Artificial intelligence (AI) resume screening is often sold as a time-saving, bias-eliminating fix-all for high-volume hiring teams. However, it can also create more problems than it solves by filtering out top talent or adding costly inefficiencies.
So, is AI screening the game-changer vendors promise or an overrated solution? Knowing its strengths and weaknesses means businesses can invest in the right technology and avoid expensive mistakes.
This guide explores how AI resume screening works, its pros and cons, and a more effective way to use AI in recruitment.
What Is Artificial Intelligence Resume Screening and How Does It Work?
AI resume screening involves using artificial intelligence to evaluate candidates’ resumes automatically during the hiring process. The aim is to save recruiters time by quickly identifying the most qualified candidates from large applicant pools.
Many AI screening tools scan resumes for specific words, match candidates’ qualifications to job descriptions, and rank applicants based on predefined criteria. These are called keyword-based resume screening tools.
Here’s how the candidate screening tool Peoplebox scores applicants for a customer success manager role based on relevant information from their resumes:
Some systems use machine learning algorithms and natural language processing (NLP) to deeply analyze and “understand” text. For instance, grammar-based resume screening software spots when candidates include the right keywords but can’t articulate their abilities in complete sentences—a sign they lack written communication skills.
However they apply AI technology, and all automated screening tools assess candidates solely on self-reported information. And this may not reflect applicants’ true capabilities.
Note: Not all keyword-based screening tools need AI to function. Some use simpler, rule-based algorithms or manual input to match keywords (without generating insights from the results). While less sophisticated, these tools are still effective for basic screening. Think of them as a “Ctrl+F” search for resume documents.
The Benefits of Artificial Intelligence Resume Screening
While AI resume screening’s adoption rates remain low, the technology suggests significant potential for optimizing hiring processes.
A Mercer survey found that only 23% of talent acquisition and HR leaders use AI to eliminate bias in resume screening. Confusingly, an even smaller proportion (18%) said they use artificial intelligence resume screening in general.
However, the few companies that use AI screening must see its value—which suggests that others can benefit from it, too.
So, what are AI screening’s key selling points?
1. Efficiency
Manual resume reviews take a lot of time. By automating the initial screening process with AI, busy hiring managers can free capacity for other tasks, like:
- Building strong talent acquisition strategies
- Collaborating with team leads to gauge workforce needs
- Delivering great candidate experiences
- Assessing suitable candidates’ soft skills (a more accurate way to predict on-the-job performance)
Let’s say an e-commerce business needs 200 new support agents to meet seasonal demand. More than 1,600 job seekers submit resumes, each of which must be checked manually by a human recruiter. The process takes a small team three days.
An AI system can quickly parse those resumes for terms like “years of experience,” “communication skills,” and “customer service,” and then give the recruiter a shortlist of what it deems the best candidates. By filtering out resumes from less suitable applicants, the software cuts the recruiters’ workload to one day, freeing two days to manage other job postings.
2. Scalability
The ability to scan lots of resumes quickly allows companies to grow faster, seize unexpected opportunities, and meet surging demand without expanding their hiring teams.
Let’s say a software startup’s product launch exceeds expectations. Adoption skyrockets in the first week, and the company must onboard 100 new support technicians in 30 days. That’s a daunting task for its sole in-house recruiter.
AI eases the strain by filtering out irrelevant resumes using predetermined screening criteria. It lets the business capitalize on its unexpected growth without pausing to hire more recruiters or getting an agency’s support.
Hiring quickly doesn’t always mean hiring well, so AI screening is more of a helpful tool than a comprehensive solution. Without an accurate skills assessment, the recruiter must still interview shortlisted candidates to gauge their suitability. Even then, there’s no guarantee for quality of hire or future performance.
3. Consistency
AI tools can make the resume screening process more consistent by applying the same rules to every candidate. They’re a fairer alternative to human reviewers who may have personal biases or let factors like tiredness and mood influence their decisions (we all do it!).
For example, AI can reliably identify resumes with specific skills or qualifications, even if they’re laid out differently. This keeps the focus on applicants’ abilities rather than formatting or font choices.
Screening automation software may also help underrepresented groups access opportunities. Kings College London’s analysis of 12,000 job applications revealed that candidates with English-sounding names received 21.2% of positive responses for non-leadership positions, compared with 11.6% for those with non-English names. This disparity highlights the impact of human bias in hiring—which carefully applied AI tools can help mitigate.
But while AI reduces some unconscious biases, it still depends on the data used to train it. If that source data has biases, the AI algorithm can repeat them, which may not entirely solve inconsistency issues.
AI Resume Screening’s Limitations: 5 Reasons to Reconsider
Using AI to screen resumes be more efficient and save time, but it can also lead to additional problems if not applied correctly.
Here are five reasons resume screening isn’t an effective use of AI for hiring teams:
1. Resumes Don’t Predict Job Performance Accurately
The biggest issue with AI resume screening also applies to resume screening more generally: resumes are a poor indicator of how well a candidate will perform in a specific job. They focus on experience, education, and self-reported skills, which often don’t reflect actual capabilities.
For example, a technically qualified candidate may excel in working alone but struggle to collaborate with others. A resume wouldn’t reveal this detail, which could hinder the applicant’s performance as part of a large team.
AI screening tools inherit this limitation because they rely on parsing information that may be incomplete, misleading, or even false. A worrying 70% of the US workers said they’d lied in resumes, with more than a third saying they lie frequently.
2. Poorly Trained AI Can Still Be Biased
AI recruiting tools are only as unbiased as the datasets used to train them. If the training data reflects historical biases—like favoring candidates from specific backgrounds or demographics—the technology will likely perpetuate those patterns.
Let’s say a retailer trains its AI recruitment software using five years of hiring data. Historically, the company has recruited more men than women for technical support jobs (an unfortunate reality), so the algorithm assumes men better suit these roles’ requirements. It then inadvertently prioritizes male candidates when screening for open positions.
Bias can have serious consequences by limiting diversity, worsening systemic inequality, and damaging morale. However, fair-treatment policies (including the careful use of AI) can counteract these issues to create a happier, more inclusive workplace. Almost three-quarters (72%) of employed US adults told a Pew Research survey that fair-treatment policies for hiring, pay, and promotions positively impacted their companies.
3. False Positives and Negatives Skew Results
AI screening tools that focus on spotting specific keywords and criteria can’t fully capture a candidate’s nuances and potential. It means they’re prone to producing false positives (passing unqualified applicants) and false negatives (rejecting strong applicants), so users never truly identify the best candidates for their roles.
For example, an applicant with relevant skills but unconventional experience might be filtered out of the pipeline because their resume doesn’t contain specific terminology. On the other hand, a candidate with a pristine resume full of keywords might pass through even if they lack relevant experience.
False positives frustrate recruiters, who waste time interviewing unsuitable candidates. False negatives frustrate applicants, who might feel unfairly excluded from the running. The inaccuracy helps no one.
4. Applicants Are Openly Gaming the System
The more popular AI screening tools become, the more job seekers learn to optimize their resumes to pass through automated filters.
Online guides now teach candidates how to tailor their resumes with the right keywords, phrasing, and formatting to increase their chances of being selected by an AI tool. Just search “AI resume screening” on Google to see what we’re talking about:
While this guidance may benefit applicants, it undermines the integrity of the screening process. A candidate who knows how to game the system can get ahead of more qualified peers who don’t optimize their resumes.
Just like this Reddit user:
Alternatively, a great candidate who knows a company uses AI may feel they need to submit an optimized CV to progress rather than show off their unique character and skillset. This means the employer still can’t accurately assess their potential.
5. A Lack of Human Nuance Creates Poor Candidate Experiences
AI screening tools lack the human ability to assess context and nuance in resumes. They can’t recognize unique experiences or traits that don’t fit neatly into predefined criteria, such as:
- Career changes that justify limited experience
- Unconventional skillsets that may aid performance
- Unpaid work that demonstrates leadership and adaptability
We’ve already discussed how this rigidity causes false positives and negatives, but it also hinders candidate experiences. Applicants may feel undervalued or frustrated when an automated system overlooks their qualifications (both formal and informal), especially if they don’t receive human feedback.
This frustrated Redditor rightly points out that companies are “missing out on a wealth of amazing candidates” and “distancing themselves [...] from human connection”:
Over time, repeatedly frustrating applicants can damage a company’s employer brand, making it even more challenging to attract top talent.
If this thread’s title (“AI resume screening should be illegal”) doesn’t further prove candidates’ dislike of AI screening, scientific research shows that 70% of job applicants have concerns over AI’s lack of nuance in human judgment or human touch. Other candidate concerns include low accuracy and reliability, algorithm biases, and a lack of transparency when interacting with AI.
There’s a Better Way to Use AI for Talent Acquisition
The biggest issue with AI resume screening is that it can’t accurately predict on-the-job performance. Fortunately, it’s the “resume” part that causes problems—AI can still be a powerful tool for streamlining recruitment processes.
Companies can instead embed AI soft skills assessments high in their hiring funnel to more accurately see how applicants are likely to perform in specific roles. The AI analyzes candidates’ responses to real-world challenges for fluency, relevance, and tone, then generates objective scores to aid shortlisting.
For example, Fundraising Direct uses this AI-led approach to save time and increase quality of hire, as Operations Manager Sarah Wise explains:
“We’re using [an AI skills assessments] to assess people so we don’t have to interview them. If they score above a certain percentage, they get put straight through.
“I used to batch candidates into group interviews to save time. Today I have the gift of time and I am more confident than ever in our hires.”
Here’s what this kind of AI-driven pre-hiring assessment could look like:
In addition to speaking fluency, the assessment tool’s AI analyzes candidates’ responses for signs of relationship-building, negotiation, and active listening skills. It then ranks applicants based on how well they demonstrated these core abilities, giving the company an accurate shortlist.
The shortlist contains people from various backgrounds with a broad range of qualifications, but the assessment has ensured what matters: they’re all capable of doing the job well. It means the hiring team can focus on the most qualified candidates, which speeds up their work. It also supports growth by handling large talent pools without compromising quality or fairness.
This approach is helping companies get real results. For instance, one Fortune 50 online retailer that used HiringBranch’s soft skills assessments to standardize its pre-hire screening processes reduced bad hires by 58% and achieved a 300% return on its AI investment.
Realizing AI’s Full Potential in Recruitment
Despite offering some benefits, artificial intelligence resume screening is let down by its reliance on an outdated hiring practice: resumes. They just aren’t a reliable way to assess a candidate’s skills anymore.
Consider pivoting to AI skills assessments for a more accurate and fruitful recruitment process. These tools measure real-world competencies, provide actionable insights, and ultimately lead to better hiring outcome.
Ultimately, the future of recruitment lies in harnessing AI to enhance—not replace—the human element of hiring. Recruiters who do so can build stronger, more diverse teams while improving the candidate experience. Everybody wins!
Image Credits
Feature Image: Pexels/Markus Winkler
Image 1: Via Peoplebox
Image 2: Via Mercer
Image 3: Via Imgflip (adapted from Reddit)
Image 4: Via Google
Image 5: Via Reddit
Image 6: Via Reddit
Image 7: Property of HiringBranch. Not to be reproduced without permission.
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