This study develops a human-centered Artificial Intelligence (AI) framework enabling rapid ecodesign prioritization for Ecodesign for Sustainable Products Regulation (ESPR) compliance while demonstrating Large Language Model (LLM) integration in sustainability strategy. A 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 the value chain context. The framework applied to two Italian building-integrated photovoltaic (BIPV) small-medium enterprises (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 one, three, and four actions and 82% for priority two actions, validating framework robustness. The framework provides empirical evidence for augmentation-not-automation in AI-assisted strategic planning, contributing a replicable methodology for responsible LLM integration across manufacturing sectors. Results demonstrate that combining AI synthesis efficiency with human contextual judgment enable regulation-aligned, business-model-specific sustainability strategies.
Pracucci et al. (Fri,) studied this question.