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.

That gap is the whole story. The capital that builds breakthroughs is going to one place and it isn’t the jobsite. It’s the office.

For an industry that has spent a decade waiting on autonomous bricklayers and exoskeletons, this should be clarifying. If great robots were ready, contractors would be deploying them. Hardware that has to operate safely in unstructured environments, around humans, in weather, on schedule, is genuinely hard. Capital markets know this and are voting accordingly.

Meanwhile, the systems behind the field-to-finance workflow like the expense report, the pay app, the change order and the submittal are being rebuilt right now with most of that $211 billion behind them. AI agents that read, route, approve and reconcile administrative tasks are in market, deployable this quarter. Leaders waiting for the robots to arrive are waiting in the wrong room.

The Hidden Constraint on Scale

Every construction firm carries administrative load. Time tracking, expense reports, pay apps, submittal logs and change-order approvals don’t disappear thanks to AI. But across many firms, back-office process flows still rely on spreadsheets, email and on-premise file servers. Even for firms using established project management systems, many handoffs remain manual and error-prone.

As firms grow, this is not an area where efficiencies are gained. A change order that took one review when the firm ran out of a single office takes five when a second branch opens. Data gets rekeyed across the project management system, the accounting system and the field reporting tool, now in two locations, with two AP teams reconciling against each other. Senior estimators and PMs end the week having spent ten hours on tasks no client paid them to do. The default response is to hire. Add an AP clerk, add a project coordinator, add another controller. It works in the short term. But every added admin head raises G&A permanently and adds another handoff where information can stall. Overhead starts compounding faster than backlog.

Why the Math Has Changed

The common objection from controllers and operations leaders is straightforward: Can an agent really do what a senior accountant, project coordinator or estimator does today? That is the wrong question.

The right question is whether an agent can create 45 hours of capacity across a five-person team: nine hours per person, per week. Framed this way, the answer changes quickly. Capacity compounds across workflows, not job titles.

On a fully burdened basis, an office hire is a six-figure annual commitment that improves incrementally with training and tenure. An AI agent, by contrast, holds a relatively stable cost profile and improves multiple times a year as underlying models are updated. Firms that deploy agents early inherit those gains automatically, without renegotiating compensation or restructuring teams.

That framing is supported by emerging labor-market data. In March 2026, research comparing what large language models could theoretically perform across occupations with what they are actually doing in live workflows today highlighted a significant gap. One chart from this report (a radar showing theoretical versus observed AI task coverage) captures the dynamic clearly.

Office, administrative, finance and professional roles show some of the largest gaps between capability and adoption. The ceiling is already visible. Most organizations are simply operating far below it. The binding constraint is not technology; it is deployment.

For construction firms, this matters. It explains why AI is not arriving as a single moment of labor replacement, but as incremental capacity gains embedded inside existing teams. The early wins are administrative: expense routing, time reconciliation, pay applications, submittals. Firms capturing that capacity now are not eliminating roles. They are changing how much work a fixed headcount can reliably support.

The Hiring Filter

Before any open requisition becomes a job posting, it should pass through a new gate: Can the work be handled by an agent? If the answer is yes for even half the role, the remaining work can be redistributed across the existing team and the role may not need to be posted.

If the answer is no—a body is genuinely needed—the requisition still has to clear a second test. Every new hire should be accretive, not dilutive, to the firm’s AI readiness.

That sounds abstract until you measure it. A simple internal benchmark works: What percentage of the team is AI-literate (can use the tools), data-literate (can structure information for them) and AI-curious (will reach for them unprompted)? In recent survey work with one client’s accounting department, those numbers came in at 55%, 85% and 70%. That’s the baseline. Every new hire either raises the average or drags it down.

This shows up in the job description before anyone is interviewed. Hiring a senior estimator? Add “fluent with AI-assisted takeoff tools” to the requirements, not the preferences. Hiring a project coordinator? “Comfortable building and refining agent prompts” belongs above “proficient in Excel.” The wrong hire isn’t the one who can’t do AI work today, it’s the one who has no interest in learning.

Burdened, an office hire is a six-figure annual decision. It’s worth the extra week to make sure that decision compounds in the right direction.

A Strategic Shift in How Firms Scale

Contractors that have moved furthest are no longer treating automation as a one‑time initiative, but as a permanent operational discipline shaping how they scale. Deploying agents aligned to real workflows lowers long‑term cost structures while improving execution and governance.

The capital markets have already made their bet. More than $200 billion is flowing into the systems that power paperwork and office workflows, not into autonomous jobsite labor. That investment is already at work in the back office, whether contractors deploy it intentionally or keep hiring around it.

SEE ALSO: THINKING OF AI AGENTS AS MEMBERS OF A CONSTRUCTION CREW

The post How Contractors Are Shifting Headcount Budgets to Agent Budgets first appeared on Construction Executive.

Cofounder of the Boston-based contech company, Cyvl, Pelaez is helping over 100 U.S. cities rethink how they build and rebuild their roads.

“Through vehicle-mounted sensing and analytics already embedded in day-to-day operations, Cyvl is effectively powering the reconstruction of roads up and down the country—determining what gets fixed, when and why,” says a representative of the company.

Having just been named in Cemex Ventures’ Top 50 Contech Startups for 2026—which recognizes companies that are already influencing how construction and infrastructure decisions are made on the ground—Cyvl sat down with Construction Executive to discuss their evolutionary work further.

Maybe even stay tuned for a sneak peek inside one of their AI-powered cars.

What was the impetus for starting this product/company?

DANIEL PALAEZ: It’s a fun story. It started with the problem and we found the technology to solve that problem. One of my first ever jobs was working on the road crew for a public works department in Southbury, Connecticut. And I was seeing firsthand how hard it was to manage infrastructure. The town was reacting to issues every single day, whether that be residents hitting a pothole or trees falling down or missing signage no one noticed until it caused an accident. This was a smaller city that was relying on outdated data that lived on paper in a three-ring binder.

I was only about 18 or 19 at the time, but I figured there had to be a better way. So, I started talking to lots of other communities and realized they were relying on similar techniques. When I entered college in 2020 for engineering is when I started to learn about these sensors used for self-driving vehicles, and that was the “a-ha” moment of seeing the massive problem firsthand, recognizing that pretty much every community across the U.S. is battling with outdated infrastructure and information. Then it was about figuring out how technology from self-driving cars could be applied to automatically map out infrastructure, and how to use AI to perform condition assessments, recommend treatments, budgets, etc. That is really where we saw the magic of applying that tech to this problem. Ever since then it’s been a very fun journey helping hundreds of governments implement this into their day-to-day workflows.

What was the process of getting this off the ground?

About halfway through college, I thought this would be a fun application of the tech. It was never a class project. I just started conceptualizing it in my mind and I recruited one of my best friends and roommates—we’ve actually known each other since we were seven years old—who is way smarter than I am to help me build it out.

We didn’t know anything about starting a company; we just thought we were solving a cool problem. We began looking for funding to make prototypes and entering innovation contests across the country. After winning a few of them, everyone kept telling us, ‘Hey, this is a really big problem that our country and the world needs to solve,’ which motivated us to take it more seriously. So, shortly after graduating, we decided to become cofounders and brought on a third friend of ours from college to help us with the AI side of things. We raised $100,000 from our first ever investors, which felt like a lot of money at the time—we were all just 22 years old. We made that last more than a year until we got our first customers. And it’s been a wild ride since then.

When did this product officially hit the streets?

At the beginning of 2022, we started our first projects with municipalities and a few civil engineering firms in Massachusetts. Those were crazy days where the product barely worked, but we were determined to make it better and to keep getting feedback from the cities and towns we were working with, which was invaluable to us at time.

Do these sensors/vehicles operate as a service (i.e. SaaS)?

It’s an annual investment the cities make in our technology. What that gives them is access to the sensors, which they can put on their vehicle so they don’t need to buy them, and access to our software program, which is what’s taking all the data from the sensors, automatically processing it, doing the condition reports and then creating plans and budgets so the city can get to work with more speed and accuracy.

Was it hard to get cities to agree to bring this tech on board?

Yeah, it was incredibly hard. Imagine a 22-year-old placing cold calls into every single town or city in the state telling them, ‘Hey, I have a better way to manage your very expensive roads and sidewalks.’ There was a lot of skepticism—especially when they asked how many customers we had and we had to say zero. We got a lot of nos, but, slowly but surely, we started building a reputation. It’s still challenging these days, but I think that’s what’s really been special.

Cities do see immediate value when they start working with our technology. Now there’s less perceived risk when we can present them with data from working with some of the biggest cities in the nation. It was never easy, but, as with any business, you just have to keep pushing through.

What is the smallest/biggest city you’ve worked with?

We’ve worked all over Massachusetts, Iowa, Wisconsin in some pretty small communities, about 5,000 people or under. Our first big customer was the city of Atlanta. We partnered with them about a year and a half ago, and we were just selected by the city of Nashville to do a full five-year infrastructure plan. We have a few other big cities that we cannot formally announce yet, and we’re even beginning talks with some state DOTs.

How do you hope to see your company evolve?

I’d say our number one goal and our mission as a company is to enable government agencies to build infrastructure 90% faster and 50% cheaper than they’re doing today. And if we can encourage a digital-first standard, I’m sure there will be more companies like us popping up, and I’m sure some of the major civil engineering firms are going to start to embrace these technologies, too. If we can make that the de facto standard for the U.S. by 2030, that will be a huge accomplishment for us. We’re really trying to change an entire industry here, and we’re not going to do that alone. So, bringing more people along with that shared digital-first mentality and the new operating model for infrastructure is incredibly important.

How do you hope that your story might inspire the future of construction business owners and innovators?

I’ve been so fortunate just to be surrounded by mentors and advisors and friends that have been cheering for us, supporting us, rooting for us unconditionally, because the construction industry is definitely not the quickest to adopt technology and rightfully so—there are major risks on the line when you’re talking about critical infrastructure. I think for good reason people are conservative about trying new things.

My advice is simply ‘don’t give up.’ It’s very easy to be told no 99 times and to return to whatever you were doing in life before. But, I guarantee you, that if you just keep your mind to it, you don’t give up, you persist, whatever you’re working on, whether it’s a business in this industry or not, you’ll be successful. Then surround yourself with like-minded folks, because you need that positivity. You have to be a default optimist.

SEE ALSO: NEW WALL-SCALING ROBOTS ARE SAVING CONSTRUCTION COMPANIES TIME, PRODUCT AND PERSONNEL

The post Contech Company Cyvl Uses Vehicle-Mounted AI Sensors to Collect Fresh Data for Some of America’s Biggest Cities 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.

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.

KEY FINDINGS FROM THE REPORT INCLUDE:

• Governance concerns: Nearly 80% of executives said they lack strong confidence their organization could pass an independent AI governance audit within 90 days.
• Performance divide: Companies with fully integrated AI are nearly four times more likely to report AI-driven revenue growth than organizations still in the pilot phase.
• Strategy gaps: While most companies are investing in AI, only 22% reported having a fully developed and implemented enterprise AI strategy.
• Workforce readiness: Just 12% of executives said their workforce is truly prepared to effectively use AI technologies.
• Construction and real estate investment: In the construction and real estate sector, 79% of boards have approved AI investments, but only 40% have established formal AI governance policies.

Grant Thornton noted that organizations seeing the strongest AI outcomes are prioritizing governance, employee training and measurable performance metrics as AI adoption accelerates across industries.

SOURCE: “2026 AI Impact Survey” Grant Thornton // grantthornton.com/insights/survey-reports/real-estate/2026/construction-and-real-estate-insights-2026-ai-impact-survey

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The post The AI Accountability Gap 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.

But beyond recognizing exceptional work, the data behind these projects may also offer a roadmap for delivering better outcomes across the broader construction industry.

Can the performance metrics from award-winning projects help owners, designers and contractors make decisions that lead to safer jobsites, stronger budget performance and more predictable schedules? According to ABC’s growing EIC data set, the answer appears to be yes.

Over the past several years, ABC has enhanced and automated the EIC application process, enabling the organization to capture and analyze detailed project-performance data across hundreds of projects nationwide. The findings reveal several clear trends among top-performing projects: value-based procurement dominated over low bid selection; preconstruction services consistently improved schedule reliability; and contractors investing in technology and early collaboration often delivered stronger safety and budget outcomes.

The 2025 EIC portfolio—recognized at ABC Convention 2026 in Salt Lake City—provides one of the clearest pictures yet of what project excellence looks like in practice.

The portfolio includes 341 national-level projects totaling $15.09 billion in construction value, representing 38 million work hours, 48 million square feet of construction and 3.6 million acres of developed land. The projects span every major market sector, including data centers, healthcare, multifamily, petrochemical, institutional, renewable energy and aviation.

Collectively, the projects achieved a total recordable incident rate of 0.68—70% below the 2025 industry average TRIR of 2.3. Ninety-eight percent finished within the final contract budget despite collectively overcoming 2,647 days of delays. Across the portfolio, contractors deployed a wide range of technologies, including robotics, telematics, drones, virtual reality and artificial intelligence.

The data also challenges several longstanding assumptions about high-performing construction projects. While many EIC projects exceed $100 million in value, nearly 200 are valued below $20 million, suggesting the practices driving excellence are not limited by project size. The portfolio also demonstrates that merit shop contractors are successfully delivering large-scale, technically complex projects while achieving industry-leading safety performance.

Taken together, the portfolio significantly outperformed broader industry benchmarks in safety, budget reliability and schedule management—raising an important question: What were these projects doing differently?

The data points to several leading indicators that consistently show up in award-winning construction.

THE VALUE OF EARLY ENGAGEMENT

Project success begins with procurement strategy and the services purchased during project development.

All procurement methods were represented across both general and specialty contracting. However, 60% of projects were procured primarily on value- or qualification-based selection, while 40% were procured primarily on price. Of the price-based projects, half were awarded through select bidding processes involving prequalified contractors.

While much of the broader construction market still relies heavily on price-driven procurement, EIC projects skewed strongly toward qualification- and value-based selection (encompassing both the “value-based” and “solely negotiated” designations in the preceding and above infographics).

Every market sector selected contractors primarily on qualification at least 50% of the time. The high-tech/data center sector and renewable energy sector relied most heavily on qualification-based procurement, at 74% and 75%, respectively.

“Owners are increasingly seeing that selecting a construction partner based on best value, not just low price, leads to savings overall,” says Buddy Henley, president of Gaithersburg, Maryland-based Henley Construction. “Having a trusted partner engaged during design allows risks to be identified earlier and problems to be solved before they become costly. That early collaboration improves cost certainty, supports smoother schedules and significantly reduces surprises in the field.”

Government projects relied most heavily on open hard bid or price-based procurement, using that method 27% of the time. By contrast, the high-tech/data center market procured just 15% of projects through open hard bid.

Among general contractors, 74% were selected primarily on a qualification basis. Nearly one-quarter of those projects were solely negotiated, and those projects represented the highest average contract value at approximately $101 million.

“The most successful projects are the ones that bring the design team and construction manager together at the very beginning. Early collaboration improves constructability, clarifies scopes for the trades, and reduces gaps that create risk,” Henley says. “That early alignment leads to better budget predictability and stronger trade relationships, which directly supports safer and more successful projects.

When owners focus on value-based procurement instead of low bid alone, the outcomes are consistently stronger. Choosing teams based on experience, collaboration and the value they bring creates real partnership across the project. We see better cost control, higher-quality results and safer jobsites when the right team is selected from the start.”

Lorri Grayson, partner and founder of Rehoboth, Delaware-based GGA Construction, echoes Henley’s sentiments. “Early involvement creates an atmosphere of strong collaboration among project stakeholders. It enables the project team to define the scope of work, identify long lead items and, most importantly, control costs from the earliest stages,” she says.

“It also helps develop a bid strategy that responds to current market conditions. For example, early involvement on our current project, The Continental, located at the University of Delaware, saved the owner over $5 million on a $90-million project,” Grayson says. “Early coordination allowed us to identify critical procurement needs and minimize budget risks, resulting in a more efficient and cost-effective process.”

That emphasis on early collaboration and value-driven procurement was reflected throughout the EIC portfolio data. Just 18% of general contractor projects were procured through open hard bid, and those projects represented the lowest average contract value at approximately $15 million.

The owner perspective also supports early contractor involvement.

“Successful projects are built on strong partnerships rooted in trust, transparency and psychological safety—where titles are set aside and teams feel comfortable speaking openly to solve problems together,” says Spencer Moore, vice president and chief facilities officer for globally renowned cancer center UT MD Anderson. “Early collaboration creates that foundation, allowing teams to address challenges with humility and ownership before moving toward solutions.”

That same emphasis on qualifications, collaboration and long-term value also appeared in how specialty contractors were selected across the EIC portfolio. Specialty contractors were procured primarily on qualification 46% of the time, while 31% were selected through select bid and 24% through open hard bid. Contract value did not appear to significantly influence procurement methodology for specialty trades.

One of the more surprising findings in the data was that procurement strategy did not necessarily dictate contract structure. Regardless of how projects were awarded—whether through qualification-based selection, negotiated work or open bid—top-performing projects utilized virtually every form of contracting across the portfolio.

One notable distinction emerged between general and specialty contractors: Lump-sum contracting overwhelmingly dominated among specialty contractors regardless of procurement method.

THE PRECON ADVANTAGE

Across all market sectors, preconstruction services were provided on 74% of projects—well above what many contractors would consider typical across the broader marketplace. General contractors delivered preconstruction services on 73% of projects, while specialty contractors did so on 75%.

“Early involvement allows specialty contractors to contribute practical, experience-based insight before key decisions are locked in. When we’re engaged during preconstruction, we can identify coordination challenges early, help refine scope and sequencing, and offer practical yet innovative solutions to tough problems–reducing rework,” says Matt Terry, president of Dallas-based mechanical contractor TDIndustries. “That upfront collaboration directly improves schedule reliability, cost certainty and overall project performance.”

Steve Grauer, executive vice president for Hensel Phelps, agrees. “In my experience, the best outcomes to project success on complex projects are rooted in project teams that exemplify a high level of trust, practice transparency, have accountability, truly collaborate, and have a high level of executive commitment and engagement in the project and where there is open communication by all the stakeholders,” he says. “These traits are best embedded when project teams have some type of early engagement, giving them an opportunity to work collaboratively to resolve early challenges and work to build personal relationships prior to the start of construction.”

Notably, no preconstruction services were provided on 51% of projects procured through open hard bid.

While preconstruction services were utilized across all procurement methods and contract structures, the combination of open hard bid procurement and lump-sum contracting most frequently resulted in projects without preconstruction involvement.

Twenty-six percent of all EIC projects did not utilize preconstruction services, and 75% of those projects followed the open hard bid/lump-sum model.

FROM PLANNING TO PERFORMANCE

At a time when the construction industry continues to battle cost escalation, labor shortages and schedule disruption, EIC projects significantly outperformed broader industry benchmarks in safety, budget reliability and schedule management. The EIC portfolio overcame more than seven years of cumulative delays, delivering 98% of projects within budget and achieving a TRIR of 0.68.

The next question? Whether procurement methods, contract structures or preconstruction services contributed to those outcomes.

When comparing planned project duration to actual construction duration—including delay recovery—projects utilizing preconstruction services demonstrated stronger performance.

Projects with preconstruction services showed slightly better schedule outcomes overall. Fifty-two percent achieved shorter actual durations compared to projects without preconstruction services.

More importantly, projects with contractor involvement during preconstruction overcame more delay days and exhibited lower schedule variation.

Projects without preconstruction services experienced a 14% variance between planned and actual construction duration, including delays.

The same trend appeared across contract structures. Lump-sum contracts—which included preconstruction services on only 47% of projects—experienced an 11% variance between planned and actual duration. Construction manager-at-risk projects, where preconstruction services were included on 96% of projects, experienced only a 1% variance.

The trend became even more pronounced among general contractors. Design-build and CMAR delivery methods significantly reduced schedule duration variance compared to lump-sum and time-and-materials contracts. This aligns with the fact that design-build and CMAR projects incorporated preconstruction services on 90% and 96% of projects, respectively.

“The biggest gains we see come under the construction-manager-at-risk approach. Early cost validation during design helps identify savings without sacrificing quality,” explains Henley. “Clarifying scopes and resolving issues early reduces financial risk and improves safety across all trade partners.”

Among specialty contractors, preconstruction services also reduced schedule variation regardless of procurement method or contract type. Projects without preconstruction services experienced a 25% schedule variance. When specialty contractors participated in preconstruction, that variance was reduced by at least half.

Although 98% of EIC projects finished within the customer’s final approved budget, variation still existed between original and final contract values through approved change orders.

Projects utilizing CMAR and design-build delivery methods experienced lower budget variation than lump-sum and time-and-materials contracts, suggesting fewer scope changes and change orders over the course of construction.

No significant correlation emerged between procurement method, contract type and safety performance, with one exception: time-and-materials projects recorded an exceptionally low TRIR of 0.04.

Time-and-materials contracting was most common in industrial, infrastructure and renewable energy markets.

CONSTRUCTION TECHNOLOGY

Technology adoption continues to expand across the construction industry.

• The four most commonly used technologies during the past three years remained consistent:
• Project-management platforms
• Safety workflow technologies
• Drones
• Jobsite security technologies

TECHNOLOGY MOVES TO THE CENTER

Just three years ago, artificial intelligence was virtually absent from EIC projects. Today, 18% of projects report using AI technologies.

Importantly, no major technology category has declined in usage over the past three years, suggesting contractors continue to realize measurable return on investment.

Technology deployment also spanned all contract types and project sizes.

Construction technologies were utilized across projects of every size category. Data indicates that technology deployment frequency remains consistent regardless of project dollar value.

“During the past three years, we have experienced unprecedented growth which we attribute to two key factors,” says Rob Griffith, chief operating officer of Gaylor Electric. “First, we are collaborating with our customers earlier in the precon process. The second—related—factor is our commitment to utilizing innovations that increase efficiency and safety for our workforce and the speed at which we deliver reliable outcomes on projects.”

Projects utilizing safety technologies consistently reported lower TRIR rates than projects without those technologies.

The trend suggests that investments in safety-focused technology are contributing to improved jobsite performance.

“Technologies like BIM, VDC, field-management platforms and real-time reporting help teams communicate more effectively and make informed decisions faster,” Terry says. “Our longstanding investments in these tools give our teams a distinct advantage when delivering high-quality results. This amounts to better productivity, safer jobsites and more predictable outcomes for owners.”

WHAT THE INDUSTRY CAN LEARN

Companies qualifying for EIC awards should take pride in their performance. The data confirms that ABC’s EIC-awarded projects significantly outperform industry averages across safety, budget and schedule metrics.

• The analysis also reinforces several broader conclusions:
• Preconstruction services deliver measurable value
• Technology adoption is accelerating and becoming a key differentiator
• Safety leadership remains foundational to project excellence
• High-performing contractors consistently deliver quality projects regardless of procurement method or contract structure

The data also provides insights worth sharing broadly across the industry. Regardless of project size, market sector or delivery method, the conditions for excellence can be created through intentional planning, collaboration, innovation and leadership.

SEE ALSO: ABC UNVEILS AWARD-WINNING CONSTRUCTION PROJECTS, NATIONAL CONTRACTOR OF THE YEAR, SAFETY AND DIVERSITY EXCELLENCE IN THE INDUSTRY

The post Inside Construction’s Highest-Performing Projects first appeared on Construction Executive.