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Alison Lands is Vice President of Employer Mobilization at Jobs for the Future.
Artificial intelligence promises to make hiring smarter and more objective. But in reality, AI-powered tools introduce new layers of discord and doubt at a time when trust in employment is already fragile.
Employers are currently being inundated with more applications than they can realistically handle. Overwhelmed recruiting teams are turning to AI-powered solutions to prioritize speed and scale in an increasingly tense labor market and prioritize high volumes of applications. They are leveraging new tools to screen resumes, schedule interviews, assess competency, and even predict job suitability.
In a self-reinforcing cycle, the increased use of generative AI on the candidate side is driving an influx of applications. These tools allow candidates to quickly create resumes and cover letters, and nearly half of job seekers are using AI to increase the number of applications they receive.
As a result, we are in a de facto “AI arms race.” A volatile labor market, driven in part by increased uncertainty due to the introduction of AI, has produced a recruitment system that moves faster than ever before, but with less clarity, confidence and shared understanding of what qualifications actually mean.
To maximize what AI can offer employers in terms of efficiency, while working towards successful recruitment outcomes for job seekers, there are fundamental changes that business leaders need to address as a top priority.
A crisis of confidence in the employment system
A special report from the University of Phoenix Career Institute, “The Illusion of Progress in Skills-Based Recruiting,” documents the increasing use of AI-powered solutions in the hiring process and the associated challenges.
Nearly 30% of hiring professionals say AI tools are starting to perform tasks once performed by humans, raising urgent questions about fairness, transparency and trust, according to the report. More than half of candidates (57%) and almost half of hiring professionals (47%) believe that AI will impact the objectivity of the hiring process. Half (50%) of recruiters are concerned that these tools may screen out qualified candidates.
Concerns about the use of AI in the recruitment process are widespread, but no action has yet been taken to address them. I’ve said before that AI is an impatient technology and traditional recruiting and talent infrastructures weren’t built to move at this speed. This discrepancy amplifies the very suspicions these tools were intended to alleviate. According to a study by the University of Phoenix, only 37% of organizations using AI in their recruitment processes currently audit the tools for fairness, leaving an alarming gap between risk and responsibility.
As AI tools become integral to both job seeker and employer recruitment, the stakes have never been higher to build trust in this technology and establish best practices for its use.
Create a new standard for talent management with AI-powered support
As employers and job seekers navigate the disruption of AI, companies are also exploring another change: moving toward skills-based hiring and talent management processes. At Jobs for the Future, we believe skills-based hiring will change the way jobs are defined, promoted and hired. This approach brings objectivity to talent decisions by assessing what can be done on the ground, expanding the workforce and increasing access to opportunity. Most employers are moving in this direction.
According to a University of Phoenix study, the majority (82%) of hiring professionals say their processes are moving toward skills-based practices.
But adopting the language of skills is not the same as building a skills-based system.
A University of Phoenix special report finds that many organizations promoting skills-based practices are not taking complementary measures to make those practices a reality. 53% of employers report a lack of standardized hiring practices, and 57% of hiring professionals say they need better training to assess candidate skills.
The result is a system that lacks a consistent framework, shared evaluation criteria, and interviewer preparation. The gap is clearly visible in readiness. Almost a quarter (24%) of hiring professionals say they don’t receive training or materials before interviewing new candidates.
In that vacuum, hiring teams often fall back on familiar shortcuts like intuition, referrals, and subjective notions of “fit.” These traditional tactics undermine the benefits of increasing fairness and transparency in the hiring process, and even when combined with AI, these inequalities will not disappear but increase. When AI adoption outpaces training and governance, risks grow faster than outcomes. In that case, trust is lost not because skill-based practices are flawed, but because implementation is incomplete.
In other words, skills-based recruitment cannot be just an aspiration. For an AI tool to support it, it needs to become a true operating system. When standardized and implemented consistently across teams and processes, a skills-based foundation gives AI tools something objective and job-relevant to measure, combining efficiency and objectivity to drive real progress toward a more successful employment system for everyone.
Future Directions: Taking Skills-Based Recruitment Models from Intent to Reality
Currently, the solution to opaque and untrustworthy AI is not more sophisticated tools. It’s the stronger foundation underneath. But how can companies achieve that?
- For employers, it starts with operationalizing skills-based practices end-to-end. This means defining clear skills criteria, using structured and consistent assessments, and providing training to hiring teams to ensure they are assessing skills rather than defaulting to alternatives such as familiarity, referrals, or perceived “fit.”
- Technology integration processes must treat equity as non-negotiable. AI tools used in recruitment should be regularly audited for bias and validated against legitimate job-related standards. Organizations should also be transparent with candidates about how and when AI is used, and ensure that human oversight is central, especially in high-stakes decisions.
- Finally, governance must be continuous. By implementing cross-functional oversight, continuous monitoring, and feedback loops from both candidate and hiring teams, you can keep the process as objective as possible, even as technology and job descriptions continue to evolve.
Skill-based models give AI meaningful measurements and restore trust while maintaining efficiency. Trust in AI-powered hiring will be earned the old-fashioned way: through clear standards, trained talent, and visible accountability. When organizations invest in clear standards and consistent practices, AI can ultimately deliver on its promise of supporting better decisions, fairer outcomes, and hiring systems that people actually trust.
