If you speak to any estimator or preconstruction lead today, the message is the same: the pipeline keeps growing, but estimating capacity isn’t.
Dodge’s Momentum Index remains elevated, with commercial and institutional construction planning activity continuing despite month-to-month fluctuations.
The labor outlook tells the same story. U.S. construction employment is projected to expand through the decade, but cost estimator employment is projected to decline (–4% from 2024 to 2034, according to the Bureau of Labor Statistics). Demand is rising. Supply is falling.
So we spoke to 5 industry leaders, from GCs to specialty trades, to understand how AI is reshaping their preconstruction workflows, and why this shift is arriving not as a necessity.
Their insight is remarkably consistent: Estimating capacity is now the core bottleneck in preconstruction. AI is emerging as the backbone that will determine who keeps up and who falls behind.
The estimating capacity problem no one can ignore
In commercial construction, everything starts with one variable: how many projects your team can bid.
Material takeoffs, still largely manual, consume up to 50% of the bid cycle. When deadlines stack up, contractors face only two options: rush or pass.
Besides, the estimating workforce is aging. The largest age cluster for US cost estimators is 55–59, and the average age is ~46.6 for men and ~41.9 for women, a demographic heading toward retirement.
Roy Cabrera, Lead Estimator at Pilkington Construction, describes this pressure clearly:
“We used to have one estimator doing takeoffs for only a few trades, taking nearly a week. And estimating tools are highly fragmented, we’re constantly hunting for specific details in the drawings, and that slows everything down.”
The industry is responding with its wallet. In AGC’s 2025 Construction Hiring & Business Outlook, 44% of firms 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’re looking for leverage that helps them do more.
Where jobs are won or lost
Bid volume determines your pipeline. Pipeline determines your growth. Growth can come from pricing jobs competitively. Pricing depth determines margins. Margins come from consistent bidding.
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 drawings change.
Irana Perez, Chief Estimator at Heavy Civil at Petticoat-Schmitt Civil Contractors, put it plainly:
“When timelines compress, the first things to suffer are pricing depth and alternate analysis. That’s where good data and consistency make all the difference.”
Her team moved from 2–3 days per project to same-day using Beam AI, shifting time back to strategy, client conversations, and pricing precision.
Preconstruction doesn’t just need faster yet disjointed tools. It needs a backbone. A shared software ecosystem that takes care of your project from plan to bid.
What AI is and what it isn’t in preconstruction
AI will not replace estimators. It cannot. The expertise, context, and judgment needed to price risk are uniquely human.
But AI can and should eliminate the repetitive, high-friction work that consumes half the bid cycle:
- Manual quantity takeoffs
- Addenda rework
- Drawing revision tracking
- Version reconciliation
This frees estimators to focus on what actually moves the needle: pricing strategy, vendor alignment, scope clarity, and risk analysis.
Bryan Ramirez, Senior Estimator at Rays Stairs Inc., captured this shift:
“With Beam AI, we’re saving two workdays a week. I can focus on getting vendor pricing, talking with clients, and sharpening our bids. Those are the big three it frees me up to do.”
At BPMT, where engineers perform their own estimating, the impact is even more pronounced.
Ricardo Pacheco, CTO, BPMT shared:
“We used to spend nearly 40 man-hours on each estimate. Now that time goes toward refining unit costs and validating design details. AI surfaces details we might have missed and saves us from expensive mistakes.”
The first benefit teams feel is relief via incredible time savings that allow them to truly focus on 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 infrastructure, a shared operating 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
Not scattered folders and local files.
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Smooth handoffs among estimating, PM, and BD
Bid strategy tied directly to sheets and specs - not recreated from memory.
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Human-in-the-loop validation
So outputs are trusted, auditable, and bid-ready.
As Cooper Simmons from Impact Concrete put it:
“Preconstruction tech doesn’t evolve overnight, but Beam AI has been a real shift. We’re moving faster on bids without losing accuracy, and that’s a big deal in our line of work.”
What comes next
The construction industry is at a structural inflection point. More projects are entering the pipeline than ever before, but the systems that support preconstruction have not 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 either let preconstruction bottlenecks cap how much work we pursue, or we can build an operating backbone that frees estimators to do the work only humans can do.
That future is clear: AI handles repetitive work. People make the decisions. A backbone ties it all together.
