INTEGRATION OF NEW TECHNOLOGIES INTO SCIENCE TO IMPROVE RESEARCH

The integration of Artificial Intelligence (AI) and cloud infrastructure into scientific research processes opens new opportunities for more efficient data processing, automation of routine tasks, and interdisciplinary collaboration.In the context of growing volumes of scientific information, increasing demands for research reproducibility, and the need for scalable computational capacity, modern science increasingly requires the implementation of comprehensive digital solutions.This paper outlines practical approaches to implementing AI tools (machine learning, natural language processing, big data analytics) in combination with cloud platforms (AWS, Azure, Google Cloud) in scientific environments.These tools are applied for automatic data classification, anomaly detection, predictive modeling, intelligent publication search, accelerated hypothesis testing, and support for reproducible research workflows.Special attention is given to the role of cloud infrastructure in enabling: remote access to computational resources; real-time collaboration on research projects; integration of open APIs, visualization and analytics platforms (Jupyter, GitHub, Tableau, Power BI); dynamic scalability according to experimental or academic program needs.The author presents examples of such solutions implemented through system integration projects for research institutions, universities, and engineering centers across Europe and Central Asia.For instance, cloud-based processing of sensor data in climate modeling labs enables real-time reporting and analysis in distributed locations.In bioinformatics laboratories, AI accelerates protein structure identification and mutation analysis.From a practical perspective, IT consultants and system integrators play a key role in aligning scientific objectives with technical execution.Their responsibilities include: designing adaptive and scalable IT architectures; configuring secure environments for data access; ensuring regulatory and standards compliance for data handling; maintaining compatibility among tools, platforms, and formats.This approach can serve as a scalable foundation for developing inclusive digital strategies and flexible IT entry points -aligned with today's labor market dynamics, mobility trends, and the growing demand for transformation-ready talent.