White-Collar Workers Fear AI, But For Blue-Collar Workers, It Can Be A Savior

By Daniel Walsh

For white-collar workers, AI looks like a pink slip. But for the millions of blue-collar individuals who struggle to land a job, not because they lack ability, but because of the lack of an effective hiring infrastructure, AI is emerging as the green light they’ve been looking for.

The inability to connect the demand for blue-collar labor, given the substantial supply of skilled talent, is not only an embarrassing systemic failure, but it’s also a huge blow to the American economy. A study of just one part of the blue-collar workforce — manufacturing — by Deloitte and The Manufacturing Institute projects that about 2.1 million U.S. manufacturing jobs will be unfilled by 2030, with the gap potentially costing $1 trillion in that year alone. We should expect to see higher costs, missed deadlines and slower growth (if any at all) across industries that face similar shortages of skilled talent.

That is, unless something changes.

Blue-collar hiring infrastructure is broken

It starts and ends with hiring. The applicant-tracking and recruitment-management systems that most companies rely on are designed to uplift top candidates who meet the norms in white-collar industries, leading to a critical divide. As Harvard research demonstrates, these tools filter out capable candidates who don’t match historical patterns or standardized checklists that they are trained to recognize.

This design is especially problematic given the realities of America’s blue-collar workforce. Foreign-born workers are overrepresented in these roles: the construction industry, for example, employs the largest percentage of immigrants of any industry.

U.S. résumé conventions, from formatting to phrasing, are unfamiliar to many immigrants, and automated résumé systems often down-rank these applications based on immaterial factors rather than work experience or relevant skills.

Credentials, critical across numerous blue-collar jobs, are another obstacle: Licenses and certificates earned abroad often map poorly to domestic job codes, even when the underlying skills are equivalent. For example, a 20-year veteran electrician certified in Nicaragua starts at the same place as a novice in the U.S.

For those who don’t speak or write English fluently, if at all, these issues are compounded. A key reason: automated systems are trained to reject applications that contain typos, incomplete phrases or grammatical errors. With all these issues combined, it’s easy to imagine why so many qualified candidates don’t bother to apply for jobs at all.

How AI is clearing the gutters of hiring

AI succeeds where these legacy hiring infrastructure systems have failed: nuance. Machine learning platforms can circumvent the obstacles that prevent immigrants from landing jobs at scale. Natural language processing allows the systems to interpret nontraditional résumés, conduct interviews in multiple languages, and verify credentials automatically.

A welder without a formal résumé can be matched to an employer based on verified training records earned in another country. A warehouse worker with limited English can be assessed by their abilities, not their syntax on a resume. This reality would have massive, positive implications for blue-collar employment numbers and the American economy. Better still, it’s possible.

Further, when these candidates are hired, the business case doesn’t stop. Employers that have brought refugees into shop-floor roles report meaningfully higher retention in manufacturing, logistics and blue-collar industries, which traditionally experience high turnover.

Put simply, hiring systems that prioritize skills, credentials and language inclusivity don’t just expand candidate reach, they drive lasting productivity and growth.

The competitive edge

For businesses, the payoff of implementing AI to improve or overhaul blue-collar hiring practices goes beyond altruism. It’s good business. AI-driven hiring platforms can shrink vacancy times, lower onboarding costs and expand labor pools — advantages that matter for companies individually, and for the American economy at large.

The challenge for executives and policymakers isn’t to slow AI down, but to deploy it wisely. Used correctly, these tools can rebuild the connective tissue of the labor market, helping millions of workers find the jobs that need them most.

The workers are out there. The jobs are waiting. The system is broken — but not beyond repair.

AI won’t take every job. Not even close. And for many, the technology will actually do the opposite: unlock one.

Daniel Walsh is the CEO of VeroSkills, an AI-powered hiring platform solving blue-collar America’s $1 trillion workforce crisis. He was previously the CMO of Credntia, a top-five globally ranked coding boot camp that has trained thousands of software engineers.

Illustration: Li-Anne Dias