(untitled) (Eureka Journal of Business, Economics & Innovation Studies, 2026)
This article examines methods for using artificial intelligence technologies under conditions of economy-wide digitalization, with a focus on applications relevant to an economic university context. The central argument is that effective AI adoption is not defined by the presence of algorithms alone, but by the quality of methodical integration across data governance, process redesign, human capital development, and institutional decision-making. The paper conceptualizes AI methods as an operational toolkit that includes predictive analytics for forecasting demand and risks, machine learning for credit scoring and anomaly detection, natural language processing for document management and customer interaction, computer vision for compliance and asset monitoring, and generative AI for knowledge work support in analysis, reporting, and academic writing. Particular attention is paid to the “method layer” that connects technologies with measurable outcomes: defining use cases, specifying performance metrics, ensuring data quality, controlling bias, and establishing responsible-use safeguards. The study also emphasizes the need for organizational methods such as pilot-based implementation, MLOps practices for lifecycle management, interdisciplinary project teams, and competency-based training for students and staff. In the context of Uzbekistan’s digital transformation agenda, the article highlights pragmatic pathways for integrating AI into economic education and practice through applied laboratories, industry datasets, simulation cases, and institutional analytics. The expected contribution is a structured, practice-oriented model that helps universities and economic organizations select AI methods, implement them responsibly, and evaluate results in terms of productivity, service quality, and decision reliability.