Artificial intelligence has already become an indispensable part of many multinational companies. They recognized such technology in time and use it to advance their business models and processes to a new level. One example is Amazon, which uses artificial intelligence in its smart stores. The need for in-depth data processing arose precisely because companies generate large amounts of data every day, and this data often hides useful information that needs to be discovered and new knowledge created from it. Apart from the business world, artificial intelligence is also used in medicine, public administration, transport and other fields. The goal of the research is to investigate the usefulness and ways of applying artificial intelligence in supply chains and to compare software and generated results based on in-depth data processing methods. One of the areas in which artificial intelligence is intensively used is the business function of procurement, i.e. supply chain management. The goal of supply chain management is to optimize the business process itself, and one of the ways to do it is the application of artificial intelligence. On the example of data from a manufacturing company, in this paper, two software for in-depth data processing, Weka and Statistica, are presented, and three methods of in-depth data processing are used. The methods that were used are artificial neural networks, random forest and decision tree. The results showed that companies benefit greatly from the application of in-depth data processing methods, and the selected in-depth data processing methods predicted with high precision the results about the acceptability or non-acceptability of an individual supplier.