Artificial intelligence is moving quickly into construction workflows, promising optimized schedules, automated risk detection, improved coordination, and faster project decision-making. But in an industry where margins are tight, safety is critical, and sustainability outcomes depend on what is actually delivered in the field, AI-enabled insights need to be validated before teams can rely on them.
This session explores one of the most important questions in AI adoption: how do we know the data is right before it informs construction decisions?
Panelists will discuss how construction leaders, technology providers, investors, and practitioners are evaluating AI-enabled tools against real project data. Through practical examples, the session will examine how teams can verify AI-generated outputs � including schedules, estimates, risk signals, field observations, coordination issues, material data, and performance-related insights � against ground-truth project conditions. The conversation will also address common AI pitfalls such as poor historical data, hallucinated outputs, algorithmic bias, fragmented construction records, and garbage-in/garbage-out decision-making. Attendees will learn how project teams can establish quality-control workflows, train teams to critically evaluate AI outputs, and determine when AI-enabled insights are reliable enough to support project decisions. The goal is to help teams move from experimentation to accountable implementation: using AI not simply to generate more information, but to support better, faster, and more trustworthy decisions that advance project delivery, sustainability outcomes, and long-term building performance.
