
What are the benefits of conversational AI in retail?
Retailers can use conversational AI–based chatbots to answer customers’ basic questions, letting human customer service agents address more complex questions that AI can’t handle.
Learn how retail businesses can use AI to help predict the unpredictable.