AI is now shaping both sides of the hiring process. Employers use it to filter applications, accelerate recruiting and even conduct initial screenings. Job seekers use it to optimize résumés, insert keywords and refine language. What’s left is a sea of polished, algorithm-friendly applications that look nearly identical.
That uniformity comes at a cost.
One study found that high-ability workers are being hired 19% less often than lower-ability candidates because AI-polished résumés mask any meaningful differences.
When every application looks strong, hiring becomes a guessing game. But job targeting and behavioral assessments help remove the guesswork, surfacing what résumés and LLMs can never replicate: the drives and motivations a hiring manager actually defined for the role.
The problem when every résumé looks “perfect”
When every candidate looks qualified, hiring stops working the way it should.
For job seekers, the rise of AI in hiring has made the process harder to trust. According to a recent World Economic Forum report, 90% of employers now use automated or algorithmic systems to prioritize, rank or deselect candidates. Some companies even have AI agents conducting initial interviews, with mixed results. Job seekers describe being screened by AI agents that cut them off mid-sentence, misunderstand their answers and leave them unsure whether a human will even review their responses.
For employers, AI tools pose challenges as well. LLMs make it easier for candidates to try to game the system. Some embed hidden chatbot instructions in their résumés to manipulate screening tools. Résumés, cover letters and interview responses appear increasingly immaculate, erasing authenticity and unique personality traits that used to shine through.
In the end, both sides are optimizing for the algorithm, not for the fit. Résumés can’t be taken at face value. AI filters are being gamed, genuine talent struggles to break through the noise and the early-stage tools organizations have long relied on have become exercises in impression management rather than talent identification.
Hiring teams and job seekers alike need a process they can trust, starting with a clearer definition of what the role actually requires before the first application ever comes in.
Why behavioral assessments are harder to game than you’d think
A behavioral assessment can measure what a résumé never could, but only if candidates can’t reverse-engineer it.
A candidate can feed a job description into ChatGPT and produce a perfectly tailored résumé. They can’t do the same with a job target they’ve never seen. Because the target lives entirely within the hiring team’s evaluation process, invisible to the applicant, a behavioral assessment reflects how someone is actually wired to work instead of how they’ve been coached to present.
That’s what makes the combination defensible. There’s no algorithm to optimize for, just a clearer view of how a candidate is likely to show up in the role.
How job targeting helps you sift through AI-saturated applications
The value of a job target depends entirely on how it’s built. A generic benchmark or a target set by a single stakeholder will reflect assumptions more than reality. A target built with the right people, around the right questions, is harder to game and more predictive of actual performance.
A few principles that make a major difference when hiring the right people:
- Bring in more than one voice. Hiring managers, current top performers in the role and HR representatives each bring a different context. The hiring manager knows what the day-to-day demands are; top performers know what it actually takes to succeed; HR ensures the criteria connect back to job performance rather than personal preference.
- Anchor to behaviors, not backgrounds. A job target should define the drives and motivations that predict success in a specific role, not the credentials or experience of the last person who held it. Those are proxies. The behavioral requirements are the signal.
- Revisit your job targets when the role changes. A job target set three years ago for a role that has since shifted is both outdated and actively misleading. As team structure, scope and expectations evolve, the target should too.
When the process is built this way, the conversation shifts naturally. Hiring stops being about who interviewed best and starts being about who is actually built for the role.
