Retailers have been using AI for several decades, but its widespread adoption and significant impact on the industry have been more noticeable in recent years.
The use of AI in retail started gaining momentum in the early 2000s, with the development of more advanced machine learning algorithms and the availability of large amounts of data.
Initially, retailers used AI for basic tasks, including inventory management and demand forecasting. However, as advances have been made, it has become more prevalent in other aspects of retail, such as personalized marketing, customer service, pricing optimization, and supply chain management. With the rise of ecommerce and the increasing importance of data-driven decision-making, AI adoption in retail has accelerated. Retailers now rely on AI to enhance the shopping experience, optimize business operations, and gain an overall competitive edge.
Generative AI has potential in customer service
Since OpenAI launched ChatGPT in November 2022, there has been a lot of hype around how groundbreaking generative AI will be across industries. In the retail and apparel sectors, its main impact will be around virtual shopping assistants. Generative AI-powered chatbots and virtual assistants can handle 24/7 customer inquiries. They can respond instantly to frequently asked questions about product availability, order status, or return policies. Answering routine inquiries and providing personalized shopping advice frees human agents to focus on more complex issues.
AI-powered systems can also analyse customer complaints and generate appropriate responses or resolutions, ensuring the consistent and timely handling of customer issues.
AI is being used by retailers to tackle ESG, inflation, and supply chain disruption
AI can contribute to retailers’ ESG goals by improving personalized shopping, inventory management, and logistics. Tools from Salesforce and Dynamic Yield can help retailers personalize promotions, product recommendations, and offers for customers. By analysing customer data, retailers can reduce waste by offering relevant products to customers, increasing sales and customer satisfaction. IBM’s Order Management System (OMS) can also help retailers improve order accuracy, reducing the need for returns and exchanges and minimizing waste.
AI can help retailers optimize their supply chains by analyzing data and identifying cost-saving opportunities to better manage pressures caused by inflation. AI algorithms can help optimize transportation routes, streamline procurement processes, and identify alternative suppliers or sourcing options to mitigate the impact of inflation on supply chain costs.
Demand forecasting and inventory management are other ways in which retailers can use AI to manage costly supply chains. AI-powered demand forecasting models can help retailers anticipate changes in consumer demand due to inflation. By accurately predicting future demand, retailers can adjust inventory levels, ensure the availability of popular products, and prevent overstocking or understocking. This helps optimize working capital and reduce the risk of inventory obsolescence or stockouts, which can be costly in an inflationary environment.
출처: Retail-insight-network.com
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