Further to our latest blog that explained what CV Parsing is and why it’s important to understand how it works… we wanted to explain in a bit more detail exactly how A.I. is used in the screening of your CV.  Understanding something means you know better how to prepare for it, and this blog, as well as the next one will gear you (and your CV) up for success!

What does the A.I. do when it analyses my CV?

When an A.I. reviews a CV, it is basically doing exactly what a human hiring manager would do – but just a lot faster and mechanically. An A.I. won’t be able to recognise someone that you used to work with, a company that you used to work for or react in an emotional way as you might when you read a CV.  When you see a great CV which is a fit, you might get excited… A.I. does not!

However, it functions in a programmatic, unemotional way by automatically comparing it back to the relative job description, and generally providing an associated score to each CV.

See below the various processes that the A.I. screening ‘bot’ goes through when it’s reviewing your CV:

Keyword Matching

– Skill-Based Keywords: AI systems are designed to look for specific skills mentioned in the job description. If a job posting lists “data analysis” or “project management”, the AI will check for these keywords or phrases in the candidate’s CV.

– Role-Specific Terminology: Industry-specific terms or jargon (e.g. “CRM” for sales roles, or “Python” for data science) are also scanned. Matching these terms indicates familiarity with the tools or concepts needed for the role.

– Soft Skills Keywords: Some systems even scan for soft skills like “teamwork”, “communication”, or “leadership” if these are highlighted in the job description.

Contextual (big word!) Analysis

– Synonyms & Related Terms: Modern A.I. systems don’t rely on exact keywords alone, rather they use Natural Language Processing (NLP) to understand related terms or synonyms. If the job mentions “client relationship management”, an A.I. might recognise similar terms like “customer success” or “account management”.

– Skill Relevance & Hierarchy: The A.I. can evaluate which skills are most relevant based on the context of the job specification, weighing highly relevant skills mentioned at the top of the CV or within recent job experiences more than those mentioned further down or less commonly.  Very clever hey!?

Experience & Achievements Analysis

– Experience Duration: Most A.I. tools have the ability to evaluate the number of years a candidate has spent in relevant roles.  So, a candidate with 3 years’ experience in a specific field or position would be ‘scored’ differently (higher) than someone with only 1 year.

– Quantifiable Achievements: A.I. systems also look for quantifiable metrics like “increased sales by 20%” or “managed a team of 10”.  These metrics help the A.I. gauge the impact and scale of a candidate’s achievements, with any key accomplishments articulated and backed up with numbers, often scoring higher.  Great tip right there!

Educational Background & Certifications

– Degrees & relevance: If a role requires specific educational qualifications, A.I. will look for degrees, certifications, or licenses included in the job spec.  More advanced systems can assess the relevance of education to the job e.g. a software engineering degree for a developer role.

– Certifications & Training: A.I. can also recognise relevant industry certifications which can significantly boost a candidate’s score.

Employment Patterns

– Career Progression: A.I. algorithms sometimes analyse how a candidate has progressed in their career. A steady advancement in job titles or responsibilities may be a positive indicator.

– Gaps & Job Stability: Extended gaps in employment or frequent job-hopping can sometimes impact scores. Advanced systems, however, may contextualise gaps or short stints if the CV explains these in a positive light (e.g. freelance or project-based work).

Formatting & Organisation (Technical Factors)

– Section Labelling: A well organised CV that contains headers for sections like “Work Experience”, “Skills”, and “Education” makes it much easier for the A.I. to parse.

– Readable Formatting: Avoiding unconventional formats, graphics or fonts improves compatibility with the A.I..  While visuals like a photo of yourself or a company logo of somewhere you have worked may look appealing, A.I. often struggles to process these – which could impact your score, or even the chances of your CV being put forward at all.

Language & Tone Analysis

– Positivity & Active Language: AI systems sometimes analyse the tone of the language used in a CV.  Phrases with positive or action-oriented verbs like “achieved”, “implemented” or “led” can enhance readability and impact.

– Avoiding Red Flags: Certain phrases may raise caution – a bit of a BS indicator if you will!  Something like “familiar with” (indicating limited experience) or passive expressions may detrimentally affect your CV’s score.

How can you optimise your CV for A.I.?

A.I.’s effectiveness in CV review has really improved with recent advancements and is widely used by recruiters and companies in the hiring process.  Understanding how it works can help you to design your CV in a way that increases your chances of successfully passing the A.I. screening process – and our next blog “How to Optimise your CV for A.I.” will give you some more pointed tips on how to do this.