Scaling Urban Retrofitting with AI-Enabled Modular Facades: An Economic Feasibility Framework for Sustainable Business Models

Urban retrofitting is increasingly critical for achieving global sustainability goals, yet economic feasibility remains a significant barrier to widespread adoption. This study presents an economic feasibility framework for AI-enabled modular adaptive façade systems, aimed at enhancing the scalability of retrofitting initiatives in diverse urban contexts. Building on validated simulation benchmarks and performance data from eleven peer-reviewed sources, this research evaluates financial metrics including Net Present Value (NPV), Internal Rate of Return (IRR), and Payback Period across three building archetypes: high-density towers, mid-rise public structures, and heritage zones.

Using a simulation-supported cost-benefit model, the framework incorporates energy savings data (25%–84%), installation cost ranges ($300–$800/m²), and lifecycle operational costs. Results indicate that simplified AI-controlled façades can achieve ROI within 5–7 years under typical retrofit conditions, and scale effectively in both developed and emerging urban environments. Scenario-based sensitivity analyses further assess scalability under different budgetary and policy constraints.

This study contributes to the literature by linking adaptive façade design, AI integration, and sustainable business models, providing entrepreneurs, developers, and policymakers with a replicable economic toolkit for low-carbon urban transformation.