This study examines the potential of generative artificial intelligence (AI) in architectural design processes, focusing specifically on the energy-efficient renovation of building envelopes. The research explores the use of AI-based image generation tools to assist in the conceptual design phase of ventilated façade systems aimed at improving the thermal performance of existing buildings.

Based on photographic documentation of façades—obtained through conventional photography and photogrammetric imaging (SFM) to ensure geometric accuracy—the proposed methodology generates multiple design alternatives with AI assistance. These alternatives are constrained by a predefined catalog of construction products and materials provided by façade system manufacturers. The objective is to evaluate the current capability of generative AI tools to produce, within a reduced timeframe, multiple façade design options that are both visually coherent and technically feasible, in accordance with specific constructive and material parameters.

The results are assessed according to both architectural criteria—such as aesthetic consistency, contextual adaptation, and formal diversity—and technical parameters, including constructability, compliance with energy efficiency requirements, and correspondence with manufacturers’ specifications.

This research seeks to establish a framework for integrating generative AI into the early design stages of energy retrofit projects, providing architects with intelligent and responsive tools that enhance creative exploration without compromising technical rigor. Ultimately, this work contributes to the ongoing reflection on the role of AI in intelligent design processes, proposing its use as a collaborative and adaptive system that bridges aesthetic decision-making with energy-conscious architectural design.