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How to Write a CV for AI Recruitment Tools

CVSense® InsightsCircle
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How to Write a CV for AI Recruitment Tools

Writing for AI screening and writing well for humans are now the same task. This guide shows the rewrite method: strip the adjectives, add the specifics, and let the evidence do the work.

There is a comforting thing about the current state of CV screening that almost nobody says out loud: writing a CV for AI recruitment tools and writing a CV that impresses a human have converged. They used to pull in opposite directions, with keyword padding for the machine and polished prose for the person. They no longer do, because modern screening reads for the same thing a good recruiter reads for, specifics.


This guide gives you the rewrite method, with before and after examples, and the small number of mechanical rules that still matter.

The Shift That Changed Everything

For years CVs opened like this: "A dynamic and highly motivated professional with a proven track record of delivering results in fast-paced environments, combining strong leadership with excellent communication skills."


Read it again and notice that it contains no information. Every clause is a self-assessment that cannot be checked, and it would apply equally to a warehouse supervisor and a hospital consultant. It survived because it sounded like what a CV was supposed to sound like.


Modern screening assigns that paragraph close to zero weight, because there is nothing in it to assess. And so does an experienced recruiter, who has read ten thousand variations of it.


What replaced it is not a different kind of adjective. It is evidence: what you did, where, how much of it, and what changed.

The Four Components of an Evidenced Statement

Every important claim on your CV should carry as many of these as honestly apply.

  • Action: the specific thing you personally did. Not "was responsible for" but "rebuilt", "negotiated", "diagnosed", "migrated", "trained".
  • Context: for whom, within what system, under what constraint.
  • Scale: a number. Volume, value, headcount, frequency, duration, size of caseload.
  • Outcome: what measurably changed.

Three worked examples, across different sectors:


Weak: "Responsible for managing social media accounts and increasing engagement."
Evidenced: "Ran four social accounts for a 30-branch retailer, moving from ad hoc posting to a planned fortnightly calendar. Engagement rose 34% over six months and enquiries from social reached about 60 a month, up from roughly 15."


Weak: "Provided excellent patient care in a busy ward environment."
Evidenced: "Held a caseload of 8 to 10 acute medical patients per shift on a 28-bed ward, acting as practice assessor for two student nurses and leading the shift handover three times a week."


Weak: "Helped improve financial reporting processes."
Evidenced: "Rebuilt the month-end reconciliation for 11 cost centres in Excel and Power Query after the manual process began overrunning close by two days. Close returned to schedule and the handover pack dropped from 14 spreadsheets to 3."


Notice none of these use the word "excellent", "dynamic" or "passionate", and all three are more persuasive for it.

Verbs to Retire, and What to Use Instead

Certain phrases signal an absence of substance, and they are worth hunting down.


Retire: responsible for, involved in, assisted with, helped to, participated in, tasked with, worked on, supported the delivery of.


These describe proximity to work rather than performance of it. "Involved in the migration" tells a reader you were nearby.


Use: built, rebuilt, designed, led, negotiated, diagnosed, reduced, recovered, automated, trained, introduced, resolved, migrated, secured, restructured.


One caution. Certain verbs have been so heavily used by AI drafting tools that they now read as generated: "spearheaded", "orchestrated", "used", "championed", "pioneered". They are grand where a plain verb would be stronger. "Led" beats "spearheaded" every time.

The Attribution Problem

This is the most common way genuinely strong candidates undersell themselves, and it takes ten minutes to fix.


Search your CV for "we". Every instance describes work you are attributing to a group, which means a reader cannot tell what you personally contributed. Evidence-based screening is specifically designed to distinguish the two, so collective language gets discounted.


You do not need to overclaim to fix it. Name your actual part. "I owned the data migration within a five-person team replatforming the CRM" is honest, specific and far stronger than "we replatformed the CRM".

Where to Put Your Effort

You do not need to rewrite everything, and attempting it is why people abandon the exercise.

  1. Find the three or four requirements that genuinely decide the role. Most adverts list a dozen; a handful matter.
  2. Evidence exactly those, using the four components, in your two most recent roles.
  3. Replace the adjective summary with three factual sentences of positioning.
  4. Trim the skills list to things the CV goes on to demonstrate.
  5. Leave older roles brief. A role from a decade ago gets one line.

This is perhaps ninety minutes of work on a CV you will use for months.

The Mechanical Rules That Still Matter

Content is most of it, but a beautifully written CV that cannot be read scores nothing. Keep these:

  • Single column. No tables, text boxes, headers or footers.
  • Standard section headings.
  • One consistent date format throughout.
  • Contact details as plain text in the body.
  • Text-based PDF or .docx, never an image.
  • Use the employer's vocabulary where it is genuinely accurate, if they say "safeguarding" and you wrote "child protection", align it.

Frequently Asked Questions

Should I Write My CV Differently for AI Screening than for a Human?

No, not any more. Both reward specifics and discount unsupported adjectives. The only AI-specific considerations are mechanical: clean structure so the text extracts properly, and standard headings so sections are identified correctly.

Do I Still Need a Professional Summary?

A short factual one is useful, because it orients a human reader in five seconds. Three sentences, no character adjectives: what you do, at what level, in what domain, with one concrete anchor.

What if I Cannot Quantify My Work?

Most roles have more countable dimensions than people assume, how many people, how often, over what period, how many cases, what value, how long something used to take. Where nothing is measurable, substitute scope and constraint: the complexity, the regulation, the legacy system you worked around.

Is It Bad to Use the Same Wording as the Job Advert?

Mirroring their terminology where it accurately describes your experience is good practice, because it removes ambiguity. Pasting their requirements back as if they were your achievements is different, and produces claims with nothing behind them.

How Long Should a UK CV Be?

Two pages is the norm, one page is fine early in a career, and clinical and academic CVs legitimately run longer. There is no length rule enforced in software. Relevance beats brevity, and neither benefits from padding.

The Useful Conclusion

Stop optimising for a machine and start being specific. The specificity is what modern screening measures, what a recruiter remembers, and what an interviewer will ask you about.


CVSense assesses the context and outcome attached to a claim rather than counting keyword frequency, which means the same plainly written, well-evidenced CV works for the software and the human reading after it.


Sources

National Careers Service. CV Sections and Advice.
https://nationalcareers.service.gov.uk/careers-advice/cv-sections

Chartered Institute of Personnel and Development.
https://www.cipd.org/uk/

Information Commissioner's Office. Guidance on AI and Data Protection.
https://ico.org.uk/for-organisations/uk-gdpr-guidance-and-resources/artificial-intelligence/guidance-on-ai-and-data-protection/


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