Ecodesign Prioritization for BIPV Manufacturers Under ESPR Compliance: An LLM-Assisted Multi-Criteria Framework with Use Cases Application

This study develops a human-centered AI framework enabling rapid ecodesign prioritization for ESPR compliance while demonstrating Large Language Model (LLM) integration in sustainability strategy. Four-stage hybrid methodology combining LLM-assisted action identification (30 ESPR-aligned interventions) with Multi-Criteria Decision Analysis with Analytic Hierarchy Process (MCDA-AHP) is developed. Expert validation addressed LLM-driven interventions limitations with practitioners evaluating AI suggestions based on value chain context. The framework applied to two Italian BIPV SMEs demonstrated strategic differentiation based on Feasibility vs. Desirability vs. Affordability producing systematically different action portfolios within regulation-aligned aggregate structures. Sensitivity analysis showed 100% priority stability under ±10% AHP variations for priority 1, 3 and 4 actions and 82% for priority 2 actions, validating framework robustness. The framework's provide empirical evidence for augmentation-not-automation in AI-assisted strategic planning, contributing replicable methodology for responsible LLM integration across manufacturing sectors. Results demonstrate combining AI synthesis efficiency with human contextual judgment enables regulation-aligned, business-model-specific sustainability strategies.