Using AI to optimize management processes and decision-making in large organizations
TL;DR
The article examines the potential of artificial intelligence to optimize management processes and improve decision-making in large organizations and proposes a conceptual AI-based management framework that integrates data sources, analytical models, and decision support systems into a unified adaptive cycle with a feedback mechanism.
Abstract
The article examines the potential of artificial intelligence to optimize management processes and improve decision-making in large organizations. The study is conducted within the framework of a business engineering approach. The analysis is based on data from the Stanford AI Index 2026 database. It evaluates the level of implementation of artificial intelligence–based decision support systems across different industries and business functions. The results reveal statistically significant differences in AI adoption between industries and functional areas (Friedman criterion, p < 0.05). The highest level of integration is observed in the technology sector, particularly in IT, software development, marketing, and sales. In contrast, manufacturing and supply chain/inventory management remain the least digitized areas. The study proposes a conceptual AI-based management framework. The framework integrates data sources, analytical models, and decision support systems into a unified adaptive cycle with a feedback mechanism. The findings confirm that the effectiveness of artificial intelligence depends not only on the technological sophistication of the solutions. It also depends on the depth of their integration into management processes and on the development of effective human–AI collaboration models. Finally, the article provides practical recommendations for scaling AI-based solutions in large organizations.