This article examines the application of intellectual analytics based on artificial intelligence (AI) to improve the quality of software (SW) releases. The main AI methods that significantly enhance the processes of testing, error detection, and prevention are analyzed. It is emphasized that their use contributes to more accurate defect identification, reduced testing time, and increased team productivity. Additionally, an experiment is conducted, comparing the results of SW development using traditional methods and AI tools. The research findings show that AI tools, such as GitHub Copilot, Facebook Infer, and SonarQube with predictive analytics, positively impact code quality by reducing the number of errors, improving structure, and accelerating the development process. Experimental data confirm that AI tools can significantly increase the efficiency of testing and quality management. The article also highlights the need to account for initial training and integration costs for incorporating AI into workflows. In conclusion, it is noted that, in the long term, the use of AI in SW development becomes an essential factor for improving quality and enhancing the competitiveness of products. #CSOC1120.