The United States is facing a severe shortage of skilled trade workers—plumbers, electricians, HVAC technicians, and carpenters—that threatens to delay infrastructure projects and drive up costs for homeowners. According to the Associated General Contractors of America’s Workforce Survey, nearly 70% of contractors report difficulty filling hourly craft positions [AGC 2017]. Demographics make the challenge even starker: BLS data show that almost 40% of the 12 million Americans in trade occupations are over 45, while fewer than 9% are under 25 [BLS 2024]. Looking ahead, the Center for Strategic and International Studies projects that by 2030 the U.S. will need at least 140,000 additional trade specialists to meet infrastructure and AI-related development demands [CSIS 2025].
For entrepreneurs like Bekhruz Nagzibekov, founder of United Plumbing, HVAC & Electrical and a recognized voice in AI-driven operations, this shortage is more than a macroeconomic trend—it’s a daily business challenge. His company sits at the intersection of this labor crisis, and his work with artificial intelligence tools offers a case study in how technology might bridge one of America’s most pressing workforce gaps.
A Founder’s Bet on AI
Few business leaders in home services have addressed this labor shortage as directly as Bekhruz Nagzibekov. An entrepreneur and digital transformation advocate, Nagzibekov has positioned his company as a testing ground for how artificial intelligence can reshape workforce management in trades—an industry that has historically been slow to adopt technology.
Under his leadership, United Plumbing has developed an AI-driven HR assistant that screens candidates, evaluates technical and soft skills, and produces structured scorecards for hiring managers. The system reduced average hiring cycles from seven weeks to two, while cutting mismatches by more than half. “Every wrong hire costs a small business weeks of lost time and tens of thousands of dollars. I want to reduce those mistakes from 70% to 20%,” Nagzibekov says.
Why Traditional Solutions Fall Short?
For decades, the U.S. has relied on apprenticeships and vocational schools to supply plumbing, HVAC, and electrical talent. But these pipelines are increasingly misaligned with today’s technological demands. Enrollment in vocational programs is stagnant, while many training curricula omit competencies in IoT-enabled systems, smart diagnostics, or energy-efficient hardware.
At the same time, retention challenges intensify. High turnover, minimal career progression, and job instability continue to thin the labor pool. In the construction sector, 94 % of firms report they are having difficulty filling hourly “craft” positions such as plumbing and electrical roles. Moreover, more than half of contractors say these staffing shortfalls have directly delayed projects. [AGC Survey, 2024]
From a cost perspective, the consequences are severe. According to CareerBuilder’s survey, the average cost of a bad hire is about $14,900 [CareerBuilder Survey, 2017]—a figure that includes recruitment, onboarding, lost productivity, and replacement hiring. For a small contractor, even one flawed hire can ripple into tens of thousands in losses—precisely the kind of risk that Bekhruz Nagzibekov’s AI-based hiring system is designed to mitigate.
These structural gaps are precisely why Bekhruz Nagzibekov pushed United Plumbing to adopt AI-based hiring methods—tools designed to bypass stagnant pipelines and align recruitment with real field conditions.
Turning Candidate Data Into Predictive Workforce Intelligence at United Plumbing
Nagzibekov emphasizes that the goal is not automation for its own sake, but actionable intelligence that directly impacts both technician performance and candidate experience. Rather than treating AI as a replacement for HR interviews, he has positioned it as a data engine for workforce planning inside United Plumbing. The assistant is layered on top of SendPulse’s automation stack, but its real strength lies in the analytics pipeline Nagzibekov’s team engineered around it.
Every candidate interaction generates structured datasets: transcripts, paralinguistic markers (tone shifts, pauses, stress), and decision-path logs from trade-specific scenarios. These raw streams are ingested into a scoring framework that maps four key vectors: technical competence, adaptability, reliability, and cultural alignment. Unlike generic HR chatbots, the system is continuously retrained on United Plumbing’s own field-performance data—linking how applicants answered in screening with how similar hires performed on real jobs months later.
That feedback loop turns hiring into a predictive function. If the model detects that applicants with certain response profiles tend to fail retention after six months, it automatically downgrades similar candidates in future cycles. Conversely, signals correlated with high-performing technicians—such as fast decision-making under time pressure or consistent communication style—are weighted upward.
Importantly, the system also benefits candidates. Instead of waiting weeks for feedback, applicants receive structured evaluations much faster, with clearer signals about where they stand. Those who advance to final interviews arrive with a transparent scorecard that highlights their strengths and development areas, which makes the process feel less arbitrary and more merit-based. For motivated candidates, this creates an incentive to engage more deeply: knowing that adaptability, communication, and cultural alignment matter as much as technical skill encourages them to prepare and present themselves in a fuller light.
For United Plumbing, the effect is twofold. The AI is not only accelerating hiring cycles, it is teaching the company what a “successful technician” actually looks like in data terms. That insight has allowed Nagzibekov to align recruitment with operational economics—lower turnover, fewer callbacks, and faster scaling into new territories. At the same time, candidates encounter a process that is faster, fairer, and more informative, strengthening their motivation to stay and grow with the company. In effect, the assistant doubles as both an HR tool and a business intelligence platform, closing the loop between people analytics, candidate engagement, and service outcomes.
“I want candidates to feel they are being judged fairly, not randomly. If they see why they advanced—or didn’t—it builds trust. And trust is the first step to retention,” says Bekhruz Nagzibekov.
Why Is Nagzibekov’s Model Different?
Most AI-driven hiring tools were built for enterprise HR and later adapted to other industries. Bekhruz Nagzibekov flipped that logic. At United Plumbing, his system was engineered directly into the contractor workflow—tracking the realities of plumbing, HVAC, and electrical services, where missed hires can halt projects and erode customer trust.
The model integrates features absent in generic platforms: trade license verification, seasonally adjusted demand forecasting, and role-specific testing scenarios. Instead of screening candidates only on resumes, it stress-tests them against field conditions—like diagnosing an HVAC fault under time constraints or handling plumbing calls during peak winter demand.
Nagzibekov treats AI not as a static product but as a living system. United Plumbing’s in-house specialist continuously retrains the model on hiring outcomes and technician performance, creating a self-correcting feedback loop. Error rates in soft-skill and technical classification have steadily fallen, making the platform more reliable with each cycle.
The Bottom Line
This experimentation carries implications well beyond one company. The U.S. home-services market, valued at $211.71 billion in 2024 and projected to reach $893.18 billion by 2032 at a CAGR of 19.59%, faces a widening labor gap that threatens its growth trajectory. [Verified Market Research, 2024] Without scalable solutions to workforce shortages, much of this projected expansion will be at risk.
Research from McKinsey shows that predictive analytics can reduce downtime and optimize workforce allocation across manufacturing and service operations, enabling firms to sustain productivity even when headcount growth is constrained [McKinsey, 2018].
Nagzibekov’s AI initiative at United Plumbing demonstrates how small-scale experimentation can address a challenge of national scale. By converting hiring into a predictive, data-driven process, his model provides a roadmap for how contractors across the United States might counter the labor shortage that threatens to slow growth in critical infrastructure and home services.
Author: Bekhruz Nagzibekov. Expert in the digital transformation and artificial intelligence-driven optimization of the home services industry.