The retail sector remains one of the dominating business forces on the planet. Capital One research indicates that US retail sales totaled $8.33 trillion in 2023 and are expected to reach over $11.5 trillion by 2030.
Despite the dynamic growth of e-commerce and online shopping, traditional retailers remain strong. According to the National Retail Federation (NRF), most e-retailers own a physical store, with only one out of ten e-commerce websites needing to be connected with a brick-and-mortar store.
Retail is an immense industry with great potential. Also, this industry is increasingly interested in implementing AI-based solutions.
This text covers:
- What is Artificial Intelligence
- AI in the Retail Industry
- AI Use cases in retail
- AI in Retail stores
What is Artificial Intelligence (AI)
Modern Artificial Intelligence is a set of techniques that enable machines to acquire skills not from programming and software development, but from the vast amount of data they collect and analyze in a process called machine learning. This enables computers to perform tasks once deemed impossible for machines, including image recognition, running predictive analysis, or maintaining Turing-test breaking conversations.
AI in the Retail Industry
Artificial Intelligence has transformative potential regarding many aspects of business, retail-specific and more general alike. The disruptive abilities of AI can be applied to multiple departments and processes that are not exactly tied to retail specifics. To name just a few examples:
Changes in business models
Artificial Intelligence brings automation and new capabilities to many levels of retail operations. The new capabilities bring precision and quality that were unseen before. With that, services or offers are unseen before they become available. Retail companies may, for example, offer super-tailored, AI-powered counseling regarding products sold.
Operational efficiencies
AI applications are not only about the total disruption of the business model. They can also help make data-driven decisions regarding day-to-day operations in an already-running and effective organization. Running AI in Accounting or AI in HR are just impromptu examples.
Market dynamics observation
AI is also a powerful tool for observing, learning about, and reacting to market dynamics. AI-powered systems may help a company gather information and intelligence both from the inside and from the outside. Internal intelligence encompasses a multitude of examples, like demand forecasting and stock management.
External intelligence may include automated competitive research or market intelligence. A related example comes from Granular.ai, a Tooploox client, where Artificial Intelligence solutions were used to monitor international raw and recycled steel and predict resource prices.
The shortlist above shows only a fraction of AI’s possible applications in the retail industry. But what about more specific use cases?
AI Use Cases in Retail
Apart from general business-related use cases in retail, there are multiple retail-specific applications. These include not only back-office support but also customer-facing solutions that transform customers’ and users’ experiences.
The most common examples of AI applications in retail include (but are not limited to):
Customer service automation
Customer service is one of the most challenging parts of running a retail business. The bigger the scale of operations, the more difficult this gets. Moreso, users expect the contact to be direct, humane and tailored to their expectations, delivered by something other than automatons on a mass scale.
According to SuperOffice data, enhancing customer experiences is currently one of the top ways to maximize income and boost business efficiency. According to SuperOffice, up to 86% of buyers are willing to pay more for a great customer experience, and 81% of organizations already cite CX as a competitive differentiator. In this area, Artificial intelligence can:
- Analyze customer data to harvest insights. This may include spotting the most common problems customers face and building the most efficient procedures to deal with them.
- Handle first-line problems, saving the time and effort of human employees for more complicated matters.
- Harness the power of Large Language Models to personalize the experiences of individual customers when interacting with the automated systems.
Inventory and supply chain optimization
Managing stock levels and warehousing is a super complicated matter. When stocking multiple physical locations, a company faces the challenge of the traveling salesman. Also, it needs to neither overstock nor understock every location in order to avoid freezing resources and losing profits.
In this context, artificial intelligence in retail can be applied as follows:
- Demand forecasting—With time series predictive analytics, a company may leverage its historical data to forecast demand. What’s more, the granularity can be as deep as one wishes. For example, it can be adjusted to the physical location of a store, the time of year, and other relevant factors, like the weather forecast.
- Automatic stock management—When paired with IoT sensors and automated systems, an AI solution is perfectly equipped to oversee and balance stock in multiple locations. For example, the system may control cash PoS systems and track stock on upcoming orders.
- Route optimization – the traveling salesman problem is an iconic math problem supply chain managers must deal with daily. Regarding the retail context, the challenge can be adjusted by external factors.
For example, the transport team needs to visit certain locations before a particular hour or within a specific time window. Weather conditions and traffic also influence this, further impacting the complexity of the transport process. Artificial intelligence proved to be one of the most efficient ways of tackling the traveling salesman problem, even with additional disturbances.
Personalized marketing and product recommendations
According to research by the University of San Diego, up to 35% of Amazon sales are driven by the company’s sophisticated product recommendations system to fit customer preferences. Artificial intelligence is effective in delivering personalized recommendations based on a multitude of factors. For example, it can take into account:
- Demographic data may affect sizes or patterns used on fabric, colors, or popular themes in products.
- Behavioral data: The system may spot patterns in customer behaviors to classify customers into a particular, repetitive group based on their shopping journeys. Examples may include early tech adopters, traditionalists, etc.
- Purchase history—The system can access the full history of customer interactions, so it may use complementary products in other offers. For example, it may suggest a gaming mouse for a powerful laptop or an elegant backpack for a business tablet.
Fraud detection and loss prevention.
The retail industry is prone to multiple types of fraud. These include (among others):
- Transaction fraud – where users buy goods using stolen credit cards
- Wardrobing – where a user purchases fashion or apparel items without the intention of wearing them or using them only once and later returning them to the shop.
- Account takeover fraud occurs when a user account is compromised and used for malicious intents. This is most common when the user has any form of e-wallet connected to the system.
- Bonus abuse fraud – this type of fraud requires a more sophisticated approach, yet it can be devastating. The fraudster targets a promotion and maximizes the outcome from it beyond the retailer’s expectations. This may include creating multiple accounts and referring circles to amass the bonus benefits.
Artificial intelligence is a powerful tool that can check real-time data and analyze customer data to prevent fraud more efficiently.
Pricing strategy optimization
Dynamic pricing is one of the key tools in the retail industry that can help you gain a competitive advantage. On the other hand, keeping prices low hurts profit and may bleed out the margin.
AI-powered pricing systems can analyze data and consider a nearly infinite number of aspects and factors impacting final pricing. This may include giving discounts that are weather-specific, location-specific, and competition-specific.
AI in Retail Stores
Contrary to the e-commerce and digital environment, the retail industry relies on physical localizations. These stores are also an arena in which a company can build a competitive advantage in the fight for customers.
The fact that the physical store is offline and is based on non-digital relations is not a blocker for implementing AI-based solutions. It is rather a new opportunity to spark creativity and encourage companies to find a new way to approach the challenges of business reality creatively.
- Autonomous checkout systems—AI can enrich smart shelves and checkout systems to streamline the user experience and elevate the purchasing process. This may include self-checkout technologies, no-checkout store concepts, or a combination of both.
- In-store analytics—powerful AI-powered analytics can also be used to improve or research customers’ in-store behavior. Camera systems or Bluetooth beacons connected with mobile apps can allow operators to check customers’ in-store paths.
Generative AI in Retail
Using generative AI to transform the retail business is a totally different story from the traditional explanation of generative AI and its potential applications in retail. Contrary to the traditional approach, generative AI helps create new content and responses rather than only using existing data.
Yet this doesn’t mean that technologies like Large Language Models or Stable Diffusion can’t find multiple applications in the retail industry. Examples of how to use this technology include:
Content creation for marketing
Generative AI can be used as a part of the marketing processes in a company. Large Language Models like Claude, GPT or Mistral can generate content on a massive scale, for example for ads or in analyzing unstructured customer feedback.
Product photography
The same goes for generating images. In a modern retail environment, all communication is image-heavy, reliant on photographs and their creation. One of many generative AI in retail examples is eBay’s tool that uses Stable Diffusion to change an ordinary photo into professional product photography. Tooploox proudly contributed to the tool’s development, supporting the e-commerce giant with engineering and AI knowledge.
Customer interactions
Generative AI has tremendous potential to transform company-customer interactions by making communication more personal on multiple levels. Generative AI systems may respond to customers and participate in conversations by providing necessary information from company-wide databases.
Conclusion – the Future of AI in Retail
The retail environment is highly competitive, and the transformation brought on by AI-enabled tools will be one of the key forces driving change. Yet this rapid growth may need to be maintained in an increasingly regulated environment, with new AI regulations popping up constantly.
The best way to deal with these challenges is to find a reliable tech partner, like Tooploox, so your company can focus on building the business while taking care of all the AI-tech-related aspects. So, if you have any questions, don’t hesitate to contact us now!
