Let software do the first pass: score every resume against a clear job description, rank the applicants, and have a recruiter review the top of the list and spot-check the rest. It only works if three things are right: a precise job description, resumes read for real experience, and an applicant pool drawn from the right audience.
What are the three moving parts?
Your ask. The JD is the instruction set for your screening. If it is vague or stuffed with keywords, screening will run and still surface the wrong people.
Resume quality. Many candidates write resumes to pass filters, not to communicate. Good candidates with poorly structured resumes get dropped, and keyword-tuned resumes get through. Screening that reads actual experience handles this better than keyword matching.
Audience. If you reach the wrong people, even good screening will not fix your conversion rate.
What goes wrong when one part is off?
Wrong ask: screening runs fast and surfaces the wrong candidates at scale.
Poor resume reading: real talent gets filtered out, and the shortlist favours good resume writers.
Wrong audience: low conversion at every stage, whatever the screening quality.
The three are connected. Fix one and the other two still limit you.
What does good screening look like?
A precise ask, the right audience, and a method that reads each candidate's experience and trajectory, not just whether the right words appear.
AI resume screening that compares a resume to a JD by meaning gets closer to this than keyword matching. Add a relevancy threshold that decides who is invited to the next stage, such as an AI phone interview, and keep candidates below it in the ATS for a recruiter to review rather than rejecting them automatically. More in What Is AI Resume Screening, and Does It Actually Work?