Investigating the Impact of AI Integration in Design Workflows on Architects’ Workload During the Pre-Design Stage, with a Focus on Optimizing Parametric Design Options
Kush Goyal,Student, Christ University, Bengaluru, kushgoyal707@gmail.com
Second Author- Prof. Arun Baby,Guide - Assistant Professor, Christ University, Bengaluru, arun.baby@christuniversity.com
Third Guide - Dissertation Coordinator - Prof. Ashik S, Affiliation - Assistant Professor, Christ University, Bengaluru, ashik.s@christuniversity.in
Fourth Author - Dr. Shilpa Madangopal, Affiliation - Professor, Christ University, Bengaluru, shilpa.madangopal@christuniversity.in
Abstract - The integration of Artificial Intelligence (AI) into architectural design workflows is transforming early-stage decision-making processes. The pre-design phase, traditionally characterized by iterative exploration, analytical evaluation, and conceptual development, demands significant time and cognitive investment from architects. With the emergence of AI-driven generative systems integrated within parametric design environments, there is growing interest in understanding whether such technologies reduce workload or fundamentally redistribute it.
This research investigates the impact of AI integration on architects’ workload during the pre-design stage, with a specific focus on optimizing parametric design options. A mixed-method research approach was adopted, combining survey data, workflow simulations, and comparative time analysis between traditional parametric modeling and AI-assisted generative workflows. Results indicate a substantial reduction in repetitive modeling tasks and base-form generation time, alongside an increase in evaluative and decision-making responsibilities. While AI accelerates option generation and performance filtering, architects remain central to contextual adaptation, qualitative assessment, and strategic refinement.
The findings demonstrate that AI integration does not eliminate architectural labor but restructures it. Workload shifts from production-oriented activities to analytical oversight and design curation. The research proposes a layered workflow framework to ensure effective integration without compromising architectural agency.
Keywords: Artificial Intelligence, Parametric Design, Architectural Workflow, Pre-Design Stage, Computational Design, Workload Optimization
