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.

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.

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.

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.

DEEPER INTERGRATION OF DESIGN AND ESTIMATING

Today’s cloud-based tools have improved collaboration, but design and estimating still offer major opportunities for improvement. As projects grow more complex and timelines compress, tighter integration will become essential.

In the near future, design and estimating will be highly data-driven and in real time. Cost feedback will surface instantly as designs change, allowing teams to understand the financial impact of decisions as they’re made. RFIs, issues and design updates will flow seamlessly between design and preconstruction, reducing silos and helping teams identify problems earlier.

The result will be faster iterations, better alignment and greater transparency. Contractors gain stronger cost and schedule certainty, while owners and designers gain confidence in the decisions they make upstream.

PREDICTIVE ANALYTICS FOR PROACTIVE RISK MANAGEMENT

Predictive analytics already help teams anticipate budget and design risks, but their capabilities will expand significantly over the next several years. By analyzing historical data and patterns, AI will surface potential risks earlier and more accurately.

Machine learning will also inform broader contingency planning by accounting for factors like market volatility and supply-chain disruption. These insights will allow teams to adjust designs, budgets and strategies before risks materialize, leading to more reliable outcomes and a competitive edge for those who adopt early.

AUTOMATION AND THE EVOLVING ESTIMATOR ROLE

Automation will continue to remove repetitive work such as manual takeoff and data entry, but it won’t replace estimators. Instead, estimators will shift from tactical execution to strategic advisory roles.

With AI handling routine tasks, estimators can focus on scenario analysis, value engineering and client guidance. As a result, data literacy and technology fluency will become as important as traditional estimating skills.

2030 PREPARING FOR 2030

Preconstruction is moving toward a more intelligent, connected future. AI and automation are already shaping workflows today and that impact will only grow. Teams that start planning now by investing in tools, building data skills and rethinking workflows will be best positioned for success in 2030 and beyond.

The post The Future of Preconstruction: Looking Ahead to 2030 first appeared on Construction Executive.

At this year’s CONEXPO-CON/AGG, autonomous equipment and AI-enabled machinery emerged as one of the industry’s hottest topics, dominating product showcases and conversations about the future of the jobsite. Industry experts told Construction Executive that the transformation this will usher in for the construction industry extends far beyond the specs and capabilities of new equipment: Once scaled, it has the potential to make construction work safer, more efficient and more possible for a broader pool of workers, at a time when lack of skilled talent remains a bottleneck for firms of all sizes. Mines, ports and build sites are soon to be where the rubber meets the road as humans collaborate with robotic fleets—a shift which requires firms to help workers develop new skills to keep up with fast-changing tech. 

“We’re seeing rapid growth in technologies that change how people learn as much as how they work,” says Lisa Strite, chief learning officer at the National Center for Construction Education & Research, pointing to evolution in building information modeling in workflows, digital layout tools, equipment telematics and jobsite guidance supported by augmented reality tech. 

Here’s what leaders should know about developments in autonomous construction, the changes they’ll bring about on jobsites and in the workforce, and how companies can start evolving now to make the most of these technologies.

WHAT’S NEW OR CHANGING SOON? 

While automated functions and teleoperation have been increasingly integrated into equipment in recent years, developments in autonomous technology— which is capable of operating with or without a human manning the controls—are progressing in ways that will trickle down to jobsites, according to John Somers, vice president of the construction and utility sector for the Association of Equipment Manufacturers.

While human oversight will still be critical to safety and success, these technologies are now ready for more dynamic, unstructured work environments, Somers says. They’re able to ease the load of time-consuming tasks like earth moving, material handling and site coordination. 

“If you don’t have anybody to operate the machine but you still need work done, it’s a great time to not have to have somebody in the cab,” says Somers.  

Advancements in hardware, software and cost efficiency are combining to make a broader array of technologies possible. 

There’s been a decline in costs for critical sensors and leaps in edge computing, which allows the processing of data locally where it is gathered. Closer proximity to data at its sources “can deliver strong business benefits, including faster insights, improved response times and better bandwidth availability,” according to IBM—immediacy which has obvious benefits for real-time, high-stakes decision making on jobsites. 

An influx of funding into autonomous heavy equipment is boosting progress: Venture capital investment in construction tech hit a record high of $2.6 billion in 2025, a 63% year-over-year increase as more top firms direct talent toward embodied AI and retrofit platforms for heavy equipment, according to Silicon Valley Bank.  

Improvements in vision-based AI are making it possible to navigate tricky environments without use of lidar: In January, Heidelberg Materials announced that it had successfully used a mixed-fleet autonomous hauling system from Komatsu and Caterpillar to move over two million tons of limestone at its quarry in Lake Bridgeport, Texas. 

The integration of AI into equipment software systems is also changing the game. Through a collaboration with NVIDIA, Caterpillar recently introduced autonomy and AI assistant technology to machines such as excavators, dozers, compactors, loaders and haul trucks, which it says will help make jobsites safer and more productive.

Bedrock Robotics is developing systems that make it possible to modernize “fleets contractors already own,” according to Laurent Hautefeuille, Bedrock’s chief operating officer, “turning existing heavy equipment into autonomous machines through a fully reversible upfit that takes a couple of hours.”

The company is starting with excavators, Hautefeuille explains, because they’re constrained by a shortage of skilled operators despite being “among the most complex machines on any jobsite, with six or more degrees of freedom, significant variability in soil conditions and several years of experience required to operate with precision.” 

Hautefeuille says the system will offer contractors a real-time window into what’s happening on jobsites through “production data, machine performance, cycle counts,” all information that traditionally has been logged manually, living in spreadsheets or on clipboards.

Michael Gidaspow, chief digital officer at Komatsu, says the company has been trying to make “contractors’ hard work easier” by translating autonomous tech from mining to construction, through both software and heavy equipment like autonomous haul trucks. 

“Autonomous construction equipment is a journey and as we progress toward a fully autonomous site,” says Gidaspow, “we see machine technology and jobsite technology as the first steps.” 

Regarding Komatsu’s most recent line of intelligent equipment, Gidaspow says: “While these machines have operators, you see more automation in these machines and you can use the automation more often.” 

For instance, a utility model in its new excavator improved trenching operations “by helping ensure that the machine is always centered over the planned trench and by helping you always come back to the center of the trench after dumping a bucket into a truck,” Gidaspow says. And through My Komatsu, the company’s centralized digital hub, “customers can get all of their key machine information and jobsite information in one spot.” 

While the biggest change is coming in fully autonomous equipment, advancements in remote operation—where a human controls a machine from a safe distance—have also made it more accessible and cost effective.

“When you look at remote operation, you can do it from almost anywhere,” says Gidaspow.

HOW WILL THIS SHAPE HOW WORK IS DONE AND WHO CAN DO IT? 

These leaps in technology are “helping make construction careers more accessible by reducing some physical barriers and placing greater value on problem-solving and digital fluency,” according to Strite, “which allows training programs to engage a broader population of learners with different strengths.”

Expansion of the construction labor force is essential at a time when the industry needs to attract roughly 349,000 net new workers in 2026 to meet demand for construction services, according to analysis from Associated Builders and Contractors. And with construction spending growth poised to rebound for the first time in years in 2027, the industry will need to bring in 456,000 net new workers to meet demand, ABC projects.

Potential to transform what a construction role can look like and who can do it is one of the reasons that “autonomy is one of the most compelling workforce development stories the industry has had in decades,” in Hautefeuille’s opinion.

For excavators, for instance, it takes five years of trade school and nearly a decade working in the trades to “reach real proficiency,” Hautefeuille notes, a significant time commitment that can also be a turn-off to younger workers. Safety concerns also contribute to the industry’s recruitment challenges, he says, given “the high risk of injury or death, especially relative to most other industries.” 

But going forward, as machines can absorb more demanding, repetitive and dangerous tasks, and proficiency in technology becomes indispensable, Hautefeuille says, “the job starts to look less like years of grinding physical repetition and more like operating and orchestrating intelligent machines.”

He continues: “People who may never have considered construction before will be drawn to new jobs on construction sites as a result of the infusion of technology, and experienced employees can shift to other, fine-grained aspects of building that require a human touch, leveraging their expertise to the fullest.”

One of the benefits of more tasks becoming mechanized and less laborious is that workers may be able to have longer careers that take less of a physical toll over the course of their lives, according to Vinz Koller, vice president of the Center for Apprenticeship & Work-Based Learning at Jobs for the Future, a national nonprofit. 

For those joining the construction field during this shift toward automation, Koller says, “We still see a large growth in the number of job classifications and the active recruitment” in years to come. But there will still be certain roles consisting mostly of tasks which are not yet able to be automated. 

“Rule-based physical tasks that can be standardized, they’re likely going to be taken over by machines over time,” Koller says. “If you think of an electrician, it’s very hard to automate a lot of tasks an electrician does because they’re not very predictable or rule based.” 

Ty Findley, co-founder of Ironspring Ventures, which invests in early-stage companies in construction, manufacturing and more, says he believes “this evolved occupation definitely will help recruit the next generation who are already digitally inclined and see this as an attractive skilled-occupation path.”

And for more experienced workers, Findley adds, “Allowing those amazing skilled operators to evolve their occupations into digitally oriented supervisory roles watching over multiple machines at one time is a great outcome.”

HOW WILL AUTONOMOUS TECH IMPACT THE BOTTOM LINE? 

According to Hautefeuille, as a greater variety of autonomous technologies become more accessible for family shops and big firms alike, they can expect to see benefits on schedule compression and quality. 

Progress can be made more quickly by extending productive hours and reducing unexpected downtime by incorporating effective automation, and “faster projects mean earlier completion, reduced carrying costs and the ability to take on more work with the same team,” he says. And because “autonomous systems operate with a consistency that’s hard to maintain over a long shift,” gradual gains in consistency will ultimately “translate into fewer errors and less remediation—which is where a lot of margin gets lost on complex projects.” 

Companies that make the most of these technologies should also be able to grow their capacity, Hautefeuille says, because “the companies that figure out how to scale using autonomy will be able to bid and build more work than their current headcount would otherwise allow.” 

“Contractors who move early will have a real competitive advantage when it comes to winning and delivering large projects,” he predicts. 

Somers says that companies looking to explore these technologies would be well-served by looking at their workforce issues and seeing if new tools might be able to ease the load, as opposed to “finding the solution and looking for the problem.” 

“If you don’t have enough people to run a compaction roller, then look into automated rollers,” he suggests. Or, in potentially dangerous environments such as demolitions or underground mine sites, take advantage of advancements in remote tech. “Why put somebody in a potentially hazardous situation?”  

The best return on investment for these technologies will generally come when they’re used to solve a sticky issue on a jobsite, according to Gidaspow, such as fuel usage or scheduling issues. 

While autonomous tech can help optimize any job, it is “most beneficial on the most challenging jobs,” he added. 

“When challenges arise, the technology can shine,” Gidaspow says. “It can help you get back on track more quickly and make more educated decisions.”

WHAT ARE POTENTIAL BOTTLENECKS? 

Gidaspow stresses that integrating automation is a complex process: “Automating the machines is one part of it, but making sure that the machines do the right things is another part—and then there is the change-management piece.” 

Although “construction technology hasn’t always been easy to adopt,” Gidaspow says, Komatsu is “actively trying to fix that issue by making our tools more user friendly and making sure that they all work together.” By combining smarter software with more advanced autonomous equipment, “bringing these types of tools together allows you to operate the machines much more efficiently with more novice operators,” says Gidaspow.

“We created technology to allow the operator to move material more quickly. Then you use the jobsite technology tools to be able to make better decisions based on the overall progress of the job,” Gidaspow says. “That means that you get more precise machines and technology that allows you to make better decisions.” 

Cost may be a barrier to early adoption for some firms, but autonomous technologies also have the potential to lower operating costs by making it easier to complete arduous work more quickly with fewer human hands needed throughout the process. 

Nurturing talent that can make the most of these tools will be critical, with capability development “becoming a competitive advantage,” Strite notes. 

“Organizations are seeing the strongest returns when they treat training as part of the technology investment, rather than an afterthought,” Strite said.  “When workers understand both the tool and the underlying workflow, companies experience faster adoption, fewer errors and more consistent productivity gains.” 

HOW CAN COMPANIES PREPARE NOW?

For executives, “the planning horizon for this is closer than most people think,” says Hautefeuille, noting that the necessary groundwork will take some time, given the magnitude of change required to get up to speed. 

“If you’re not thinking now about how autonomous-capable machines fit into your company strategy, you may find yourself behind,” he cautions. The contractors who will benefit most from this technology are the ones building relationships with it early—understanding how it integrates into their workflows, training their teams and getting comfortable with what it can do.” 

As more new technology becomes available, one of the biggest challenges for firms will be “ensuring training keeps pace with how quickly tools and workflows evolve,” Strite predicts. Companies can be ready to meet the moment by designing “learning pathways that are modular, stackable and closely aligned to real job tasks.” 

By embedding learning into everyday work, firms can “prevent skills gaps from slowing adoption,” she adds.   

“Employers are increasingly requesting training that builds digital confidence alongside craft expertise, including interpreting model-based plans, using connected equipment interfaces and understanding data generated on jobsites,” Strite says. “There is also growing interest in simulation-based learning and testing that allows workers to practice skills in lower-risk environments.”

As more of the construction work of the future becomes done on computers, Somers says it will be essential to make sure the workers nearing the end of their careers—skilled machine operators, superintendents, foremen—pass on knowledge “as soon as possible” about topics like machine maintenance and other tasks that are best understood after years of hands-on experience. 

Before companies really jump into this, they need to get those newer folks up to speed,” Somers says, pointing to the possible dangers of having future generations of workers spend more of their time away from jobsites. “Maybe they’re in the office looking at equipment telematics data, looking for workflow challenges or analytics on predicting when a machine needs maintenance. They might not understand the impact if they don’t act on that stuff.”

The post Hands Off, Hard Hats On: Autonomous Construction Technology Takes Over first appeared on Construction Executive.

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

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

A New Vision: Technology as a Bridge Between Generations

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

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

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

Augmenting the Worker With AI

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

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

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

The Coach in the Cab

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

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

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

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

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

Minimizing Downtime With On‑Demand Expertise

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

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

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

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

Enhancing Jobsite Awareness With a Smarter View

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

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

A New Era of Human–Machine Collaboration

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

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

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

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

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

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

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

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

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

2. Environmental Erraticism and Data Quality and Consistency

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

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

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

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

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

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

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

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

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

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

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

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

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

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