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This research paper explores the transformative potential of intelligent economic systems in the context of sustainable economic growth. This potential determines the impact of integrating artificial intelligence, data analytics, and automation into economic processes on key dimensions, including economic growth, resource efficiency, and environmental sustainability. Through a multidimensional approach, this research employs quantitative analyses, qualitative insights from stakeholder interviews and expert surveys, and cross-regional comparisons to provide a comprehensive perspective on the subject. The findings reveal a compelling positive relationship between the adoption of intelligent economic systems and economic expansion. Regions that have embraced these systems consistently outperform their non-adopting counterparts, demonstrating higher economic growth rates, enhanced resource efficiency, and improved environmental sustainability. Innovation emerges as a central driver of growth, while challenges related to workforce adaptation and data administration require careful consideration. This research paper not only underscores the significance of intelligent economic systems in shaping a more sustainable and prosperous future but also offers practical recommendations for policymakers, businesses, and individuals. It calls for a collaborative global effort to harness the transformative power of intelligent economic systems, highlighting the potential to create a harmonious balance between innovation, intelligence, and sustainability in the pursuit of economic prosperity. The research carries significant practical value for a wide range of stakeholders, including policymakers, business leaders, investors, and the broader community. By empirically examining how intelligent economic systems, characterized by the integration of technologies like artificial intelligence, big data analytics, and automation, impact economic growth, resource efficiency, and sustainability, the research offers actionable insights and recommendations that can guide decision-making in both the public and private sectors.

An important issue in forecasting the probability of bankruptcy is the formation of an optimal set of financial-economic performance indicators with high forecast capacity. The article is aimed at optimizing the indicator system, which can be used to build a model for diagnosing the probability of corporate failures. The known methods and models for diagnosing bankruptcy were analyzed and it was found that they were based on the financial performance indicators, which use empirical data only. A set of financial performance indicators has been formed that can be used to forecast probability of corporate bankruptcy or to plan for anti-crisis measures. The practical significance of the study suggests developing a theoretical basis for solving issues arising in the diagnostics of probability of bankruptcy of corporate enterprises. Prospect for further research in this direction is to develop an integrated indicator using the fuzzy logic theory, taking into account the qualitative and quantitative performance indicators of enterprise.