AI-Enabled ESG Compliance Audit for Stakeholders

Environmental, social, and governance (ESG) disclosures face credibility risks due to Scope 2 Greenhouse Gas (GHG) reports lacking standardized compliance checks, raising concerns about their reliability. This study therefore develops and evaluates an AI-enabled artefact for ESG compliance auditing. This artefact applies natural language processing (NLP) to extract reported values, implements rule-based checks grounded in the GHG Protocol, and produces transparent output. A design science research (DSR) approach guided the design, demonstration, and evaluation of the artefact, which was applied to sustainability reports from five technology companies. The results revealed that it replicates auditor judgments and reduces workload by over ninety percent in the sample. These findings serve as a proof-of-concept for automation in ESG compliance auditing. The theoretical contributions include extending the literature on AI in ESG auditing by reframing its role from producing interpretive scores to enabling transparent compliance verification. This study also demonstrates how DSR can help produce artefacts that embed rule-based logic into ESG assurance with rigor and practical relevance. The practical contributions include highlighting how a lightweight tool can enable auditors, regulators, boards, and investors to screen disclosures and benchmark credibility without sacrificing professional judgment.