Artificial Intelligence in retail is not only about futuristic autonomous shops, interactive shelves or bots as shop assistants. Although seemingly attractive, such solutions will be impossible for most retailers to implement, if only because of the need to bear significant costs for the infrastructure and store equipment. However, the use of Artificial Intelligence for data analysis is relatively cheap and business-efficient.

The implementation of the Data-Driven Retail approach involves analyzing billions of receipt data, information about thousands of products and dozens of retail-specific factors in order to predict future business events. Thanks to the use of advanced statistical methods on a large scale, it is highly probable that trends can be identified, demand for individual products can be predicted, marketing activities can be optimized and sales can be increased by adjusting the offer to individual customer needs “just in time”. Machine learning models are able to “learn” a new reality within two weeks, i.e. react to changes in an automatic way. In-depth statistical analysis supported by expert knowledge gives the business a new perspective on the current market situation.

Artificial Intelligence makes it possible to forecast sales individually for each store and each product, and to generate automatic orders to replenish stock in stores. In warehouses, it is possible to optimize the availability of goods and allocated capital on the basis of demand forecasts and to generate automatic orders for suppliers. The use of the latest Big Data technologies ensures effective monitoring of data flow and sales processes in real time and the generation of alerts in case of anomalies. All this can happen automatically, taking into account industry-specific factors and new variables that will be provided by the new retail reality.

We are talking about specific opportunities to build a competitive advantage by minimizing out-of-stocks and reducing over-stocks to increase sales and reduce losses. Artificial Intelligence makes it possible to forecast sales individually for each store and each product, and to generate automatic orders to replenish stock in stores. In warehouses, it is possible to optimize the availability of goods and allocated capital on the basis of demand forecasts and to generate automatic orders for suppliers. The use of the latest Big Data technologies ensures effective monitoring of data flow and sales processes in real time and the generation of alerts in case of anomalies. All this can happen automatically, taking into account industry-specific factors and new variables that will be provided by the new retail reality. If we add the automation of business processes to this, we can talk about a transformation towards Data-Driven Retail.