This paper presents a comprehensive sustainability assessment framework for document intelligence within supply chain operations, centered on agentic artificial intelligence (AI). We address the dual objective of improving automation efficiency while providing measurable environmental performance in document-intensive workflows. The research compares three scenarios: fully manual (human-only), AI-assisted (human-in-the-loop, HITL), and an advanced multi-agent agentic AI workflow leveraging parsers and verifiers. Empirical results show that AI-assisted HITL and agentic AI scenarios achieve reductions of up to 70-90% in energy consumption, 90-97% in carbon dioxide emissions, and 89-98% in water usage compared to manual processes. Notably, full agentic configurations, combining advanced reasoning (thinking mode) and multi-agent validation, achieve substantial sustainability gains over human-only approaches, even when resource usage increases slightly versus simpler AI-assisted solutions. The framework integrates performance, energy, and emission indicators into a unified ESG-oriented methodology for assessing and governing AI-enabled supply chain solutions. The paper includes a complete replicability use case demonstrating the methodology's application to real-world document extraction tasks.

翻译：本文提出了一种针对供应链运营中文档智能的综合性可持续性评估框架，以智能体人工智能为核心。我们旨在实现双重目标：提升自动化效率，同时在文档密集型工作流中提供可量化的环境绩效。研究比较了三种情景：完全人工（仅人类）、AI辅助（人在回路，HITL）以及一种利用解析器和验证器的先进多智能体AI工作流。实证结果表明，与人工流程相比，AI辅助的HITL和智能体AI情景在能耗上实现了70-90%的降低，二氧化碳排放减少了90-97%，用水量减少了89-98%。值得注意的是，结合高级推理（思维模式）和多智能体验证的完整智能体配置，相较于纯人工方法实现了显著的可持续性收益，即使其资源使用量相对于较简单的AI辅助解决方案略有增加。该框架将性能、能耗和排放指标整合到一个统一的、面向ESG的方法论中，用于评估和管理AI赋能的供应链解决方案。论文包含一个完整的可复现性用例，展示了该方法在现实世界文档提取任务中的应用。