South Carolina is at the center of the Southeast’s ongoing population boom, attracting buyers from across the country with its lower taxes, strong schools, and recreational amenities. The ...
AI Was Supposed to Fix Construction's Labor Shortage. The Industry Is Still Waiting.
Written by:
Date:
29 Jul 2026
Share via Facebook
Share via Linkedin
Share via X
Copy to clipboard
Most homebuilders still run on spreadsheets, phone calls, and manual site inspections. That’s not for lack of trying; it’s a symptom of a broader gap between what AI can theoretically do for construction workflows and what builders have actually implemented. The industry’s underlying problem is severe enough to explain the urgency: construction needs roughly 499,000 new workers in 2026 alone, and nearly 41% of the current workforce is expected to reach retirement age within the next five years.
Against that backdrop, AI adoption is accelerating on paper but lagging badly in practice. Most contractors expect AI to matter to their business, but only a small fraction have actually rebuilt their workflows around it, and the majority of the industry remains in early testing rather than production use. Even where AI has been adopted, results have been inconsistent; the overwhelming majority of enterprise AI pilots across industries fail to produce measurable returns, and in construction specifically, a large share of contractors still say they don’t fully trust the technology.
That gap between stated intention and operational reality is what makes it notable when a builder claims to have moved past it. According to Ting Qiao, Co-Founder, CEO & Chairman of Texas-based build-to-rent developer Wan Bridge and its technology arm AiWB, the company’s home starts increased by 40%, and completions increased by 100% between 2025 and the first half of 2026, while headcount decreased by 40% over the same period. Wan Bridge attributes those gains to AI agents integrated directly into daily construction and property management workflows, the kind of full operational integration that, industry-wide, few companies have reached.
Where Companies Get Stuck
Industry research points to a consistent reason most AI pilots stall: data quality and organizational readiness, not the technology itself. Qiao’s account of Wan Bridge’s own process reflects that same pattern. Getting from pilot to full integration, he says, took the company two to three years of restructuring workflows, standard operating procedures, and team organization from the ground up, not a software rollout so much as an operational rebuild.
One example Qiao points to is vendor payment verification. When a subcontractor completes work, they upload a photo, and an AI agent analyzes the image to confirm whether the job is done. Qiao says the accuracy runs at 94 to 95%, not perfect, but fast enough, in his account, that both sides tolerate the error rate in exchange for turnaround measured in minutes rather than days. “Even though it’s not 100% accurate, because the vendor knows the AI is not 100% accurate, we know the AI is not 100% accurate, but it’s good enough,” he says.
Despite the scale of automation Qiao describes, he frames the technology as handling the bulk of operational work while people retain a smaller but decisive share, the institutional knowledge and judgment calls that, in his view, separate a profitable company from a merely functional one. “You can be 95% perfect, but if you are missing that 5% you are still in a business not making money,” he says. “This 5% is that human beings’ know-how.”
The Jobs That Remain
A common assumption about AI-driven headcount reduction is that it simply eliminates roles. Qiao’s account suggests something more specific: a shift in which roles remain and what they’re asked to do. Fowler Knight, who works with the company, says the change is about the kind of work left for people once repetitive tasks are automated. “We are really looking for our human capital and our talent to be people that want to solve meaningful problems, want to be able to connect with employees,” Knight says. “They’re not ones just being burdened by administrative tasks.”
Qiao says the technical side of scaling AI integration has moved faster than expected; the company’s SaaS platform spans dozens of modules, and work that once took months to rebuild now takes weeks, according to Qiao. But he describes the actual bottleneck as organizational rather than technical: coordinating how new capabilities roll out across employees, subcontractors, and supply chain partners, each adopting at a different pace. That distinction – technology outpacing an organization’s ability to absorb it – matches what industry research more broadly identifies as the real barrier to AI adoption in construction: not whether the tools work, but whether a company is structured to use them.
About the Expert: Ting Qiao is Co-Founder, CEO, and Chairman of Wan Bridge, a Texas-based build-to-rent developer, and its technology arm AiWB, focused on integrating AI agents into construction and property management workflows.
This article is based on information provided by the expert source cited above. It is intended for general informational purposes only and does not constitute legal, financial, or real estate advice. Readers should conduct their own research and consult qualified professionals before making any real estate or financial decisions.
Similar Articles
Explore similar articles from Our Team of Experts.
