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AI Use Cases

A collection of over 250 uses for artificial intelligence

A continually updated list exploring how different types of AI are used across various industries and AI disciplines,including generative AI use cases, banking AI use cases, AI use cases in healthcare, AI use cases in government, AI use cases in insurance, and more

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Product identification and tracking

Introduction

In the current world where the retail industry is constantly changing and developing, it is clear that ML and AI have become major factors that define the development of the industry. Another important area of application of ML and AI in the retail sector is product identification and tracking. This includes the use of smart systems in identifying, sorting and monitoring of products in the course of their movement along the supply chain network right from the source of supply, through storage and distribution, to the point of sale and even after sales services. The implementation of artificial intelligence and machine learning in product identification and tracking does not only increase efficiency in the supply chain but also enhances the experience of the customers through providing a more streamlined and efficient shopping experience.

Challenges

There are, however, a number of challenges that can hinder the application of ML and AI in product identification and tracking. Some of these challenges include; high costs of implementing AI, skill gap, data privacy, and challenges in implementing AI in to current systems. Also, there is a problem of accuracy and reliability of AI systems for instance, false positives or false negatives may cause tremendous losses. The responsibility on the retailers is also cast on the issue of explaining the advantages of AI to the customers since some may consider AI as an intrusion of their privacy. Also, the ever evolving nature of the retail industry only makes it that much harder to implement AI solutions.

AI Solutions

In order to address these issues there is a number of AI solutions that are being created and applied. These are AI-based image recognition for product recognition, predictive analytics for inventory and supply chain, and AI chatbots for customer service, among others. For example, Google’s Cloud Vision API can help in identifying products through images while IBM’s Watson provides a high level of predictive analytics for inventory management. Other AI techniques involve the use of machine learning models that can learn about customers’ behaviour and their preferences, which helps retailers provide a more personalised experience. Also, AI can help to perform routine functions, so that employees can focus on important work. These solutions are usually compatible with current systems, thus minimising the expenses and time required for the reconstruction of the IT structure.

Benefits

There are numerous advantages of employing ML and AI in product identification and tracking. Some of the advantages include better inventory management, improved customer satisfaction, higher sales, and lower operational costs. For example, proper product identification can minimize the errors that are common in inventory control while predictive analytics can assist in demand forecasting to avoid stocking up or stocking out of goods. AI can also improve the customer experience through providing personalized recommendations, shortening the checkout process, and improving the after-sales service. Also, AI can boost sales as it enables retailers to address customers properly and to understand their wants and needs. Last but not the least, AI can minimize the costs by performing routine tasks that would otherwise need a lot of resources.

Return on Investment

The ROI of using ML and AI in product identification and tracking can be very valuable. A study by Capgemini has revealed that retailers can cut their costs by 20% with the help of AI while their sales can go up by 15%. Also, a research done by Boston Consulting Group revealed that retailers who adopt AI and ML can boost their revenue by 6-10%. Still, the ROI may differ greatly based on implementation size, the complexity of the AI system, and the retailer’s capability to capitalize on the information that AI offers.

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