Texas-based Central Builders was established in 1989 and specializes in large-scale remodels, expansions and ground-up new construction of supermarkets and grocery distribution facilities. As the company expanded, on-time project delivery and profitability were increasingly threatened by disconnected workflows and data silos that delayed job-cost updates, required duplicate data entry and limited insight into project performance.

The company invested in a connected construction ecosystem that aligns project management, financial operations and reporting to ensure that all teams work from a single source of truth across projects. Time and again, this decision has proven to be critical to project success.

The Challenge: Building on a 30,000-Square-Foot Pond

Central Builders has completed well over $500 million in grocery store projects over the past five years. One was a new $10-million Sprouts grocery store built in 2025 in an unlikely place: directly on top of a 30,000-square-foot pond. The challenging location was only the beginning of the complexities that tested the company’s capabilities and illustrated the value of the investment in connected technology. Persistent rain, the coordination of more than 40 subcontractors and a simultaneous Sprouts project in North Texas left zero margin for error.

Before vertical construction could begin, crews had to demuck the site, excavate unstable material and rebuild the pad with engineered fill. Central Builders completed the site preparation 10 days ahead of schedule, an advantage that proved vital when the weather turned.

Moisture affected nearly every downstream activity. Dry weather windows opened and closed quickly, and sequencing often shifted by the hour. Coordinating crews, materials and inspections under these conditions required clear communication and real-time visibility into job progress and costs.

Despite environmental and logistical challenges, the Sprouts project reached key milestones ahead of schedule. Steel erection and decking were finished five days ahead of plan, and the project closed on time and on budget.

Connecting the Field and the Office

Central Builders relies on a cohesive technology ecosystem built largely around Trimble solutions to bring people, data and workflows together in a shared environment.

Field teams utilize Trimble ProjectSight to manage RFIs, submittals and drawing updates. “With ProjectSight, everyone has real-time access to the most current information,” says Shellie Gregg, financial controller for Central Builders. “Shared visibility reduces rework and keeps our teams aligned as schedules shift and as documents are updated or added.”

In the office, the Trimble Vista financial management solution serves as the system of record for accounting, payroll, job costing and subcontractor billing. With field updates flowing directly into financial reporting, manual data entry has decreased by 90%. “Job-cost data reflects exactly what is happening on the jobsite,” says Gregg. “Plus, visibility into field updates enables our teams to closely track performance and respond quickly when conditions change.”

Gregg credits improved job-costing visibility with increasing field-budget forecasting accuracy by more than 30%, helping project managers hit margin targets.

Driving Efficiency and Cash Flow

The investment in connected construction didn’t take long to pay off. “Within eight months, we realized a return on investment,” says Gregg. “Payroll savings, reduced administrative overhead and better, faster operational decision-making enabled by real-time data collectively transformed our business.” 

The impact of a connected construction approach is visible across Central Builders’ operations:

• More Timely Job Costing: Integrated project and financial management systems bridged the field-to-office divide, allowing managers to align job cost with actuals in near real time.
• Faster Financial Reporting Cycles: Live dashboards connected to project financial data shortened monthly close cycles from 12 days to five.
• Labor Transparency: With real-time visibility into labor through phase-level time tracking and automated burden calculations, teams can assess performance weekly and adjust forecasts before a project drifts off course.
• Streamlined Vendor Management: Automating compliance and shortening approval cycles with Trimble Pay has reduced subcontractor payment processing time from two weeks to less than five days, keeping vendors engaged and materials flowing to the site.

Predictability in an Unpredictable Environment

Technology adoption has not only improved workflows at Central Builders but also changed how people work across the company and how they feel about their jobs.

“In a fast-paced construction environment where job costing, documentation and approvals can grind morale into dust, connected technology has become the backbone of clarity, speed and sanity,” Gregg concludes.

Project managers now spend fewer hours reconciling numbers and more time directing work. Predictability has reduced burnout and improved morale. Late nights reconciling numbers or “guessing” when trades should be on site have decreased dramatically, and office staff report a 35% decrease in rework caused by outdated or missing documentation.

These outcomes at Central Builders demonstrate the power of connected construction workflows for responding quickly, sharing data across the organization and making decisions based on reliable information rather than assumptions.

SEE ALSO: POWERING PROFITABILITY WITH CONNECTED CONSTRUCTION WORKFLOWS

The post Building on Water:  How Central Builders Leveraged Connected Construction to Conquer a Complex Jobsite first appeared on Construction Executive.

In 2025, Buildcheck officially launched after raising $5.9 million in seed funding—with help from Uncork Capital, Peterson Ventures, XFund, and founders and senior executives at OpenAI, Opendoor, CBRE, Zillow and more.

Michalatos sat down with Construction Executive to discuss the rigorous creative and prototyping process for this product, how he expects the technology to evolve over the next decade, as well as many details in between.

What type of technology is Buildcheck?

Buildcheck is an AI-powered design review platform. We’ve trained computer vision models to read construction drawings—the language of construction—so we can catch coordination errors, missing scope and cross-discipline conflicts before they turn into RFIs, change orders or city comments. We work with real estate developers, general contractors and design firms, and our customers typically see a 10x to 40x return on what they spend with us.

What was the impetus for creating Buildcheck?

The impetus is personal. I grew up in a construction family in Vancouver—my father built single-family homes for decades—and I spent my own career on the owner, general contractor and design sides: Stantec, Honeywell, EllisDon on hospital design-builds and QuadReal on mixed-use development. Across every one of those roles, I kept seeing the same pattern: small inconsistencies scattered across hundreds of sheets that nobody caught until steel was going up or concrete was being poured. Globally, that’s a $200-billion problem. When I stepped back, it was obvious: Construction doesn’t run on contracts or emails, it runs on drawings. Until AI could actually read the lines on a sheet, it was going to stay peripheral to real construction risk. That’s what Buildcheck is built to solve.

Who is Buildcheck’s main type of client within construction?

Primarily general contractors and real estate developers, with a growing number of architecture and engineering firms using us as an internal QA/QC layer. Our customers include EllisDon, AvalonBay, Novo Construction, Dempsey Construction and many more.

The common thread is that they’re all managing design risk—not just building. That’s a bigger group than it used to be. Public infrastructure is increasingly delivered through design-build and alliance contracts, which push design responsibility onto contractors. Mid-market developers are engaging general contractors earlier through preconstruction services or GMP structures. Architects are carrying more professional liability exposure on bigger, more complex drawing sets. All of them share the same underlying need: find the coordination gaps before they get priced into a change order.

We tend to fit best with firms that have already decided design coordination is a bottleneck. They usually know exactly where it hurts; our job is to show them that AI can now do something about it.

Do you work with clients outside of construction?

No, and that’s intentional. Computer vision on construction drawings is a uniquely hard problem—drawings aren’t standardized, symbol sets vary by firm and discipline, and layering conventions shift project to project. Training a model that genuinely understands an MEP sheet versus a structural sheet or civil sheet takes years of labeled data and domain expertise. That depth is the moat, and it only comes from staying focused.

That said, the underlying technology could apply to other drawing-heavy industries—shipbuilding, aerospace manufacturing and certain industrial engineering verticals. For now, the opportunity in construction alone is enormous. The global design-error problem is $200 billion annually and nobody has solved it. We’d rather be the best in the world at one thing than average at several.

Do you still run into problems with designers/companies hesitant to use AI for preconstruction? How do you create buy-in/convince them to get onboard in the first place?

Yes, and I think that skepticism is healthy. There’s a lot of AI-washing in construction tech right now and buyers are right to demand proof.

For our clients, a demo is the starting point, but the real conversation begins when we run their drawings through Buildcheck. In most cases, we come back with dozens to hundreds of flagged issues that their experienced team hadn’t caught. If that first project lands, a companywide rollout tends to follow naturally. We also invite any prospective customers to talk to current customers and hear directly from them, not us.

The other piece is framing. We aren’t replacing labor; we’re enabling your people to do more. Your senior reviewers still make the judgment calls. We just compress the hours of repetitive pattern-matching work—the missing power, the mismatched fire ratings, the clashing service connections—so your people can spend their time on the decisions that actually require expertise. When buyers understand that, the skepticism usually shifts from “will this work?” to “how fast can we roll it out?”

Does this type of tech only apply to the preconstruction process?

Preconstruction is where we start because that’s where the ROI is most obvious. Catching a coordination issue on a sheet costs almost nothing to fix; catching it in the field can cost six figures and weeks of schedule. Front-end planning research from the Construction Industry Institute has consistently shown returns of roughly 10:1 on investment in document quality before construction.

But computer vision on drawings unlocks workflows across the entire project lifecycle, such as shop drawing cross-checks against the IFC set, change-order quantification, automated takeoffs and, eventually, as-built verification. The same underlying models that detect errors today can drive proactive design improvement tomorrow—through real-time coordination feedback as drawings and design optimization evolve.

The long-term vision is that drawings stop being static PDFs and start being structured, machine-readable artifacts. Once that happens, everything downstream—estimating, procurement, coordination, closeout—gets meaningfully faster.

What was the development/prototyping process like for getting this product out the door?

Our process was and continues to be rigorous and disciplined. We’ve spent over three years building proprietary models trained specifically for construction drawings, but also building the user experience around it. Neither of these can be vibecoded because the domain expertise and customer feedback loops take time to establish.

Two things shaped the process. First, we chose quality at every step—from labeling the data and fine-tuning the models to having construction experts verify every AI output before it reaches the customer. Second, our earliest customers did more to shape the user experience than any internal plan ever could have. General contractors and developers willing to give us feedback are how we figured out what reviewers actually want to see first, how to surface severity and how the interface should mirror the way their teams already work.

How has this type of technology evolved since Buildcheck’s inception? Where do you see it going by the next decade?

When we started, most construction AI was optical character recognition, chatbots layered over contract text or basic clash detection inside a BIM model. Useful, but peripheral to the core risk. The shift over the last two years has been toward specialized vision models that can actually interpret 2D drawings—the medium construction still overwhelmingly runs on.

Looking out ten years, I’d expect three things. First, error detection becomes table stakes—every major project will run through automated review the same way it runs through code check today. Second, the tooling moves from reactive to proactive: Rather than flagging problems after a set is issued, AI will provide real-time coordination feedback inside the design-authoring tools as drawings evolve. Third, we’ll start to see genuine design optimization—AI that suggests smaller duct runs, more efficient structural layouts and value engineering moves grounded in both code and constructability.

Is there fear that this type of AI will ‘take people’s jobs’?

It comes up and it deserves an answer. AI isn’t going to replace most construction jobs—it won’t sequence concrete pours, negotiate a subcontract or lead a toolbox talk. Leadership and judgment in construction remain deeply human. And besides, there is a huge backlog of work for the entire industry. We want to do more and technology enables that; we can’t afford to lose people.

What AI removes is the repetitive, pattern-based review work that consumes hours: hunting missing dimensions across 400 sheets, cross-referencing fire ratings, chasing broken callouts. Given the industry’s labor gap and flat productivity, the real risk isn’t AI taking jobs. It’s the industry being unable to deliver enough projects, affordably, because we can’t scale human expertise fast enough. AI is a leverage tool for the people already here.

How has this tech saved money, time, safety, productivity?

Money and time are the easiest to quantify. Design errors and coordination gaps drive an estimated $200 billion in global overages annually. On individual projects, we regularly see six-figure savings and multi-week schedule protection—on one 230-unit multifamily project, over $500,000 in cost avoidance and 27 days of schedule saved. Under conservative assumptions, customers see 10-40x ROI.

Safety is the most underappreciated. The highest-severity inconsistencies we catch are life-safety issues—mismatched fire ratings between disciplines, undersized electrical feeds to fire pumps and missing sprinkler branches. Finding those in the documents, before installation, is meaningfully better than catching them at commissioning.

Do you believe this type of technology is gaining momentum within the industry? Is it helping give AI a friendlier reputation within construction?

Yes, clearly. Recent industry surveys show up to 64% of construction organizations experimenting with AI. Two years ago, the first meeting was about, “Does this work on drawings at all?”

Today it’s about which vendor, what pilot structure, what rollout. That’s a meaningful shift.Construction is actually one of the better industries for AI to land in, because it’s pragmatic. Professionals don’t care about hype—they care about dollars saved, days saved and risk reduced. When they see AI flag a real issue on a real drawing set, skepticism fades quickly. The caveat: Overpromising vendors can set the category back. The industry has a long memory for broken tech promises.

Anything else?

Don’t conflate AI with LLMs. A lot of construction buyers assume ChatGPT-style tools are what AI looks like. Those models are excellent at text—contracts, specs, RFIs—but construction’s highest-value problems are visual: drawings, models, site conditions. The vendors solving those problems are doing specialized computer vision work that looks very different from a chatbot wrapper. When you’re evaluating AI tools, the first question to ask is what the models were actually trained on.

SEE ALSO: SIX AI SOLUTIONS DRIVING PRODUCTIVITY AND PROFITABILITY IN CONSTRUCTION OFFICES

The post A New Vision for AI: Construction Design-Review Platform Lends Extra Eye to Image Analysis first appeared on Construction Executive.

The result is redundant data entry and lag times that leave the back office relying on financial data that’s sometimes two to three weeks old. Without timely integration between the field and the office, budget overruns are difficult to catch early, making it harder to protect profitability before the damage is done.

Construction financial management is also inherently complex because it’s not just about moving money; it’s about managing a mountain of legal defense. Manually tracking insurance certificates, safety logs and lien waivers for dozens of subcontractors is logistically challenging. A missing signature or expired policy can halt a multi-million dollar project or expose the general contractor to massive legal liability.

That’s why more contractors are turning to financial management technologies to navigate increasingly complex financial environments—shifting from reactive accounting to fully connected construction workflows that more easily improve cash flows, reduce lag time errors and align teams.

With an integrated system in place, the finance team can act as a true strategic partner to project teams, helping them not just increase profitability, but take on bigger, more complex jobs—ultimately positioning the business for sustained growth and a stronger competitive advantage.

Integrated Workflows Start from the Field

Shifting to more integrated financial workflows begins with field data, which serves as the critical link connecting what’s happening in the field with back office personnel who must then process that information in a timely manner.  This includes information like labor hours, material and equipment costs, RFIs, submittals and change orders, which are recorded daily in the field.

Within many construction companies, the “field” (project managers and foremen) and the “office” (accounting and finance) operate in disconnected silos. Field teams use project management applications and field logs to track daily progress, while accounting relies on separate financial systems to manage invoices and payments. This lack of timely data flow can create a lag, delaying the discovery of financial issues sometimes weeks after they occur.

A connected ecosystem bridges this gap by unifying field and project data with financial management via a single, shared flow of information. This ensures that both the project lead in the field and the person cutting the check in the office are working from the exact same data.

This workflow ideally starts by transferring bid data directly into a field or project management solution. A common cost-code structure acts as the connective tissue between the field and the office. As crews work, they log hours and materials and other costs against those specific cost codes. This isn’t just “status tracking;” it’s the foundation of accurate job costing.

Hours estimated versus hours spent against the budget can be more easily seen and managed. For example—if a contractor budgeted 40 hours for framing and has already spent 38 hours with half the work done, the overrun will be flagged. This allows the problem to be fixed that week, rather than finding out at the end of the month when the bank account is empty.

Field to financial integrations can also help with change orders, which are often hidden costs that aren’t noticed until the very end of a project. But when systems are linked, the change order process moves from being a paperwork headache to a proactive financial strategy.

For example, when a subcontractor encounters an onsite conflict that results in a potential change order, it’s immediately logged in the financial management system as a pending item. The system automatically calculates the potential change order’s impact on the project’s bottom line and alerts the project manager if it threatens to consume the remaining contingency. Once the owner approves the change, it’s converted into an official change order and synced with the ERP, where it automatically updates both the subcontractor’s contract and the owner’s prime contract.

The Subcontractor Relationship: Facilitating Accuracy, Speed and Trust

Another critical workflow is invoice, compliance and lien waiver management—especially for subcontractors, who are often left chasing payments through emails and phone calls. Automated payment portals like Trimble Pay act as a digital bridge between project managers and subcontractors, standardizing payments through a digitized system. It replaces tedious paperwork with a guided digital experience, shifting the dynamic from chasing checks to simply verifying progress.

Subcontractors submit invoices through a dedicated digital portal, where built-in checks ensure everything is in order before submission. If a certificate of insurance has expired or a required license is missing, the system prompts them to upload the necessary documentation on the spot. It also automatically generates the appropriate conditional lien waiver based on the invoice amount, streamlining compliance and reducing delays.

Once an invoice is submitted, subcontractors gain full visibility into the review process, including who is currently evaluating the bill—whether it’s the project manager or accounting personnel. After approval, payments are released via ACH directly through Trimble Pay.

This transparent, end-to-end process replaces the back-and-forth of phone calls and emails. Subcontractors can track their invoice status from submission to final payment, fostering greater trust and strengthening working relationships.

The Connected Future

Moving from manual, siloed operations to an integrated data ecosystem provides three major benefits to contractors:

Elimination of the Visibility Gap: By integrating field data with back-office accounting, contractors achieve more timely project and financial visibility. This allows them to catch budget overruns (such as labor hours exceeding estimates) and track pending change orders when they occur, rather than discovering financial damage after a project is already over budget.

Reduced Risk and Automated Compliance: Managing compliance and lien waivers on spreadsheets is a gamble. By integrating workflows, financial solutions automatically collect and validate documents before money moves, reducing the risk of costly claims or financial disputes. The transition to automated payment portals reduces administrative friction, fosters trust and strengthens the long-term partnership between the contractor and their trades.

Scalability Without Overhead: Integrated systems allow contractors to take on more complex projects or a greater volume of work without expanding back-office staff. The efficiency gains in payment processing alone allow the finance team to act as strategic partners to the project teams.

Shifting to a fully connected financial ecosystem transforms construction management from a reactive struggle into a proactive strategy. Bridging the gap between the field and the back office helps contractors protect their bottom line through more immediate visibility, automated compliance and stronger professional partnerships. In an industry where a single missing signature or an unrecorded change order can erase a year’s profit, these digital workflows serve as the essential foundation for building a truly scalable and profitable business.

SEE ALSO: WHEN BUILDINGS FORGET: DIGITAL DATA SILOING

The post Powering Profitability With Connected Construction Workflows first appeared on Construction Executive.

Contractors are increasingly pursuing work tied to power generation, utilities and site preparation as tech companies race to expand AI capacity nationwide. Industry leaders also noted growing challenges tied to labor availability, electrical equipment lead times and power access, which are becoming critical factors in where projects move forward. Despite those pressures, firms remain bullish on long-term demand for AI and data center construction.

SEE ALSO: NONRESIDENTIAL CONSTRUCTION SPENDING GROWTHS ON PUBLIC SECTOR STRENGTH IN APRIL

The post Data Center Demand Drives Construction first appeared on Construction Executive.

The DEWALT study, AI in the Trades, underscores a paradox: Construction professionals overwhelmingly believe AI will define the future of the jobsite, yet very few are actually using it today. In the United States, 90% of construction professionals say AI will be indispensable within five years, but only 8% report using it as part of their day-to-day work.

This gap is not about skepticism. It is about access—specifically, access to training that is practical, relevant and aligned with how work actually gets done in the field.

A WORKFORCE READY—BUT NOT EQUIPPED

The data reveals a workforce that is not only open to AI, but actively expects it. Eighty-eight percent of respondents anticipate increased adoption in the next year, and 83% believe it will become standard within three years.

That level of consensus is rare in construction, an industry often characterized by cautious adoption curves. But belief alone does not translate into implementation. The critical barrier, cited consistently across the survey, is the lack of formal, job-relevant training.

“Tradespeople are the backbone of our industry, and their hands-on expertise is what brings every project to life,” says Bill Beck, president of tools & outdoors at Stanley Black & Decker. “As jobsites become increasingly complex and technology-driven, the need for practical AI training has never been more important.”

What Beck’s statement captures is a subtle but important shift: The industry is not asking whether tradespeople can adapt to AI—it is recognizing that they must. The question is whether the ecosystem supporting them—educators, contractors, manufacturers and associations—is moving fast enough to prepare them.

THE TRAINING GAP IS STRUCTURAL

One of the most striking findings from the study is where workers are currently turning for AI education. Instead of structured programs, tradespeople are largely self-teaching through informal channels: 40% rely on YouTube, 39% on platforms like Coursera and 42% prefer video tutorials.

This patchwork approach to learning is not sustainable for an industry where precision, safety and coordination are paramount. It also signals a deeper issue: Traditional training pathways—especially trade schools and apprenticeship programs—have not yet integrated AI into their curricula at scale.

That disconnect is widely recognized within the workforce itself. An overwhelming 87% of respondents say AI education must be embedded in trade schools and technical programs, while 59% emphasize the need for hands-on training tied directly to real construction tasks.

In other words, the workforce is not asking for abstract knowledge about AI. It is asking for applied learning: how to use AI for estimating, site monitoring, scheduling, procurement and quality control.

This is a critical distinction. Without contextualized training, AI risks becoming another underutilized technology—promising in theory but marginal in practice.

EARLY USE CASES POINT TO IMMEDIATE VALUE

Among the minority of early adopters, the use cases are already clear—and closely aligned with core construction workflows. Forty-six percent report exploring AI for site operations and monitoring, another 46% for planning and design, and 41% for estimation, procurement and supply-chain processes.

The benefits they report are equally tangible: increased productivity (35%), improved quality control (35%) and cost savings (34%).

These are not experimental gains. They go straight to the heart of project performance. Yet the fact that these benefits are concentrated among a relatively small group highlights the risk of a bifurcated workforce—where a subset of AI-enabled professionals pulls ahead, while the majority remains constrained by limited exposure and training.

From a workforce development perspective, that kind of divide could exacerbate existing labor challenges, including retention, wage disparities and skills shortages.

IMPLICATIONS FOR THE SKILLED LABOR GAP

The construction industry is already grappling with a well-documented labor shortage. AI has often been framed as part of the solution—helping workers do more with less and improving efficiency across projects.

But the DEWALT study suggests that without deliberate investment in training, AI could actually widen the skills gap rather than close it.

If AI proficiency becomes a differentiator, employers will increasingly compete for workers who possess both traditional trade skills and digital fluency. That raises the bar for entry into the workforce and places additional pressure on training systems that are already stretched.

At the same time, there is a clear opportunity. AI, when properly implemented, can enable skilled workers to focus more on high-value tasks—problem-solving, craftsmanship and decision-making—while automating repetitive or data-intensive processes.

As Beck notes, “Empowering our workforce with AI education is not just about keeping pace with technology—it’s about equipping tradespeople with the tools and knowledge they need to solve real-world challenges, drive productivity, and lead the industry forward.”

That framing positions AI not as a replacement for skilled labor, but as an amplifier of it.

RETHINKING APPRENTICESHIP AND EDUCATION MODELS

Perhaps the most immediate workforce implication is the need to rethink how training is delivered.

Traditional apprenticeship models have long emphasized hands-on learning, mentorship and incremental skill development. Those principles remain relevant—but they must now incorporate digital competencies alongside physical ones.

Recognizing this, DEWALT has begun piloting new approaches in partnership with Associated Builders and Contractors. A pilot program with ABC’s Central Florida chapter aims to deliver jobsite-relevant AI training to apprentices and early-career professionals, including case studies and real-world applications.

The company has also committed $75,000 to ABC’s Trimmer Construction Education Fund to support AI-related training initiatives nationwide.

These efforts are notable not just for their scale, but for their focus. They emphasize applied learning—connecting AI directly to the tasks workers perform every day.

“Education is vital to bringing fundamental AI skillsets to our future workforce,” says Matthew Abeles, ABC’s vice president of construction technology and innovation. “DEWALT’s commitment to providing AI resources to craft professionals… will be invaluable to improving safety and productivity on jobsites.”

For workforce leaders, the takeaway is clear: AI training cannot be an add-on. It must be integrated into the core of how the industry develops talent.

THE ROLE OF EMPLOYERS AND INDUSTRY LEADERS

While trade schools and associations play a critical role, employers themselves will need to take a more active stance in workforce upskilling.

The study shows that many workers already feel “somewhat or very prepared” to work with AI, despite limited formal training. This suggests a foundation of confidence that employers can build upon.

But translating that confidence into competence will require structured programs, mentorship and access to tools.

Initiatives like ABC’s “AI Toolbox Takeaways” webinar series—designed to help companies understand and adopt AI—represent one approach. But broader adoption will likely depend on integrating training into everyday workflows, rather than treating it as a separate activity.

That could include:

• Embedding AI tools into project management platforms
• Offering on-the-job training modules tied to specific tasks
• Creating cross-functional teams that pair tech-savvy staff with field professionals
• Incentivizing continuous learning through certifications and career advancement pathways

The goal should be to make AI literacy as fundamental as safety training or equipment operation.

A DEFINING MOMENT FOR THE WORKFORCE

The construction industry stands at a pivotal moment. AI is not a distant trend; it is already reshaping how projects are planned, executed and managed.

But the pace of technological change is outstripping the pace of workforce preparation.

If the current trajectory continues, the industry risks creating a mismatch between the tools available and the people equipped to use them. That mismatch could slow adoption, limit productivity gains and deepen existing labor challenges.

On the other hand, closing the training gap presents a powerful opportunity. By investing in AI education now—particularly at the entry and apprenticeship levels—the industry can build a workforce that is not only prepared for the future, but capable of shaping it.

As Beck emphasizes, “AI is starting to reshape the future of construction, and we need to make sure tradespeople are ready for it… [These programs are] about giving early-career workers and current pros access to the tools and skills that will matter on tomorrow’s jobsites.”

The message is straightforward: The technology is ready. The workforce is willing. The missing piece is training.

How the industry responds to that challenge will determine not just how quickly AI is adopted, but how effectively it transforms the construction workforce for decades to come.

SEE ALSO: VIRTUAL REALITY AND 360-DEGREE VIDEOS FOR THE JOBSITE

The post AI Hits the Jobsite: The Workforce Training Gap first appeared on Construction Executive.

Recent research from CBIZ and The National Center for the Middle Market found that while 31% of construction-industry respondents named increasing efficiency as a top strategic priority (more than any other industry), only 26% of construction firms cited investing in technology as a priority. This begs the question: Where do construction leaders see these efficiency gains coming from if not from technology investments?

This discrepancy represents a general mistrust of modernization in the construction industry. While it’s unlikely robots will take over jobsites altogether, the truth is that software, AI and other technological advancements are powering growth in the construction industry by helping with offsite tasks like material management, financial processes, administrative duties, and safety and compliance. These tools can enhance employees’ capabilities, free up time spent on repetitive tasks and increase efficiency enterprisewide.

Construction firms looking to enhance efficiency should consider the following tech tools:

Artificial Intelligence

AI can help construction firms reduce rework and expedite timelines every step of the way, both onsite and in-office.

AI can:

• Help estimate costs and promote more competitive bids by analyzing historical data, material prices and labor availability
• Avoid turning preventable hiccups into change orders by identifying sequences related to scheduling and issuing alerts regarding material availability
• Track progress, ensure compliance and oversee quality assurance programs
• Reduce repetitive work, manual tasks and time spent preparing reports
• Analyze jobsite photos to flag potential safety risks (i.e., missing PPE or hazards)

It’s important to note that AI’s output is only as good as the data it receives, so firms should prioritize clean data, consistent coding structures and clear governance to ensure reliable insights. Try starting with a narrow, high-value use case—such as AI-powered invoice coding or photo-based progress tracking—then prove the ROI and scale to adjacent workflows.

Enterprise Resource Planning

An ERP centralizes data to promote accessibility, integrates and shares information across departments for greater visibility, and provides real-time insights to help leaders make timely and informed decisions. The right ERP should become the source of truth for operational and financial data across estimating, procurement, project management, accounting and HR/payroll.

ERPs can also:

• Deliver cloud-based service to reduce burdens on in-house IT teams
• Ease demands related to construction-specific workflows
• Provide a single access point for key information from across various systems
• Track inventory
• Help with project management and job costing
• Deliver comprehensive audit trails, apply strong controls and streamline reporting processes

Look for solutions that integrate with field applications (i.e., timekeeping and daily reports), support mobile approvals and offer configurable workflows. Robust role-based permissions, embedded analytics and standardized dashboards can help executives and PMs make faster, more informed decisions. Consider mapping your current processes and pain points, identifying essential features and piloting with real data. Plan for data migration and change management upfront to avoid operational disruption.

Drone Technology

Many construction firms are already using drones to monitor jobsites. These drones can also use advanced sensors to chart out obstructions underground. Eventually, there could be the potential for additional functionality to actively analyze and identify potential code violations as they occur.

Building Information Modeling

BIM software can also help teams visualize what hasn’t been built yet, with some systems even enabling teams to test materials and methods virtually. 4D (time-based) and 5D (cost-based) simulations can help teams evaluate sequencing and budget impacts before mobilization.

Time for Change

When implementing new technologies, there are bound to be growing pains. The process of updating and modernizing systems will always be different for each company due to each having uniquely complex processes and distinct goals. In order to ensure that implementation is successful, a strategic approach to change management is crucial.

Change management refers to the process of aligning leadership on important changes and communicating the value of the changes with employees. This usually involves engaging professionals to formalize the changes and develop a strategic approach to introducing new systems and processes.

Change management is effective when employees feel empowered to contribute to a successful implementation. By helping employees understand the benefits the changes represent, they should feel excited to be able to contribute to the company in a new way.

Implementation Tips

• Make change incremental by breaking implementation into phases.
• Establish baseline KPIs and track improvements.
• Consider field input during design to avoid workarounds and unsuccessful adoption.
• Define data ownership, retention and access controls across internal teams and vendors.
• Implement role-based access and multifactor authentication.
• Fund training and provide support for employees during the initial rollout.

Technology is not a silver bullet for the construction industry’s challenges, but it is a practical lever for boosting efficiency, improving safety and protecting margins. Firms that pair targeted tools (AI, ERPs, drones and BIM) with disciplined change management can capture measurable gains: fewer change orders, faster cycles, better visibility and safer jobsites. Start small with a high-impact pilot, invest in training and track outcomes. With a clear roadmap and an empowered workforce, construction companies can support the craft at the heart of the industry while operating with the speed and precision today’s market demands.

SEE ALSO: WHICH CONSTRUCTION TECHNOLOGY INVESTMENTS ACTUALLY MOVE THE NEEDLE ON PROFITABILITY

The post Practical Technology Upgrades for Construction Firms to Improve Efficiency first appeared on Construction Executive.

Today, that expertise is at risk of disappearing. With more than 40% of the U.S. construction workforce expected to retire by 2031, the industry isn’t just losing people—it’s losing institutional memory. This challenge is already playing out across jobsites, service bays and project offices nationwide.

The impact goes far beyond headcount. When experienced workers leave, they take with them the intuition that keeps projects moving, the shortcuts that save hours and the situational awareness that prevents incidents. Training new workers without access to that depth of knowledge becomes exponentially harder. The industry is approaching a knowledge cliff, and once that expertise falls over the edge, there’s no easy way to recover it.

A New Vision: Technology as a Bridge Between Generations

Addressing this challenge will require more than recruiting campaigns or accelerated training programs. While those efforts matter, they don’t fully solve the problem of transferring experience at scale.

What’s needed is a new vision for workforce development—one that treats technology as a bridge between generations rather than a replacement for people.

This approach creates a different model for learning. Instead of relying solely on classroom instruction or shadowing, knowledge can be delivered at the moment it’s needed—on the jobsite, in the cab or in the service bay. It’s the emergence of what might be called the “augmented apprentice”: a worker who gains confidence and competence faster because institutional wisdom is embedded into the tools they use every day.

Augmenting the Worker With AI

Artificial intelligence plays a central role in making this shift possible. When applied thoughtfully, AI supports people rather than replaces them, countering the common assumption that automation inevitably leads to job loss.

Across the construction ecosystem, AI is beginning to show up in practical, worker‑centric ways. The most effective applications focus on reducing complexity, lowering barriers to entry and reinforcing best practices. The goal isn’t to eliminate the human from the process, but to give workers better information and support as they make decisions.

From an equipment perspective, this means embedding intelligence directly into machines—helping new operators navigate unfamiliar tasks while enabling experienced professionals to work more efficiently. At Bobcat Company, for example, this philosophy has driven efforts to place guidance closer to the operator, turning equipment into a more active partner in daily work. AI, when deployed responsibly, elevates human capability.

The Coach in the Cab

The next frontier of skills training isn’t in a classroom. It’s in the cab of the machine.

Historically, apprenticeship depended on proximity—learning by watching and listening to someone more experienced. As that workforce retires and crews become leaner, that model becomes harder to sustain. Intelligent equipment helps fill that gap by providing guidance in real time.

Voice‑activated interfaces and guided diagnostics are beginning to transform how operators learn. Instead of stopping work to consult a manual or call for help, operators can ask questions, adjust settings and troubleshoot issues while staying focused on the task at hand.

One example of this evolution is Bobcat’s Jobsite Companion. The AI-enabled feature allows operators to use voice‑activated automation to manage more than 50 machine functions, automatically optimize attachment settings and answer questions about operation, including fault codes. This functionality allows them to keep their hands on the controls while operating for greater efficiency throughout the day. For a newer operator, that experience mirrors what it feels like to have a seasoned mentor nearby—offering suggestions, reminders and reassurance.

The value isn’t convenience alone. It’s acceleration. When learning curves shorten, productivity improves and confidence increases. The ultimate goal is not simply to build smarter machines, but to create more capable workers.

Minimizing Downtime With On‑Demand Expertise

The knowledge cliff doesn’t stop with operators. It extends into maintenance and service operations as well.

Experienced technicians are retiring at the same time equipment is becoming more technologically complex. Downtime—which remains a costly and preventable threat to project timelines and overall profitability—becomes harder to manage when specialized expertise is in short supply.

AI‑enabled service platforms are beginning to change how the industry approaches this challenge. As OEMs can collect and learn from hundreds of thousands of service interactions and equipment touchpoints, these systems can continuously improve how knowledge is captured and applied. For technicians, this translates into immediate access to repair documentation, guided diagnostics and historical case data, dramatically reducing troubleshooting time.

One possible approach to this problem is represented in systems like Bobcat’s prototype Service.AI, placing step‑by‑step guidance into the hands of its dealers and service teams. Instead of waiting hours for the right expert or escalating every issue, technicians can move faster and with greater confidence. More broadly, this trend points to how AI can help less‑tenured technicians perform at a high level much earlier in their careers—protecting uptime and keeping projects on schedule.

Enhancing Jobsite Awareness With a Smarter View

Experience also manifests as situational awareness—the ability to read a jobsite and anticipate risk before it becomes a problem. That sixth sense is one of the hardest skills to teach and one of the most critical as jobsites become increasingly complex.

Advanced detection systems are helping novice and seasoned operators improve their situational awareness, especially on busy and dynamic jobsites. Camera-based systems integrate additional visual information into the operator display, while radar-based solutions offer automated support by detecting nearby objects and people. Built using a combination of real-world data and AI-simulated scenarios, these smarter systems support operator awareness and decision‑making. Technologies like Bobcat Sense Rear Object Detection and Avoidance take this a step further, audibly alerting operators to potential hazards or even slowing or stopping the machine altogether. The value this provides is confidence, giving operators more information about their surroundings within an advanced display, to support efficient, confident operation that allows operators to stay focused on getting work done.

A New Era of Human–Machine Collaboration

The future of construction won’t be defined by humans or machines working alone. It will be defined by how effectively they work together.

An augmented workforce—where AI supports human judgment rather than replaces it—offers a practical response to shrinking labor pools and aging workforces. It preserves expertise, accelerates learning and helps the next generation step into roles once defined by decades of experience.

This isn’t about chasing technology for its own sake. It’s about continuity. When the wisdom of the master foreman can be captured and delivered as a digital mentor, the industry doesn’t lose its past—it builds on it. And in doing so, construction positions itself not just to survive the labor challenge ahead, but to emerge stronger, more confident and more resilient.

SEE ALSO: WHY AI IS A LABOR COST, NOT A SOFTWARE COST

The post The AI Apprentice: Mentoring the Future Workforce and Preserving Past Knowledge Construction Technology first appeared on Construction Executive.

When used to achieve general safety goals, such as real-time monitoring with video-based AI to detect risks, it can be a valuable tool if there are frontline supervisors available to respond to alerts. The inconsistent nature of a construction jobsite, where new problems and dangers can happen at any time, and workers often make split-second decisions to save themselves and their colleagues from injury or in some cases take unnecessary risks, makes relying on video-based AI highly impractical, and due to often very complex work conditions, prone to inaccuracies.

Here are five reasons why video-based AI has limitations in construction safety:

1. The Full Time Power That is Needed to Support Video-Based AI Isn’t Available in 90% of the Highest Hazard Construction Settings

AI is very energy consuming and in order to process video-based AI, full time power is required. Given that 90% of the work areas that are associated with the highest hazard construction work do not have full time dedicated power available, the battery-powered fixed-point camera systems that are needed in the highest risk work zones would require changing batteries frequently, which isn’t practical or even viable in some work areas.

2. Environmental Erraticism and Data Quality and Consistency

High-performing video-based AI depends on large volumes of consistent, high-quality data. Manufacturing naturally produces uniform datasets, improving model performance. In healthcare, hybrid-AI and human-intelligence approaches help ensure accuracy in complex situations.

Construction video data is often fragmented and inconsistent: Camera angles change regularly and are often far away from work activities; different weather patterns and lighting can impact clarity; obstructions are common; and high hazard work behaviors are unpredictable. These factors reduce data quality and increase the likelihood of missed or inaccurate video-based AI detections.

3. Roaming Video-Based AI Systems Such as Robots or Helmet Cams Miss 90% of the Story and Cannot Access the Highest Hazard Work Zones

As with almost any work environment, worker behaviors are heavily influenced when a manager is present. Roaming video-based AI systems inherently change worker behaviors when they are present but are unable to evaluate or impact 90% of the work activities when such roaming systems aren’t present. Fixed-point camera systems that record 100% of the work activities and which are then “smart sampled” by expert human video annotation construction specialists are much more impactful. Robotic camera systems are also unable to access the highest hazard work areas especially on the top floors of buildings as they are being constructed, and helmet-fixed camera systems are mostly worn by staff who aren’t permitted to access these highest risk work areas.

4. Unsafe Worker Behaviors Often Occur in a Just a Few Seconds Making Video-Based AI “Real-Time” Alerts Ineffective and Can Overwhelm Frontline Supervisors With Too Many Alerts, a Subset of Which Will Be Inaccurate

Many of the extremely dangerous worker behaviors in construction take place in 1-5 seconds, making real-time video-based AI alerts extremely impractical. Overwhelming a single safety professional on a construction project who oversees safety for 100+ workers with a barrage of real-time video-based AI alerts will almost never result in a worker being notified in real time of the high-risk action they just took.  Without human video annotation specialists being used to ensure that all videos sent to frontline supervisors are accurate, there are going to be many inaccurate videos sent which will quickly erode the confidence in the video data integrity.  Furthermore, “smart sampling” and sending a small subset of the overall coaching clips can drive safety performance to extremely high levels without overburdening safety professionals with too many alerts.

5. Video-Based AI Solutions Do Not Provide Construction Clients With the Consultative Video Coaching Services Needed to Truly and Significantly Impact the Safety Culture on Projects

There are countless examples over the last 50 years of frontline supervisors in many industries being overloaded with automated data streams that are often underutilized and wind up not materially improving safety, quality or productivity. Building a safety culture that can drive the 97%-100% compliance levels needed to significantly reduce the risk of workplace injuries requires a highly consultative coaching approach, which video-based AI alone cannot accomplish.

Over time, video‑based AI can contribute meaningfully to construction, but it should complement versus replace time-tested, human‑led consultative video coaching methods. Its greatest value lies in supporting human teams by:

• Providing additional visibility across sites in the small number of work areas where there is full-time power available
• Capturing rare, but potentially high-risk video-based AI events with fully powered cameras that can be reviewed first by human video annotation specialists before the video events are sent to frontline supervisors

A balanced approach of combining AI capabilities with experienced human oversight offers the most practical path forward.

While video‑based AI is often presented as a compelling solution for construction safety, its effectiveness is limited by the dynamic and unpredictable nature of jobsites. Unlike industries such as manufacturing and healthcare, construction lacks the consistency and stable power infrastructure needed for AI to perform reliably. As a result, the greatest impact will come from selectively combining video‑based AI with human video annotation specialists and proven consultative video coaching programs.

SEE ALSO: THE NEWEST MEMBER OF YOUR FACILITIES TEAM? YOUR BUILDING.

The post Five Reasons Why Total Reliance on Video-Based AI for Construction Jobsite Safety Isn’t the Answer first appeared on Construction Executive.

From technology’s ever-increasing enmeshment with the construction workforce to persistent data-siloing issues and more, Jon Fingland, vice president and category general manager at Trimble, sat down with Construction Executive to reveal the findings of this first-ever study.

The throughline of this survey seems to be the enmeshment of construction technology and the workforce. How have you seen construction technology and the workforce become more intertwined as technology develops and the workforce shortage worsens? 

I lived through the move to the cloud, the transition to mobile devices. Now we’re living through the age of AI. If I rewind to getting paper cuts from blueprints—that’s how old I am—then fast forward to today’s ability to have the latest drawing set available at the touch of a device in the field, that seems profound. That was one of the first big problems we had to solve in the industry: making sure people are working off current data.

I think what’s interesting about this moment in time is that you need some outside factors to really get people serious and honest about making the change of how they deliver the project and how they process internally. If you look at what’s happening in our market right now, it’s all coming to a head of supply-chain pressures and tariffs and workforce shortages.

We need a half million more workers this year. And 40% of the workforce is going to retire in the next five years. Combine the complexity of the work with the pressures on the supply chain and you start to see signals of shifts even in how people contract work.

They just don’t have enough workers. It’s a real problem—they must figure out how to do more with less and how to collaborate and create more predictable construction. That’s where this survey really comes into play and serves as validation that our customers are truly feeling it; that it’s not something they can continue to absorb. It’s forcing them to look at different techniques.

Would you say that, while your contractors seem pretty optimistic, they are still behind the eight ball when it comes to certain action items?

If we rewind again to 10 years ago, I don’t think the environment felt so different, but I think that now the industry is in a place where with labor and supply-chain constraints, contractors are needing to make changes. So, I look at that as a glass half full. If over 80% of contractors have an optimistic outlook about AI, but only 40% are implementing it, that 40% is still probably higher than it was 5-10 years ago. That’s a material amount of the market; I’ll take it.

Why is survey and positioning tech some of the most sought after pieces of technology that you found in this survey?

A lot of it comes from what our customers are asking us for, and it’s triangulated based on market research. There’s also an influx of outside investments from the VC and PE community investing in this space which is good for our industry. Every day it’s changing and it’s interesting to see it at the top of the survey list this year.

When it comes to companies either not adopting contech at all, not adopting enough of it, or the right kind, how can the industry as a whole continue to encourage or ramp up the buy-in of this technology?

We need to change behavior, which is hard to do. You still hear, “It’s just the way we’ve operated. This is how we schedule, how the spec is written…” There is a lot that must change. So, we have to encourage people to continue to do more, to commit to the journey.

On the vendor side, to really change behavior, you’ve got to build trust. There’s no doubt that AI can get involved in helping with decision making and automation. But when we do those things with AI, we’ve got to be able to show the vendors how we decided so that they can trust that and see where that recommendation came from.

When it comes to data siloing, one silo of sorts is the 40% of the workforce that’s set to retire. How can the industry ensure that that knowledge isn’t gone when that set of people retire?

If we take a lesson learned in the industry from 15 years ago: They started to build VDC departments, but they kept their normal estimating, scheduling and project management departments, creating an us-versus-them mentality. What we need is the experience from all domains that all these folks leaving have, plus these new folks coming in. The next generation is coming in more accepting of AI, more accepting of 3D and VDC workflows, et cetera. But you’ve got to bring those teams together. If you’re trying to change culture and behavior, you need to merge those folks in. You must use that experience and adopt a new process.

In an earlier conversation, the word ‘democratized’ was used regarding the advancements of scanning and positioning technology. Elaborate on what that means.

The way the industry has historically worked with data is in starts and stops. You design it, you hand it over and then you start to construct it, but often when you hand it over, you lose knowledge. It’s that stair step or see-saw. Every functional area and every stakeholder have their own data and data model. We’ve got to find a way to liberate that data so that it can be used throughout the process. We want the data to be available to everyone across the lifecycle. With new technology, what was once only accessible to bigger, more advanced companies is now also accessible to smaller contractors.

The construction industry’s attitude around AI has already gotten so much better. Would you say the industry is on a cusp when it comes to AI adoption and implementation?

Exactly. That’s why I get so excited when talking about the 40% who are already implementing. That’s very close to 50%. That’s what I need. I don’t need 100% right now. We need 40-50% to make certain moves. And I’ll bet, once the other half sees those moves, they won’t be able to unsee them and they’ll want to make them themselves.

SEE ALSO: THE BENEFITS OF INCORPORATING AI INTO THE CONSTRUCTION LIFECYCLE

The post Trimble Launches New Survey at Annual Dimensions Conference first appeared on Construction Executive.

Construction Technology That Actually Works

Construction technology should target sources of profit loss, such as downtime, rework, budget overruns and safety incidents. Here are proven investments with measurable ROI.

Building Information Modeling

The concept of 3D modeling has been valuable in producing geometric representations of blueprints. Building information modeling takes it a step further by creating and managing extensive project data, including costs, materials and schedules.

Companies can expect better stakeholder collaboration and reduced rework when they connect on these crucial elements and stay aligned. BIM is already expected to increase from $10.27 billion in 2026 to $27.12 billion by 2034 with a CAGR of 12.90%.

Reality Capture and Machine Learning

Reality capture through drones and sensors is versatile, as these tools can be used for site surveys, progress monitoring and safety inspections. Combined with machine learning, progress reports become accurate enough to improve worksite safety for workers.

The collected data can also be used to improve equipment quality and lifespan. Use it as a basis for predictive and preventive maintenance, helping companies experience 52.7% less unplanned downtime than those using reactive maintenance.

Centralized Project Management Software

Centralized project management software can streamline communication, documentation and scheduling all in one platform. A report found that 77% of optimized users saw increased profit margins.

The software also offers productivity gains. By unifying project tasks and enhancing real-time visibility into project progress, construction businesses can reduce their administrative errors and minimize field delays.

Overvalued Tech With Uncertain ROI

As important as technology is, certain buzzwords have innovative potential but are risky or have low ROI. Firms should practice more careful consideration to avoid poor technological investments and implementations.

Metaverse

The metaverse has become more possible through a combination of virtual reality, augmented reality and artificial intelligence. It has the potential to improve virtual visualization and 3D modeling, thereby impacting energy efficiency and reducing construction waste.

However, despite its power, technological limitations and data privacy concerns still hinder its widespread adoption in the construction industry. Investing in it now without proper development might involve little to no return.

Blockchain for Supply-Chain Management

Tracking materials and other resources from a factory to a jobsite can involve numerous documents. Integrating blockchain solutions can boost operational efficiency by 25% while reducing fraud by 50%, which improves transparency between workers and stakeholders.

However, it’s hard to ignore the high implementation costs associated with blockchain. It can also be complex to understand, which isn’t ideal for construction start-ups that may prefer simpler options, such as centralized project management software.

Generative Design

Another application of AI is generative design, which entails creating plans with minimal human input. It’s intended to simplify the planning process, but there are concerns about the unethical use of this technology, given GenAI’s challenges with domain knowledge and model accuracy.

Generative design is prone to hallucination and mistakes. Removing the input of experienced architects and engineers early on in the process can also lead to further delays and expenses in the long run.

How to Choose the Right Technology for the Firm

Differentiating between technological investments that deliver measurable results and those that are simply buzzwords can be challenging. It’s important to take the time to analyze these prospects. Here are tips to help construction industry professionals choose the right ones.

1. Identify Problems Instead of Products

Instead of chasing technological solutions based on what’s popular, focus on what problems the construction business is actually facing. For instance, to improve equipment lifespan, predictive maintenance analytics would be most effective.

Start by defining a problem and then work from there. If there are no clear areas for improvement, take the initiative to identify what would positively impact the company’s profits.

2. Consider the Investment Risks

As high as the ROI may be for certain technologies, it’s still important to understand any risks associated with their implementation. Small- to medium-sized companies, especially, should be vigilant about where they invest their money.

For instance, BIM is ideal for improving planning. However, implementation costs can be relatively high, with some services charging monthly or annual fees, depending on the scale and complexity of the project.

3. Implement a Pilot Project First

Before widespread technology adoption, it’s safe to implement a pilot project first. This will involve a limited number of users to evaluate functionality and gauge the budget and resources required to use the tool effectively. It’s best to have an IT specialist who can help lead its integration.

A pilot project should have a specific timeline and a list of goals that would warrant its implementation. Profitability is a key performance indicator, but it’s also important to assess other elements, such as reduced delays and errors compared to traditional methods.

4. Get Employees Involved Through Training

Incorporating new technologies can become much smoother when employees are involved in the process. Managers should provide adequate training sessions to familiarize construction teams with using these tools, ensuring greater success and better results.

Training can also translate to a more positive evaluation of the pilot project and encourage buy-in from business leaders. Seek their feedback to explore any adjustments in the implementation process when it goes companywide.

Be Intentional With Technology Adoption

Technological adoption should enhance profitability and productivity in the construction industry. Evaluate industry solutions before integrating them into the project process to ensure that upcoming investments have a real impact.

SEE ALSO: EXECUTIVE INSIGHTS 2026: LEADERS IN CONSTRUCTION TECHNOLOGY I

The post Which Construction Technology Investments Actually Move the Needle on Profitability? first appeared on Construction Executive.