Construction is a roughly $13 trillion global industry where productivity has barely improved in decades. In some advanced economies it has declined. Nearly 40% of the skilled workforce is expected to retire this decade. These are not surprises to anyone who works in the sector. What is still being worked out is where, exactly, AI changes the equation.
Where Most People Are Looking
Much of the public conversation focuses on the jobsite: automation, robotics, computer vision. Those applications are real and increasingly visible. But in a new piece for Engineering News-Record, Saurabh Mishra, Founder and CEO of Taiyo.AI, argues that the larger economic impact sits somewhere else entirely.
Read next The Myth of the Monolith: Why AI is a Mosaic, Not a Single TechnologyMost of a project's cost, risk, and timeline is determined long before construction begins. Decisions made during planning, selection, and design shape what happens in the field. By the time crews mobilise, many of the key outcomes are already locked in.
The Work Before the Work
For many contractors, the issue is not access to projects. It is choosing the right ones. Estimating teams are stretched, pipelines are noisy, and time is spent pursuing work that is misaligned, underpriced, or structurally difficult to deliver. AI, drawing on project pipelines, historical outcomes, and market signals, can help building teams focus on opportunities that align with their capabilities and risk tolerance.
Cost estimation follows a similar pattern. Estimates frequently fail not because they are careless, but because they cannot fully account for how projects evolve. AI can compare bids to similar projects, identify outliers in assumptions, and surface risks that are not yet visible in the numbers. The goal is not to replace estimators. It is to extend their field of view.
Coordination, Knowledge, and the Compounding Advantage
If there is a consistent source of underperformance in construction, Mishra argues, it is not capital. It is coordination. Projects rarely struggle because financing is unavailable. They struggle because design, execution, procurement, and field operations are working from different assumptions, and by the time that inconsistency reaches the site, it is expensive to resolve.
The workforce dimension adds urgency. Construction has always depended on tacit knowledge, the kind built over years and now at risk of disappearing as an experienced generation retires. AI can help capture that institutional memory, surface patterns from past performance, and make hard-won lessons accessible to newer teams. That is not a replacement for experience. It is a way of preserving it.
Read next Publication: Cognitive Infrastructure for an Uncertain WorldThe piece closes on an argument that speaks directly to where the industry is headed. Firms that develop these capabilities will bid more selectively, price risk more accurately, and avoid repeating known mistakes. Those that do not will still have access to work, but with less visibility, and over time, that difference compounds.
Construction will remain an execution-driven industry. The source of advantage is shifting from who can build to who understands the job early enough to build it right.
Read the full piece at Engineering News-Record.
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