Supporting organizational decision-making in building adaptation: a scenario-based multi-criteria analysis framework
This study introduces an integrated decision-support framework to aid early-stage planning for building adaptation. It aims to support structured decision-making and priority-setting within organizations by combining scenario development, stakeholder evaluation and AI-enhanced communication. The framework integrates cross-impact balance (CIB) analysis, analytic hierarchy process (AHP), Fuzzy-TOPSIS and generative AI techniques for scenario communication and visualization. It was applied within a Paris-based social housing association through participatory workshops with internal stakeholders, including architects, sustainability officers and project managers. The integrated framework produced 21 internally consistent scenarios, prioritized them through stakeholder-weighted objectives and identified high-performing adaptation pathways. Results revealed that strong CIB-based scenario filtering substantially conditioned downstream MCDA behaviour, producing relatively robust but convergent ranking outcomes across structurally distinct scenarios. AI-generated narratives and visuals further supported communication of complex trade-offs and exploratory planning within the organizational context. Application was limited to a single case and stakeholder group. Future research should test the method in broader multi-actor settings, incorporate participant validation and explore automation of CIB construction and weighting to improve scalability and reduce resource demands. The approach helps decision-makers co-develop and compare building adaptation pathways aligned with organizational goals. Its modular design supports integration into asset management and planning systems. This is the first study to combine CIB-based scenario planning with MCDA for building adaptation, enhanced with AI-supported scenario communication. Beyond methodological integration, the study contributes new insights into how upstream scenario-space conditioning influences downstream ranking behaviour, evaluative convergence and discriminatory capacity within exploratory decision-support systems.