By TIFFANIE REYNOLDS

The commercial construction industry is in the middle of a technology reckoning.

Artificial intelligence has moved from talking points to active deployment. But the distance between early adoption and meaningful transformation is still wide, and many firms are actively working to embrace and activate AI to change how they operate.

To get a read on where the industry stands and where it’s headed, CNR Magazine put forth questions to four of the people tracking it most closely: Ken Simonson, chief economist at the Associated General Contractors of America; Chris Love, vice president of association and industry innovation at AGC; Patrick Scarpati, director of construction technology and innovation at Associated Builders and Contractors; and Tomislav Žigo, AIA, LEED AP, CEO of BEXEL USA.

CNR: ABC projects the industry needed nearly 350,000 additional workers in 2026 alone. How much of the AI investment today is driven by that shortfall versus genuine productivity ambition?

Simonson:

“Need” is subjective since it depends on the perceived volume of work available at a given price. Most demand for workers in construction comes from the need to replace workers who are retiring or leaving for other occupations, education, family needs, etc. AI will affect the mix of skills needed but the impact is likely minor so far, especially among craft workers.

Scarpati:

By 2030, I expect AI to help augment some of the workforce gap by reducing administrative burdens, improving planning and allowing field teams to focus on higher-value work. The labor shortage challenge won’t disappear, but AI should make the industry more resilient and efficient.

Žigo:

The next several years, or to be safer, the next decade will introduce significant transformation associated with these not-so-emerging technologies, but also with the industry’s level of digital maturity, which is still a work in progress.

CNR: Data quality keeps coming up as a barrier to AI reliability. Do you see firms addressing that by 2030?

Love:

Data quality is one of the biggest barriers to reliable AI in construction, but it is a solvable problem. The firms making the most progress are not treating data as just an IT issue. They are treating it as an operating discipline. By 2030, the likely winners will be the ones that improve data discipline enough to make AI reliable for estimating, scheduling, reporting, safety and risk management.

Scarpati:

Firms are recognizing that better data collection and standardization are prerequisites for successful AI implementation. As digital project management platforms become more integrated, the quality and consistency of construction data will need to improve significantly. The companies investing in their data today will likely see the greatest return from AI tomorrow.

Žigo:

Those organizations that early on understood the need for knowledge management, data governance and quality are in a much better position to capitalize on interacting with reliable AI. Placing the timeline for solving this problem is like placing the timeline for reaching a carbon-neutral economy. I firmly believe that those who are not willing to start a comprehensive internal digital maturity introspection process are setting themselves up for a horizon line beyond 2030.

CNR: AI adoption has taken hold in office and administrative functions far faster than on the jobsite. What’s driving that gap, and when does the field catch up?

Simonson:

Many office and administrative functions in construction firms are identical or like those in other industries or lend themselves to similar analysis and “training” methods for AI applications, since the work is relatively standardized. But every construction site has unique characteristics, making it difficult to produce applications.

Scarpati:

The office side naturally adopted AI first because it relies heavily on digital information, documentation, scheduling and reporting. Field operations are more complex because they involve changing jobsite conditions, multiple trades and real-world variables that are harder to automate. As mobile devices, wearables, reality capture and connected equipment become more common, I see field adoption accelerating significantly over the next five years.

CNR: When AI delivers on its promise, what does a well-run commercial construction firm look like in 2030?

Love:

The well-run commercial construction firm of 2030 will be much more proactive than today’s average firm. Instead of mainly documenting what already happened, firms will use AI to surface risk earlier, improve decision-making and reduce manual administrative work. The defining advantage will not be AI replacing people. It will make people better informed and faster to act.

Scarpati:

A successful commercial construction firm in 2030 will combine skilled people, advanced technology and strong operational discipline. While technology will play a larger role, the expertise and judgment of construction professionals will remain the foundation of successful project execution.

Žigo:

It will never be just about AI. The well-run commercial construction company of tomorrow is first and foremost the one that is capable of clearly identifying its own problems, devoid of internal human bias. It is the company that has a set of well-documented, time-tested processes that can be intentionally used to train its own AI models while maintaining IP and becoming more competitive.

The firms positioned to lead in 2030 are doing the unglamorous work right now –  cleaning data, documenting processes and building the organizational discipline to make AI deliver on its promise.