IGLC.net
EXPORT DATE: 19 June 2026

@CONFERENCE{Dong2026,
author={Dong, Yaxian and Chatterjee, Sangaa and Zhan, Zijun and Hu, Yuqing and Doe, Daniel Mawunyo and Han, Zhu },
editor={Hamzeh, Farook and Poshdar, Mani and Garcia-Lopez,, Nelly P. },
title={AI-impacted construction people readiness via skill valuation and talent trajectories}, 
journal={Proceedings of the 34th Annual Conference of the International Group for Lean Construction (IGLC 34)}, 
booktitle={Proceedings of the 34th Annual Conference of the International Group for Lean Construction (IGLC 34)}, 
year={2026},
pages={1288-1299},
url={http://www.iglc.net/papers/details/2575},
doi={10.24928/2026/0296},
affiliation={Ph.D. student, Department of Architectural Engineering, The Pennsylvania State University, University Park, USA, yzd5221@psu.edu ; Undergraduate student, Department of Computer Science and Engineering, The Pennsylvania State University, University Park, USA, sjc6940@psu.edu ; Ph.D. student, Department of Electrical and Computer Engineering, University of Houston, Houston, USA, zzhan@uh.edu ; Assistant Professor, Department of Architectural Engineering, The Pennsylvania State University, University Park, USA, yfh5204@psu.edu ; Assistant Professor, Department of Electrical and Computer Engineering, Prairie View A&M University, Prairie View, USA, dmdoe@pvamu.edu ; Moores Professor, Department of Electrical and Computer Engineering, University of Houston, Houston, USA, zhan2@uh.edu },
abstract={Artificial intelligence (AI) is reshaping architecture, engineering, and construction (AEC) workflows by redistributing cognitive and coordination tasks across human and digital resources, thereby changing cost structures and value generation mechanisms in lean construction. However, domain-specific and data-driven evidence on how AI restructures the construction workforce remains limited. Framing the construction industry as a production system that allocates work to capable resources, this study examines how AI exposure varies across construction roles and how it influences career transitions and capability development. Using job postings from Indeed, we quantify AI exposure across diverse AEC positions. Results show the emergence of AI-specific roles (e.g., AI trainers), high exposure in administrative positions, and comparatively low exposure in executive, human resources, and certain roles related to building information modeling. To understand workforce dynamics, we construct a directed graph of construction professionals’ career trajectories from LinkedIn data and model transition patterns. By mapping transition-level task and skill gaps to an AI capability taxonomy, we quantify alignment between required capability upgrades and AI-performable functions. The results offer a structural perspective on how AI exposure gradients may reshape capability flows within the construction production system and inform workforce adaptation strategies. },
author_keywords={Construction workforce, Artificial Intelligence, collaboration, reliable promising. },
address={Singapore, Singapore },
issn={2789-0015 },
publisher={ },
language={English},
document_type={Conference Paper},
source={IGLC},
}