Artificial intelligence-based decision support systems: Integration, adaptation, and performance evaluation

Aim. The work aimed to conduct a comprehensive analysis of decision support systems (DSS) based on artificial intelligence (AI) technologies, with an emphasis on their integration into business processes and performance evaluation. Objectives. The work seeks to study the main stages of AI-based DSS development, to determine key performance indicators for assessing their financial, operational, and strategic impact, to select the main challenges in such implementations and the long-term effects of the systems, as well as to formulate recommendations for improving their interpretability and adaptability. Methods. The study employed methods of system analysis, generalization of practical experience, and research. The article considers modern trends in the use of AI, successful cases from the practice of large companies (JPMorgan Chase, General Electric, Amazon), and the concept of the J-curve productivity for analyzing long-term effects. Results. The integration of AI into DSS provides the best potential for increasing work efficiency, reducing costs, and improving the quality of management decisions. A comprehensive efficiency assessment model has been developed, which includes both quantitative and qualitative indicators. Conclusions. AI-based DSS can be used not only to increase the accuracy and rate of management decisions, but also to optimize the resource utilization and adapt to a fast-paced market environment. However, successful integration of such systems requires solving a number of problems, including improvement of data quality, enhancement of the interpretability of algorithms, and adapting the personnel to new technologies. Hybrid models that combine AI capabilities and cognitive methods open up a promising direction capable of improving the efficiency and adaptability of DSS under conditions of uncertainty. The implementation of the proposed approaches leads to increased competitiveness and sustainability of companies.