AI-Enabled Green Supply Chains: A Cross-Case Empirical Analysis of Sustainable Sourcing Disclosures in Indian Manufacturing Firms

Abstract

The growing integration of artificial intelligence into supply chain systems has begun to reshape how firms approach environmental responsibility and sustainable sourcing. While discussions around green supply chains often remain conceptual, limited empirical attention has been paid to how companies actually disclose and operationalize AI-enabled sustainability practices. This study examines the extent to which artificial intelligence is embedded within green supply chain strategies by conducting a cross-case empirical analysis of selected Indian manufacturing firms, including Tata Motors, Mahindra & Mahindra, and ITC Limited. Drawing upon publicly available sustainability reports, ESG disclosures, and annual reports over a multi-year period, the research analyses patterns in sustainable sourcing commitments, supplier transparency mechanisms, digital traceability initiatives, and environmental performance indicators. Using structured content analysis and comparative evaluation, the study develops an AI-enabled green sourcing disclosure index to assess the maturity of sustainability integration. The findings reveal variation in the depth of AI adoption, the credibility of sourcing disclosures, and the linkage between digital tools and measurable environmental outcomes such as emission reduction and responsible procurement practices. The research contributes to the literature on Sustainable Supply Chains 4.0 by providing empirical evidence from an emerging market context and by proposing a practical disclosure-based evaluation framework. It further offers managerial insights into how AI can move firms beyond compliance-oriented reporting toward measurable sustainability optimisation.