As far as analysing, updating, and optimising data is concerned, AI in retail has been impeccable
Retail is a sector that generates chunks of data every day which includes product spanning, promotion, customer service, and mapping sales trends. To streamline the entire process, automation and machine learning have been indispensable.
Today, retailers are looking forward to an experience that is lightyears away from the traditional way of making a purchase. It is all about upgrading the sector to the next level of personalisation with increased efficiency; in maintaining supply chain, customer satisfaction, product tracking, increasing sales, and cost reduction.
In the retail landscape, AI will have a daunting contribution in making shopping an immersive and personalised affair. Similarly, generative AI is anticipated to make the sector sustainable and environmentally friendly by reducing carbon footprint. Since AI, when fed with the optimum data, could forecast consumption demand, it could prevent overproduction and waste. Experts are also optimistic about AI’s potential to detect fraud by noticing anomalous activities.
How well retail has implemented and manipulated AI is an insightful topic itself.
AI to Locate Products
Meandering in the vast menagerie—what some product tracking might feel like to some customers. To ease the process, brands have been quick to launch mobile robots to enquire what customers need, navigate a physical store, browse locations, and fetch relevant item(s). In many stores, AI performs a visual search: customers show a picture which enables the robot to find the right or at least the relevant match. Besides doing manual work, robots / AI churn out data to direct customers or share knowledge. AI is now being used increasingly to take a quick stock of inventory to reduce surplus and scarcity of items.
Retailers have been successful at slashing operational costs in myriad ways. While automating manual tasks, which include inventory management and planning, has decreased the need for manual labour, it has allowed retailers to save monetary resources simultaneously. AI generates product designs and prototypes thereby doing away with the extravagant, if not redundant, product development costs as well. With inventory management, one can now plan in a bid to avoid excess inventory or risks of stockouts. Since AI results have unchallenged accuracy, retailers are keenly dependent on it to minimise inventory holding costs.
AI, so far, has done a marvellous job at analysing historical data to calculate the optimum number of employees a store would require at a given time. This is a major help to HRs in recruitment and staffing as they grapple with the persistent issue of the highest turnover. Besides staffing, AI is also bestowed with the capacity to attract the right candidates which looks after resource management in the sector. The industry has also been using AI tools to detect and prioritise the most crucial and difficult positions to fill. This extraordinary feature provides for a prompt and targeted approach towards talent acquisition.
Predicting future demand patterns and increasing customer traffic is a job that AI excels at. With this predictive analysis, recruiters are better equipped to understand staffing needs. Since overstaffing or understaffing could wreak havoc financially, resorting to AI for optimisation has gained momentum. Brands, such as Uniqlo, have rolled out another revolutionary feature of AI—one which analyses each customer’s reaction to recommend products. For this activity, AI relies on brain signals. Automation, therefore, has reduced the need for staffing at many levels. Optimisation has enabled urban retailers to sell well even in a fiercely competing market.
