Digital Workforce Augmentation in Construction: Intelligent Tools, AI Assistants, and Next-Gen Operations

Authors

  • Darshit Jasani

Keywords:

Digital Workforce Augmentation, Construction Industry, Artificial Intelligence, Intelligent Tools, AI Assistants, Operational Efficiency, Human–AI Collaboration

Abstract

Digital workforce augmentation through intelligent tools, AI assistants, and next-generation operational systems is driving a major revolution in the construction sector. With an emphasis on how artificial intelligence enhances rather than replaces human capabilities, this study investigates the degree of acceptance and perceived impact of digital workforce augmentation in construction environments. Data were evaluated using descriptive statistical approaches, such as frequency and percentage analysis, using a hypothetical, mixed-methods study framework in order to evaluate operational outcomes and adoption patterns. According to the data, among the most extensively used technologies are AI-enabled BIM tools, intelligent safety monitoring, and AI-assisted project planning. These technologies largely contribute to increased operational efficiency and decision-making accuracy. The findings, however, also point to a gap in organized reskilling programs and workforce skill development, indicating that technical progress is surpassing attempts to increase human capabilities. The study comes to the conclusion that in order to promote successful human–AI collaboration, a balanced strategy integrating intelligent systems with workforce training, change management, and organizational preparedness is necessary for sustainable digital transformation in the construction industry.

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Published

28.10.2024

How to Cite

Darshit Jasani. (2024). Digital Workforce Augmentation in Construction: Intelligent Tools, AI Assistants, and Next-Gen Operations. International Journal of Intelligent Systems and Applications in Engineering, 12(23s), 4109 –. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/8082

Issue

Vol. 12 No. 23s (2024)

Section

Research Article

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