There is a new term circulating in the HR hiring world that addresses the challenges recruiters are seeing as AI helps candidates better present themselves across the hiring process. Coined by SHRM, the term describes the gap between how a candidate presents their abilities during the interview process and their ability to do the job.
The term may be new, but the problem is not. Candidates have always overstated experience. What changed is the effort required. AI has made it faster and easier to build a resume that mirrors the job description, generate polished interview answers, and look more qualified than the candidate may be.
In the same SHRM article, the organization highlights survey findings from 2,000 U.S. workers and HR professionals showing just how common the issue has become.
What is more interesting is that skillfishing is not only a candidate problem. It exposes process weaknesses. If the interview is built around assumptions, loose questions, and subjective readouts, polished candidates can pass through too easily. The hiring process starts treating presentations like proof, and that is where the risk shows up.
Remote hiring changed part of that. AI changed the rest. Before AI, a targeted resume took time. Candidates could exaggerate, but tailoring every resume to every job description required effort. Interview preparation also had limits. A candidate could rehearse common questions, but not at the level now possible with AI. That is the issue hiring teams are running into now. A candidate can look right on paper, sound prepared in a screen, and still not be able to do the work once hired.
For example, a candidate can list cloud migration experience, mirror the language from the job description, and give a rehearsed answer about project ownership. The real question is whether they can explain the tradeoffs they made, what broke, what they would do differently, and where their actual role started and ended.
This is no longer a fringe concern. Gartner found that 39% of candidates used AI during the application process. Among those candidates, 54% used AI to generate resume or CV text, 50% used it for cover letters, and 29% used it to generate answers to assessment questions. Gartner also found that 6% of candidates admitted to interview fraud, either posing as someone else or having someone else pose as them. By 2028, Gartner predicts that one in four candidate profiles worldwide will be fake.
Even if every number varies by role, market, and screening method, the direction is clear. Hiring teams are moving from an attraction problem to a verification problem. It is no longer enough to get more applicants into the funnel. Teams need to know which candidates can support what they claim before hiring managers, technical leads, or client-facing leaders spend time with them.
Skillfishing also carries real costs. It slows the team down before a hiring decision is ever made. Recruiters spend more time sorting through candidates who look qualified but don’t have real credibility. Hiring managers get frustrated with the quality of candidates, and SMEs have to spend more time vetting candidates instead of talking to people who are truly qualified. Worse yet, qualified candidates end up waiting and moving on.
The cost gets very real when the wrong person makes it through. CareerBuilder found that 75% of employers have hired the wrong person, with one bad hire costing nearly $17,000 on average. In the same research, nearly half of employers said the person’s skills did not match what they claimed they could do when hired.
For technical and technical-adjacent roles, the risk can be much higher. When a specialized engineer, IT lead, or customer-facing technical employee does not work out, the company is not only replacing a person. It is absorbing lost ramp time, rework, missed delivery, added manager oversight, and the cost of pulling experts back into the process to fix what should have been caught earlier.
AI candidate screening tools like Right Hire are built to validate capability before hiring managers and subject-matter experts spend time figuring out whether a strong resume is real. Right Hire reviews resume claims, conducts first-round screening, asks role-specific follow-up questions, monitors cheating or identity risk, and gives hiring teams evidence-backed reporting before candidates move forward. The point is not to judge candidates on how polished they sound. It is to understand whether they can explain their experience, support their answers, and show that they understand the work.
This is where loose screening breaks down. A polished resume may be enough to get through a keyword screen. A prepared answer may get through a basic first round. But when a candidate has to explain the work, walk through their reasoning, or answer a follow-up they did not rehearse, the gaps become easier to see.
The goal is simple: validate the resume against what the candidate can explain before they move forward.
AI is now part of how candidates apply and prepare. That isn’t changing, and not all of it is a problem. The issue is when AI is used to hide gaps, support cheating, or make someone look ready for work they can’t do.
Hiring teams don’t need to treat every candidate like they’re doing something wrong, but they do need to validate skills sooner. AI structured interviews are one tool teams can use to do this. To see how The Functionary leveraged Right Hireto help improve hiring, read the case study here.
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Replace early interviews with structured evaluations, so your team sees qualified candidates sooner with clear supporting evidence.
