THE USE OF ARTIFICIAL INTELLIGENCE IN AIDING THE STRATEGIC DECISIONS OF BUSINESS LEADERS IN A VUCA ENVIRONMENT

Abstract

The ability to make decisions in crisis situations has always been an extremely important quality for leadership. Moreover, the ability to make good decisions is the element that makes the difference between a company that thrives and one that fails. For a long time, it has been assumed that this decision-making ability is exclusively human orientated, and it relies on available data and intuition. The purpose of this article is to show how, in the years ahead, artificial intelligence can be used to make informed and predictive decisions.

Design thinking programmes and agile working methods are already being used to improve decision-making processes, but given the VUCA environment we live in, proper decisions require a much more complex information base, which can be provided by artificial intelligence. This article will show how specific predictors can be combined to create predefined sets that help determine key information for management decisions, but more importantly, what kind of predictors can and should be considered. Too many predictors complicate the analysis process and can sabotage the result, while a small number of predictors can exclude the most important factors in the area under study. Therefore, the proper selection of predictors is very important and depends on the macroeconomic and microeconomic knowledge of the analyst processing the data. Predictor analysis is performed using artificial intelligence programs so that the decisions generated take into account a much larger number of factors than the human brain can process. The analysis is based on scenarios that span a longer period of time and takes into account the interdependence between the causative factors. Business decisions are already made based on various predictors or parameters, but the use of artificial intelligence can significantly improve the accuracy and reliability of decisions. However, artificial intelligence alone does not guarantee high prediction accuracy; the business knowledge and skills of the programmer are a key factor in achieving high accuracy. To reduce the risk of poor accuracy even though artificial intelligence is used for prediction, we have developed a scheme for selecting the right parameters. In addition, the use of artificial intelligence in this area will enhance business managers' understanding of the impact of various predictive factors on their business.