Quick answer: Applicant Tracking Systems don't read CVs — they parse them for specific signals and assign a match score. These are the six patterns that reliably suppress that score, why they appear so often in AI-generated CVs, and what the fix looks like in each case.
The most common piece of job search advice is also the most frustrating: tailor your CV to each role. Everyone says it. Almost no one actually does it, because doing it properly — extracting the keywords from a job description, restructuring your achievements around them, adjusting the weighting of each section to match the seniority signal the hiring manager is looking for — takes between two and four hours per application. At that rate, a serious job search becomes a second job.
So people compromise. They update the personal statement. They swap one or two bullet points. They run the CV through a generic AI writer and tell themselves it's tailored. The ATS disagrees. The match score comes back low, the application doesn't surface in the recruiter's shortlist, and the cycle continues.
The six patterns below are not random CV mistakes. They're the specific structures that applicant tracking software is built to penalise — and they appear in the overwhelming majority of AI-generated CVs precisely because a language model writing without role context produces exactly the kind of generalised, responsibility-led, keyword-thin document that ATS algorithms are designed to filter out.
The Six Patterns and What to Do Instead
1
Responsibility-led bullets with no outcomes
Bullets that begin with "Responsible for…" or "Managed…" describe a job function, not a contribution. ATS systems parsing for seniority and impact signals find nothing to score. More advanced platforms use machine learning to assess achievement density — a section of pure responsibility statements scores near zero against a job description asking for demonstrated results at scope.
Rewrite every bullet using the CAR structure — Context, Action, Result. The result doesn't have to be a precise percentage; scope, scale, and direction of impact all satisfy achievement scoring. "Managed the UK social media accounts" becomes "Grew UK social media following from 12K to 47K across 18 months by shifting 60% of content to short-form video — ahead of a category shift that competitors adopted six months later."
2
Synonym mismatch on required skills
A job description asks for "stakeholder management." Your CV says "senior relationship building." A human recruiter reads both and understands. An ATS keyword parser looking for the string "stakeholder management" finds zero instances and scores accordingly. The same mismatch happens constantly with tool names ("Excel" vs "Microsoft Excel"), methodologies ("agile" vs "scrum"), and seniority markers ("P&L responsibility" vs "budget management").
The job description is the keyword brief. Every required and preferred skill listed in the posting needs to appear in your CV using the exact phrasing the employer chose — not a synonym, not an expansion, not a paraphrase. Read the posting as a specification document, not a narrative. The words that appear in the requirements section are the words your CV needs.
3
Multi-column and table formatting
Visually sophisticated CV layouts — two-column designs, skill rating bars, sidebar contact sections, tables used for experience entries — are among the most common causes of ATS parse failures. The software reads text sequentially. A two-column layout produces garbled output: left-column content and right-column content are merged into nonsense strings, dates get detached from job titles, and the parsed document is structurally unusable for scoring. The application may never surface at all.
Single-column, left-to-right, top-to-bottom. Standard heading names: "Experience," "Education," "Skills." Dates in consistent format (Jan 2022 – Mar 2024 or 01/2022 – 03/2024). Contact details in plain text at the top, not in a header text box. No tables. Save design for a PDF portfolio linked in the contact section.
4
A generic personal statement that addresses no one
The summary section of a generic AI CV reads identically across twenty different applications: "A results-driven professional with X years of experience seeking a challenging role where I can contribute to a forward-thinking organisation." This statement contains no searchable keywords, signals no understanding of the specific role, and tells a recruiter — when it reaches one — nothing about fit. Many ATS systems now score the summary section for role-specific language density; a generic summary contributes nothing to the match score.
The summary should be the first place role-specific language appears. Three to four sentences: your current title and years of relevant experience, your two or three most role-relevant achievements at the scope the target job requires, and the specific value you bring to this type of organisation. Rewrite it for every application. It takes eight minutes when you have the job description in front of you.
5
Skills section populated with soft skills
"Team player. Strong communicator. Problem solver. Adaptable." These are what every applicant claims and what no ATS system is scanning for. A skills section populated with soft skills instead of hard skills and named tools is occupying space that could be scoring keyword matches against the job description's requirements. The system is looking for "Salesforce," "SQL," "HubSpot," "IFRS," "Figma" — the specific technical vocabulary of the role — not attributes that are untestable from a document.
The skills section should list hard skills, named tools, certifications, and methodologies that appear in the job description or are standard in the sector for this role level. Soft skills belong in the achievement bullets where they can be evidenced — not in a standalone list where they're an assertion.
6
Seniority mismatch between the document and the role
A Director-level application submitted on a CV structured like a graduate application — education leading, short achievement bullets, no indication of scope or commercial impact — signals a misunderstanding of the level being applied for. Conversely, a junior candidate whose CV uses executive framing ("drove organisational transformation," "architected strategic vision") triggers a credibility gap when the underlying experience doesn't support the language. Both are seniority mismatches, and both suppress both ATS scoring and recruiter confidence.
Section weighting, summary tone, and achievement depth should all reflect the level of the role. Senior and executive CVs lead with commercial impact and strategic scope; the education section moves to the bottom. Graduate CVs can lead with education but need achievement evidence — from internships, projects, or extracurriculars — structured to the same CAR standard as professional experience.
💡
The common thread
Every pattern above is a symptom of the same root cause: writing a CV about your history rather than writing a CV in response to a specific job. The document that fixes all six issues is built around the job description — which means the job description has to be read, analysed, and used as a brief before a single bullet is drafted.
That gap is exactly what the Resume & CV Builder skill for Claude was built to close.
What Role-First Research Produces Instead
The NovaKit Resume & CV Builder skill treats the job description as the primary input, not the candidate's existing CV. The posting is analysed for required skills, preferred tools, seniority signals, outcome language, and the specific vocabulary the company used when they wrote the role — before any content from the candidate's background is structured into the final document.
The CV that passes ATS isn't better-written. It's written against the right brief — the one the hiring company already published.
Here's the same candidate experience, written generically versus after role research, for a data analyst role at a healthcare technology company:
Data Analyst — MedTech Co (2023–present)
• Responsible for analysing large datasets to provide insights and support business decisions.
• Used various data tools to create dashboards and reports for stakeholders across the organisation.
• Collaborated with cross-functional teams to improve data quality and reporting processes.
Data Analyst — MedTech Co (2023–present)
• Built and maintained patient outcomes dashboards in Tableau for 4 clinical teams — reducing ad-hoc reporting requests by 60% and cutting time-to-insight from 5 days to same-day for standard metrics.
• Automated monthly KPI reporting pipeline in Python (pandas, SQLAlchemy), replacing a 3-hour manual process; flagged a billing reconciliation anomaly that recovered £84K in the first quarter after deployment.
The generic version uses zero keywords from a typical healthcare data analyst job description. It describes activity — analysing, using tools, collaborating — with no specificity about what tools, what data, or what the analysis produced. The researched version names the exact technologies the role requires (Tableau, Python, SQL), quantifies the impact in the units a healthcare hiring manager values (time-to-insight, process hours saved, financial recovery), and signals the seniority appropriate for a mid-level hire. The ATS scores the keyword match. The recruiter reads the proof of capability.
NovaKit Skill
Resume & CV Builder — Built around the job description, not your history
Paste in the role you're targeting. The skill analyses the JD, extracts the right keywords, and writes achievement-led bullets that score well and read well.
A Quick Self-Audit Before Your Next Application
✓
Check these before you send
Open your CV and the target job description side by side. Count how many of the required skill phrases in the job description appear word-for-word in your CV. If the answer is fewer than five, the document isn't tailored — it's edited. Count how many bullets start with "Responsible for" or "Managed" with no outcome clause. If the answer is more than two, your achievement density is below the threshold for competitive screening at most seniority levels above entry.
The audit takes three minutes. What it reveals usually takes longer to fix manually — which is exactly why most candidates don't do it for every application. The skill does it automatically, against each specific role, every time.
ATS systems are not obstacles to navigate around. They're the hiring manager's first filter — a mechanism to surface the candidates whose documents demonstrate the clearest match to what the role actually requires. A CV that scores well in that filter isn't gaming the system. It's doing what a well-targeted application has always been supposed to do: making the case that this person, for this role, is worth thirty minutes of a recruiter's time. The six patterns above are what stop that case from being heard.
The next piece most people tackle from here is a cover letter that earns the interview, not just the read. If you're working across the full Student workflow, the Student bundle covers everything in one place.
Ready to try it?
Resume & CV Builder for Claude
Role-analysed, ATS-optimised, achievement-led. Covers UK CV and US resume formats. Runs inside Claude with your free or Pro account.
Put this to work: the Resume & CV Builder skill for Claude turns everything above into one guided workflow you run in a normal Claude chat. Not ready to buy? Start with a free Claude skill and see how it works first.
Related reading: Your CV Isn't Being Rejected by a Recruiter
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Resume & CV
ATS Optimisation
Job Search
Claude AI
AI Skills
Career