Your CV is read twice. Most people only write it for the second reader.
A parser reads it first, and it does not care how your CV looks. Here is exactly what it extracts, what it drops, and how to fix yours in an evening.
Before a person sees your CV, software turns it into fields: job title, employer, dates, location, skills, education. That step is not optional at most large employers — Harvard Business School's research on hiring technology found automated screening in near-universal use among big US firms, and SHRM's data shows resume screening is the second most common AI application in recruiting.
So your CV has two readers with opposite tastes. The parser wants structure. The human wants evidence. Most candidates write only for the human, then wonder why a well-matched application never got a reply.
What gets extracted, and what quietly disappears
Extracted reliably: standard section headings, month-and-year dates, employer names, job titles, plainly listed skills, degrees. Lost regularly: text inside images or logos, skills shown only as a graphic rating bar, two-column layouts read in the wrong order, dates written as "Summer '23", headers and footers, tables used for layout, and anything in a text box.
The fix is unglamorous and fast. One column. Real headings. MM/YYYY dates. Skills written the way the job ad writes them, because matching is more literal than candidates expect — if the posting says "Kubernetes" and you wrote "K8s", you have created a gap that does not exist. Then, for the human, one measurable outcome per role in your own words.
The ten-minute pass
Collapse to one column and remove every text box and table.
Rewrite dates as MM/YYYY, including gaps.
Match skill vocabulary to the posting — the exact words, where they're true.
Add one number per role: scope, volume, saving, or time.
See the parsed version before you send it
You do not have to guess at any of this. HireSuite's Resume Builder produces ATS-optimised documents [and, if the parsed-field view is in the product, shows you the extracted fields — confirm before publishing], so you can see what survived the parse before an employer does. Candidates who fix parse errors before applying report [X%] more first-round responses.
On using AI to write it
Use it as an editor, not a ghostwriter. Keep every rewrite that makes your work more specific — a number, a scope, a tool, a constraint. Delete every rewrite that only makes it sound more important. That discipline matters more than it used to: recruiters now report AI-exaggerated CVs as the fraud they see most (Greenhouse, 2026), which means generic, inflated language is actively suspicious rather than merely dull.
Chloe Nguyen
Content Strategist
Chloe writes career guides for job seekers on the HireSuite.ai careers platform.
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