DESIGN AND MODELING

Generative AI as a driver of BI technology transformation

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GOST Chasovskikh V. P., Kokh E. V. Generative AI as a driver of BI technology transformation // STROITEL'NYE I DOROZHNYE MASHINY. 2026. Vol. 70. No. 9. P. 139-150. DOI: 10.25726/q7996-4225-1560-d
APA Chasovskikh, V. P. & Kokh, E. V. (2026). Generative AI as a driver of BI technology transformation. STROITEL'NYE I DOROZHNYE MASHINY, 70(9), 139-150. https://doi.org/10.25726/q7996-4225-1560-d

Abstract

This article examines the impact of generative artificial intelligence on the development and transformation of modern BI technologies in the digital economy. It substantiates the relevance of generative AI in business analytics, driven by the growth of data volumes, increasing demands on the speed of management decision-making, and the need for more accessible and intelligent analytical tools. The paper explores the essence of BI technologies and characterizes their main components, including data sources, ETL/ELT processes, data warehouses, OLAP tools, visualization tools, reporting, and analytics. Particular attention is paid to the application of generative AI in BI systems: natural language analytics, automatic report generation and analytical insights, intelligent data visualization, SQL query generation, forecasting, and scenario analysis. It is shown that generative AI facilitates the transition of BI systems from traditional reporting and visualization tools to intelligent analytical platforms for supporting management decisions. The key advantages of implementing generative AI in BI are identified, including increased speed of analytical information acquisition, greater accessibility of business analytics for users without specialized technical skills, reduced workload for analysts and IT departments, and personalization of analytics. The risks and limitations of generative AI application are also considered, including the potential for generating unreliable answers, the dependence of results on the quality of the source data, information security threats, insufficient model transparency, and organizational barriers to implementation. It is concluded that generative AI does not replace traditional BI technologies, but rather serves as a means of developing and enhancing them. Its effectiveness depends on data quality, the maturity of the IT infrastructure, the digital competencies of personnel, and the availability of regulations for the safe use of artificial intelligence.

Keywords

generative artificial intelligence Business Intelligence BI technologies business analytics digital economy data analytics natural language analytics intelligent visualization management decisions

Funding

The authors did not declare any external funding for this research.

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