TY - CONF TI - AI-impacted construction people readiness via skill valuation and talent trajectories C1 - Singapore, Singapore C3 - Proceedings of the 34th Annual Conference of the International Group for Lean Construction (IGLC 34) SP - 1288 EP - 1299 PY - 2026 DO - 10.24928/2026/0296 AU - Dong, Yaxian AU - Chatterjee, Sangaa AU - Zhan, Zijun AU - Hu, Yuqing AU - Doe, Daniel Mawunyo AU - Han, Zhu AD - Ph.D. student, Department of Architectural Engineering, The Pennsylvania State University, University Park, USA, yzd5221@psu.edu AD - Undergraduate student, Department of Computer Science and Engineering, The Pennsylvania State University, University Park, USA, sjc6940@psu.edu AD - Ph.D. student, Department of Electrical and Computer Engineering, University of Houston, Houston, USA, zzhan@uh.edu AD - Assistant Professor, Department of Architectural Engineering, The Pennsylvania State University, University Park, USA, yfh5204@psu.edu AD - Assistant Professor, Department of Electrical and Computer Engineering, Prairie View A&M University, Prairie View, USA, dmdoe@pvamu.edu AD - Moores Professor, Department of Electrical and Computer Engineering, University of Houston, Houston, USA, zhan2@uh.edu ED - Hamzeh, Farook ED - Poshdar, Mani ED - Garcia-Lopez,, Nelly P. AB - 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. KW - Construction workforce KW - Artificial Intelligence KW - collaboration KW - reliable promising. PB - T2 - Proceedings of the 34th Annual Conference of the International Group for Lean Construction (IGLC 34) DA - 2026/06/22 CY - Singapore, Singapore L1 - http://iglc.net/Papers/Details/2575/pdf L2 - http://iglc.net/Papers/Details/2575 N1 - Export Date: 19 June 2026 DB - IGLC.net DP - IGLC LA - English ER -