USING ARTIFICIAL INTELLIGENCE TOOLS TO FORM PAYBACK BUSINESS MODELS

Abstract

The article examines the areas in which modern analytical tools (software solutions) integrated with AI can be applied, as well as the tasks that can be addressed with their help, with the aim of developing profitable business models and supporting business and management decisions of both tactical and strategic significance. As key issues that can be solved, and processes (related to the substantiation of business and managerial decision-making) that can be improved or optimized with the help of AI tools – and which, therefore, will have a direct impact on the formation of effective business models as ways of generating profit from selected types of activity by business entities (BEs) – the following are considered: tasks, processes, and decisions related to the generation, review, and evaluation of various options for market environment segmentation; alternative scenarios of events and business behavior; alternative business and managerial actions; as well as the justification of their optimal parameters and the selection of the most appropriate options (according to specified criteria) among them, which should be incorporated into the business and strategic plans of BEs. Specifically, these are: a) processes and decisions related to selecting business directions, the so-called strategic business zones (SBZs) or strategic business areas (SBAs), attractive for business entities (BEs) in terms of potential future profitability, based on computerized algorithms for constructing and applying the multidimensional Zwicky morphological matrix (the method of morphological analysis);b) processes for determining the values of absolute strategic potential and competitiveness of BEs (calculated using an algorithm that compares the values of their individual components with those of the conventionally strongest competitor within the SBZ), according to the key success factors (KSFs) for particular SBZs;c) processes of identifying and evaluating potential opportunities and threats (risks) for the development of a BE’s business in these SBZs;d) processes of forming and making decisions regarding the choice of strategic behavioral (action) alternatives at all levels of business managemen t – corporate, individual businesses (i.e., at the level of so-called strategic business units, SBUs), functional (business process level), and operational. A selection and list of AI tools currently available on the market has been compiled and presented. Attention is focused on their functionality and potential for practical application in various aspects – that is, for solving different tasks related to the information support of developing and operating profitable business models. Consideration is given to their ability to provide systematic substantiation of adequate, high-quality, accurate, and effective business and managerial decisions to be offered to business entities and their management. This capability, in turn, is determined by the possibilities embedded in such technological concepts (ideas) as “Big Data Analysis,” “Data Mining” (DM), “Deep Learning” (DL), “Machine Learning” (ML), and “Knowledge Discovery in Data” (KDD), as well as the algorithms on which they are based. Attention is drawn to the fact that the foundation of AI tools for solving tasks of effective management, building profitable and prospectively lucrative business models, and substantiating rational business and managerial decisions lies in a variety of methods of classification, modeling, evaluation, and forecasting. These are based on the use of principles such as decision trees, artificial neural networks (ANNs), probability theory, mathematical statistics, as well as algorithms and computer programs for predictive modeling and machine learning. Such tools are applied to the analysis of current and retrospective data in order to forecast (predict) future events, dependencies, and trends in the business environment – or those that otherwise could not be detected or defined. The application of AI tools using various methods and algorithms for processing and deep analysis of large datasets (databases) helps business entity (BE) management to identify new trends or relationships in business, as well as new business insights (knowledge) that become practically useful for making effective management decisions and generating optimal tactical and strategic alternatives for business behavior. In particular, deep data analysis (Data Mining – DM, Deep Learning – DL, Machine Learning – ML, Knowledge Discovery in Data – KDD) is already widely applied in business and business management today for forecasting market trends; for identifying critical directions to strengthen the competitiveness of business entities in prospective industries, taking into account both existing and potential risks as well as key success factors (KSFs) in such businesses; for evaluating and identifying possible ways to improve the efficiency of individual business processes; for automating business and strategic planning (both optionally and systematically overall); and for performing other tasks that enable business entities to develop profitable business models and ensure their long-term profitable operation thanks to adequate and timely access to information – and consequently, readiness to respond promptly and appropriately to detected and forecasted (predicted, anticipated) changes in the external business environment.