That changes what retention actually means in practice. The conversation used to be about stopping people from leaving. Now it’s about how to plan around predictable attrition, develop the people you already have, and make assignment decisions before the exit interview rather than after it. The data backs this up clearly. Senior superintendents and project managers turn over at roughly a quarter the rate of their non-senior counterparts, which means the contractors who hold onto people through the first four years end up with a senior bench that competitors can’t easily hire in.

What follows are 50+ statistics across nine categories: the labor shortage, how turnover has shifted, what it costs, who stays and who leaves, why people leave, the commute factor, the treadmill effect, the rookie ratio, and where the workforce is heading. Most are public data from BLS, AGC, ABC, the Construction Industry Institute, the Census Bureau, and Deloitte; some come from 做厙勛圖’s , which analyzed anonymized data from 233 companies and 114,000 people, including nearly 40% of the ENR 400.

TL;DR

• Quit rates are at a nine-year low and hiring is at a record low, but the industry still needs roughly 349,000 net new workers in 2026
• Net international migration is projected to fall from 2.7 million in 2024 to about 321,000 in 2026, removing the demographic backstop the industry has leaned on
• Senior superintendents and project managers turn over at one-quarter the rate of their non-senior counterparts, making early-career retention the highest-leverage move
• The Top 50 of the ENR 400 face roughly the same attrition as the broader industry but grow at three times the rate, because they plan around turnover rather than try to eliminate it
• The average team rookie ratio sits at 36.4% and climbs to 56.2% on teams of 51+ people, with measurable consequences for safety, quality, and project outcomes
• 41% of the current construction workforce will reach retirement age by 2031, which makes the next four years of senior-tenure retention decisions disproportionately important

The construction labor shortage in 2026

Demand is outrunning supply across nearly every category, and the gap is widening as immigration policy and demographics compound the pipeline problem.

worked in construction as of March 2026, with year-over-year growth of 57,000 positions. The headline employment number is up, but the unmet demand is bigger:

• report difficulty filling open positions (AGC 2025 Workforce Survey)
• , and 80% report the same for salaried openings (AGC 2026 Outlook)
• The industry needs , rising to 456,000 in 2027 (ABC)
• Every creates demand for approximately 3,450 jobs
• report delaying projects due to labor shortages
• Project abandonment activity in August 2025

The immigration pipeline is part of why this is harder to solve through hiring alone. Net international migration (U.S. Census Bureau, January 2026). Immigrant workers make up , and that share exceeds 40% in high-activity states like California and Texas. A report being affected by immigration enforcement in the past six months, with 24% saying subcontractors lost workers as a result.

How turnover has actually shifted

Despite the shortage headlines, turnover itself has been falling, which is the part of the story most leaders miss. The in July 2025, and by February 2026 the hiring rate had dropped to , with quits at 1.5% and layoffs at 1.8%. Job openings fell to , a decline of 53,000 year-over-year.

Average employee tenure in construction sits at roughly four years according to , which is among the shortest of any major industry. The combination of lower quits, lower hiring, and short tenure overall describes a cooling labor market rather than a stable one. Fewer people are leaving voluntarily, but the industry still can’t find enough new workers to grow. The retention question shifts accordingly: less about how to stop the bleeding, more about how to keep and develop the people you’ve already invested in.

What turnover costs

The financial impact of losing people goes well beyond the cost of posting a job. Replacing a worker costs , depending on specialization and seniority, with junior craft workers at the lower end and superintendents, project managers, and specialized trades at the higher end. The cost compounds with role complexity, because the harder roles take months to source and longer still to bring up to full productivity.

The Construction Industry Institute found that a . The hidden costs sit underneath that headline number. Lost productivity during a vacancy, quality issues and rework from less-experienced replacements, and overtime to cover open positions all accumulate well beyond the direct cost of recruiting and onboarding.

“Company morale goes down, employees are burnt out because they’re going to do whatever it takes to get the job done,” says Shawn Gallant, COO at Columbia Construction. “It affects your employee retention and increases safety incidents on a project. You never want an unsafe site because you’re cutting a dollar on staffing.”

At the macro level, McKinsey projects that construction output could fall if current workforce trends persist.

Who stays and who leaves

Not all turnover is equal, and the seniority split inside two key roles is where the most useful retention data lives. The 做厙勛圖 Benchmark Report shows a striking gap between senior and non-senior attrition for the two roles that have the greatest impact on project outcomes:

Senior superintendents and project managers turn over at roughly a quarter the rate of their non-senior counterparts. The growth data tells you why: non-senior supers saw while senior supers showed , and the same pattern holds for PMs (non-senior +4.8%, senior 0.0%). Senior-level supers and PMs simply don’t move around. Trying to hire experienced talent away from competitors is an expensive long shot. The more reliable path is hiring earlier in the career arc and being intentional about keeping people through the first four years.

The tenure data shows where the retention cliff sits:

If a superintendent or PM stays past the 3.7-year median, they’re on the path to senior tenure. The contractors that figure out how to retain people through that window end up with something competitors can’t easily replicate.

“What happens when you don’t have a clear picture of your staff is you don’t see ‘John Smith’ is ready for a promotion,” says Lisa Villasmil, VP of People & Culture at Cauldwell Wingate. “So you hire an outside senior PM instead of promoting internally and backfilling the open position.”

The Benchmark Report also draws on findings from 做厙勛圖’s 2025 State of Workforce Planning survey: 100% of construction leaders agree a project team’s collective experience plays a significant role in creating positive project outcomes, and 93% have experienced talent-related impact on operations.

Why construction workers leave

Career development is the leading reason workers leave, and it outpaces compensation by nearly 2:1 across industries. The breaks down the top departure drivers:

That ranking matters in construction specifically because earlier-career superintendents and PMs are actively evaluating employers based on the work itself: the project types, owners, and delivery methods that will define their reputation and open future doors. The Benchmark Report reinforces this directly. Top contractors that plan according to the unique needs of each project type can offer newer team members the variety of experience that keeps them engaged, while ensuring they’re paired with senior talent who can mentor them.

Mental health is another factor that compounds compensation alone. CDC data shows that , and (CDC MMWR, National Vital Statistics System). Hours compound the strain: , 25% work 60 or more, and .

Commute as an actionable retention lever

One of the most actionable retention variables in construction is commute distance, because it’s a decision the contractor controls during assignment planning. The superintendent commute distribution from the Benchmark Report , which is where retention risk concentrates. Peer-reviewed research published in AERA Open found that (Santelli & Grissom, 2024). The study examined public-sector workers, but the underlying mechanism (commute fatigue compounding job dissatisfaction) generalizes naturally to construction roles where supers and PMs travel meaningful distances daily.

The construction-specific commute data shows how demanding the travel can be:

• each way
• Another
• each way

The point of pulling commute into the conversation is that it’s a variable you can adjust ahead of time. Knowing which superintendents face long commutes on their current assignments, and factoring that into the next assignment, turns retention into a planning decision that happens months before anyone starts thinking about leaving.

The treadmill effect

Attrition shows up as a growth problem, not just a people problem. 做厙勛圖’s Benchmark Report names the dynamic the “treadmill effect,” where attrition offsets hiring so companies have to run hard just to hold their position. The math is simple: an organization aiming to add 100 people with a to net the growth. At .

The real-world impact shows up in the growth distribution. In 2025, and another 26% remained flat. Nearly half the industry failed to achieve net headcount growth.

The most useful finding in the report sits in the comparison between the Top 50 ENR 400 and the rest of the industry. Top 50 contractors face , but their . The largest contractors aren’t winning because they’ve solved turnover. They’re winning because they plan around it. Proactive hiring and workforce planning, rather than lower attrition, is what separates the leaders from the pack.

McKinsey’s productivity research supports the same point from another angle: productivity for major construction projects each time labor markets tightened. The contractors that maintained planning capacity through tight markets came out ahead.

Rookie ratios and the experience mix

With high attrition flowing through project teams, one metric that has emerged among strategic contractors is the “rookie ratio,” which measures the share of newer team members (typically under one year of company tenure) relative to the total team. 做厙勛圖’s Benchmark Report puts a number on it for the first time. The , and the average masks meaningful variation by team size:

On teams of 51 or more people, the rookie ratio averages above 56%, meaning more than half the team is in their first year with the company. That has direct consequences for safety, quality, and project outcomes. Travelers’ , which analyzed more than 1.2 million workers compensation claims, found that first-year employees account for approximately 36% of all workplace injuries and 34% of overall claim costs across industries. In construction, the early-tenure injury rate skews higher because new workers are concentrated in the most physically demanding tasks before they build the muscle memory and risk awareness that come with experience.

NCCER’s research with the Construction Industry Institute shows the flip side. Trained craft workers achieve a in training, with productivity targets met more reliably and retention improved when formal training programs are in place.

The contractors who use rookie ratio well don’t just track it. They set targets for it by project type and complexity, pairing newer team members with experienced mentors and routing the most straightforward projects toward teams that can accommodate a higher share of newer workers without compromising outcomes.

Where the workforce is heading

The sectors driving the most workforce demand are also the ones requiring the largest teams and longest commitments. Year-over-year growth data from the Benchmark Report shows where the workforce is being pulled:

Solar projects stand out for the sheer scale of workforce commitment, with a . Data center construction spending , per the AIA Consensus Construction Forecast, making it the only sector showing strong growth in an otherwise weak market.

The pipeline of new workers is showing early signs of improving. The share of young adults interested in construction in 2026 (NAHB), and at a said they would reconsider construction. currently offer median wages at or above that threshold.

The demographic shift is already visible in the data. Gen Z’s share of the construction workforce , while Baby Boomers over the same period. Women in construction reached (NAHB analysis of BLS data). Deloitte estimates , which puts a clock on how much time contractors have to develop the next generation before the senior bench thins out.

What contractors who plan ahead do differently

The Top 50 of the ENR 400 are , nearly two years further than the . That gap reflects something specific about how they operate. Reliable data, integrated systems, and company-wide coordination are what extend a planning horizon, and the longer horizon is what creates the optionality the rest of the industry doesn’t have.

“Strategic workforce planning gives our clients confidence that we can provide the right people to build their projects,” says , Vice President of Operations at W.E. O’Neil Construction. “If we don’t consider all the necessary strategic factors, we won’t be able to assign the appropriate teams, and those clients won’t keep coming back.”

The supporting evidence is consistent. Contractors with realized profits on more jobs, completed more projects on or ahead of schedule, and posted better safety performance, according to the Construction Industry Institute. Career development opportunities correlate with (Work Institute). NCCER’s research with the Construction Industry Institute shows that .

The common thread across these findings isn’t a single tactic. It’s visibility. The contractors pulling ahead know who they have, where those people are, what they’ve built, and what they need next. Experience-based staffing tools like 做厙勛圖 consolidate the people, project, and pipeline data so that retention decisions happen during assignment planning, not during exit interviews. Whether the goal is closing the planning horizon gap, getting ahead of attrition before it constrains growth, or building teams with the right experience mix, the work starts with the data being in one place.

The post 50+ Construction Workforce Retention and Turnover Statistics for 2026 appeared first on 做厙勛圖.

There is also a lot of noise around AI in construction right now. This article is designed to separate signal from hype by consolidating 60+ statistics from industry associations, consulting firms, and trade publications into a measured picture of where the industry actually stands, where the money is going, what’s working, what isn’t, and what it means for the people running construction operations today.

TL;DR

• 79% of construction organizations have either implemented no AI at all or are testing in limited ways, yet 87% expect it to transform the industry
• AI-specific funding claimed 68% of construction tech VC capital in Q2 2025, up from 20-25% historically
• Early adopters report saving 500-1,000 hours and $50,000+ annually, but ROI typically takes 2-4 years to materialize
• 95% of enterprise AI pilots deliver zero measurable ROI, with 85% of failures tracing back to poor data quality
• The construction workforce needs 499,000 new workers in 2026 while 41% of existing workers approach retirement age
• 46% of firms cite a lack of skilled personnel as the top barrier, ahead of integration challenges and data quality

AI adoption rates in construction

The of 2,200+ construction professionals worldwide paints the clearest picture of where adoption actually stands:

• 45% have implemented no AI at all
• 34% are in early pilot phases
• Under 12% report regular use in specific processes
• 1.5% use AI across multiple processes
• Less than 1% have achieved organization-wide adoption

Compare that to what those same organizations expect. according to , but only 19% have actually adapted their workflows to incorporate it. The gap between expectation and execution is wide, but it is closing. The AGC’s annual survey found that , up from 44% in 2024. Where firms are deploying AI, 45% use it for office and administrative functions, 23% for estimating, and 20% for design and preconstruction (AGC).

AI adoption by company size

Larger firms are moving faster, and the gap is growing. , compared to 69% of small and mid-sized firms (Dodge). The cost barrier hits smaller firms harder: 49% of smaller firms cite the cost of AI investment as a significant concern, versus just 26% of large firms (Dodge).

For context, (McKinsey), making construction one of the least digitized major sectors. Among specialized trades, for design optimization, estimating, analysis, and error reduction (Dodge SmartMarket Brief).

AI investment in the construction industry

Investment is accelerating even as most firms remain in pilot mode, and the VC numbers tell a story that market projections alone cannot.

Construction AI market size

Market research firms project significant growth, though estimates vary widely by methodology:

North America accounts for 35% of global AI construction market revenue (Precedence Research). The U.S. market alone is projected to grow from $427M in 2025 to $6.7B by 2035.

Construction tech VC funding

In , a 75.2% increase from Q2 2024 (Construction Today). Of that total, 68% went to AI and machine learning startups, nearly triple the historical 20-25% AI allocation.

On the demand side, over the next three years (PwC Future of Industrials Survey). Among contractors specifically, the found approximately 25% plan to increase AI spending in the next 12 months, while 28% have no AI investment plans and 22% remain unsure about their direction.

Measurable results from AI in construction

The results that exist are real, but they cluster in specific applications rather than broad transformation. The honest picture is that proven ROI comes from well-defined, repeatable problems where the data is already structured.

AI ROI data from early adopters

The strongest documented results come from targeted applications:

• Field layout: (PwC)
• Scheduling: (PwC)
• Digital workflows: (Deloitte 2026 E&C Outlook)
• Cost estimation: through better estimates and error mitigation (Deloitte)
• Hours saved: using AI tools (Bluebeam AEC Technology Outlook)
• Cost savings: (Bluebeam)

Among those early adopters, 95% now use AI frequently across the building lifecycle (Bluebeam), suggesting that once firms get past pilot stage, usage becomes habitual. Contractors also see the potential clearly: once AI is implemented, and (Dodge).

AI ROI timeline expectations vs. reality

The less comfortable story is that most AI investment has not yet translated into measurable returns. , and typical satisfactory ROI , significantly longer than the 7-12 month norm for technology investments (Deloitte). For construction, where adoption is further behind and data infrastructure is less mature, those timelines are likely on the longer end. That is not a reason to delay, but it is a reason to set realistic expectations internally.

Why most AI projects fail in construction

The failure data is the most underreported part of the AI conversation. While the industry focuses on possibility, the implementation record across all sectors is sobering, and the root cause has direct implications for construction.

AI pilot and implementation failure rates

Across all industries, according to the MIT NANDA Initiative. (IBM CEO Study), meaning 84% stall.

The proof-of-concept graveyard runs deep:

• after initial testing (Gartner)
• report their AI PoC success rate is lower than 5% (Omdia)
• reported more than half their PoCs accepted into production (Omdia)
• of companies have the capabilities to move beyond PoC (BCG)
• have advanced AI capabilities deployed across functions (BCG)

The companies that do succeed see a real reward. BCG’s AI leaders report , but (BCG).

Data quality in construction

The data problem is why construction is especially vulnerable to AI project failure. , and construction’s data house is not in order.

reported that bad data caused an estimated $1.8 trillion in global construction losses in 2020, with 14% of avoidable rework traced directly to poor data at a cost of $88 billion. The underlying numbers are worse: 30% of construction firms say more than half their data is bad or unusable, and 45% lack a formal data strategy entirely. Without centralized, clean data, AI tools have nothing meaningful to work with regardless of how sophisticated the algorithms are.

AI and the construction workforce

This is where AI meets construction’s most urgent challenge. The labor crisis is not a forecast, it is the operating reality for every contractor hiring right now, and it shifts the AI conversation from optional to essential.

Construction labor shortage statistics

The scale of the shortage is difficult to overstate:

• in 2026, up from 439,000 in 2025 (Deloitte)
• in potential lost output from unfilled positions (Deloitte)
• 93% of contractors report difficulty finding skilled workers (做厙勛圖 2025 State of Workforce Planning)
• employing craft workers cannot fill craft positions (AGC)
• will reach retirement age by 2031 (Deloitte)
• consider construction careers (AGC)

Wages have responded, with compared to 8.2% across all occupations. But higher wages alone have not been enough. That 21% wage increase (Deloitte), which underscores why contractors are looking to AI and technology for capacity that hiring alone cannot provide.

Workforce planning benchmarks

做厙勛圖’s , drawing on anonymized data from 233 companies and 114,000 people, illustrates how the workforce challenge plays out at the company level.

The top 50 of the ENR 400 face attrition rates similar to the broader industry, but their median growth rate is 3x higher. Proactive hiring and workforce planning, not lower turnover, is what separates the leaders from the pack.

Construction industry AI and jobs sentiment

The sentiment data offers a counterpoint to the “AI will take jobs” narrative. According to the :

• 45% of contractors expect AI will positively impact construction jobs by automating manual, error-prone tasks
• 44% believe AI will improve job quality and make workers safer and more productive
• Only 12% worry about negative job market impact, down from 17% two years ago
• 47% report difficulty filling AI specialist positions, up from 30% in 2024

Barriers to AI adoption in construction

The provides the clearest ranking of what’s standing in the way:

The skills barrier and the data barrier reinforce each other. Companies lack the people to implement AI well, and the data those people would work with is not ready. Across the RICS dataset, 74% of construction organizations have minimal or no AI capability, 29% have no capability or plans in place, and only about 20% are engaged in strategic planning and proof-of-concept testing.

AI adoption in construction scheduling

Scheduling offers a telling example of where adoption remains low despite clear opportunity. , with 60% reporting no plans to adopt it (ConstructionOwners.com). Meanwhile, only 12% of baseline schedules meet high-quality standards, and less than 5% maintain quality through project completion. The gap between how poorly the current approach works and how little the industry is doing to change it is one of the clearest opportunities in construction AI.

Where this leaves contractors

The data across this report points to a consistent theme: AI in construction works when the data underneath it is structured, centralized, and trusted. The 做厙勛圖 , drawing on anonymized workforce data from 233 companies and 114,000 people, found that the median attrition rate across the industry sits just below 20%, and nearly half of all companies didn’t achieve net workforce growth in 2025. At the same time, companies that centralize their workforce data around experience, skills, and availability are seeing 3x higher growth rates than those that don’t, even when facing similar attrition.

That’s the real AI readiness story for contractors. The question isn’t which AI tools to buy. It’s whether your workforce planning data is in good enough shape to make any of them useful. For most, the first step is the same one the data keeps reinforcing: get your workforce data in one place, make it reliable, and build from there.

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做厙勛圖 AI has now been in the hands of customers for several months, and we���re working every day to further its capabilities and provide more value to our customers. At the same time, we���re continuing our commitment to transparency by offering another look behind the scenes at how 做厙勛圖 is strategically embracing AI.

I previously shared about cultivating a culture of AI curiosity, and how internal hackathons have helped spark creativity and bring new ideas to life. For this post, I want to pull back the curtain on another side of our AI journey: how we’ve built an AI-enabled technical stack, and how we’ve approached the challenge of keeping an entire organization learning together. 

Every company is navigating the AI transition differently, and my hope is that sharing our approach might serve as inspiration for organizations facing similar challenges. To be clear, we don���t have all the answers. With a new tool launching every week���or so it seems���the pace of change has felt dizzying at times. But that’s precisely why we’ve focused on building systems for learning, rather than chasing every new release. I���m excited to share what that looks like for us.

The developer tooling evolution

Like most engineering teams, our AI journey started with GitHub Copilot. It was a useful introduction, but felt limited in scope. The arrival of 2025 brought an explosion of AI-assisted coding tools.

This presented an interesting challenge: How do you balance budget discipline with the need to let people experiment and find what works best for them? At 做厙勛圖, our philosophy has been to lean toward experimentation, while putting lightweight guardrails in place: monitoring spend to avoid surprises and ensuring tools are logged for security purposes.

As the year progressed, we saw adoption of Cursor and JetBrains’ Junie grow steadily. By the second half of the year, Claude Code emerged as a strong contender (and it’s where much of the industry seems to be converging).

As usage matured, we started establishing standards to make AI tooling more consistent and effective across the team. We’ve introduced AGENTS.md rules across our most-used repositories, defined a shared configuration for common MCP servers, and created LLM-friendly markdown files for frequent operations. The goal is to make it easier for anyone on the team to get reliable, high-quality results from these tools – without having to reinvent the wheel each time.

We’ve also started experimenting with the product team on a more ambitious front: having Claude Code build features directly from detailed specs. The results so far have been promising, and it’s opened up interesting conversations about how we might rethink parts of our development workflow.

We believe AI fluency is now fundamental to engineering work, which is why we’ve incorporated it into our performance review criteria. Rather than tracking specific tool usage, we’ve wrapped it into a broader criterion called “Growth Mindset.” This focus helps ensure everyone takes the time to level up and explore new approaches, regardless of which tools they choose.

Collective learning as a strategy

One principle has guided our approach from the start: learning can’t be left to individuals alone. In a moment of rapid change, it has to be an organizational responsibility.

On the engineering side, we’ve created dedicated Slack channels for sharing discoveries and asking questions. We also host monthly dev demos, where much of the conversation has naturally gravitated toward showcasing what people have accomplished with new AI tools. These sessions have become a highlight, and are equal parts knowledge-sharing and inspiration.

For the broader organization, we’ve taken a similar approach. We established company-wide channels for AI learning and have used our quarterly kickoffs and Friday all-hands meetings to run demos and talks. These have ranged from basics, like building a shared glossary of AI terms, to more forward-looking discussions about where the technology is headed.

We also ran a company-wide survey to understand how comfortable people are with AI and what they’d most like to learn. This has helped us tailor training to actual needs rather than assumptions.

Choosing the right tools

On the tooling front, we started with a team trial of ChatGPT but quickly found ourselves gravitating toward Anthropic’s Claude. Our general sense is that Claude is better suited to workplace and organizational needs, particularly with its connector capabilities for integrating with other tools. We’ve since made Enterprise plans available to employees upon request and enabled additional capabilities like Claude Desktop.

On the automation side, we reintroduced Zapier across the organization – this time with its AI features enabled. We did evaluate alternatives like n8n, which is a great product with strong technical flexibility. Ultimately, we chose Zapier for its breadth of out-of-the-box integrations and its gentler learning curve for non-technical team members. The goal was to empower people across the organization to build their own automations, not just those comfortable writing code. It’s been a good opportunity to not only build AI fluency but also to embrace automation more broadly.

What’s next

One key piece we’re actively working on is securing and enabling the use of MCP servers. Most of our developers already use them locally with a shared configuration, but we want more visibility and control there. In the same vein, we’re exploring ways to connect Claude to more tools in our stack that don’t yet have official MCP integrations.

On the engineering side, we’re also preparing to collectively delve into the world of AI agents, learning how to build them properly for production. It’s one thing to experiment with agentic patterns in a hackathon; it’s another to deploy them reliably at scale. That’s the next frontier for us.

Beyond that? It’s hard to say what 2026 will bring. But with the learning infrastructure we’ve put in place, we’re confident we’ll be ready to adapt. The pace of change in AI shows no signs of slowing down, and we’re excited to keep building alongside it. If your organization is navigating similar challenges, I hope our experience offers some useful inspiration

The post Behind 做厙勛圖 AI: What we’ve learned building our AI stack appeared first on 做厙勛圖.

The $20 million project sits in limbo while Phoenix and Denver wait for their weekly sync-up meeting. By the time everyone connects, Phoenix has already started conversations with external recruiters. Denver’s PMs have been tentatively assigned elsewhere.

This coordination problem is playing out at construction companies across North America. , with 42% citing reduced ability to take on new projects as a direct consequence.

It’s a challenge of scale and market pressure, not just about budget. You need the right processes and technology to support how your company actually works.

The realities of scaling construction workforce planning

Traditional methods of tracking workforce data don’t scale across multiple offices. Spreadsheets that worked perfectly when you had one location and 50 people become unmanageable when you’re coordinating across three states with 200+ team members.

But even when you move past spreadsheets to more sophisticated systems, you often lack the experiential tracking that makes the difference in winning a competitive healthcare bid or landing a repeat client. Knowing someone is “available” doesn’t tell you whether they have the specific market sector experience, client relationships, or delivery method expertise that a project actually needs.

Your regional offices and divisional leaders aren’t operating as independent kingdoms. They’re working toward their own objectives with the information they can access. The problem isn’t bad intentions. Nobody has a bird’s-eye view of capacity across all locations combined with the ability to coordinate effectively.

The clipboard-and-spreadsheet approach doesn’t let you quickly text everyone when a project timeline shifts or keep people synchronized without scheduling yet another meeting. As your projects multiply and locations expand, these constraints become bottlenecks that slow down decisions and eat into your margins.

What high-performing multi-location contractors actually do

The construction companies that manage multi-location teams most effectively aren’t necessarily the ones with the biggest budgets or the most sophisticated org charts. They’ve built systems that let them see their entire workforce clearly, make decisions quickly, and share resources efficiently across locations.

They create a single source of truth

Power Construction expanded into 14 states after acquiring a national design-build firm. Their previous approach (one person handling workforce planning through Excel spreadsheets, PDFs, emails, and phone calls) couldn’t scale.

Monthly leadership meetings became exercises in frustration. 80% of the time went to establishing basic facts: who was working where, which projects were active, whether the data was even current.

After implementing a centralized workforce planning platform, Power’s approach transformed into a cross-organizational effort where all leaders work from the same real-time data. “We would not be able to manage all of our work today without [a modern workforce planning platform] in place,” says Matthew Walsh, Senior Operations Technology Manager at Power Construction. The platform became their “source of truth” for construction schedules, locations, and staff data. Marketing, operations, pre-construction, and IT all access the same reliable information.

They track experience, not just availability

MYCON General Contractors faced 500% job growth in some divisions within a single year. Their Excel-based approach quickly became “a massive headache” characterized by outdated data, lack of transparency across locations, and massive time waste.

Switching to a purpose-built workforce planning platform gave them more than better organization. It gave them the ability to track team member experience across past projects.

This experiential data allows MYCON to assemble balanced teams that complement each other’s strengths and avoid redundant hiring by making better use of existing talent. “做厙勛圖 has become the source of truth for anything related to people, location, and assignments,” notes Chris Martin, VP of Technology Services at MYCON.

This approach aligns with broader industry trends.. When asked what factors contribute most to successful project teams, 59% cite build-type experience as the top consideration, followed closely by industry experience at 53%.

They shift from reactive to proactive

Queensland-based Mosaic Property Group recognized that as their portfolio expanded to over $2 billion AUD in projects, they needed to evolve beyond reactive hiring. By implementing workforce planning software that provided clear visibility into resource allocation, project timelines, and contract statuses, they gained the ability to forecast 12 months ahead.

“做厙勛圖 has given us a level of insight into our workforce planning that we simply didn’t have before,” says Melissa Hockey, People and Culture Manager at Mosaic. “This visibility is now critical for making proactive decisions about resource allocation and future growth.”

Suffolk Construction took the same idea further. With dozens of major projects running concurrently in their Boston office alone, they connected their preconstruction estimating system to 做厙勛圖 so that the staffing plan for every project was generated from the estimate itself. The two-hour line-by-line staffing meetings became reviews, not reconciliations.

The shift meant that significantly more meeting time could be spent planning for future projects rather than scrambling to address immediate staffing gaps.

Four decisions to help you scale workforce planning

As your construction company expands across multiple locations, you’ll need to make four key decisions about how workforce planning actually operates. These aren’t about choosing between rigid centralization or complete autonomy. They’re about building systems that work as you grow.

Who can see what workforce data?

The first decision is about visibility and permissions. Does everyone across all offices see all workforce data, or do you limit visibility to specific regions or divisions?

High-performing multi-location contractors typically lean toward broad visibility with smart permission structures. Operations leaders need to see availability across all locations to identify resource-sharing opportunities. Project teams need to see who’s worked with specific clients or on similar project types. Finance needs accurate labor allocation data for forecasting.

The key is making information accessible without creating information overload. Purpose-built workforce planning platforms allow you to set permissions so that team members see the information relevant to them. Regional managers might see their full division, while project managers focus on their specific teams.

Who decides on assignments?

This is often where multi-location workforce planning gets contentious.

The answer isn’t to have corporate dictate every assignment, nor to let each office operate in complete isolation. Instead, establish clear decision rights: Who makes the call when there’s competition for a senior PM between two projects? Who has authority to pull resources across regional boundaries?

Many successful multi-location contractors use a model where project assignments within a region stay local, but cross-regional resource sharing requires collaborative decision-making with visibility from operations leadership. The platform enables the conversation. Clear protocols determine the outcome.

How do you share resources across locations?

Create a process that makes cross-office staffing easy rather than exceptional. MYCON’s integration between their CRM and workforce planning platform means that opportunities are automatically pushed into the system, giving estimators and pre-construction teams immediate visibility into staffing across all locations.

Power Construction uses mapping features and automatic distance calculations to efficiently staff projects across multiple states, factoring in employee home locations and travel considerations.

When resource sharing is frictionless and data-driven, regional leaders are more willing to collaborate because they can see the business case clearly. When it requires endless email chains and negotiation, everyone defaults to working within their silo.

What data matters beyond availability?

This is where experience tracking becomes transformative. shows that 59% of construction leaders consider build-type experience the most important factor in assembling successful project teams, followed closely by industry experience (53%) and market-sector experience (50%). Yet many workforce planning systems only track whether someone is available, not whether they’re the right fit.

Track the experience data that drives your business decisions: market sectors, build types, client relationships, delivery methods, architect or engineering firm experience, regional expertise. This is the information that helps you win competitive bids and assemble teams positioned for success.

Using workforce planning software to scale across multiple offices

The companies that successfully manage teams across multiple locations aren’t just implementing better processes. They’re layering human judgment with technology systems that make those processes actually work.

A modern workforce planning platform serves as a central repository for all workforce information, breaking through the spreadsheet chaos and eliminating single points of failure in your knowledge systems. When Power Construction’s leaders were asked to quantify the time saved by their workforce planning platform, “they said it’s unquantifiable at this point.” MYCON reports saving “hundreds of hours a week” across the organization.

But time savings are just the beginning. The real value comes from matching experience to projects in ways that help you win more bids.

Power’s marketing team now relies on experience tracking when putting together proposals, while executive leadership uses forecasting capabilities to communicate strategic plans to the board. MYCON’s experience tracking allows them to assemble balanced teams and avoid redundant hiring.

Perhaps most importantly, the right platform shifts your staff meetings from tactical firefighting to strategic planning. When everyone starts the meeting knowing that all the data in the system is current and accurate, you can spend meeting time on decisions that actually matter: which projects to pursue, how to develop emerging talent, where to invest in new capabilities. You’re not just trying to establish basic facts.

The industry recognizes this shift., with 99% planning to invest at least $100,000. This isn’t aspirational spending. It’s recognition that modern workforce planning tools have become essential infrastructure for companies operating at scale.

做厙勛圖 is purpose-built to solve these exact challenges for multi-location construction companies. With real-time workforce visibility across all offices, robust experience tracking, powerful forecasting tools, and seamless integrations with your existing tech stack, 做厙勛圖 helps you coordinate teams efficiently while maintaining the local autonomy that makes your regional offices successful.

Learn how 做厙勛圖 can help you manage teams across multiple locations

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But it’s important to be honest about where the line is. ChatGPT handles text-in, text-out work well. Give it raw notes, get back a formatted report. Give it context about a schedule change, get back a professional email to your sub. What it can’t do is anything that requires construction-specific judgment, structured project data, or the kind of experience that comes from years on the jobsite. Knowing that boundary saves you time and prevents the kind of expensive disappointment that 95% of AI pilots end up in.

What works well for construction PMs

The use cases where PMs get consistent value all share a profile: they involve turning unstructured text into structured output, or turning rough notes into polished communication. The data doesn’t need to be precise to the decimal. The tone needs to be professional. And if something’s slightly off, you catch it in review.

Daily reports and documentation

Taking raw field notes, walk-through observations, or site log data and turning them into formatted daily reports is one of the best uses of ChatGPT for a construction PM. Feed it the day’s observations: weather, crew counts, work completed, issues, equipment on site. Ask for a structured report with status, blockers, and next steps. The output needs a review pass, but it cuts the formatting work from thirty minutes to five.

The same approach works for progress reports. If you’re pulling from multiple sources to compile a weekly update, ChatGPT handles the formatting and narrative structure while you focus on accuracy and the “so what” of the numbers. Over a week of project documentation, the time savings compound into hours you can spend on the site, in meetings, or on the problems that actually need a PM’s judgment.

Meeting minutes

This is the use case PMs mention most. After an OAC meeting, coordination meeting, or sub check-in, paste your rough notes or a transcript and ask for structured minutes with action items, owners, and due dates. Five minutes of review instead of thirty minutes of writing.

It works especially well for PMs running multiple coordination meetings per week across different projects. The documentation overhead stacks up fast, and it’s work that’s important but doesn’t require high-level judgment. Having a reliable first draft of every meeting’s minutes means your review time goes to accuracy and nuance rather than starting from scratch.

Email drafting

Drafting emails to subs about schedule changes, RFI responses, follow-ups on submittals, and coordination requests. Give ChatGPT the context: who you’re writing to, what the situation is, what you need from them. It produces a professional first draft you can edit in a few minutes.

A practical approach that PMs report working well: build a set of prompts for your most common communication types. A change order notification, a schedule update to the owner, a coordination request between trades. Refine them over a few weeks until the outputs match your tone and the level of detail your stakeholders expect. That’s when ChatGPT stops being something you experiment with and becomes part of your actual workflow.

Spec and contract searching

, turning what can be hours of searching through a hundred-page spec set into a quick query (Construction Dive). One attorney described using it as a “gut check” for checking references, citations, and provisions that might be easy to miss in a long document.

The important distinction: it’s effective for searching and summarizing. It’s not effective for interpreting what those provisions mean for your project. Use it to find the relevant section. Apply your own judgment to what it says.

Where ChatGPT falls short on the jobsite

The limitations matter as much as the capabilities, and the construction trade press has been more honest about them than the general tech coverage.

The data problem

An documented a pattern that keeps repeating across the industry. A mid-size civil firm spent six weeks and about $40,000 trying to feed closeout reports into an AI to surface patterns in cost overruns and change orders. They killed the project. The data was spread across three formats, two SharePoint sites, half the PDFs were scanned images, and naming conventions had changed twice in eighteen months.

The same article described an MEP contractor that tried AI-powered submittal reviews, comparing submittals against specs to flag discrepancies. It failed because specs came from a dozen architects with different formats, submittals lived in Procore and emailed PDFs, and in one case, photos of a whiteboard. The AI consultant quoted in the piece called the pattern “enthusiastic pilot, quiet cancellation, nobody talks about it.”

These stories aren’t unique. They’re the norm. 85% of AI projects fail because of data quality, and construction’s data is more fragmented than most industries. ChatGPT can’t fix that. It can only work with what you give it.

Hallucination and construction accuracy

ChatGPT doesn’t actually know construction. It produces plausible-sounding text, but it will confidently cite standards that don’t exist, reference code sections incorrectly, and generate contract language that any experienced PM would flag immediately. who said she’d be “terrified” to hear of anyone using it to generate a construction contract. Hallucination rates in newer AI systems have reached as high as 79% in some testing contexts (Construction Dive).

Use ChatGPT to draft the email. Don’t use it to decide what the email should say about a contractual issue.

Staffing and workforce decisions

ChatGPT can help you write a staffing memo or format a resource plan, but it can’t tell you which superintendent should run your next healthcare project based on their build-type experience, client relationships, and commute distance. That requires structured workforce data that a general-purpose AI tool doesn’t have access to.

The found that the factors most predictive of project success, team experience mix, rookie ratio, and senior-role retention, are exactly the kind of data points that require purpose-built workforce planning tools. General-purpose AI is good at text. Domain-specific decisions need domain-specific data.

Getting real value from ChatGPT

The PMs who get the most from ChatGPT treat it as a documentation assistant, not a construction expert. They use it for tasks that eat time without requiring deep expertise, and they keep the judgment calls where they belong: with the person who has project context and experience.

If you’re just getting started, meeting minutes and daily reports are the highest-return, lowest-risk place to begin. These are the tasks that consume hours every week, where “professional and accurate” is the quality bar rather than “contractually precise,” and where ChatGPT with light editing is consistently faster than starting from a blank page.

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Launching 做厙勛圖 AI was a huge milestone for our company, and the customer feedback we���ve heard so far has been positive. It has been extremely rewarding to further equip those who rely on our software everyday with additional tools for building project teams and winning work. 

In a previous post about becoming an AI-first company, we pulled back the curtain on the process and technology that turned 做厙勛圖 AI into reality. For this post, I wanted to share another piece of the puzzle that has made a significant impact in the development of 做厙勛圖 AI: product hackathons.

Sparking creativity

Engineering presents an interesting paradox: how do you cultivate a culture of consistent, predictable delivery, all while leaving room for creativity and discovery? At 做厙勛圖, one of the ways we strike this balance is through product hackathons.

Hackathons aren���t a new concept, but they remain one of the most impactful ways to rally a team, break down silos, and spark new ideas. This is the approach that we���ve learned works especially well:

• Frequent: We try to hold a hackathon three times each year. Participation isn���t mandatory, but highly encouraged.
• Inclusive: Two weeks before the event, we gather ideas from across the entire organization���not just engineeringThis ensures that everyone feels a sense of ownership, and that the problems we choose to tackle are grounded in real business needs. We want to start by identifying clear problems to solve, rather than jumping straight to solutions.
• Collaborative: Two weeks before, participants form teams and select an idea to pursue.
• Engaging: Over two focused, in-person days, teams work toward a single goal: building a workable demo that clearly demonstrates how their solution addresses the chosen problem. The event culminates in demos presented to the whole organization, reinforcing the focus on real-world impact.
• Democratic: The winner is chosen through public voting following the demos. This adds a fun strategic element, since teams need to pick ideas that not only work well but also resonate with their teammates.
• Fun: We offer great prizes for the winning team and a lighthearted physical challenge on the first night (with bonus points on the line!).
• Actionable: Following the event, a small committee of engineering and product leaders review each project and determine next steps, including whether any of the ideas will be developed further as part of our product roadmap.

Our first AI hackathon

Our most recent hackathon���held in September���was centered all around AI. We called it Reconstruct. Here���s an excerpt from the theme that set the tone for the event:

We���re entering an era where AI isn���t just changing what we build – it���s changing how we think about building it.

Join us for Reconstruct, a hackathon where we throw out the old playbook and imagine what software looks like when it thinks with us. Over 48 hours, we���ll explore how AI can transform workforce planning – from automating the tedious to unlocking new insights.

This isn���t about polishing features or cleaning up code. It���s about reimagining them through a new lens. The goal of this hackathon is to think with an AI-first mindset – whether through the tools you use, the features you build, or the prototypes you bring to life.

To support that exploration, we opened the floodgates in terms of AI resources, leveraging tools like Cursor, Claude Code, and plenty of AWS Bedrock. (Let���s just say our token usage graph made it pretty easy to spot the hackathon dates!)

And our teams delivered. The creativity and technical depth of the demos were truly inspiring. Here are just a few examples of what came to life:

• 做厙勛圖 MCP – Connected Claude Desktop directly to our public APIs, enabling workforce planning right from an AI assistant.
• AI org charts – Using common knowledge of construction company structures, generated org charts in PDF form from existing roles in 做厙勛圖.
• AI activity summaries – Automated daily summaries of recent account activity.
• AI PDF resume parsing – Simplified importing existing resume into 做厙勛圖, strengthening our experience data foundation.
• AI project templates – Used historical experience data to automatically scaffold new project templates.
• Mobile voice-based broadcasts – Leveraged a local LLM to interpret voice commands into organization-wide notifications.
• Agentic project updates (our winner ) – Allowed users to describe project updates in natural language and let AI handle the rest.

Will all of these projects make it into production? Probably not. But that wasn���t the point���creativity was. The goal was to experiment, imagine, and reframe how we build with AI. And in that sense, Reconstruct was a complete success. Stay tuned for what���s next; we can���t wait to share which of these ideas do make their way into our product roadmap!

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The takeaway isn’t that AI doesn’t work. Construction-specific applications are producing real results in scheduling, estimation, and workforce planning. But you’re not behind the curve if your company hasn’t figured this out yet. Most of the industry is in the same place. This piece looks at why AI projects fail, what the small percentage of companies getting value have in common, and what you should be thinking about as a contractor evaluating your options.

How often AI projects actually fail

The failure rates are higher than most people expect, and they’re consistent across multiple studies. MIT’s NANDA Initiative found that . Gartner’s puts the number at 28% of AI infrastructure projects fully paying off, with one in five failing outright and 57% of managers reporting at least one AI failure.

BCG has tracked this across two consecutive years, and the trend is going the wrong direction. Their found 74% of companies struggling to get value from AI. Their found 60% seeing “hardly any material value,” while just 5% are generating substantial returns. That top 5% is achieving 5x the revenue increases and 3x the cost reductions of everyone else (BCG).

The proof-of-concept stage is where most projects die. The found nearly one in three firms report their AI PoC success rate is lower than 5%. Only 9% said more than half their PoCs made it into production (Omdia). Gartner predicts over 40% of agentic AI projects will be canceled by the end of 2027.

These numbers can feel discouraging, but they’re actually useful. They tell you that the problem isn’t the technology. The companies seeing 5x returns are using the same AI tools as everyone else. The difference is what they did before they turned the tools on.

Why most AI projects fail in construction

The reasons are surprisingly consistent, and they’re worth understanding because they’re all avoidable.

Bad data is the top cause

(MIT). Construction has it worse than most industries. , and 30% of firms say more than half their data is bad or unusable (Construction Dive).

Here’s what that looks like in practice. An documented a mid-size civil firm that spent six weeks and about $40,000 trying to use AI to find patterns in their closeout reports. They killed the project. The data was spread across three formats, two SharePoint sites, half the PDFs were scanned images with no searchable text, and naming conventions had changed twice in eighteen months. The AI consultant quoted in the piece described the pattern as “enthusiastic pilot, quiet cancellation, nobody talks about it.”

A found that 81% of companies still struggle with AI data quality, and here’s the kicker: 90% of director and manager-level data professionals say their leadership isn’t paying enough attention to the problem. The people building the AI systems see the data quality issue clearly. The people funding the projects often don’t.

People and process, not technology

BCG found that , with only 20% from technology and 10% from algorithms. In construction, the confirmed the same pattern: 46% cite a lack of skilled personnel as the top barrier, ahead of integration (37%) and data quality (30%).

(Deloitte). That’s the C-suite telling you the problem isn’t budget or technology. It’s knowing where to aim. And when you buy a tool before you’ve figured that out, you end up with an expensive pilot that works in the demo but never makes it into your actual operations. Ask anyone at a GC who’s been through it; the story usually involves an enthusiastic start, a quiet wind-down, and a reluctance to bring up AI at the next leadership meeting.

Unrealistic timelines

(Deloitte). Satisfactory ROI takes 2-4 years. (Mavvrik). When leadership expects transformation in six months and the realistic timeline is two years, the project loses support before it has a chance to prove anything. Setting honest expectations upfront is one of the simplest things you can do to improve your odds.

What the companies getting it right have in common

The 5% generating substantial returns from AI share a few patterns, and none of them are about having a bigger IT budget.

They start with specific, well-defined problems

The construction-specific ROI data consistently shows results clustering around targeted applications: layout robotics, scheduling optimization, digital twin workflows, estimating automation. These are areas where the data is already structured, the problem is repeatable, and you can measure whether the output improved things.

The lays out a phased timeline that matches the data. Quick wins arrive in the first six months from operational efficiency and back-office automation. From six to twelve months, predictive capabilities start showing up in schedule variance, safety metrics, and rework rates. After twelve months, strategic value emerges in forecasting accuracy, margins, and cash flow. You can show quick wins to leadership while building toward the bigger applications.

They invest in data before tools

Every study in this space points to the same prerequisite. BCG’s top performers , but the differentiator is that they also invest in data infrastructure, cross-functional alignment, and change management at rates that dwarf the 60% seeing no value.

MIT found another useful distinction: (Fortune/MIT). For GCs without deep technical teams, that finding has practical implications. You don’t need to build AI capability in-house. You need to build data readiness in-house and then use tools purpose-built for your workflows. That’s the approach behind 做厙勛圖 AI, which works on top of centralized workforce data to surface insights about team composition, availability, and experience that would take hours to compile manually. The AI is useful because the data foundation is already there.

They centralize their workforce data

For construction specifically, workforce data is one of the strongest starting points because the quality of staffing decisions directly affects project outcomes, growth capacity, and retention. The 做厙勛圖 found that companies centralizing their workforce data see 3x higher growth rates, even while facing similar attrition rates to the broader industry. The mechanism is straightforward: better data produces better decisions, and AI amplifies whatever quality of data you give it.

Where this leaves you

(RICS). If that describes your company, you’re in the majority, and you’re in good company. The contractors that pull ahead over the next few years won’t be the ones that bought the most tools. They’ll be the ones that built the data foundations and organizational readiness that make those tools worth the investment.

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That gap between how leaders want to staff projects and how they actually do it is where most workforce planning problems start. The industry’s labor shortage makes it worse. When your talent pool is tight, every staffing decision carries more weight, and the margin for getting the team composition wrong gets thinner.

Why availability-based staffing stops working

When you’re running a handful of projects, staffing by availability works fine. You know your people. You know who’s wrapping up, who’s ready for the next thing, and who’s the right fit for the healthcare job versus the data center. The breakdowns start when you scale past the point where one or two leaders can hold all of that in their heads.

The math alone creates problems. With a across the industry, contractors face a constant treadmill. A company hiring 100 people to support growth actually needs to hire 125 to account for turnover. At 35% attrition, that number climbs to 154. The 做厙勛圖 found that nearly half of all companies in the dataset didn’t achieve net workforce growth in 2025. When you’re running that hard just to stay even, putting the wrong people on a project has ripple effects across your entire portfolio.

The senior PM who gets assigned to a healthcare project when their background is in data centers isn’t just less effective on that job. They’re also unavailable for the data center bid you’re pursuing next quarter, the one where their experience and client relationships would have made the difference in your win rate. And the less-tenured PM who could have used the healthcare project as a development opportunity gets passed over because nobody had the data to see the fit.

(AGC). 93% of construction leaders say workforce shortages have impacted operations (做厙勛圖). When the talent pool is that tight, the difference between staffing based on who’s available and staffing based on who’s the right fit for the project is a real competitive advantage.

What experience-based staffing looks at

The factors that make a project team successful go well beyond availability and job title. Most ops leaders know these factors matter. The challenge is tracking them consistently enough to use them across 20 or 30 concurrent projects.

Build type and market sector. A superintendent who has delivered three data centers brings capabilities to a data center project that someone with a healthcare background doesn’t. The benchmark data shows that the top 50 of the ENR 400 concentrate their portfolios in complex, regulated sectors like industrial manufacturing, healthcare, and transportation. Matching build-type experience to project requirements is a strategic decision at that scale.

Client and architect relationships. Repeat business depends heavily on the team the client worked with last time. If your best PM for a particular owner is buried on a different project because nobody checked the relationship history, you’re leaving win rate on the table.

Team chemistry. A PM and superintendent who’ve delivered three projects together communicate differently than two people meeting for the first time. They anticipate each other’s needs, handle problems more efficiently, and need less oversight. That’s hard to measure but easy to track if you know who’s worked with whom.

Commute distance. The benchmark data found this is one of the most overlooked factors in superintendent retention. Contractors who keep commute distances reasonable see better retention outcomes, and the ones who can demonstrate that to recruits use it as a hiring advantage.

Certifications and training. Certain projects require specific qualifications, clearances, or safety training. Tracking these as structured data means compliance isn’t a scramble the week before a project starts.

100% of construction leaders surveyed agree that a team’s collective experience significantly impacts project outcomes (做厙勛圖 2025 State of Workforce Planning). The gap between knowing that and having the data to act on it is what experience-based staffing closes.

Rookie ratio and team balance

One of the more practical applications of this approach is managing what the benchmark data calls the rookie ratio: the share of team members with less than one year of company tenure.

Across the dataset, the average rookie ratio is 36.4%, climbing to 56.2% on teams of 51 or more people. That’s a significant percentage of any team still learning your company’s systems, relationships, and standards.

A high rookie ratio isn’t automatically a problem. Some projects are well-suited for development: straightforward owner, familiar build type, flexible timeline. A less-tenured PM or super can get valuable experience on that kind of work. Other projects, particularly those with demanding clients, complex delivery methods, or tight schedules, need people who’ve done it before. Strategic contractors set rookie ratio targets for each project rather than letting team composition happen by default.

Consider a contractor running 25 concurrent projects across three offices. Each project needs a mix of senior leadership, mid-career staff, and newer people building their portfolio. Without structured data on who’s where and what experience they carry, the default is to staff by availability. That fills rosters but doesn’t build balanced teams, and it doesn’t develop the junior staff in a way that builds your senior bench over time.

How experience data affects retention

The link between experience-based staffing and retention is one of the strongest findings in the benchmark data, and it runs in both directions.

Senior superintendents and PMs have attrition rates just a quarter of their non-senior counterparts. Their median tenure is roughly double: 7.0 years versus 3.7 for superintendents, 5.6 versus 3.7 for PMs. And here’s the critical point: senior roles show zero growth across the dataset. Companies aren’t hiring senior supers and PMs from the outside. They’re developing them internally, which means the investment timeline is measured in years. Losing one is expensive in a way that’s hard to quantify but easy to feel.

Experience-based staffing supports retention for less-tenured staff too. When a superintendent’s career development is factored into project assignments, giving them exposure to new build types and clients that expand their portfolio, they’re more likely to see a future at your company. The alternative is getting stuck on repeat work that doesn’t build their skills, which is one of the reasons people start looking elsewhere. Protecting senior staff from burnout assignments matters just as much. A senior super who keeps getting sent to projects with long commutes and unfamiliar territory because they were available will eventually find a firm that’s more intentional about how they deploy their best people.

Where AI fits into experience-based staffing

The concept behind experience-based staffing is straightforward. The practical challenge has always been scale. No one person can hold the complete experience profile of every team member across every relevant factor when staffing decisions happen across dozens of concurrent projects. Build type, certifications, relationships, tenure, commute, team chemistry, development goals, availability. It’s more data than any spreadsheet or planning meeting can realistically process.

That’s the problem purpose-built workforce planning tools are designed to solve. 做厙勛圖 AI approaches this through features like Smart Suggestions, which recommend team compositions based on the full set of experience data, and Ask 做厙勛圖, which lets anyone on the team query workforce data conversationally. Instead of calling the ops leader who keeps it all in their head, a PM can ask “who has data center experience and is available in Q3?” and get an answer in seconds. The benchmark data shows the results: companies centralizing their workforce data see 3x higher growth rates than those that don’t. They’re making better decisions because they have better information, and AI makes that information actionable at a scale no spreadsheet can match.

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“When you’re placing a person, you’re not just placing a robot. You’re placing a human. And understanding their relationships can be a big part of a project team,” says Matthew Walsh, Senior Operations Technology Manager at Power Construction.

The construction experience gap

The numbers behind this are straightforward. , according to the National Center for Construction Education and Research. The industry needs approximately just to meet current demand. And the people replacing retirees aren’t staying long, with average employee tenure in construction sitting at just according to the Bureau of Labor Statistics.

The workforce is turning over faster than institutional knowledge can transfer. A project manager who started three years ago is now one of the more experienced people on staff. The superintendent with 20 years of relationships, sector knowledge, and judgment about which subcontractors actually deliver is approaching retirement and has no direct replacement in the pipeline. That superintendent’s knowledge isn’t documented anywhere. It lives in their head, in the relationships they’ve built with owners and architects, and in the instincts they’ve developed from seeing hundreds of situations play out on job sites. report having a hard time finding workers to hire. That’s one of the top challenges facing construction right now, and it affects who ends up on which project just as much as it affects headcount.

What the rookie ratio looks like on a project

To make this concrete, consider an eight-person project team:

Three experienced members and five newer ones. Whether that works depends on the project. For a straightforward commercial build where the superintendent has done dozens of similar jobs, it probably does. For a first-time healthcare project with a demanding owner, that balance creates risk that nobody officially measured or accounted for.

Newer team members can and do perform well, especially with strong leadership around them. The concern is that nobody tracks this balance across the portfolio, so it happens by accident rather than by design.

What happens when too many team members are new

A high rookie ratio doesn’t usually produce a single dramatic failure. The effects are more like friction that builds across dozens of small interactions over the course of a project.

Submittals take longer because the PM doesn’t know the architect’s preferences from a previous project. RFIs increase because field engineers are learning systems they haven’t encountered before. The superintendent spends more time coaching and less time managing subcontractors. Decisions that an experienced team would handle in a hallway conversation become formal escalations because people don’t yet have the relationships or pattern recognition to move quickly.

“Team dynamics are really important to us. We don’t just throw people on a project without thinking about how they’ll work with each other. We look for strengths that complement each other,” says Jamie Miller, Director of Engineering Development at Sellen Construction. That kind of complementary pairing requires knowing who has worked together before, who has sector-specific knowledge, and who needs mentorship on a given build type. Without that information centralized and visible, team assembly becomes a best guess.

There’s also a safety dimension that deserves plain language. “Company morale goes down, employees are burnt out because they’re going to do whatever it takes to get the job done. It affects your employee retention and increases safety incidents on a project,” says Shawn Gallant, COO of Columbia Construction. “You never want an unsafe site because you’re cutting a dollar on staffing. It just doesn’t make sense.”

A team where experienced supervision is stretched thin across too many people who need guidance creates conditions where things get missed. Not because anyone is careless, but because one superintendent can only be in one place at a time. The newer assistant superintendent who hasn’t seen a particular sequencing issue before won’t necessarily catch it without someone nearby who has. These aren’t dramatic failures that make the news. They’re the kind of everyday quality slippage that shows up later as rework, schedule delays, and strained client relationships.

Measuring experience across your project portfolio

The rookie ratio becomes useful when you can see it at the portfolio level.

You probably know who is assigned to which project. Building balanced project teams requires answering a harder question: across all your active projects, which ones have the highest concentration of newer team members? Which projects have experienced people who could be shared or reallocated. Where the mentorship opportunities are, and where the risk concentrations sit.

“Previously, there was a gatekeeper. We would communicate project needs, and they would go back to their desk to figure it out. It was siloed,” says Gallant. “We quickly identified that we could improve how we manage our workforce. It needs to be collaborative. You need the input of your leaders and your team.”

When experience data is centralized, a few things become possible that spreadsheets can’t support. You can assess a proposed team lineup before it’s finalized, checking whether it has enough sector experience, enough tenure, and enough people who have worked together before. That assessment takes minutes instead of a week of phone calls and emails. A newer PM in one office might benefit from pairing with a senior superintendent from another office who has deep experience in the relevant build type. If the rookie ratio across your portfolio is trending toward inexperience in a specific role type, your HR team can start recruiting before the gap becomes a crisis rather than reacting to it after a project struggles.

“I think we’ve been building better teams from the project’s inception. We have the RFP in hand and are all on the same platform. We collaborate on the team we assemble immediately, which can help us win a project,” says Miller.

How much experience does a project team need

There is no universal benchmark here. The right rookie ratio depends on project complexity, client expectations, build type, and the specific strengths of the individuals involved. But some patterns hold.

Higher-risk projects need a lower rookie ratio. A first-time-for-the-company build type, a project with a difficult owner history, or a job with tight schedule constraints all need more experienced coverage. The margin for learning on the job is thinner, and the cost of rework or schedule delays on these projects is proportionally larger.

Growth phases require particular attention. When a contractor is winning more work than usual, the natural tendency is to spread experienced people thin across more projects and fill in with newer hires. Gilbane Building Company faced exactly this when expanding into data centers and advanced manufacturing, sectors that required specialized expertise their existing teams hadn’t built before. 

“做厙勛圖 gave us a centralized, data-driven approach that’s fully integrated and user-friendly. It’s helped us eliminate silos and turn workforce data into a competitive advantage,” says Alexander Gutman, Gilbane’s Chief Technology Officer. Without that kind of visibility, the projects that suffer tend to be the ones where the experienced person was technically assigned but practically unavailable because they were covering problems on another job.

Mentorship also requires deliberate pairing, not just proximity. Putting a newer PM on the same project as an experienced superintendent doesn’t automatically create knowledge transfer. The two roles interact differently with the work, the owner, and the subcontractors. A newer PM learns project management from a senior PM, not from a superintendent, no matter how experienced that superintendent is. Effective mentorship happens when someone with relevant role-specific experience is paired with someone who needs that particular guidance, and that pairing is planned rather than coincidental.

Experience tracking and workforce planning

The practical challenge is that the underlying data, who has experience with what, rarely lives in one place.

Internal resumes that track build type, market sector, client history, and past team collaboration give operations leaders the information they need to assess experience distribution before assigning teams. The question “who has done this before?” should take seconds to answer, not a series of phone calls across offices.

When that experience data connects to workforce forecasting, the rookie ratio becomes forward-looking rather than reactive. Instead of discovering experience gaps after a project starts and problems emerge, you can see six months out where the portfolio will be heavy on newer team members and plan accordingly. That might mean accelerating a hire, reassigning an experienced PM from a project entering closeout, or making a go/no-go decision on a pursuit based partly on whether the right team exists to deliver it.

“I never really felt like I had the time to keep up with the resource management during my day job. To be honest, it was a Saturday when I used to try to hammer through all of our resource planning,” says Jeremy Moe, Operations Manager at The Boldt Company.

The rookie ratio should be a visible dimension of the workforce data that already informs weekly planning meetings, pursuit discussions, and hiring conversations. The contractors who track this balance are building teams that perform consistently even as the workforce gets younger. Experience won’t take care of itself, not at the rate people are retiring and turning over. Workforce planning platforms like 做厙勛圖 make it possible to see that balance before the project starts, rather than discovering it after problems surface.

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“We spent too much time simply trying to display our data correctly and it detracted from the problem-solving and thoughtfulness that workforce planning and scheduling deserve,” says Keyan Zandy, CEO of Skiles Group.

Revenue per person in practice

A project generating $2 million in monthly revenue with eight assigned team members produces $250,000 in revenue per person per month. The same project with twelve people assigned produces $167,000. Both projects might show healthy utilization numbers because everyone is busy, but the second project is carrying four more salaries against the same revenue and that difference compounds across a portfolio.

The metric gets more useful when you compare it across projects of similar type and size. Here’s what a hypothetical portfolio might look like:

The numbers don’t tell the whole story on their own. Healthcare B might need more people because the scope is genuinely more complex. Commercial D might be overstaffed because the team was sized for peak activity and nobody adjusted when the project settled into a steady phase. But without the metric, those conversations don’t happen. The project just feels busy, and busy looks like it’s working until the margins come in lower than expected.

Overstaffing in construction

The construction industry has an understaffing narrative and the data supports it. report having a hard time finding workers to hire. The labor shortage dominates every industry conference and trade publication.

Overstaffing is the quieter problem, and it happens in predictable patterns. A project gets staffed for peak activity during preconstruction or early site work. The team size made sense when there were 15 submittals a week and daily coordination with three design teams. Six months later, the project is in a steady construction phase, the submittal volume has dropped, and the same team size persists because nobody flagged the change. The people are busy, just not generating proportional revenue.

Growth compounds it. When a contractor wins more work than expected, the instinct is to hire and assign. The urgency of getting people on projects overrides the discipline of right-sizing teams. “Because of effective planning, we can probably get a few more projects than we typically would’ve because it’s a huge risk when resources are your biggest limitation,” says Johnathon Grammer, Director of Operational Excellence at Rogers-O’Brien. The flipside is that without that effective planning, adding people doesn’t always translate to adding capacity. Sometimes it just adds cost.

The financial impact is concrete. Labor burden in construction averages approximately , according to Construction Coverage’s analysis of Bureau of Labor Statistics data. Apply that to a salaried project manager making $95,000 and the true cost to the company is closer to $137,000 once you include benefits, insurance, and employment taxes.

One unnecessary PM on one project for six months represents roughly $68,500 in cost that didn’t need to happen. Multiply that across a portfolio of 15 to 20 active projects and the aggregate drag on profit margins becomes real.

The cost of running too lean

The opposite problem is just as real and arguably more dangerous to long-term business health. Understaffing saves on labor cost in the short term while creating schedule delays, quality issues, and team burnout that cost more down the line.

An understaffed project pushes experienced people past sustainable workloads. The superintendent covering two buildings instead of one catches fewer problems. The PM handling three projects simultaneously responds to RFIs slower. Submittals back up, schedules slide, and the overtime costs that follow can erase whatever labor savings the lean staffing was supposed to produce.

“Company morale goes down, employees are burnt out because they’re going to do whatever it takes to get the job done. It affects your employee retention and increases safety incidents on a project,” says Shawn Gallant, COO of Columbia Construction. “With any safety incident, there’s a cost, but more importantly, somebody’s personal life could be impacted.”

The retention effect matters here too. The industry’s means contractors are already fighting to keep experienced people. Chronic understaffing accelerates departures. The cost of replacing a senior PM, including recruiting, onboarding, and the productivity gap while the replacement gets up to speed, dwarfs the cost of adding a team member when the workload justified it.

Revenue per person per month helps identify both sides. If the number is consistently above the portfolio average for a project, the team may be stretched too thin. If it’s well below average with no complexity justification, the project is probably carrying more people than the work demands. Either way, the metric only works if you know what normal looks like for your organization.

Setting a revenue per person baseline

The metric is only useful as a comparison tool. An isolated revenue-per-person number for one project tells you very little. The value comes from establishing a baseline across project types and then noticing outliers.

Start with historical data. Pull the last 12 months of project financials and team assignment records. For each project, divide monthly revenue by the number of salaried staff assigned that month. Group the results by build type and project phase:

These numbers are illustrative. Every contractor’s baseline will differ based on market, geography, and how they scope project teams. The point is establishing what normal looks like so that deviations trigger a conversation rather than going unnoticed.

Phase matters because team size should change as a project moves through its lifecycle, and your utilization rate should reflect those transitions. Preconstruction teams are smaller but so is the monthly revenue recognition. Peak construction has higher revenue and larger teams. Closeout should see team sizes shrink as revenue winds down. A project where closeout RPP drops below the baseline suggests people stayed on the project longer than the work required.

“We have total transparency in our metrics now. Fast-forward from last year to this year, our utilization has exceeded our targets. The increased utilization rate contributes directly to higher than forecasted profits,” says Ed McCauley, VP of Corporate Services at Wohlsen Construction. Revenue per person per month makes that utilization-to-profit connection visible at the project level, where staffing decisions actually get made.

Four staffing decisions this metric informs

Revenue per person per month becomes a planning tool when it informs the decisions you’re already making.

For team sizing on new projects, compare the projected monthly revenue against your portfolio baseline for that build type. If the expected RPP falls below baseline with the proposed team size, the project may be overstaffed from day one. If it’s well above baseline, the team may be too lean for the scope. The Forecasting Dashboard that connects your pursuit pipeline to workforce data makes this comparison possible before you finalize a team.

For phase transitions, most projects don’t adjust team size as they move from preconstruction to construction to closeout. The preconstruction team stays on through construction, and the construction team stays on through closeout. Revenue per person per month makes the case for right-sizing at each transition. If the metric drops 30% from peak construction to closeout, that’s a signal to reassign people to projects where they’re needed more.

For portfolio rebalancing, when you can see RPP across all active projects, you can spot where people are underutilized and where teams are stretched. A project running 40% above baseline RPP while another runs 20% below suggests a reallocation opportunity. That conversation belongs in a weekly planning meeting, not a quarterly financial review when it’s too late to act.

For pursuit strategy, scenario planning becomes more grounded when you can model the RPP impact of winning a new project. Gilbane Building Company faced this when expanding into data centers and advanced manufacturing, sectors that required specialized expertise their existing teams hadn’t built before. With workforce data centralized across regions, they could evaluate pursuits with confidence that the right teams could be assembled. Without that kind of visibility, the projected RPP for a new-sector project staffed primarily with newer hires will tend to be lower than baseline, because those team members take longer to reach full productivity.

“做厙勛圖 saves me personally at least 4-6 hours a week. And then if you multiply that across the company, we’re saving hundreds of hours a week,” says Chris Martin, VP of Technology Services at MYCON. Those reclaimed hours shift what operations leaders spend their time on. Instead of data entry and spreadsheet maintenance, they can look at metrics like revenue per person and ask whether the staffing configuration is producing the returns the business needs.

Getting started with what you already track

Revenue per person per month doesn’t require new systems or data that most contractors don’t already have. Monthly revenue by project comes from accounting. Team assignments come from whatever system tracks who is on what. The calculation is division.

The math is simple. Getting those two data sets into the same place, at the same cadence, so the metric updates in real time instead of being a quarterly exercise in hindsight, is the actual challenge. The contractors who connect workforce data to financial outcomes in real time are the ones who catch overstaffing before it erodes margins and understaffing before it burns people out. Workforce planning platforms like 做厙勛圖 bring team assignments, project data, and forecasting into one view, making revenue per person visible across the portfolio without the spreadsheet gymnastics.

The metric won’t tell you what to do. It tells you where to look. In an industry where margins are tight, labor costs are rising, and the difference between a profitable project and a break-even one comes down to having the right number of the right people at the right time, knowing where to look is worth the effort.

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