366: How to Improve Your Resume for Applicant Tracking Systems (ATS) - Part 1 of 2
For the first episode of 2026, I am covering how to improve your resume to score higher with Applicant Tracking Systems, known as ATS. There’s a lot of content here, so I’m breaking this into 2 episodes.
I want to be clear in saying that different ATS will provide different approaches to the information they give to recruiters, but my content today is based on several major platforms including Workday, LinkedIn Recruiter, and Greenhouse.
AI summaries help recruiters screen candidates in seconds rather than minutes. The recruiter will often see a “cheat sheet” generated by the ATS that highlights why you are—or aren't—a match for the specific job description.
I’m not covering resume issues that fall outside of the ATS scoring realm in this episode; know that there is much more to writing an effective resume than just what is contained in this episode. However, without these ATS strategies, your resume likely won’t be seen by the humans on the other side of the ATS.
“Fit Score” and Rationale
The first thing most AI tools do is assign you either a numerical score (85% match) or a rating (high potential), followed by a summary that tells the recruiter why you received that score.
Here’s an example: "Candidate is a strong match due to 5+ years of Project Management experience and PMP certification. Matches 4 out of 5 'must-have' skills."
The danger here should be obvious: If you have the experience but didn't use the standard keywords the job description used, the AI might generate a summary saying, "Lacks required experience in [Skill X]," simply because it didn't recognize your alternative phrasing or doesn’t know that you implied a certain skill without naming it specifically.
While the ATS might score all candidates, you can be assured that the recruiters on the other side of the ATS won’t be viewing the materials from candidates who don’t either score above a certain percentage or receive a certain ranking – they just don’t have the time to look for a possible “diamond in the rough.”
The "Highlights" Bullet Points
The next thing the AI does is extract the most relevant data points and present them as a bulleted list. This often includes:
Skills extraction: A list of technical skills you have that overlap with the job posting.
Current/Past Titles: A quick snapshot of your last 2-3 job titles to verify career progression.
Education: specifically noting if you meet the degree requirements.
Tenure: A calculation of your average time at jobs (e.g., "Average tenure: 1.5 years") to flag potential "job hopping."
A word about Job Titles: You may want to “translate” a job title or put the nature of that job in parentheses next to your actual job title to make it clearer to the ATS and humans.
A word about Tenure: While there isn't a universal "kill switch" in an ATS, recruiters and hiring managers generally use these unofficial benchmarks to categorize candidates:
The "Magic Numbers" for Tenure
Less than 1 Year: Red Flag. Almost always viewed as a "job hopper" unless the candidate is a fresh graduate or a career contractor.
1-2 Years: Yellow Flag. Often seen as a "likely hopper." Many companies believe it takes 6–12 months to become fully productive, so leaving shortly after provides little ROI.
3+ Years: Green Flag. Generally considered the "gold standard" for stability and an "A-Player" indicator.
Knockout Questions: Instead of a complex algorithm, many companies use "Knockout Questions" during the application phase (e.g., "Do you have at least 2 years of experience in your current role?"). Answering "No" can trigger an automatic rejection.
Algorithmic Ranking: Some systems rank candidates based on "stability." If two candidates have the same skills, the system may rank the one with longer average tenures higher in the recruiter’s search results.
NOTE: These factors can be neutralized by such things as industry, years in the professional arena (e.g. are you right out of college or a seasoned worker), and being a contract worker. It is important to include not just years, but also months, of employment on your resume – otherwise, you’ll have to fill this in manually in most systems.
If your most recent or current role has been a short tenure, try the following hacks in your resume summary and Experience sections:
Rapid Progression: "Promoted twice within 18 months."
Specific Outcomes: "Delivered $X in revenue within first year."
The "Why": If you left due to a merger, acquisition, or layoff, briefly note that (e.g., "Role eliminated due to company acquisition").
The "Gaps" or "Missing Requirements" Section
This is the most critical part for candidates to understand. The AI explicitly lists what you are missing compared to the job description.
What they see: "Missing Skills: Python, B2B Sales."
What they see: "Education Mismatch: Job requires Master's; candidate holds Bachelor's."
Why it matters: Recruiters often glance at this section first to disqualify candidates quickly.
Inferred "Soft Skills" and Attributes
Newer AI models (like those used by LinkedIn or specialized add-ons) attempt to infer personality traits or soft skills based on how you write your resume.
What they see: "Candidate demonstrates strong leadership potential through progression of titles from Associate to Manager."
What they see: "Communication style appears formal and academic."
Context: Some systems analyze your cover letter or the way you describe your achievements to guess at traits like "adaptability" or "collaboration."
Interview & Feedback Summaries (Post-Screening)
Some systems go beyond the initial resume screening. If you make it past the resume screen, systems like Greenhouse use AI to summarize the feedback from human interviewers.
What they see: Instead of reading 5 different interviewers' notes, the recruiter sees: "Consensus is strong on technical ability, but 2 interviewers flagged concerns regarding cultural fit and willingness to work in-office."