ARTIFICIAL INTELLIGENCE TOOLS FOR SYSTEMS ANALYSIS

Abstract

The introduction of artificial intelligence (AI) and machine learning tools into data analytics has revolutionized data interpretation, providing unprecedented insights and facilitating data-driven decision-making across sectors. Data systems analysts can use a variety of tools to improve their analysis, decision-making, and risk management processes. The choice of tools depends on the specific needs of the systems analysis, the type of data, and the goals of the analysis. Integrating AI into the activities of data analysts can significantly improve the efficiency and accuracy of their work. The article analyzes the ways in which AI tools can be used in systems analysis, including: data analysis, automation and robotics, data visualization, decision support systems, natural language processing (NLP), predictive analytics and risk analysis, and expert knowledge systems. The paper proposes strategies that will help organizations navigate the complexities of integrating AI and machine learning into systems analysis to improve their analysis, decision-making, and risk management processes. AI tools are systematized and collected into groups, depending on their specifics and in accordance with the scope of possible use in systems analysis. In each group, several modern AI tools for solving specialized tasks are presented and characterized. Such a systematic approach will allow specialists in the field of systems data analysis to quickly navigate the choice of possible tools when solving a specific task and consider possible alternative options for the toolkit. The correct and quick choice of an AI tool is important, as it helps the specialist to quickly achieve the desired result with less effort. The results obtained can be used to optimize the work of specialists of various professions in the field of systems analysis.