Top 9 Use Cases of Artificial Intelligence in the Retail Industry’s Value Chain Stages

Artificial Intelligence (AI) technology is the latest buzzword across multiple industries, both for its advantages and the concern about its implications for the labour workforce. Today, one of the notable sectors is the usage of Artificial Intelligence in retail industry. AI technologies are helping retailers to enhance the customer experience, optimize administrative tasks and empower the workforce with the modern solutions available for the retail sector.

There are several ways in which AI is transforming the traditional retail industry into a customer-centric retail sector despite the ongoing apprehensions about AI replacing human workers. 

What are the various AIs in the retail industry to perform tasks across their value chain?

Deep learning insight engines, machine learning, computer vision, intelligent applications, smart robots, augmented intelligence chatbots, Edge AI and virtual assistants are some of the AI trends in the retail industry’s value chain. 

Image source: Stellenbosch University 

The overall findings of a latest study based on the Leavitt Diamond model (that revolves around key variables: structure, technology, people and tasks) suggest that artificial intelligence (AI) is transforming them in three ways:

1. The retail value chain from linear to circular with insights and data at the core of a successful value chain.
2. How retailers attain end-goals through specific outcomes.
3. How retailers operate in the complex interplay between structure, technology, people and tasks.

Most AI use cases in the retail industry are in-store operations and support (30%) and customer use and support (34%). 

The remaining use cases are sourcing/procurement (9%), inventory management/distribution (15%), manufacturing (5%) design (3%) and fulfillment (4%).

Let’s Begin:

1. Considering Product Designing Value Chain Stage

AI is revolutionizing the product designing process by offering suggestions for new product designs, custom product designs, using customer data to design new products, and prototyping and tailoring designs. AI being one of the modern technologies for retail automation, amalgamates customer data, myriad product images and runway video analysis to supplement the product design process.

Use Case:

Tommy Hilfiger, an American lifestyle retailer, in collaboration with IBM and the Fashion Institute of Technology, presents the advantageous role of artificial intelligence in retail industry’s product designing value chain stage. The brand scrutinizes a vast archive that comprises fifteen thousand product images, sixty thousand runway images and one hundred thousand fabric patterns through AI’s deep machine-learning capabilities. By processing the data the system presents unique product designs in varied colour schemes, prints and silhouettes, minimizing the investment needed for a new product development. 

What Can Be Learned?

Generative AI in retail will act as a guiding source, empowering product designers with immediate access to diverse design options based on the AI application’s machine-learning (ML) capabilities based on an extensive product database. 

2. Considering Sourcing of Procurement

Ensuring sourcing and procurement of inventory is one of the most important trends in the retail industry needed to meet customer demand while aligning with the retailer’s financial goals. However, predicting customers’ preferences traditionally can take longer hours and even worse lead to costly errors. 

This is where AI applications, when slipped into an inventory management practice, are helping store owners to analyze various customer historical data sources, supplier details, required inventory, market and industry trends and future product demand forecasts. This way retailers can leverage customer insights to create product plans and assortments.

Use Case: 

H&M, a multinational clothing company, uses AI technology predictive analysis capabilities to monitor and predict the usage of raw materials in production. Through this, the brand ensures that sufficient quantities of products are manufactured, reducing wastage and promoting sustainability. 

What Can Be Learned?

Implementing AI technology’s insight engine capabilities will enrich sourcing and procurement strategies to anticipate customer demand while optimizing the inventory management practice.

3. Manufacturing

AI for manufacturing is forecast to reach US $3.7 trillion by 2035 and there are multiple ways that AI can transform the manufacturing process. 

The manufacturing process includes sewing, cutting, preparing, assembling and packing of raw materials to produce end products for retailers. Many manufacturing units still follow the traditional production practices which leads to downtime and errors in the process. 

However, manufacturers who have already adopted AI technology in their manufacturing operations and quality measures avoid downtime, errors and increase efficiency. 

Use Case: 

Mohammadi Group, with a focus on the garment industry, makes use of AI-enabled sewing machines to knit sweaters. This allows the manufacturer to produce clothing faster and quality ones. 

What Can Be Learned? 

In summary, intelligent applications are one of the best artificial intelligence in retail solutions revolutionizing and streamlining the product production process by making it faster, smarter, safer and more efficient. 

4. Inventory Control and Distribution 

Inventory management includes procuring, storing and forecasting product requirements to meet customer’s demands throughout the retailer’s network. Therefore, this important retail business operation requires constant monitoring and optimization, yet many are still lagging to ensure a successful inventory management strategy in place. 

This is where AI technology is modernizing the inventory management process for retailers across the globe. Whether you are looking to generate recommendations on product assortments and track inventory placements in the warehouse, analyze data coming from various customer data streams, adopt robotic process automation (RPA) technology to ensure streamlined distribution or optimize inventory data management capabilities, AI technology is the modern solution for the retail industry in 2024 and beyond.

Use Case:

Gap, a worldwide clothing and accessories retailer, implements AI-based material handling systems to manage stock reception, sorting and order picking. This way the popular retailer ensures quick adaptability to changes in the retail value chain dynamics. Moreover, ThredUp, an online consignment and thrift store, smartly generates product listing information through AI’s deep learning capabilities to assign resale values and unique codes for each stock, streamlining the loading process and enhancing data governance needed for a successful inventory management practice.

What Can Be Learned?

Generative artificial intelligence in retail in retail plays a key role in inventory control and distribution processes within the retail value chain, therefore, adopting it will eliminate stock inefficiencies, enhance forecasts and reduce costs and wastage of resources behind their operational tasks.

5. Store Operations

This is where the customer engages with the retailer for the first time, hence it is important to have the stock availability based upon the customer’s needs to deliver an excellent shopping experience.

AI is helping retailers to seamlessly integrate digital and physical shopping capabilities with augmented intelligence allowing personalized product recommendations, automated payments, fraud and counterfeit detections and real-time chat support services. 

Use Case:

Lowe’s, a home improvement retailer, has a chatbot called LoweBot that helps in-store customers easily find the product they want to purchase. They can answer customers’ questions in multiple languages.  

Moreover, Sobeys, a grocery retailer, is allowing its shoppers with AI-powered smart shopping carts to enhance their checkout experience. Sobey’s smart shopping cart automatically identifies the products added to the trolley offering a seamless payment experience.   

What Can Be Learned?

AI retail solutions like augmented intelligence enhance retail operations, customer experience and inventory management. Today, there are many AI applications to optimize retail store operations in the store operations value chain stage.

6. Sales Operations

This is also another important stage in the retail value chain where the customers and retailers come in contact with each other. Therefore, any retailer needs to ensure their stock shelves are filled with updated price tags.

Use Case:

Walmart uses the power of AI-based shelves scanning robots in some of its stores to ensure stock is available. The robot reports to the store associate in case of empty shelves or stocking of wrong items.

What Can Be Learned?

Augmented Intelligence technologies can serve as essential touch points to ensure better experience among store associates and customers.

7. Fulfilment

Fulfillment, also known as order fulfillment in retail, defines the overall process of preparing, packing and delivering customer orders. A well-optimized order fulfillment strategy can significantly enhance the retail customer experience, delighting shoppers at every turn.

Today, edge AI is being used by retailers for order collections and packing orders too.

Use Case:

Ocado, a grocery retail business uses AI to lift, move and sort items speeding up the order fulfillment process. It steers across thousands of bins stocked with products to the pick stations where associates would then pack them. 

What Can Be Learned?

Few AI technologies steer products on conveyor belts making it easy for associates to perform the packing. At the same time, other edge AI technologies for retail automation enable customers to collect orders with the involvement of a sales associate.

8. Retailers’ Customer use 

This is another broader use case of AI applications in the retail value chain. The retail industry would not exist without customers and today many AI applications assist and support customer use capabilities including virtual try-on, speech recognition and snap and shop. 

Use Case:

Ikea, a furniture retailer and Home Depot, a home improvement retailer use three-dimensional (3D) augmented reality technology to measure and place products within the customer’s home. 

What Can Be Learned? 

Many AI-based technologies are helping retail brands of all sizes to offer modern ways of product engagement and enhancing customers’ shopping experience. 

9. Retailer’s Customer Support 

Last but not least we are going to talk about customer support. In this stage of the retail value chain, retailers are seen to offer product demonstrations for building customer relationships. 

Use Case: 

Nike, the popular athletic footwear and apparel retailer, utilizes AI-powered assistance technology that allows end customers to adopt virtual training sessions. The application creates exercise plans and shares motivational messages to help customers achieve their fitness goals. 

What Can Be Learned? 

AI-based applications are helping retailers enhance customer support capabilities in many ways, including assisting in post-purchase relationship-building activities. 

Summing up

Whether you are a big box retailer, convenience store, department store, digital marketplace, fast fashion, off-price retailer, online retailer, specialty or supermarket you can see how AI technology in the retail industry is revolutionizing the value chain operations through machine learning, deep learning, augmented intelligence, insight engines, edge AI and intelligence applications capabilities.

Are you looking to enhance your customers’ in-store shopping experience through an interactive location mapping tool that can allow you to trigger personalized messages and wayfinding capabilities? Inquire now! Click to read about the 10 factors to consider in your warehouse location mapping strategy!