While some sectors have spent recent years sprinting toward digital maturity, construction and engineering organizations are finally crossing a major threshold as the potential of automation and Industrial Artificial Intelligence becomes impossible to ignore.

Faced with tightening margins and complex global pressures on materials and labor constraints, construction and engineering firms are overhauling their traditional value propositions to stay competitive. As 2026 unfolds, the construction sector is reaching a critical tipping point where A.I. and connected data move from the periphery to the core of business operations.

But, as these innovations are scaled, a fundamental issue remains—A.I. is only as powerful as the digital foundation it sits on. To move from a pilot program to enterprise-wide success, leaders must first master the essential building blocks of digital transformation. A.I. just cannot reach its transformative potential to reshape construction workflows, scale project management, and empower intelligent decision-making, without a resilient digital backbone, and the construction industry is waking up to the fact. 

BUILDING BLOCKS OF DIGITAL CONSTRUCTION

One of the biggest trends we saw throughout this past year was the rapid adoption of modern Enterprise Resource Planning (ERP) platforms that serve as digital backbones across organizations. Our Censuswide industry research study found that 63 percent of construction and engineering companies planned to adopt a new ERP system in the next one to two years.

This modernization statistic has proven to be underestimated. In terms of how many companies are embarking on digital transformation projects, nearly a year after making these predictions, we realize that the optimism around the role of A.I. being able to reshape workflows, project management, and intelligent decision-making, is fueling a greater desire to adopt a future-proof ERP platform. It is seen as the springboard for scalable, trusted A.I. deployments.

HIGH ON THE PRIORITY LIST

The latest IFS research study “The Invisible Revolution”, which surveyed more than 300 senior executives from leading construction and engineering firms in May and June of this year, found that the sector is expected to become one of the most A.I.-first industries in 2026. Momentum is clearly building, with 91 percent of firms expecting to increase A.I. investment in 2026.

RECLAIMING CAPACITY

One of the greatest benefits of A.I. that we discuss is its ability to analyze and collate data in a much faster way. This gives all their employees—from workers on the construction site to senior management in the back office—more accurate, predictable and reliable information.

This enhanced and automated information gathering and analysis can reduce the enormous amount of time it takes to prepare regular board presentations on project performance. Most companies host interdepartmental meetings monthly with all the senior leaders from each of their divisions. Typically, each team of people takes days, if not weeks, to prepare their data by gathering information across multiple software solutions. 

Industrial A.I. gives construction and engineering organizations more trusted control of their business by removing the unreliability and inconsistency of human guesswork, making it faster and easier to gather, analyse, and report on every dimension of project performance—including profitability, timeline delays, budget overruns, cost forecasting, safety incidents, quality, and more. Ultimately, leveraging Industrial A.I. to enhance reporting and data sharing across the organisation reduces business risk and delivers greater control over project results.

KEY METRICS BEHIND SUCCESS

The benefits are clearly resonating across the industry. The latest IFS research study found that the biggest applications of current A.I. deployments across construction and engineering firms were project delivery (62%) and business intelligence (59%). Out of these early adopters who are currently deploying A.I., companies are already seeing these benefits: 89 percent report profitability gains, 44 percent outperform the cross-sector average in operational efficiency, 42 percent in supply cost reduction, and 36 percent in lowering project expenditures.

With the growing economic uncertainty across the globe, the most impactful benefit of Industrial A.I. for this industry is giving companies greater control over project management. Out of all other industries, the project-centric nature of construction puts these firms most at risk of diminishing and/or unpredictable profit margins in volatile and disruptive markets.

Perhaps this is why the latest A.I. research shows that construction firms anticipate A.I.’s greatest benefits in project delivery (41%) and increased profitability (36%).

THE INDUSTRY OF TOMORROW 

It is clear to see over the last 12 months the construction sector digital evolution has outpaced even the most optimistic forecasts. A.I. is here to provide tangible advantages, but the sector has reached a point where the modernization of aging ERP systems is no longer a luxury, but a strategic prerequisite for deploying Industrial A.I.

By clearing away these technical hurdles, construction and engineering firms are securing the level of project oversight, efficiency, and productivity necessary to thrive. The ultimate result of this shift is an industry that is more agile, data-connected, and durable—ready to fundamentally transform the way global infrastructure is designed and delivered.

about the authors

Kenny Ingram is VP of C&E, IFS, and Chris Knight is global industry director of C&E, IFS. For more, visit www.ifs.com.

Whether it’s a residential construction project in the Dallas-Fort Worth area or commercial projects in Florida and Tennessee, the contractors who successfully navigate today’s most complex and demanding jobsites all have one commonality: Technology is the foundation of their success.

The modern jobsite is fraught with potential pitfalls, from increasing demands on efficiency to reducing waste to tackling the prolonged skilled labor shortage.

As jobsite tools like laser scanners, total stations, and machine control become more common, younger workers are interested not just in the jobsite itself but in the chance to use the latest sophisticated technology to solve real-world problems and advance their careers.

According to the Associated Builders and Contractors, the industry needs about 349,000 new workers this year to meet demand. That number could rise to 456,000 in 2027, but many contractors still struggle to find and retain the right people for today’s jobsites.

Technology, coupled with mentorship and opportunities, can help current employees gain new skills and younger team members grow their careers by providing a broader understanding and hands-on experience across roles and machines.

TECHNOLOGY ATTRACTS THE NEXT GENERATION

For Mt. Pleasant, Texas-based Stephenson Dirt Contracting, technology is as critical to tackling large-scale residential projects as it is to attracting new—and often younger—workers.

New solutions are user-friendly, which simplifies training for all workers. They are also a powerful tool for attracting a younger generation of digital natives, for whom technology is second nature, to the industry.

Retrofitting machine control on older equipment, such as Cat scrapers, has several advantages. First, it boosts the equipment’s value by improving productivity. It also allows Stephenson Dirt’s team members, such as 21-year-old Oscar Miranda, to operate a range of machines, including backhoe loaders, excavators, and skid steers, helping him gain a broader understanding and operating experience in various roles and machines.

“The skid steer was a lot of fun to drive, and I learned a lot about how to handle it in different tasks, from shifting dirt to moving other materials around and generally keeping the yard clear,” Miranda says. “It was definitely the best machine to start on, as I had to quickly learn how to operate the joysticks and change attachments.”

Once he had mastered the skid steer, Miranda went on to operate a backhoe loader.

“With the backhoe again being a utility machine, I found myself doing even more tasks like learning how to dig trenches,” Miranda adds. “This then helped me to progress into working on excavators. But I always had my eye on the bigger machines, so I pushed myself hard to get an opportunity to work on a dozer.”

During a recent residential project in Dallas-Fort Worth, Stephenson Dirt needed precise grading and ditch shaping for hundreds of plots. They also had to handle changes in the volume of dirt caused by water saturation.

The team needed to coordinate in real time with clients, which meant efficiently training new operators in GPS and machine control. To ensure the project is graded according to specification and to eliminate the risk of costly rework, the team installed Leica Geosystems machine control solutions on its equipment.

For Stephenson, the project wasn’t just about testing the technology. It affirmed that digital tools could accelerate the training of a new generation of operators.

TECHNOLOGY CATCHES MISTAKES EARLY

For Jacksonville-based Superior Construction, coordinating projects at Nashville International Airport and the First Coast Express in Jacksonville, Florida, required a different type of technology deployment: automating its pavers.

By mounting robotic total stations—electronic and optical instruments that enable users to lay out points more accurately—on a tripod, Superior monitored progress and collected data on each project. Doing so sped up construction and enabled the Superior team, much like the Stephenson Dirt team, to pave the road as close to the design as possible, using only the necessary materials, eliminating waste and the potential need for costly rework.

The solutions also enabled the teams to complete the project with an eye toward the future. They created a digital twin of the project, providing an exact replica overlaid with design plans, so teams can assess the entire site rather than just one-off spot checks.

The technology provides a more complete file, allowing teams to see exactly where the materials went and empowering them to address any errors quickly. It can also be recalled for analysis at any time.

“Leica Geosystems technology is now even more precise and easier to use,” says Tim Gebauer, survey manager for Superior Construction. “I feel like that technology is making it safer for operators.”

A.I. ELEVATES JOBSITE SAFETY

While GPS and machine control helped solve efficiency problems, as jobsites grow even more complex, the challenges contractors face today demand even more sophisticated solutions.

Artificial intelligence is transforming construction by accelerating workflows, helping teams manage data and projects more efficiently, and boosting safety with real-time alerts. Yet, its greatest value to operations is still emerging.

As job sites tighten schedules, operate with fewer workers, and enforce stricter rules, integrated safety tools backed by intelligent, adaptive A.I. systems help contractors to enhance jobsite safety.

By monitoring job sites in real time, teams can quickly identify potential hazards and recommend immediate protective actions. They can also analyze incidents to identify patterns and inform future safety strategies and potential preventive measures.

A.I. ensures teams are working according to plan by monitoring progress and comparing it against project models, saving money by eliminating costly repairs, and increasing productivity through automated task management. The vision is to equip every construction tool and machine with A.I., enabling teams to receive real-time data and recommendations, supporting better, faster decision-making on the jobsite.

Enter the Leica Xsight360 safety awareness solution. It uses edge-based A.I. and up to six onboard cameras to provide operators with full 360-degree visibility around heavy equipment. The end-to-end solution unites machine automation, awareness, and construction specific A.I. algorithms to tackle these challenges directly.

The system detects people, vehicles, and obstacles in real time, issuing immediate audio and visual alerts to mitigate risks before incidents occur.

Technology is no longer a nice-to-have; it’s integral to a company’s success. It saves time and money and improves jobsite safety, building talent and reducing waste, and attracting younger workers like Oscar Miranda into the fold.

about the author

Troy Dahlin is vice president of heavy construction and machine control for Leica Geosystems. For more, visit www.leica-geosystems.com. 

Due to high concentrations of sulfuric acid on the surface, chemical corrosion causes concrete sewer pipes and wastewater infrastructures to deteriorate at unexpected, rapid rates. Microbially induced corrosion in concrete (MICC) is a multistage process that converts waste products into corrosive acid. Microban, in partnership with MarMac, explored these stages and recorded how quaternary silanes that offer robust efficacy with an optimal concrete compositional design can effectively combat MICC.

THE MECHANISM OF ACID DEGRADATION OF CONCRETE

A complex microbial process generates sulfuric acid. In the sludge below the sewage surface, anaerobic organisms, those living without oxygen, metabolize sulfates to create hydrogen sulfide gas. The gas is then released into the air above the waterline and absorbed into the moisture layer on the concrete walls. Thiobacillus, an aerobic genus of bacteria living on the walls, then metabolizes the hydrogen sulfide gas into sulfuric acid. The resulting acid attacks the concrete’s cement binder, turning it into weak, crumbly substances like gypsum and ettringite that lead to structural failure. 

When concrete is freshly installed, none of the elements that support the corrosion process are present; these conditions develop slowly over time. The MICC process is gradual and relies on a succession of bacterial species. Stage 1 provides inherent protection. Since fresh concrete is highly alkaline, it naturally prevents most microbial growth. Over time, a combination of moisture and sewer gases begins to lower and neutralize the concrete surface’s pH, onsetting Stage 2. This allows a succession of acid-producing bacteria, with the major species being Thiobacillus, further lowering the pH. When the pH drops low enough, the most damaging organism, T. thiooxidans, takes over, resulting in Stage 3. This microbe thrives in high-acid conditions and drives the most aggressive phase of corrosion and subsequent degradation.

LIMITATIONS OF TRADITIONAL SOLUTIONS 

Many traditional methods to combat MICC focus on strengthening concrete against acid attack by adding densifiers. These methods ignore the original protection, inadvertently lowering the initial pH and shortening Stage 1 protection, which in turn accelerates the onset of Stages 2 and 3. To maximize inherent protection, comprehensive protection against microbial attack should also account for the extension of Stages 1 and 2. By Stage 3, concrete is actively corroding, meaning it would be too late to abort the process. 

THE ROLE OF QUATERNARY AMMONIUM COMPOUNDS 

For many decades, quaternary ammonium compounds (QACs) have been extensively used due to their good stability, relatively low toxicity, and compatibility with other chemistries. QACs are used for surface protection in an extensive range of applications, including textiles, disinfectants, soaps, hand washes, and swimming pool chemistries. QACs are also valued because of their broad-spectrum effectiveness against organisms like bacteria or fungi. Quaternary ammonium silanes are a specific type of QAC that incorporates a silane base, providing additional chemical benefits in specific applications.

OPTIMIZING EFFICACY THROUGH MOLECULAR DESIGN

Evaluating microbial effectiveness in concrete requires more than standard lab tests because freshly made concrete has a naturally high pH that inherently inhibits microbes. This can lead to a false positive when testing, because the bacterial kill is incorrectly attributed to the antimicrobial rather than to a high intrinsic pH. 

The ASTM C1904-20 test method establishes a laboratory procedure for evaluating how antimicrobial-treated concrete materials perform when exposed to microbially induced acidification. Designed to simulate years of sewer service by preconditioning samples to support the growth of Thiobacillus, this method shows how specific long-chain quaternary silane formulations maintain a “protective window,” even as biogenic acidification attempts to take hold. By comparing molecular analogs, the evaluation reveals that the right chemical structure can significantly delay microbial colonization, offering a data-driven path toward extending the service life of critical infrastructure.

DUAL-LAYERED APPROACH 

To effectively combat MICC, a dual-layered approach must be considered, focused on the first two stages of the corrosion cycle. First, prioritize Stage 1 protection by favoring concrete compositions with a high starting pH of at least 12. The primary goal is to maintain this high intrinsic alkalinity as long as possible, as it provides the first line of defense against microbial colonization. Be cautious with additives like silica fume. Although they increase strength, they can inadvertently lower the initial pH and potentially shorten the Stage 1 protection period.

Second, implement an antimicrobial Stage 2 protection. Antimicrobials should be selected through a rigorous process, including minimum inhibitory concentration testing and comparative performance evaluations in low-pH environments that support Thiobacillus growth. The ideal additive remains active across a wide pH range, allowing it to suppress microbial acid production even as conditions become more acidic.

The conclusion underscores the high stakes of infrastructure protection. Current literature suggests that without such specialized microbial intervention, the expected service life of concrete structures, typically designed for 100 years, can be reduced to as little as 30 years. By proactively addressing the biochemical mechanisms of decay, municipalities and engineers can move toward more sustainable and durable wastewater systems.

about the author

James Rapley is liquid formulations manager at Microban International. For more, visit www.microban.com. 

Commercial construction isn’t short on data. We track schedules, RFIs, submittals, cost codes, daily reports, safety observations—and we still pass around plenty of emails and PDFs. The problem is that most of this information shows up too late, or in a format that doesn’t match how people actually work. That’s when dashboards start to feel like “something corporate wanted,” not something that helps the team pour concrete, close ceilings, or hit turnover.

When teams can’t explain a metric in plain language, they won’t trust it. When they don’t trust it, they won’t use it. A.I. doesn’t magically fix that. If the inputs are messy or the workflow is unclear, A.I. just makes the confusion faster.

The path to value is pretty practical: Give each role the information that fits its decisions, at the pace those decisions are made, and with enough context that a superintendent or PM can say, “Yep—that matches what I’m seeing.”

PRE-MORTEM VS. POSTMORTEM

Executives are right to focus on margin, schedule certainty, and predictable delivery. Those outcomes keep companies healthy. But they’re lagging indicators. By the time they move, the job has already absorbed the problem.

Outcomes-only reporting usually breaks down for three reasons:

Timing: The signal shows up after the corrective window closes.

Attribution: the metric doesn’t point to a controllable behavior—so nobody knows what to change.

Trust: the number is presented without the story and evidence behind it.

A.I. enablement starts earlier—closer to where work happens—and then connects those field signals to the decisions leadership needs to make.

A ROLE-BASED MEASUREMENT MAP

A single dashboard can’t serve a field engineer and a project executive at the same time. They operate on different clocks and on different decisions. A role-based map is less about software and more about decision rights.

Here’s a practical way to think about the signal each role needs.

Project Engineer / Field Engineer (hourly–daily): activity measures:

• RFI/submittal aging and stuck items
• Inspection outcomes and what failed
• Constraint status and ownership
• Document completeness for upcoming work

Superintendent (daily–weekly): readiness and leading indicators:

• Look-ahead reliability (planned vs. truly ready)
• Constraint closure rate
• Near-term float stability
• Crew/material readiness (1–3 weeks)

Project Manager (weekly–monthly): controllable drivers and exposure:

• Buyout/commitment progress and gaps
• Change order aging by discipline
• Forecast drivers + risk identification/management
• Procurement status vs. schedule needs

Project Executive / Portfolio (monthly–quarterly) outcomes with drill-down:

• Margin performance + forecast reliability
• Delivery risk and intervention triggers
• Cross-project patterns (what keeps repeating)
• Portfolio actions tied to controllable drivers

The non‑negotiable is vertical connectivity: Field actions should roll into leading indicators, and those indicators should support executive decisions. If that chain isn’t visible, adoption usually stalls.

PROJECT EXECUTIVE FOCUS

Cash management belongs at the project executive level because it’s where execution, forecasting, and owner-facing strategy come together. Cash is not a separate finance exercise—it’s the consequence of how reliably the project turns progress into approved billings.

In practice, strong project executives do three things consistently:

1. Monitor and improve forecast quality. Cost-to-go (CTG) should be driver-based and exposure-aware—not a month-end re-spreadsheeting ritual. Forecast volatility is a signal. If CTG swings without a real scope event, something is unstable: commitments, productivity assumptions, change tracking, or cost-coding discipline.
2. Tie CTG to the schedule that will produce the costs. If schedule logic is weak or look-ahead plans aren’t reliable, CTG becomes guesswork. Executives should insist on basic schedule health—logic completeness, realistic durations, and float stability—because that’s what makes future costs predictable.
3. Stay cash positive (or at least neutral) through billing strategy. Align pay applications to verified progress, protect payment prerequisites (submittals, inspections, close-the-loop documentation), and plan around owner review timelines. A common failure mode is doing the work but not having the proof package ready—so billing lags the field.

A.I. can help by flagging missing documentation, stalled approvals, and forecast drift early so teams can act before the month-end rush.

RIGHT PEOPLE, RIGHT SEATS

A.I. and analytics amplify whatever operating system you already have. If responsibilities are fuzzy, decision rights are mismatched, or the fundamentals are weak (schedule discipline, forecasting hygiene, procurement cadence, scope/change controls), A.I. will mostly accelerate churn.

Before rolling out advanced automation, it’s worth running a simple competency and decision-maturity check by role. The point isn’t to grade people, it’s to confirm the basics are stable enough for A.I. to add value.

THE MEASUREMENT SHIFT

A connected measurement system has three layers. Outcomes are what leadership reports. Leading indicators are what teams can influence. Activities are what people do every day.

Outcomes: delivery performance, margin reliability, portfolio risk.

Leading indicators: predictive drivers you can influence.

Activities: daily behaviors that create (or reduce) risk.

Shift-left examples contractors can implement quickly:

Schedule: Track missing logic, long durations in look-ahead, unstable float, and constraint closure—before a milestone slips.

Cost: Track forecast volatility (“zig-zag” projections), open commitment gaps, buyout delta, and aging potential change items—before the month-end surprise.

Quality/rework: Track recurring inspection failures and RFI churn—before rework hits job cost.

OPERATIONAL DATA STREAMS

Role-based A.I. is only as good as the signals feeding it. Four streams tend to matter most:

Scheduling as a lifecycle discipline: Publish a baseline, enforce update rules, and retain as-built history (use fragnets/window analysis where needed).

Production planning cadence (pull planning): Weekly planning and daily huddles create the heartbeat for leading indicators.

Project execution data: RFIs, submittals, changes, and procurement form a risk map; AI should classify, prioritize, and route.

Daily progress: standardized daily reports and photos capture planned vs. done vs. blocked—and support credible month-end billings.

WHY FORECASTING FAILS

Forecasting fails when definitions vary, workflows are siloed, and “truth” sits inside spreadsheets. Teams stop believing the numbers, and the monthly cycle becomes theater. A unified approach that connects project management and financials—RFIs with cost impacts, submittals tied to scope creep risk, and both owner-driven and internal exposures—produces a more credible forecast.

Role-based A.I. helps by standardizing definitions, integrating workflows (estimating, procurement, operations, finance), speeding up feedback, and making explainability visible—why risk is being flagged and what evidence supports it.

about the author

Mandar Joshi is an accomplished Senior Project Controls professional and Construction Project Manager with over 15 years of experience in the commercial construction sector. With a focus on data enablement, Joshi currently serves as Director of Project and Process Controls for Bernards, a trusted commercial builder and construction management company delivering complex projects in healthcare, education, and infrastructure. He plays a pivotal role in advising the C-suite on crucial data analytics – including cost, schedule, and project management KPIs – bridging insights from the jobsite to the executive suite. Joshi holds a Master of Science in Construction Management from Texas A&M University and a Bachelor of Science in Civil Engineering from Datta Meghe College of Engineering, Mumbai University, India.