Technology First Read
Construction is About to Leave $124B on the Table Due to Outdated Bidding
Contractors must deal with a rapidly changing bidding environment, bidding AI startup founder Shiva Dhawan writes
Somewhere right now, a contractor’s bidding window is closing while an invitation to bid sits unanswered in an estimator's inbox. Another contractor with a healthy backlog just passed on a qualified opportunity because no one had time to price it.
I see this pattern constantly in my work with contractors who deploy Beam AI. Before a project can be built, it has to be found, quantified, priced, and bid. Yet, when estimating teams run out of capacity, projects don't just get delayed; they disappear. The work is never pursued, never won, and never becomes construction output.
That makes preconstruction one of the industry's most overlooked capacity challenges.
Output Is Lost Long Before Crews Mobilize
Deloitte's 2026 Engineering & Construction Industry Outlook estimates that persistent labor shortages could cost the industry nearly $124 billion in lost output. The report highlights an urgent workforce challenge: construction needs nearly 500,000 additional workers this year, while the industry's aging workforce and shrinking talent pipeline make that increasingly difficult.
As alarming as those numbers are, they tell only part of the story. The report assumes output is constrained only when labor is unavailable in the field. In reality, another capacity constraint emerges much earlier, during preconstruction.
Long before crews are mobilized, estimating teams determine which opportunities will become projects. And this distinction matters because construction output doesn't begin when the first crew arrives on-site. It begins when someone translates drawings into quantities, quantities into costs, and costs into a competitive bid. Every project starts in preconstruction.
Yet preconstruction has rarely been viewed as a strategic capacity function. Contractors meticulously track backlog, labor productivity, and project margins, but few measure a simpler question: How many qualified opportunities did we never have the capacity to pursue?
Looking for quick answers on construction and engineering topics?
Try Ask ENR, our new smart AI search tool.
Ask ENR →
Your Estimating Capacity Sets Your Revenue Ceiling
For decades, that tradeoff was unavoidable because estimating remained largely manual. Advances in AI are beginning to challenge that assumption. Preconstruction platforms can now automate the repetitive tasks that consume experienced estimators' time — performing takeoffs, extracting quantities, reviewing drawings, and organizing project documents — so estimators evaluate more opportunities without sacrificing quality.
Artificial intelligence changes the equation from "How many estimators do we have?" to "How much estimating capacity can we create?"
Consider a masonry contractor in Wisconsin whose estimating team consistently capped out at roughly 25 takeoffs each month. That limit wasn't just an operational metric; it became a revenue ceiling. Every qualified opportunity beyond those 25 takeoffs represented work the company simply didn't have the capacity to pursue.
The same pattern repeats, no matter the trade. For a drywall and demolition contractor, the bid volume dropped almost immediately after losing a key estimator, even though field operations remained unchanged. The real bottleneck wasn't labor; it was capacity upstream. After introducing AI into its estimating workflow, the company doubled its bid volume target, not by hiring additional estimators, but by removing much of the repetitive work that had constrained them.
In both cases, the limiting factor was estimating capacity. Once that bottleneck was removed, the firms were able to pursue more business.
Count How Fast You Convert Demand Into Bids
Construction productivity has focused on field execution: labor hours, equipment utilization, prefabrication, and schedule performance. Those metrics remain essential, but they capture only part of the equation. In today's market, productivity must also include how efficiently contractors convert demand into bids.
Every unanswered invitation to bid represents lost capacity. Every opportunity declined because an estimating team is overloaded represents potential revenue that disappears before construction even begins. These losses rarely appear on a balance sheet, yet collectively they shape how much work the industry is capable of delivering.
Measure The Work You Never Had Capacity To Pursue
If productivity begins in preconstruction, contractors need better ways to measure it. That means looking beyond bids submitted and asking what prevented qualified opportunities from being pursued in the first place. Start with three questions: How many qualified bids went unanswered? How much estimating time was spent on repetitive work? How much revenue was left behind because capacity ran out before demand did?
Once contractors begin measuring those questions, the next step becomes obvious. Automation increases the number of opportunities a business can realistically pursue. By shifting repetitive tasks to AI while preserving human judgment where it matters most, contractors can expand bid capacity without expanding headcount at the same pace.
At the individual firm level, that means pursuing more work without a proportional increase in overhead. Across the industry, it means unlocking estimating capacity that has always existed but has never been fully utilized.
Viewed through that lens, Deloitte's $124 billion projection is both a warning that construction needs more workers and a signal of a larger opportunity.
The industry's next competitive advantage will come from expanding the capacity of every estimator already on the team, because long before labor shortages limit what gets built, estimating capacity determines what gets bid.
Shiva Dhawan is co-founder and CEO of Attentive.ai, an AI-powered bidding startup.
Facts Only
* Deloitte's 2026 Engineering & Construction Industry Outlook estimates a potential loss of $124 billion due to persistent labor shortages.
* The industry requires nearly 500,000 additional workers this year.
* A capacity constraint exists in preconstruction long before crews are mobilized on-site.
* Preconstruction involves translating drawings into quantities, quantities into costs, and costs into bids.
* Estimating teams face capacity limits when pursuing qualified opportunities.
* Automation of repetitive estimating tasks can allow estimators to evaluate more opportunities.
* A masonry contractor example showed a limit of 25 takeoffs per month constrained revenue.
* Losing an estimator caused a drop in bid volume for a drywall contractor despite unchanged field operations.
* Productivity must include the efficiency with which demand is converted into bids.
Executive Summary
Persistent labor shortages are projected to cost the construction industry nearly $124 billion in lost output, requiring approximately 500,000 additional workers. This capacity constraint occurs not just during field execution but significantly earlier, within the preconstruction phase where estimating teams determine which opportunities can be pursued. The traditional view focused on labor availability in the field, but the source material suggests that a parallel bottleneck exists upstream: estimating capacity to process demand into competitive bids.
The introduction of artificial intelligence is beginning to shift this dynamic by automating repetitive tasks like takeoffs and data organization within preconstruction platforms. This change reframes the problem from a simple shortage of estimators to a question of estimating capacity creation, suggesting that technology can allow existing teams to pursue greater volume without immediate hiring. The core tension involves shifting productivity measurement from field execution metrics to incorporating how effectively demand is converted into pursued bids.
Full Take
The narrative establishes a critical disconnect between widely reported labor shortage statistics and the actual bottleneck within the construction process: upstream capacity management. The core pattern observed is that limiting factors shift based on the phase of work—from field execution constraints to preconstruction estimation constraints. This suggests that large-scale problems like the $124 billion projected loss are compounded by an overlooked, systemic constraint in the pipeline management itself.
The argument pivots on the concept that capacity is not static; it is a function of process. When manual processes constrain estimating teams, opportunities disappear before they can ever enter the pursuit pipeline. The introduction of AI challenges the established assumption that output is constrained by headcount rather than workflow inefficiency. This shifts the competitive advantage from simply acquiring more labor to maximizing the productive throughput of existing human capital through automation.
The implication for agency is significant: if capacity is the ultimate limiter, then unlocking estimating potential becomes a mechanism for growth that bypasses traditional hiring constraints. The larger context suggests that focusing solely on field labor shortages risks overlooking organizational and technological friction points that dictate overall industry output. The question shifts from "How many workers do we need?" to "How much capability can our current team deploy?" What follow-up research is needed to quantify the ROI of preconstruction capacity improvements versus field labor investment?
Sentinel — Human
The text presents a well-structured, persuasive argument linking preconstruction estimating capacity to industry bottlenecks, utilizing anecdotal evidence and large economic forecasts to build a case for AI integration.
