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 Escalating Threat of Heat Stress in Construction

Heat stress is a serious medical condition that occurs when the body cannot adequately dissipate heat, leading to heat exhaustion and, in severe cases, heat stroke. Heat exhaustion manifests as fatigue, dizziness and excessive sweating, while heat stroke is a life-threatening emergency, potentially resulting in organ failure and death.

Construction sites present unique risk factors, including prolonged direct sun exposure, physically demanding labor and heat-trapping personal protective equipment. According to the Occupational Safety and Health Administration, environmental heat exposure resulted in 999 fatalities from 1992 to 2021. As climate change intensifies heat waves, the urgency of addressing heat stress through effective monitoring and prevention strategies must become a priority for construction professionals.

Proactive Safety: Beyond Traditional Prevention Methods

Traditional heat safety protocols, such as access to water, rest breaks and shade, are essential but often lack real-time, individualized protection for workers. These reactive measures may not address the urgent needs of workers in extreme heat conditions. By leveraging technology, the construction industry can transition from reactive to proactive safety measures for workers.

Now, the industry is also improving hazard mitigation through real-time monitoring. Technologies such as drones and robotics are not new in the sector, but are now employed to identify jobsite risks in real time, enhancing safety and reducing production delays. This existing momentum also presents an opportunity to integrate individual-level safety technologies, providing every worker adequate protection from problems like heat stress.

The New Guard: Wearable Technology for Heat Stress Monitoring

Wearable technology is revolutionizing the way safety officers monitor heat stress on construction sites by providing real-time insights into physiological indicators. These innovative devices enable construction professionals to take proactive measures to protect against heat-related conditions, helping ensure the safety and wellbeing of workers in extreme environments. Below are several types of wearable technologies designed for this purpose.

Biometric Sensors and Smart Patches

Biometric sensors and smart patches stick directly to the skin, continuously tracking vital metrics such as core body temperature, heart rate and sweat rate in real time. These devices use advanced algorithms that analyze physiological data, providing early warnings of potential heat stress before workers themselves even notice symptoms. By promptly alerting individuals and supervisors, these sensors facilitate timely interventions that significantly reduce the risk of serious heat-related incidents.

Smart Hard Hats and Helmets

Smart helmets and hard hats represent another innovative approach to heat stress monitoring. These integrated systems measure the worker’s internal state by tracking body temperature and heart rate and also assess the external environment by gauging ambient temperature and humidity. This dual functionality enables comprehensive monitoring, ensuring that workers remain informed about hazardous working conditions. Real-time data activates alerts, enabling immediate action to protect workers’ health.

Connected Vests and Apparel

Connected vests and smart clothing equipped with embedded sensors offer continuous health monitoring within the construction environment. These garments provide real-time data on physiological indicators, similar to the biometric sensors, but some vests also feature manual or automatically triggered cooling systems. This functionality enhances worker comfort and safety, keeping them cool and maximizing productivity even in extreme temperatures.

From Data to Action: Implementing a Wearable Safety Program

The true value of wearable technology lies not only in the devices themselves, but also in the actionable data they generate and the responses they enable. By implementing a comprehensive wearable safety program, construction stakeholders can harness real-time insights that improve worker safety. A central dashboard becomes an essential tool, enabling site supervisors to monitor the status of all their workers simultaneously.

This dashboard provides alerts when individuals approach critical thresholds, enabling prompt intervention before heat stress escalates to a more severe condition. By adopting a proactive, prioritized, data-driven approach, construction teams can significantly accelerate decision-making, mitigate risks and ensure a safer work environment amidst a culture of accountability and care.

Examples of How Wearable Technology Can Help

Several construction companies have successfully integrated wearable technology into their safety protocols, with NIOSH resources reporting positive feedback on the real-time integrations. A suggested scenario used the following example of the difference the technology can make.

A construction worker’s biometric sensor triggered a rising-heart-rate and core-temperature alert. The worker’s supervisor immediately instructed the employee to take a break at a designated cooling station. This timely intervention helped avert a potential medical emergency.

Additionally, the data collected from scenarios like the above can inform long-term safety planning, such as adjusting work schedules on particularly hot days and identifying workers who may be more susceptible to heat stress.

The Future of the Connected Jobsite

The integration of wearable technology on construction sites promises numerous benefits, including improved worker safety, reduced incident rates, fewer project delays and a stronger safety culture. However, challenges such as securing worker buy-in and addressing data privacy concerns remain, and stakeholders must navigate these thoughtfully. As technology continues evolving, the connected and data-driven jobsite is becoming an increasingly standardized symbol of safety and efficiency within the construction industry.

Embracing advancements in real-time wearable tech and other modern technologies will lead to enhanced protection for workers and a commitment to ongoing safety improvements in the field.

SEE ALSO: ANALYZING THE BENEFITS OF EXOSKELETON USE ON CONSTRUCTION JOBSITES

The post Leveraging Real-Time Wearable Tech to Reduce Heat Stress on Construction Sites 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.

The business owner doesn’t walk to the library themself. There’s far too much construction work to do in their office. Instead, they send couriers who know how to find books, copy pages, summarize chapters and bring information back to read. Not all couriers are equal; some have small bags, while some drive vans filled with bookshelves. Some are experts themselves and will spend time at the library answering questions, while some can barely be trusted with the checklist of information. Some couriers are part of the library delivery system, while others are contracted out based on their skill or the needs of the area.

Welcome to the world of modern AI technology, revolutionized by the growth in popularity and capabilities of large language models. The library is the AI corpus of knowledge. Couriers are AI agents, constrained only by their carrying capacity, their pre-existing expertise and their access within the library. The construction business pays for access to the library based on how much information (input tokens) they provide the couriers for retrieval and pay again based on how many books or notes (output tokens) they return.

With such vast knowledge now instantly accessible through AI,  innovative construction executives will find a way to leverage access to improve their work. The crucial question isn’t, “Can I use it”, but, “How can I use it most effectively?”

Power vs. Reliability

One of the most important decisions to make regarding AI application is the trade-off between power and reliability. AI models are probabilistic by design. They are incredibly powerful predictors of the most-likely next token, word or sentence, but without clear boundaries and guidelines an AI model can return different output for the same input. The power and creativity implicit in AI come with variability, which is beneficial during brainstorming and problem solving. Unconstrained, though, it can be dangerous in high-risk scenarios like compliance or procurement. When large projects depend on an assumption, consistency matters more than cleverness. Look for bounded intelligence over maximum capacity in high-stakes workflows when building or selecting AI products.

To simplify into our library metaphor, more powerful unconstrained models are like the courier who reads everything but interprets it slightly differently every trip to the library.

Many construction workflows don’t need that. Instead, they require a less powerful, well-constrained courier with a checklist, a pre-determined route map and a supervisor to keep them focused along the way.

When building or selecting AI products, leaders should prioritize reliability as a design feature, not an afterthought.

Trust vs. Speed

Many leaders incorrectly implicate processing speed into this decision. The thought process of, “If I can do X task in minutes, I will save Y hours,” is a valid but incomplete decision-making framework. AI workflows must be built on trust. Traceability, explainability and repeatability matter more than raw speed.

Leaders who simply replace human time with AI processing aren’t eliminating effort; they’re shifting it closer to delivery. Without trust mechanisms, speed gains can introduce downstream risk that erodes any perceived efficiency.

Critical Guardrails

There’s an often-cited anecdote in the heavy equipment space that illustrates the importance of guardrails. In the early days of self-driving vehicles, a jobsite trial on autonomous articulated dump trucks repeatedly failed. Technicians were baffled and initially assumed a telematics failure. But onsite observation revealed a simpler truth: The trucks followed their route perfectly, always in the same ruts, until those ruts became so deep the trucks began bottoming out.

The system did exactly what it was told. Without the human in the loop, the workflow became so muddied that it ground to a halt.

Construction executives should treat this as a cautionary lesson. The most effective AI systems are not autonomous; they are orchestrated. Experts must remain available to validate assumptions, intervene when conditions change and decide when not to use the AI’s output. Accountability never transfers to software. Strong AI implementations can reduce cognitive load on teams but never absolve them of responsibility.

Amplifying Human Expertise

AI acts as a force multiplier, not a replacement. Agentic workflows make it possible to apply this principle at scale.

Many executives remember a time not too long ago when bid couriers were a critical risk in construction projects. A single individual tasked with a critical step in the business could get lost or miss the deadline and create chaos for the business. In a similar vein, a single AI courier carries the same risk, but agentic workflows enable a crew-based approach to AI processes.

Mature AI systems rely on coordinated teams of specialized agents, each designed for a specific role. These well-designed agentic crews should mirror how construction teams work in the real-world: In a preconstruction example they are analyzing scope, defining quantities, validating costs and revising each as requirements change. Just as in efficient human workflows, this creates a type of local linearity. Where the large-scale problem may be convoluted, in small, constrained problem spaces the solutions start to resemble point-to-point paths ideal for AI augmentation.

The ability to think of AI agents as a crew presents another unique advantage; it reminds leaders that AI agents are not traditional software. Unlike capital-intensive software development with minimal operating costs, AI pricing (per token) scales much more like labor and material. When building or evaluating AI-driven solutions, ensure that the pricing model is robust and cost transference is clearly defined.

For the more technical, there are many well-established ways to reduce this operational overhead, especially when building AI products in-house. Consider local large language models (the equivalent of building a smaller in-house self-curated library in the earlier metaphor) or employing robust caching layers to minimize cost exposure on repeat or similar requests. If development and maintenance of AI solutions is daunting for your organization, implement point solutions from a trusted vendor that can cover these input burdens while your teams focus on the productive outcomes.

Innovative Workflows for an Innovating Industry

Modern AI offers construction executives access to an unprecedented library of knowledge and a small army of agentic couriers to interact with that knowledge, but access alone provides no real advantage. Real value emerges in the construction lifecycle when agents are developed into carefully orchestrated crews, when humans remain in the process or orchestration loops and when reliability is treated as a feature, not an obligation. The opportunities ahead for construction should be channeled to create AI systems that respect how construction firms work. As with previous cycles of revolutionary new technology, the future belongs to firms that innovate deliberately by pairing new intelligence with tested disciplines of judgement, accountability and control.

SEE ALSO: THE AI ACCOUNTABILITY GAP

The post Thinking of AI Agents as Members of a Construction Crew 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.

Construction has always relied on forecasting. Contractors estimate costs, project labor needs, sequence activities, manage procurement and monitor cash flow. Predictive analytics improves that work by using broader data sets and more advanced models to identify patterns that are difficult to detect manually.

A traditional project report often explains what already happened. Predictive analytics helps estimate what is likely to happen next. That shift matters because construction risk becomes more expensive as time passes. A delayed procurement package discovered early may be solved through resequencing or alternate sourcing. The same issue discovered late may create idle labor, schedule compression, claims exposure and margin loss.

The strongest forecasting systems do not produce one rigid prediction. They show likely outcomes, risk ranges, contributing factors and decision points. A forecast that explains why a project may finish late is more useful than one that only states the delay. Key Principle

CONSTRUCTION FORECASTING EXTENDS BEYOND SCHEDULE PREDICTION

Forecasting applies to cost, schedule, labor, procurement, safety, quality and cash flow. Schedule prediction is often the most visible use case, but project performance depends on several connected variables. A delay in submittal approvals can affect material delivery. Late material delivery can reduce labor productivity. Lower productivity can change cost projections. Compressed work can increase safety exposure and quality risk.

Forecasting Area	What It Predicts	Why It Matters
Schedule	Milestone risk, float erosion and delay exposure	Protects sequencing and completion dates
Cost	Budget variance, contingency use and final cost exposure	Improves financial control
Labor	Crew demand, staffing gaps and productivity	Reduces workforce bottlenecks
Procurement	Lead-time risk, late materials and supply constraints	Prevents idle labor
Safety	Higher-risk activities, conditions and exposure patterns	Supports prevention before incidents
Quality	Rework risk, inspection failures and defect patterns	Reduces downstream cost

A reliable forecast should also show uncertainty. Construction projects are affected by weather, owner decisions, design coordination, subcontractor performance, inspections and market conditions. Forecasting should clarify risk, not create false certainty.

CLEAN PROJECT DATA IS THE FOUNDATION OF RELIABLE FORECASTING

Predictive analytics is only as dependable as the data supporting the model. Construction data often comes from disconnected systems, inconsistent daily reports, outdated schedules, manual spreadsheets and cost codes that vary by project. Poor data quality weakens every forecast—missing production quantities reduce labor productivity accuracy, inconsistent change order coding distorts cost projections and incomplete safety observations make exposure patterns harder to identify.

Strong Construction Analytics Programs Typically Require

• Standard cost codes and work breakdown structures
• Reliable daily field reporting with consistent definitions
• Current schedules with maintained logic
• Clear RFI, submittal and change order tracking
• Integrated accounting, project management and procurement data
• Clear ownership over data review and approval

Data governance is not administrative overhead—it determines whether analytics can be trusted. The most successful construction firms treat data as a project control asset, with field teams, project managers, executives and finance leaders sharing consistent definitions so the forecast reflects actual project conditions.

AI AND MACHINE LEARNING IMPROVE FORECASTING WHEN THE USE CASE IS SPECIFIC

AI-enabled forecasting performs best when the use case is narrow enough to validate—a delay prediction model is more useful than a system claiming to predict total project success.

AI and machine learning can improve construction forecasting by detecting patterns across large volumes of historical and active project data. These tools can compare current conditions to prior outcomes and flag risks related to delays, cost overruns, safety incidents, rework or productivity loss.

AI-enabled forecasting may support delay prediction based on schedule activity patterns, cost overrun alerts based on budget burn and scope changes, labor demand forecasts tied to future schedule phases, safety risk scoring based on work conditions and procurement risk identification based on approvals and lead times.

A black-box prediction is risky in a project environment where decisions affect contracts, safety, margins and relationships. Forecasting tools should make risk more explainable, not less transparent. AI in Construction

BIM, DIGITAL TWINS AND FIELD DATA CREATE STRONGER FORECASTING CONTEXT

BIM, digital twins and field technology improve forecasting by connecting planned work to actual conditions. BIM supports visual coordination and model-based planning. Digital twins can connect digital representations of a project to updated project data. Field tools supply information from daily logs, mobile apps, sensors, drones, cameras and equipment systems.

When image capture shows an area is not ready for a scheduled trade, productivity data shows installation rates falling below plan, and sensor data shows equipment strain before failure—and all three are connected to the schedule—the forecast becomes genuinely actionable rather than directionally vague.

Technology Selection Should Match Project Complexity

• Complex hospitals, infrastructure programs and data centers may justify model-linked digital twin forecasting
• Smaller commercial renovations may gain more value from disciplined reporting and schedule analytics
• The right technology depends on contract value, schedule sensitivity, owner expectations and operational maturity

COST FORECASTING SHOULD EXPLAIN WHY THE FINAL COST IS CHANGING

Predictive cost forecasting estimates where final project costs are likely to land based on commitments, productivity, change orders, remaining scope, procurement exposure and subcontractor performance. Traditional cost reporting often identifies problems after money has already been committed. Predictive cost forecasting moves earlier by analyzing leading indicators—rising RFI volume, slow approvals, declining productivity and unresolved scope gaps can all signal future cost pressure.

Cost Risk Type	Example	Likely Response
Productivity risk	Crews producing below estimate	Adjust supervision, sequencing or staffing
Procurement risk	Materials arriving later than planned	Expedite, resequence or source alternatives
Scope risk	Unresolved design gaps	Clarify responsibility and document impact
Market risk	Material cost escalation	Review buyout timing and contingency
Contract risk	Disputed change order value	Preserve documentation and negotiate early

Contract structure also changes forecasting priorities. A guaranteed maximum price contract places heavy emphasis on contingency management. A lump-sum contract places more pressure on margin protection. A cost-plus project may require greater transparency in owner reporting. A cost forecast should help leaders decide whether the issue is operational, contractual, financial or external.

SCHEDULE FORECASTING WORKS BEST WHEN IT MEASURES FLOAT, LOGIC AND FIELD PROGRESS

Schedule forecasting predicts whether future milestones remain achievable based on progress, activity logic, resource availability, procurement status and known constraints. A schedule can appear healthy while risk is accumulating—out-of-sequence work, weakening logic ties, disappearing float and growth in near-critical activities can indicate that a project is becoming fragile before the critical path visibly changes.

Critical path movement Near-critical activity growth Float consumption Missed trade handoffs Procurement-linked activities Weather-sensitive work Approval dependencies

Field progress also needs production context. A schedule update showing an activity is 50% complete has limited value if the first half took longer than planned. The best schedule forecasts are tied to mitigation options—a delay prediction should lead to clear choices: add crews, resequence work, accelerate approvals, adjust deliveries or negotiate revised milestones.

PREDICTIVE ANALYTICS IMPROVES RISK MANAGEMENT BY MOVING ATTENTION UPSTREAM

Construction problems rarely appear in isolation. Predictive analytics connects multiple signals—RFI volume, submittal cycle time, productivity variance—to identify risk before it compounds.

Predictive analytics improves construction risk management by identifying the conditions that often appear before negative outcomes. A design conflict can trigger RFIs. RFIs can delay procurement. Late procurement can compress installation. Compressed work can increase overtime, lower productivity and raise safety exposure.

Predictive risk models can combine multiple signals simultaneously:

RFI age and volume Submittal cycle time Change order frequency Safety observations Weather exposure Subcontractor performance Labor productivity variance

A risk score is not a decision. A project executive, superintendent or project manager still needs to determine whether the forecast points to a manageable condition, a commercial dispute, a staffing issue or a planning failure. Risk Management Principle

LABOR AND PRODUCTIVITY FORECASTING ADDRESS ONE OF CONSTRUCTION'S HARDEST VARIABLES

Labor forecasting estimates how many workers, crews, supervisors and specialized trades will be needed as the project progresses. Productivity forecasting estimates whether those resources are likely to produce work at the rate assumed in the estimate and schedule. Labor is difficult to forecast because productivity changes with site access, sequencing, congestion, weather, supervision, material availability and trade stacking.

Labor forecasting also supports workforce planning across multiple projects. Contractors managing several active jobs can identify upcoming trade conflicts, staffing gaps and supervision needs before shortages affect the field. Better forecasting cannot create labor capacity on its own, but accurate labor visibility helps firms deploy available crews more strategically.

SAFETY FORECASTING MUST BALANCE PREVENTION, PRIVACY AND TRUST

Safety forecasting uses project data to identify activities, conditions or patterns associated with higher incident risk. The benefit is earlier prevention—if analytics show that fall protection issues increase during certain phases or that safety observations rise when overtime increases, leaders can adjust training, supervision or sequencing before an incident occurs.

A Credible Safety Analytics Program Should Define

• What data is collected and why
• Who can access the data and for what purpose
• How long the data is retained
• How findings are used and what limits protect workers from misuse

Safety forecasting must be handled carefully—workers may resist analytics programs that feel like surveillance rather than prevention. Safety forecasting works best when the culture is preventive rather than punitive. The goal should be identifying hazardous patterns, correcting conditions and reducing exposure.

FORECASTING SHOULD SUPPORT DECISIONS, NOT CREATE DASHBOARD NOISE

Predictive analytics can fail when dashboards become more complex than the decisions they support. Construction leaders do not need endless charts. They need timely, accurate information that leads to action. Forecasting should also fit the authority level of the user—executives may need portfolio-level risk visibility, while superintendents may need daily productivity and sequencing alerts.

What is likely to happen?

Clear prediction with a realistic range, not a single point estimate

Why is it likely to happen?

Visible drivers and contributing factors—not a black-box output

How serious is the impact?

Cost, schedule, safety or quality consequence clearly quantified

What can be done now?

Practical mitigation options with clear ownership

PREDICTIVE ANALYTICS HAS LIMITATIONS THAT CONSTRUCTION LEADERS NEED TO MANAGE

The larger risk is not that analytics will be imperfect—it is that project teams will treat imperfect forecasts as objective truth without applying professional judgment.

Forecasting cannot eliminate uncertainty. Construction projects involve human decisions, physical conditions, contractual obligations and external disruptions that models cannot fully control. Historical data can also mislead when future conditions are materially different—a contractor expanding into a new market, delivery method or project type may not be able to rely on prior project patterns.

Common Analytics Limitations

• Incomplete or inconsistent project data
• Overreliance on historical patterns when conditions change
• Poor integration between systems
• Models that do not reflect field reality
• Forecasts without clear action ownership
• Confusion between correlation and causation

Predictive analytics also raises governance issues. Project teams should know who owns the model, how predictions are validated, how exceptions are handled and how forecast-driven decisions are documented.

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THE RIGHT FORECASTING APPROACH DEPENDS ON PROJECT COMPLEXITY AND DATA MATURITY

A construction firm does not need to implement every advanced analytics capability at once. The most common mistake is buying forecasting technology before fixing the reporting process—a firm with inconsistent daily reports and outdated schedules will not get reliable predictions from a sophisticated platform. The best starting point is a high-value use case: schedule risk, labor productivity, procurement exposure or cost forecasting.

Level Typical Capability Best Next Step

Basic

Manual reporting and spreadsheets

Standardize data definitions across projects

Developing

Digital project management and cost tracking

Integrate schedule, cost and field data

Advanced

Dashboards and historical benchmarking

Add predictive alerts and risk scoring

Leading

AI-supported forecasting and model-linked data

Build decision workflows around forecast outputs

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FREQUENTLY ASKED QUESTIONS: PREDICTIVE ANALYTICS IN CONSTRUCTION

What is predictive analytics in construction?

Predictive analytics in construction is the use of historical project data, current field information and statistical models to estimate future outcomes related to cost, schedule, labor, safety, procurement and quality—enabling project teams to act before problems become visible through traditional reporting.

How is forecasting different from standard project reporting?

Standard project reporting describes current or past performance. Forecasting estimates future performance so project teams can act before delays, overruns or risks become harder to control. The shift from descriptive to predictive reporting is what creates earlier decision windows.

What data is needed for construction forecasting?

Useful forecasting data may include schedules, cost reports, daily logs, RFIs, submittals, change orders, procurement records, production quantities, safety observations and labor utilization. Data quality and consistency matter as much as data volume.

Can AI predict construction delays accurately?

AI can help predict construction delays when the model has reliable data, a clearly defined use case and regular validation. Accuracy depends on data quality, schedule discipline and whether the model reflects real field conditions rather than only historical patterns.

What is the biggest barrier to predictive analytics in construction?

The biggest barrier is usually inconsistent data. Disconnected systems, incomplete reporting and unclear definitions make it difficult for forecasting models to produce reliable results. Buying technology before fixing the reporting process is the most common mistake.

Does predictive analytics replace project managers?

Predictive analytics does not replace project managers. It gives project managers better visibility into risk, but human judgment is still required to interpret forecasts, manage subcontractor relationships, navigate contracts and make project decisions in context.

Explore More: AI Data Analytics BIM Risk Management Project Delivery Project Management Safety Workforce Management

The post Predictive Analytics and Forecasting in Construction Projects 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.

But opportunity alone does not guarantee success. As competition intensifies, communication failures and poor information management are emerging as some of the most common (and costly) reasons firms lose margins, miss deadlines or fail to secure repeat work. In data center construction, where schedules are compressed and tolerance for error is minimal, even small breakdowns in communication can have outsized consequences.

Why Data Center Projects Raise the Bar for Communication

Data centers differ fundamentally from traditional commercial construction, particularly when it comes to communication demands. These projects are not simply buildings with specialized tenants. They are mission-critical infrastructure environments where mechanical, electrical and digital systems take precedence, and where mistakes can delay commissioning at enormous cost.

In many data center projects, there is effectively zero tolerance for error. A misrouted duct, an incorrect cable path or an outdated electrical drawing can compromise airflow, redundancy or system performance. Fixing those issues late in the process often requires rework that ripples across multiple trades, extending schedules and increasing risk.

Timelines further heighten the stakes. Data center owners often pursue aggressive schedules to bring capacity online as quickly as possible. Contracts frequently include liquidated damages, meaning every missed day directly erodes profitability. Under these conditions, delays are less likely to stem from physical construction than from slow reviews, unclear approvals or conflicting information.

Confidentiality requirements add another layer of complexity. Data centers are widely considered strategic infrastructure, and owners tightly control access to designs, layouts and system details. Informal communication methods, such as unstructured email chains or unsecured file sharing, can violate contractual obligations and undermine owner confidence.

Stakeholder complexity compounds the challenge. Data center projects typically involve owners, developers, general contractors, specialty subcontractors, equipment vendors, commissioning agents, insurers and authorities having jurisdiction. Each relies on accurate, timely information. When teams lack clarity about which documents are current or which decisions are final, confusion spreads quickly.

Fragmented Communication Creates Real Risk

Despite the high stakes, many construction teams still rely on fragmented communication practices. Drawings live in shared drives, decisions are buried in email threads and approvals are tracked manually. This fragmentation increases the likelihood of errors and makes accountability difficult to establish.

Version control is a frequent failure point. When different team members reference different iterations of drawings or models, coordination errors multiply. In a data center environment, those errors can halt progress entirely.

Documentation practices also reveal a firm’s maturity. Data center commissioning requires complete, accurate records, including as-built drawings, testing results, warranties and operations manuals. Teams that delay documentation until the end of the project often scramble to assemble information, delaying handover and straining relationships with owners.

Just as importantly, communication practices directly affect reputation. Owners in the data center market tend to favor partners with proven reliability. A clear, defensible record of decisions, changes and approvals builds trust and reduces disputes. Conversely, missed deadlines, unresolved RFIs or incomplete documentation quietly damage credibility and can result in firms being excluded from future bid opportunities.

Workforce Constraints Make Communication Even More Critical

Communication challenges are intensifying as labor shortages strain the construction industry. Data center projects require specialized skills, including commissioning engineers and power systems experts. These roles are difficult to fill, yet demand continues to rise.

With limited talent available, firms cannot scale simply by adding people. Instead, they must improve how work is managed. Clear workflows, centralized information and disciplined communication allow smaller teams to handle more complex projects without sacrificing quality.

Strong information management also reduces dependency on institutional knowledge held by a few individuals. When project history is captured systematically, teams remain resilient even as personnel change.

Practical Steps to Avoid Costly Communication Failures

Firms looking to compete effectively in the data center market must treat communication as a core operational function. Several practices consistently distinguish high-performing teams:

• Centralize information. Establish a single source of truth for drawings, correspondence, RFIs and submittals. Centralization reduces confusion and minimizes version conflicts, ensuring all stakeholders are working from approved information.
• Standardize workflows. Defined processes for reviews, approvals and changes prevent bottlenecks and clarify responsibility. Automated routing and status tracking can help teams identify issues before they impact schedules.
• Build security into collaboration. Role-based access controls and secure sharing protocols are essential for protecting sensitive information and meeting confidentiality expectations.
• Document progressively. Collect and organize documentation throughout the project rather than at the end. Progressive documentation supports smoother commissioning and faster, cleaner handovers.
• Integrate systems where possible. Disconnected tools increase the risk of misalignment. A full integration improves consistency and reduces duplicate data entry.

Some organizations use dedicated platforms, like Newforma, to support these practices, but the underlying principle applies regardless of technology. Discipline, transparency and accountability matter more than any individual tool.

Communication as a Competitive Advantage

The data center boom shows no signs of slowing. Energy demand is rising, digital infrastructure investment is accelerating and owners are becoming more selective about who they trust to deliver. In this environment, communication failures are not minor inconveniences, but strategic liabilities.

Construction firms that invest in clear communication, secure collaboration and disciplined information management position themselves to win repeat work and protect margins. Those that do not risk being left behind, not because demand disappeared, but because confidence did.

In data center construction, execution defines success. That kind of success requires communication that is simple, secure and reliable from start to finish.

SEE ALSO: DATA CENTER SURGE: PRE-DEVELOPED SPACE ALREADY 90% LEASED

The post Communication Gaps Can Cost Construction Firms in the Data Center Boom first appeared on Construction Executive.