Artificial intelligence has become a standard part of recruitment, but employers remain uneasy when candidates use the same tools in their applications, according to a new Resume Genius survey.
Resume Genius based its 2026 AI Impact on Hiring Report on a Pollfish survey of 1,000 US hiring managers and hiring team members. The survey examined employer use of AI, experiences with AI-assisted candidate materials, and perceptions of AI’s effects on hiring.
The report found that 87 percent of hiring managers use AI during at least one stage of recruitment. Resume screening was the most common application, reported by 58 percent of respondents, followed by writing job descriptions and postings at 46 percent, matching candidates to roles at 44 percent, and scheduling interviews at 41 percent.
At the same time, 82 percent of hiring managers said they were concerned about candidates using AI in job applications, and 86 percent expected AI to make it harder to determine whether application materials accurately reflect a candidate’s abilities.
AI creates an authenticity gap
Most respondents said they had encountered signs of candidate AI use. Fifty-eight percent reported receiving AI-generated resumes or cover letters, 46 percent had encountered candidates using AI to answer interview questions, and 34 percent had seen AI-generated LinkedIn or other social media profiles. Twenty-nine percent reported AI-generated portfolio work, while 28 percent said candidates had used AI to cheat on skills assessments.
Seventeen percent of hiring managers also reported encountering deepfake technology during video interviews. However, the report did not provide details about how employers identified or verified those incidents.
Employers reported confidence in AI’s potential to improve their own processes. Seventy-two percent said it would make hiring faster and more efficient, 68 percent believed it would help identify stronger candidates, and 65 percent said it could help reduce hiring bias.
Transparency remains inconsistent. Thirty-five percent of respondents said their organizations always disclose the use of AI during interviews or candidate evaluations, while 26 percent disclose it only sometimes. Twenty percent said their organizations do not currently disclose their use of AI, including 12 percent with no plans to do so.
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Evaluating candidates through direct evidence
Eva Chan, a career expert at Resume Genius, says employers should address concerns about authenticity by changing how they assess candidates rather than discouraging applicants from using AI.
“Employers can’t have it both ways. They use AI to screen resumes and write job postings, so candidates should be allowed to use it too,” Chan said. “The real issue is that employers can no longer tell who someone is on paper, and that’s a fair concern. But the answer isn’t to ask candidates to stop using tools that employers rely on themselves. If companies want a true picture of a candidate, they need to change how they evaluate people. That means live problem-solving, structured interviews, and real work samples. This is something employers need to fix, not something candidates should be blamed for.”
Applying direct assessment in laboratory hiring
For laboratory employers, Chan’s recommendation offers a practical framework for evaluating candidates through direct, job-relevant evidence. Rather than relying on predictable questions that candidates can rehearse, hiring teams can present realistic scenarios and ask applicants to explain how they would respond. Using the same scenarios, follow-up questions, and scoring criteria for every candidate can help interviewers compare responses consistently and focus on how applicants process information, structure decisions, and troubleshoot operational problems.
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The evaluation should reflect the responsibilities of the position. Some examples may include a candidate:
- Interpreting an anonymized quality control trend
- Reviewing a short standard operating procedure (SOP) excerpt
- Explaining how they would respond to an instrument failure
- Prioritizing work during a sample backlog
For roles requiring specific bench skills, employers can also use a supervised work sample or technical demonstration and ask references to verify the candidate’s proficiency with particular methods, instruments, or software.
Hiring teams can also assess safety awareness by asking candidates to describe a routine laboratory procedure from beginning to end without specifically prompting them to discuss safety. Their answers can reveal whether they naturally incorporate personal protective equipment, hazard identification, waste handling, and other safety considerations into the workflow. Interviewers can also evaluate traits such as accountability, curiosity, and receptiveness to feedback through targeted behavioral questions rather than relying on conversational chemistry or a general impression of “fit.”
These practices shift responsibility back to employers to design hiring processes that measure the capabilities the laboratory actually needs. As AI makes application materials easier to produce and refine, direct evaluation can provide stronger evidence of how candidates think, work, and respond to realistic laboratory conditions.
This article was created with the assistance of Generative AI and has undergone editorial review before publishing.
