The number of people applying for unemployment benefits in the week ending June 20 fell by 12,000 to 215,000, the Labor Department reported, beating analyst expectations. Even with that improvement, about 7.3 million Americans were still looking for work as of May, according to the Bureau of Labor Statistics. As applicants sharpen their CVs - often with AI tools - employers and HR directors are using AI to screen the piles.
Shaun Pichler, Ph.D., professor at the Department of Management at California State University, Fullerton, and senior editor of the Journal of Occupational & Organizational Psychology, offered insights into how AI screening works and what actually makes a résumé rise to the top.
Where AI screening actually shows up
AI-driven screening is not the universal first-pass filter many job seekers imagine. Pichler said its use depends heavily on the role and the employer. "I wouldn't say that AI does the first pass on most resumes and applications because recent survey data indicates that a little less than half of responding organizations use AI for recruiting in general."
It appears most often for entry- to mid-level positions at large companies that receive high volumes of applications per opening. For executive roles or when a recruiter is working through referrals and priority candidates, AI may never enter the picture.
Does AI favor AI-written résumés?
Early research suggests the answer might be yes. "Since LLMs tend to prefer self-generated content, there is reason to posit that AI actually has a positive bias in favor of AI-constructed resumes," Pichler said. But he cautioned that the evidence is thin. "There just haven't been enough studies done on this topic to draw any meaningful conclusions, at least not yet - but give it a couple of years."
Skip the paid optimization tools
Many websites promise to tailor a CV for AI algorithms. Pichler is skeptical. "My suggestion to job candidates is to use multiple LLMs, like ChatGPT and Claude, to help them with their resumes instead of paying money to a third-party vendor. My personal opinion is that this is a waste of money in most cases."
What makes a résumé stand out to both AI and humans
Pichler's central advice applies regardless of who - or what - is reading the application: mirror the language of the job posting. Highlight important keywords, quantify experience wherever possible, and keep the focus on job-relevant information. Tailoring each submission to the specific role still matters most.
AI can speed up that tailoring. Give a large language model your résumé, a narrative of your accomplishments, and the job description, and it can produce a version that aligns tightly with the posting. The same principle works in reverse for HR professionals writing job ads - precise, concrete language helps the right candidates surface in a screening system.
Why this matters for HR professionals
The findings are a reminder that AI screening is far from ubiquitous and that its biases - including a potential tilt toward AI-generated content - are still poorly understood. For HR teams, that means AI tools should complement human judgment, not replace it. When building or buying screening systems, understanding how they evaluate language and where they break down is a practical necessity. For HR managers tasked with implementing these tools, a structured learning path like AI Learning Path for HR Managers provides training on recruitment automation, workforce analytics, and responsible deployment.
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