How companies use AI to screen resumes

What AI screening software actually does

Applicant tracking systems (ATS) like Greenhouse, Lever, Workday, iCIMS and Taleo have built-in or bolt-on AI layers that parse your resume into structured fields: job titles, employers, dates, skills, education, location. The AI then compares those fields against the job posting and produces a match score or a ranked position in a candidate list.

Newer tools go further. They use large language models to read full sentences, infer skills from context and flag candidates whose experience is semantically similar to the requirements even if the exact words differ. However, most enterprise deployments still weight keyword overlap heavily because it is auditable and defensible to hiring managers.

• Parsing: the system extracts your data into discrete fields
• Matching: it compares your fields to the job requirements
• Scoring: it assigns a relevance score or rank
• Filtering: recruiters often see only the top 10 to 20 percent of applicants

Why formatting matters as much as content

AI parsers read plain text. Complex layouts, tables, text boxes, headers and footers, and graphics can cause the parser to misread or skip sections entirely. A resume that looks polished in a PDF viewer may arrive at the ATS as a jumbled block of text with your job titles missing.

The safest structure is a single-column layout with standard section headings: Summary, Experience, Skills, Education. Use a standard font, left-aligned text and bullet points made with a simple hyphen or bullet character. Save as a PDF unless the application explicitly asks for a Word document, because PDFs preserve your formatting across systems.

• Avoid tables, columns and text boxes
• Use standard headings: Summary, Experience, Skills, Education
• Keep fonts simple: Calibri, Arial, Georgia or similar
• Do not put contact details only in a header or footer
• Spell out acronyms at least once: 'Search Engine Optimisation (SEO)'

How to match keywords without keyword stuffing

The job posting is your primary source. Read it carefully and note the exact phrases used for required skills, tools and qualifications. If the posting says 'stakeholder management' and your resume says 'managing stakeholders', some parsers will match them and some will not. Using the posting's exact phrasing removes that uncertainty.

Work keywords into real sentences rather than listing them in a block. A skills section is useful, but a bullet point that says 'Led stakeholder management across five product teams' is stronger than a bare list entry. It gives the AI context and gives the human reader evidence.

Prioritise the requirements listed first or marked as required. Optional or preferred skills matter less. If a skill appears three times in the posting, it is almost certainly weighted heavily in the scoring model.

1. Copy the job posting into a plain text document
2. Highlight every skill, tool, qualification and responsibility
3. Check each one against your resume
4. Add missing terms where you genuinely have that experience
5. Rewrite bullets to use the posting's exact phrasing where possible

What AI screening cannot do well

Current AI screening tools struggle with career changers, non-linear paths and roles where the title varies by company. A 'Growth Manager' at one company is a 'Demand Generation Lead' at another. If your title does not match the posting's language, you may score lower even if your work is identical.

AI also cannot reliably assess quality of experience. It can see that you managed a team; it cannot tell whether you managed two people or two hundred. This is why your bullets need to include numbers. Quantified achievements give both the AI and the recruiter something concrete to evaluate.

Gaps in employment, short tenures and non-traditional education are handled inconsistently across platforms. Some systems flag them; others ignore them. You cannot control how a specific system handles these, so focus on what you can control: clear dates, accurate titles and strong skill matching.

Writing a resume that works for AI and humans

The goal is a resume that passes the AI filter and then persuades the human who reads it. These two goals are mostly aligned. Recruiters also want to see relevant skills, clear job titles and quantified results. The main difference is that humans can infer and forgive; AI cannot.

Write a short summary at the top that mirrors the job title and two or three core requirements from the posting. For example, if the role is 'Senior Data Analyst' requiring SQL, Python and stakeholder reporting, your summary might read: 'Senior data analyst with six years of experience in SQL and Python, building dashboards and presenting findings to non-technical stakeholders.' That sentence alone will match several high-weight keywords.

Each experience bullet should follow a simple structure: action verb, task or project, measurable result. 'Reduced report generation time by 40 percent by automating SQL queries' is better than 'Responsible for SQL reporting.' Keep bullets to one or two lines. Aim for four to six bullets per role.

How Break-In Bad helps you write for AI screening

Tailoring a resume for every job posting is time-consuming when done manually. Break-In Bad's AI assistant Omar reads the full job posting, including location, visa requirements, experience level and listed skills, and scores how well your current resume matches. Jobs scoring 75 percent or above are auto-applied; jobs between 45 and 74 percent are flagged for your review.

If your resume needs work before applying, Omar can enhance it through a short conversation of up to 20 questions, drawing out the specific experience and achievements that match the role. She then writes a tailored resume and cover letter using only facts you have confirmed, and checks every line against what you said. Applications go out to Greenhouse, Lever, Workday and other employer sites with a screenshot as proof. Credits start at 25 for $9, and you get 50 free credits when you sign up.