Agentic AI Sustainability Assessment for Supply Chain Document Insights

2025-11-10

AI Summary

This study addresses the challenge of balancing automation efficiency and environmental sustainability in supply chain document intelligence. We propose the first ESG-aligned sustainability evaluation framework for document processing. Methodologically, we design a multi-agent AI architecture incorporating “thinking-mode” reasoning, an adaptive document parser, a verifiable validation module, a human-in-the-loop benchmark, and a real-time energy consumption and carbon emission monitoring model—enabling joint quantitative assessment of performance and environmental metrics. Our key contribution lies in the deep integration of agentic AI with green AI evaluation, transcending conventional human-AI collaboration paradigms. Experimental results demonstrate that the fully autonomous agent-based workflow reduces energy consumption by 70–90%, carbon emissions by 90–97%, and water usage by 89–98% compared to manual processes—robustly validating the technical feasibility and governance value of sustainable intelligent document processing.

Technology Category

• Machine Learning: Efficient ML / Green AI
• Humans and AI: Emotional Intelligence
• Intelligent Robots: Embodied AI

Application Category

• Search and Retrieval-Augmented AI: Agentic search
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Architectures and workflows that use LLMs for crowd work

Abstract

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.

Problem

• Assessing sustainability of agentic AI in supply chain document workflows
• Comparing environmental impact of manual versus AI-assisted document processing
• Developing ESG methodology for AI-enabled supply chain automation solutions

Innovation

• Agentic AI framework for supply chain sustainability assessment
• Multi-agent workflow with parsers and verifiers
• Unified ESG methodology integrating performance and emissions