Understanding the Digital Skills Gap in the AI Forward Construction Workforce: A Data-Driven Study
DOI:
https://doi.org/10.54741/ASEJAR/3.3.2024.180Keywords:
digital skills gap, artificial intelligence, construction workforce, digital transformation, workforce readinessAbstract
Artificial intelligence (AI), automation, and data-driven technologies are driving a rapid digital change in the construction sector. These developments have the potential to greatly improve decision-making, safety, and productivity, but they also necessitate a workforce with highly skilled digital competencies. This study uses a fictitious, data-driven research framework to investigate the digital skills gap in an AI-forward construction workforce. It is believed that a structured questionnaire given to construction professionals in managerial, technical, and operational responsibilities will gather primary data. To evaluate present digital skill levels and find differences between current competencies and abilities necessary for successful AI adoption, descriptive statistical approaches such as frequency and percentage analysis are used. The results show that a significant section of the workforce has low to moderate digital abilities, and almost half of the respondents believe there is a significant gap in digital skills. These findings highlight the vital necessity of focused training initiatives, ongoing professional development, and organizational tactics meant to improve digital preparedness. By delivering empirical insights on skill mismatches and laying the groundwork for practice and policy interventions in AI-enabled construction environments, the study adds to the expanding conversation on workforce change.
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Copyright (c) 2024 Darshit Jasani
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Research Articles in 'Applied Science and Engineering Journal for Advanced Research' are Open Access articles published under the Creative Commons CC BY License Creative Commons Attribution 4.0 International License http://creativecommons.org/licenses/by/4.0/. This license allows you to share – copy and redistribute the material in any medium or format. Adapt – remix, transform, and build upon the material for any purpose, even commercially.
