David Chocron
We all knew AI was going to be transformational.
And although it’s still early days, we’re already seeing vast changes in how we go about our everyday business. From planning an itinerary to drafting an email, AI is redefining how we seek solutions and perform tasks, both personal and professional.
The world of talent acquisition is no exception, with skills matching and agentic automation among the many positive innovations winning back wasted hours for overworked talent teams and boosting the quality of hires. This extends throughout the HR function. Citing a recent Gallup survey, Forbes stated that 93 percent of CHROs have integrated AI into their everyday activities, with ideation and reduced administrative work identified as areas for quick wins.
There is, however, one area in particular where AI is proving problematic.
The Rise of the AI-Powered Serial Applicant
As I speak with clients, prospects and peers, we all agree that one area that has become immensely difficult to deal with is that of ‘AI-powered candidates’.
They have always existed in some form or another: the serial applicant who applies for a hundred jobs in your organization while barely meeting the criteria for even a handful. And while they would often cause some noise, the issue was manageable. With a few automations and rules, an ATS could easily spot them, and the recruiter could filter them out in favour of the truly committed talent.
But all that’s now changed. Serial applicants are getting to grips with large language models (LLMs) and using them to tailor resumes to positions and apply in a matter of seconds. The issue is further compounded by some candidates employing AI bots to comb job boards and apply for roles on their behalf, automatically tailoring their CV and cover letter to the job description. And ATS filters are having a much harder time catching them.
An AI Arms Race Emerging Between Candidates and Recruiters
In hindsight, it was only a matter of time until this happened. Interestingly, a perfect analogy of the issue can be found in a core component of how today’s AI tools make self-improvements: generative adversarial networks (GANs).
GANs mirror human communication: there’s a sender and a recipient. And the purpose of them is to improve the technology’s ability to determine whether a message is authentic or fake. Over the course of countless iterations, the recipient learns from its mistakes to become better at spotting fakes, while the sender also learns to become better at crafting its deceptions.
If we were to transpose this digital game of cat and mouse to the talent world, we would be the cat, or the recipient. Our responsibility, therefore, is to sharpen our ability to ‘detect the fake‘ and sift out these serial candidates. As I see it, there are two key ways to do this:
- Strengthen the filtering layer of your ATS
- Incorporate sourcing strategies, an algorithm can’t game
Let’s dive deeper into these paths…
1. Strengthen the Filtering Layer of Your ATS
Savvy process design and the right technology can work together to provide extra checks, without tying up recruiters’ time. Assessments, limit-setting and recorded interviews will play a key role here.
- Pre-Interview Screening Assessments:
Not only do these provide an additional friction layer to help verify intent, but they also allow us to gain a greater understanding of the candidate’s skills and capabilities on a quantitative level. - Setting Application Limits:
Recently, we have been seeing clients implement restrictions around how often candidates can apply (e.g. 3 applications within a 12-month period). Enforcing these limits gives candidates pause to consider whether the job they are applying for really is the best fit for them. - Recorded interviews:
Removing the synchronous aspect of interviews reduces the risk of a bottleneck of interviewer availability. But it also provides an opportunity to assess candidate suitability more qualitatively without compromising on efficiency. As these tools evolve, it will become interesting to see whether AI can uplift the quality of these questions by tailoring them dynamically based on recruiter prompts and the candidate’s previous responses.
2. Incorporate Sourcing Strategies an Algorithm Can’t Game
Get Face-to-Face at Recruiting Events:
In-person meetups, careers fairs and networking gatherings have taken on a new importance in an era of AI-driven hiring. While CVs can be made to look flawless, they can’t shake your hand, hold eye contact or navigate a spontaneous conversation. These events give recruiters a chance to pick up on non-verbal cues, communication style and signs of genuine enthusiasm – something an algorithm can’t fake.
Even a brief, five-minute interaction can reveal subtle insights into a candidate’s suitability that are far more difficult to glean from a pristine CV. Companies that consistently show up at industry-specific fairs or host their own community meetups build trust, brand recognition and a pipeline of candidates who have already demonstrated real-world engagement.
Tap Into Referral Programs:
There’s never been a more important time to have someone vouch for a candidate, to raise their hand and say “I know them, they can do it”. As recruiters drown in perfect-on-paper applications, the signals we used to see as measures of intent are becoming less relevant. Tailored keywords, cover letters, all of the little details and extra discretionary effort that once showed a candidate is genuinely keen are no longer differentiators in a world in which generative AI can provide this in seconds. They’re the bare minimum.
On the other hand, a referral from someone inside the organization still speaks volumes. According to an article from Mercer, referrals convert 4 times faster and stick 70 percent longer, and cost $1,000 less per hire.
Engage Your Talent Community:
AI has brought application sprees, but what happens when you don’t have jobs to offer to a candidate? A select group will be going out of their way and engaging with you even when there are no open jobs – providing you give them the opportunity. The leads you receive from your talent community will provide a reliable source of talent with a clear demonstration of intent.
The Tech is There to Help, But We Need Humans in the Loop
As AI-powered applicants flood the system, it’s no longer enough to rely on resumes, cover letters and keyword tailoring to determine their level of intent. We need to make sure we’re applying smarter filters and sharper instincts.
The future of hiring lies in combining the efficiency of technology with targeted human engagement. Assessments and automation can help, but real intent is best spotted through conversations, referrals and community engagement. The companies that win won’t just have the best tech; they’ll have the best read on people.
Interested in learning more about how Avature can help you cut to the quality candidates? Contact me here or on LinkedIn.