COMPARATIVE ANALYSIS OF TRADITIONAL ARCHITECTURAL DESIGN AND GENERATIVE DESIGN: QUALITY METRICS, TIMEFRAMES AND LABOUR INTENSITY

Against the backdrop of global digital transformation, the architecture, engineering and construction (AEC) industry is confronted with the need to reconsider its fundamental design approaches. This study presents a comprehensive comparative analysis of traditional architectural design methodology and approaches based on generative design (GD) and artificial intelligence algorithms. The aim of the paper is to quantitatively and qualitatively verify the effectiveness of generative tools along three key vectors: design quality metrics (energy performance, functional and structural optimisation), temporal characteristics of the process (duration of project phases) and labour intensity (distribution of person-hours and team roles). The methodological framework comprises a systematic review of recent scientific literature, analysis of industry reports (AIA, RIBA, McKinsey) and an in-depth examination of documented practice-based case studies (Autodesk, Zaha Hadid Architects, LIFTbuild). The findings demonstrate that generative design delivers statistically significant improvements in asset performance (reductions in energy consumption of up to 23–28% and in material intensity of structural systems by 20–30%) and radically reshapes the temporal structure of projects, reducing option-seeking phases from weeks to days. However, the study also identifies that the adoption of these technologies is associated with an initial increase in labour intensity at the stages of data preparation and algorithm development, and requires a substantial transformation of the competences of design teams. The paper contributes to the ongoing discourse on the shift from heuristic to evidence-based and algorithmic design by offering a holistic assessment of the return on investment (ROI) of digital transformation.