AI-Driven Sustainable Procurement: Balancing Strategy and ESG Compliance

Aim: This study aims to evaluate the role of Artificial Intelligence (AI) in enhancing sustainable supplier assessment within ESG-focused procurement systems, ultimately demonstrating AI’s potential as a transparent, adaptive, and scalable tool for sustainable supplier evaluation. Methods: The research adopts a mixed-methods approach combining qualitative analysis of international ESG standards (GRI, ISO 20400), an algorithmic review of AI techniques (Machine Learning, Natural Language Processing, Explainable AI), and an in-depth case study of Unilever. The core procedure involved developing an analytical matrix to systematically compare traditional and ESG-oriented supplier selection and formulating a framework for embedding ESG-by-design principles into digital procurement platforms. Results: Findings reveal that AI-enabled procurement systems significantly improve transparency, scalability, and adaptability in supplier ESG assessments compared to traditional methods. The analytical matrix highlights AI’s superior predictive capabilities for risk mitigation, facilitating a shift from simple compliance checking to proactive risk management. Conclusion: This work substantiates the need to combine AI tools with an ethical approach to supply chain management under conditions of uncertainty and risk, establishing ESG integration not just as an ethical imperative but a strategic necessity. Recommendations: The main practical implication is the imperative for organizations to shift from reactive compliance to a proactive risk architecture, mandating the use of AI tools under strict ethical governance for reliable supply chain resilience. Strategic recommendations include embedding ESG criteria natively within digital procurement platforms and adopting the developed AI-driven classification matrix.