If you talk to any preconstruction estimator or leader today, the message is the same: the pipeline continues to grow, but the estimating capability is not.
Dodge’s momentum index remains high, and commercial and institutional construction planning activity continues despite month-to-month fluctuations.
Job prospects tell the same story. U.S. construction employment is projected to expand throughout the decade, but employment of cost estimators is projected to decline (-4% from 2024 to 2034, according to the Bureau of Labor Statistics). Demand is increasing. Supply is falling.
So we spoke to 5 industry leaders, from GC to skilled trades, to understand how AI is reshaping their pre-construction workflows and why this change isn’t coming as a necessity.
His view is remarkably consistent: capacity estimation is now the core preconstruction bottleneck. AI is emerging as the backbone that will determine who keeps pace and who falls behind.
The estimability problem that no one can ignore
In commercial construction, it all starts with one variable: how many projects your team can bid on.
The take-off of materials, still largely manual, consumes up to 50% of the supply cycle. When deadlines pile up, contractors are faced with only two options: hurry up or pass.
In addition, the estimated labor force is aging. The largest age group for US cost estimators is 55-59, and the median age is ~46.6 for men and ~41.9 for women, a demographic point towards retirement.
Roy Cabrera, Principal Estimator at Pilkington Construction, describes this pressure clearly:
“We used to have an estimator that did take offs for only a few operations, taking almost a week. And the estimation tools are very fragmented, we’re constantly looking for specific details in the drawings, and that slows everything down..”
The industry responds with its portfolio. In AGC’s Construction Procurement and Business Outlook 2025, 44% of companies plan to increase spending on AI and 35% plan to increase investment in estimating software.
Companies are no longer looking to add more people; they are looking for leverage to help them do more.
Where jobs are won or lost
Deal volume determines your pipeline. Pipeline determines your growth. Growth can come from a competitive price for jobs. Price depth determines margins. Margins come from consistent deals.
The challenge is that many estimators today are effectively doing the work of two or three people, coordinating with vendors, raising RFIs with architects and engineers, chasing addenda, and rebuilding estimates every time the drawings change.
Irana Perez, Chief Heavy Civil Estimator at Petticoat-Schmitt Civil Contractors, put it plainly:
“When timelines are compressed, the first things to suffer are price depth and alternative analysis. That’s where good data and consistency make the difference.”
His team went from 2-3 days per project to same-day use Beam AIputting time back into strategy, customer conversations and pricing accuracy.
Pre-construction not only needs faster but disjointed tools. It needs a backbone. A shared software ecosystem that takes care of your project from plan to tender.
What AI is and isn’t in preconstruction
AI will not replace estimators. can’t The experience, context, and judgment required to price risk are uniquely human.
But AI can and should eliminate the high-friction, repetitive work that consumes half of the offer cycle:
- Manual take-offs of quantities
- Addendum reworking
- Drawing review follow-up
- Reconciliation of versions
This frees estimators to focus on what really moves the needle: pricing strategy, supplier alignment, scope clarity and risk analysis.
Bryan Ramirez, senior estimator at Rays Stairs Inc., captured this change:
“With Beam AIwe are saving two working days a week. I can focus on getting prices from suppliers, talking to customers and improving our offers. Those are the big three it allows me to do.”
In BPMT, where engineers perform their own estimation, the impact is even more pronounced.
Ricardo Pacheco, CTO, BPMT shared:
“They used to spend nearly 40 man-hours on each estimate. Now that time is spent refining unit costs and validating design details. AI shows details we might have missed and saves us from costly mistakes.”
The first benefit teams feel is the relief through incredible time savings that allows them to really focus on the things that move the needle.
Why AI must become the backbone, not a plug-in
Calling AI “another tool” understates what the industry needs. Preconstruction needs an infrastructure, a shared operational backbone that ensures consistency, accuracy and traceability.
A true backbone means:
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Organization-wide measurement standards
No more estimator-by-estimator spreadsheets.
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A single source of truth for drawings, scope and takeoff history
Local files and folders not scattered.
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Smooth handovers between estimates, PM and BD
Bid strategy tied directly to sheets and specs, not recreated from memory.
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Human-in-the-loop validation
The results are therefore reliable, auditable and bid-ready.
As Cooper Simmons of Impact Concrete said:
“Pre-construction technology doesn’t evolve overnight, though Beam AI it has been a real change. We’re moving faster on deals without losing accuracy, and that’s a big deal in our line of work.”
What comes next
The construction sector is at a structural turning point. More projects are coming in than ever before, but the systems that support preconstruction haven’t scaled with that demand. This is not a hype cycle. Construction adopts technology when the work demands it, and the work is already here.
We can let pre-construction bottlenecks limit the amount of work we do, or we can build an operational backbone that frees estimators to do the work that only humans can do.
That future is clear: AI handles repetitive work. People make the decisions. A backbone ties it all together.
