Intelligent Supply Chain Governance: Integrating AI for Sustainable, Resilient, and ESG-Compliant Procurement

Aim: This study aims to evaluate how artificial intelligence (AI) can enhance ESG-oriented supplier risk assessment and strengthen sustainability compliance in global supply chains. Methods: Theoretical foundations of ESG monitoring were analyzed, the main risks were categorized into Environmental, Social, and Governance domains, and the role of AI technologies in enhancing the adaptability, transparency, and efficiency of supply chains was substantiated. The study employed a case study approach, utilizing secondary data from the Prewave platform, and applied comparative analysis to evaluate improvements in ESG risk detection efficiency. Particular attention was given to the algorithmic structure of modern intelligent platforms capable of real-time cognitive analysis of textual and numerical data. Results: The advantages of employing AI models in reducing ESG incident detection time, automating counterparty evaluation, and ensuring compliance with international regulatory frameworks (CSRD, LkSG) were empirically demonstrated. Furthermore, a conceptual structural model was proposed for implementing an AI-oriented ESG supplier assessment system, covering all stages - from data source formation to managerial decision-making. Conclusion: The study concludes that AI-based ESG monitoring systems significantly enhance transparency, operational resilience, and sustainable procurement practices, ultimately marking a paradigm shift in global supply chain governance. Recommendations: Key implementation barriers in emerging market contexts included fragmented data infrastructure, low digital literacy within public sectors, and inconsistent regulatory compatibility with international ESG standards. Future research should focus on developing and localizing AI models for ESG monitoring, specifically addressing the unique data, infrastructure, and policy environments of developing economies.