Forecasting in construction has long involved a level of estimation. Planners gauge crew requirements using partial schedules, estimate material timing from scattered procurement data, and adjust labor based on informal site updates. These inputs are often incomplete, and the methods used to interpret them can introduce further uncertainty. Artificial intelligence (AI) brings structure to this process.
AI does not replace field insight. It applies statistical methods to recurring patterns across projects, trades, and schedules. It enables firms to identify early signs of deviation from the plan and to trace the causes with greater accuracy. This shift, moving from reactive explanations to proactive understanding, defines its operational contribution.
This guide explains:
Why Forecasting in Construction Fails Without AI
How AI Models Interpret Labor and Resource Variability
Data Foundations for AI Forecasting in Construction
Governance Structures That Support AI Forecasting
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