Staggering Piles of Applicants—and Rejections
Sixty percent of firms use AI that rejects literally billions of candidates. How can firms inject humans into the process?
The hiring managers were underwater on the hundreds—sometimes thousands—of applications that reached them for every role, despite a robust applicant-tracking system. Then the head of HR saw an ad for an AI platform that could perform the HR drudgery that was slowing down his department. The platform would create teams of AI agents—the equivalent of dozens of new staffers working around the clock. He went for it. What could go wrong?
More than you might think. One leading AI platform rejects 8,950 applications every half hour, scrapping a staggering 157 million résumés annually. That platform is used by 60% of Fortune 500 firms: Do the math, and you realize that a single firm could end up rejecting over a billion applicants over the span of just a few years. And while undeniably helpful, such platforms are prone to making decisions based on errant data sets. “These firms are ceding judgement to a machine,” says Karena Man, senior client partner in the Technology and Digital practice at Korn Ferry. “Spotting and assessing for talent is a task that humans are much better at.”
Corporate use of AI to automate decision-making is not new. But the sheer power and reach of the technology continues to grow: Between 2024 and 2025, AI use in HR jumped from 26% to 43%, according to figures from the Society for Human Resources Management. In just two years, the platforms have gone from widely used to dominant.
To be sure, automating repetitive tasks sounds good in principle—but it’s often at cross-purposes with good hiring practices. Ideally, hiring managers should seek the widest possible group of qualified candidates, says Man. “I tell my clients that early in the hiring process, we want to filter in rather than filter out candidates.” This means catching traits like potential. For example, a hiring manager might look past a candidate’s limited relevant experience because of their interesting mid-career pivot, or recognize the usefulness of two seemingly unrelated skills in the context of future developments in technology and business developments. Another hiring manager might willingly overlook an applicant’s lack of retail experience because they worked twenty hours a week at their family’s restaurant from ages 12 to 20—a strong indication that they may understand more about customer service than most retail executives.
Experts say that AI filtering platforms are usually good at identifying applications that precisely match the desired candidate profile. This can be useful if a firm is trying to fill a lower-skilled role at high volume. But the platforms miss applicants with both upskilling potential and diversity of experience. “The trade-off is the loss of identifying future talent for the organization,” says Man.
The solution is surprisingly straightforward—but implementing it is labor-intensive. “Never use a single automated decision point to make a decision,” says Bryan Ackermann, head of AI strategy and transformation at Korn Ferry. For example, a hiring manager might search candidates in various ways—on an AI platform, LinkedIn, Google, or in specialized talent pools—before making a human decision. This is critical, as both platform vendors and employers have recently faced litigation due to hiring platforms’ algorithmic decision-making.
Firms have nonetheless embraced AI platforms—sometimes without the human oversight necessary. “A lot of companies trust that an AI solution is already trained, and a ready-made tool,” says Paul Fogel, sector leader in the Software practice at Korn Ferry. “It’s unfortunate, but this is what happens when there’s an AI arms race.”
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