Many resumes submitted to companies are screened out by automated screening systems before hiring staff review them, a report showed.

Tech outlet TechRadar reported on April 19 that a Global Work artificial intelligence (AI) survey of 1,000 U.S. job seekers found most applications must first pass an automated system before any human review.

Companies are using applicant tracking systems (ATS) to sort resumes first. The system reads resumes based on specific keywords, document formats and job-related terms. It checks whether items match rather than gauging potential, creativity or organisational fit. That means even if applicants carefully write resumes and cover letters tailored to job postings, an automated filter may be operating behind the lack of response.

The problem is that reasons for rejection may have nothing to do with actual job ability. The system cannot assess creativity or organisational fit and only checks whether set conditions are met. As a result, even applicants with sufficient experience can be rejected early if their phrasing differs from the wording the system expects. For example, even if an applicant wrote experience meant to convey "increased sales by 30 percent", it could be filtered out if the system is designed to prioritise expressions such as "sales growth".

Document format is also a variable. Tables and images, complex layouts and unusual fonts can cause errors as the system reads a file. That can lead to early rejection for reasons unrelated to ability, even for strong applicants. Hiring AI can process thousands of resumes per hour, but that also means it has limits in accurately identifying suitable candidates presented in unstructured formats.

In this trend, job seekers are also actively using AI. The survey found that 68 percent of job seekers said they use AI to write resumes. It also pointed to criticism that many applicants do not fully recognise that the same technology can work against them in corporate screening.

As a result, resume-writing methods are also shifting to an ATS focus. For career section headings, standardised terms such as "work experience" are more advantageous for sorting information than creative wording. Simple text-based documents or common word-processor files also help reduce errors. Matching key words in job postings as closely as possible, rather than substituting synonyms, raises the likelihood of passing the first stage.

The market is also producing resume-writing tools that include ATS optimisation features to reflect such demand. They work by checking for potential rejection factors in a document before submission. Still, the bigger limitation cited is not that automated screening tools are designed to maliciously exclude applicants, but that they "do not consider human nuances at all".

Ultimately, unless companies review their current filtering methods, qualified applicants may continue to be rejected at the algorithm stage. As automation spreads to improve hiring efficiency, designing resumes not only for content but also in a format the system can read is becoming an effective requirement for job seekers.