IJSEA Volume 15 Issue 1

Addressing Construction Workforce Shortages Through AI-Augmented Planning, Skills Forecasting, and Knowledge Retention Amid an Aging Labour Force Crisis

Adeyemi Michael Adejumobi
10.7753/IJSEA1501.1005
keywords : Artificial intelligence; Construction workforce; Skills forecasting; Knowledge retention; Aging labour force; Workforce planning

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The global construction sector is confronting a structural labour crisis driven by demographic aging, declining apprenticeship pipelines, project complexity, and cyclical demand volatility. Workforce shortages now threaten delivery schedules, safety performance, cost certainty, and the capacity to execute infrastructure programmes essential to economic growth and climate adaptation. From a broad perspective, this challenge reflects systemic fragmentation between labour supply, skills development, and project planning, compounded by limited visibility into future workforce needs and the loss of tacit knowledge as experienced workers retire. This abstract examines how artificial intelligence can function as an integrative capability to stabilise construction labour systems rather than a narrow automation tool. At the planning level, AI-augmented scheduling and resource optimisation models enable contractors and owners to align labour demand dynamically with project portfolios, reducing bottlenecks and idle capacity. At the workforce level, predictive skills forecasting leverages historical project data, regional labour statistics, and policy signals to anticipate trade shortages years in advance, informing targeted training, recruitment, and migration strategies. At the organisational level, knowledge retention systems using natural language processing and digital twins capture experiential know-how from senior trades and engineers, preserving safety practices, sequencing logic, and problem-solving heuristics. Narrowing to the aging labour force crisis, the abstract argues that AI-supported decision frameworks can mitigate the dual risks of expertise attrition and productivity decline by enabling evidence-based succession planning and accelerated upskilling of younger workers. By embedding AI across planning, forecasting, and knowledge management, the construction industry can transition from reactive labour substitution to proactive workforce resilience, supporting sustainable project delivery in the face of demographic and technological disruption across global construction markets worldwide today.
@artical{a1512026ijsea15011005,
Title = "Addressing Construction Workforce Shortages Through AI-Augmented Planning, Skills Forecasting, and Knowledge Retention Amid an Aging Labour Force Crisis",
Journal ="International Journal of Science and Engineering Applications (IJSEA)",
Volume = "15",
Issue ="1",
Pages ="24 - 34",
Year = "2026",
Authors ="Adeyemi Michael Adejumobi"}