AI-slop applications cost enterprise TA teams an average 9 hours per week

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Would it surprise you to learn your talent acquisition (TA) team spends more than one day a week sifting through AI slop applications?

Many job seekers are increasingly using AI tools in an attempt to get their foot in the door with employers. It’s affecting recruiters’ workloads: 74% of TA leaders believe that at least one-quarter of applications contain AI-generated or AI-assisted content, according to a new report from AI-powered TA platform Recruitics based on a survey of more than 150 TA leaders at companies with 5,000 or more employees.

That’s created a rise in poor-quality (and sometimes false) applications: 77% of TA leaders surveyed reported an increase in applications overstating candidates’ skills; 52% reported candidates using AI during live interviews; and 40% saw an increase in fraudulent or identity fraud applications.

These knockoff applications cost TA teams a lot of their time: 70% of respondents said recruiters spend six or more hours weekly screening candidates who turn out to be unqualified, duplicates, or misrepresenting themselves. On average, TA teams are spending 9 hours weekly sifting through such applications, the report estimated.

“What that means is it makes it much much harder for the employer, ultimately, to do their job of determining who’s the right fit for the role and who’s going to be successful in this role when we hire them,” Adam Stafford, Recruitics’ chief executive, told HR Brew.

Time to move quickly. Part of this challenge is job seekers moving faster than enterprises with AI adoption.

“A lot of enterprise organizations have, in the past year, said, ‘We’re going to invest in AI, and that’s going to make us more productive.’ But what they really weren’t prepared for was how consumers and job seekers have adopted AI at a speed that no enterprise organization can match,” Stafford explained.

Employers adopting new tools more quickly can create competitive advantage, according to Stafford. Typically, enterprises prefer vendors that provide many point solutions in one package, but catching up to job seekers’ AI use may require considering other vendors, particularly smaller ones, he added.

“What this is pushing a lot of large organizations to do is to actually get more aggressive in terms of seeking out best in breed solutions that directly handle some of these emerging problem sets,” Stafford said.

New signals. TA teams should also scrutinize the specific tools that they use to vet candidates. Currently, 73% of TA teams use résumé screening and ranking tools to vet candidates, making it the most commonly used AI solution among respondents. But this approach becomes ineffective if the applications being screened are embellished to make each candidate’s job experience appear more desirable.

“We’ve got to completely rethink how we are evaluating candidates at the point of submission,” Stafford said, adding that the process of “let’s take a resume and a JD and have a machine tell me which one works or which one fits—at this point it’s almost entirely obsolete.”’

Stafford encourages TA leaders to look at other ways to screen candidates, particularly those who can verify whether a candidate and their claimed job history is authentic. For example, identity or credential verification tools are already used by 54% of respondents, with 53% planning to increase their spend on them over the next year.

These methods have also seen the largest increases in trust over the last two years: Live interviews saw the largest net increase in purported trust, followed by internal referrals, and using verified employment or credentialing data. Meanwhile, traditional signals including cover letters, résumés, and references saw the smallest change in net trust (cover letters, in fact, have declined as a signal of trust among TA leaders).

“The enterprises that move quickly to establish trust signals…that’s going to go a long ways in terms of helping those recruiters claw back some of that 9,10 hours a week that they’re currently using, trying to sift through the haystack here,” Stafford said.