The Résumé Screening Blues

Consider that the recruiting process is a five-step process made up of requirements gathering, sourcing, screening, interviewing, and offering. So far, we’ve explored the first two phases and highlighted how using Artificial Intelligence (A.I.) can simplify and streamline each step. This post will focus on the screening stage, elaborating on the challenges recruiters face and how artificial intelligence can successfully address those challenges.

In an ideal scenario, model screening criteria would be based upon the combination of résumé content, business needs, and predictive analytics. This blend would allow for consistency in the screening process, advancing the best-fit potential hires, while filtering out non-ideal candidates. But we’re not yet looking at an ideal situation.

Picture this: one job opening receives, on average, 250 resumes, most of which aren’t aligned with your needs. It’s a laborious process to sift through the résumés that don’t fit your job description nor meet your minimum requirements. Not to mention that you need to don your Sherlock Holmes hat to deduce truth from fiction as, according to Inc.com, over 85% of applicants pad their experience.

Manual Résumé Review

Let’s have a little fun with math. If each resume review takes an average of 3.14 minutes, it will take one person 785 minutes, or over 13 hours, to review all 250 resumes—for only one job!

Let’s look at a real-life example. Johnson & Johnson, a consumer-goods company, receives annually 1.2 million applications for 25,000 positions. Manually sifting through those resumes could take over 62,800 hours. You can be sure that their Human Resources’ theme song isn’t Styx’s “Too Much Time on My Hands.” We’re guessing you can relate.

The screening process is laborious and complicated beyond the hours it takes to review resumes. Recruiters are making decisions on who to interview based on how well the résumé matches the job description and requirements.

Further complicating this step are the challenges that arise around hiring criteria and its consequent interpretation. Regarding criteria, there’s a lack of standardization in what constitutes a good candidate versus an unsuitable one. This lack opens the door to high variability with who the company decides to interview or forego.

Artificial Intelligence Screening

Contrast the manual-résumé riffle with Artificial Intelligence (A.I.)-enabled systems which can scan applications far more quickly than humans and work out whether candidates are a good fit, thus saving recruiters thousands of hours of time. A.I. eliminates the drudgery and allows recruiters to focus on higher level tasks.

Saving time isn’t the only way A.I. can assist in screening candidates. Let’s explore common screening problems and examples of companies successfully utilizing A.I. to solve them:

Challenge: Applications vastly outnumber available jobs requiring hassled recruiters must sort through reams of submissions.

A.I. Example: Nvidia, a chipmaker, also gets more résumés than it can comfortably cope with, so it spent a year building its system to predict which candidates are interview-worthy. Their A.I. recognized patterns that its recruiters might not. For example, candidates who submitted overly long résumés turned out to underperform those who submitted concise resumes so that those extra words will count against them.

Challenge: Many candidates never hear back from potential employers and feel that their résumés seem to go into a “black hole.”

A.I. Example: Mya Systems helps in the screening process by directly engaging with candidates via text. Mya screens candidates based on a pre-programmed assessment model or moves them along to the next part of the interview process. “Now 100 percent of candidates are getting a response; everyone is getting a chance,” said Mya founder Eyal Grayevsky in a CNBC article. “Candidates feel like they really get a chance to express themselves to the company with more than just a résumé.”

Challenge: Applicants with unconventional qualifications aren’t considered for roles they may be ideally suited for.

A.I. Example: Pymetrics, a startup whose clients include companies such as Unilever, a consumer-goods giant, and Nielsen, a research firm, offers a set of games for candidates to play, usually at an early stage of the recruitment process. These games ignore factors such as gender, race, and level of education. Instead, they test candidates for some 80 traits such as memory and attitude to risk. Pymetrics then uses machine learning to measure applicants against top performers and predict their suitability for a role.

Challenge: Saving data on highly qualified candidates who are not a good fit for the position being filled.

A.I. Example: A.I. makes it possible to “repurpose candidates we have attracted before,” says Sjoerd Gehring, vice-president of talent acquisition for Johnson & Johnson, which uses HiredScore to grade candidates. When a vacancy opens up, the system automatically generates a shortlist of candidates, thus saving big bucks.

Implementing a robust A.I. solution will have your human resources department singing a happier tune—perhaps Jennifer Warnes and Bill Medley’s “The Time of My Life” from film Dirty Dancing.