Could Artificial Intelligence Help You Find Your Dream Job?

Artificial intelligence leverages data and personality assessments to source and match job seekers and employers. By incorporating AI, employers can learn about an applicant's soft skills like emotional intelligence and adaptability, and eliminate some of the guesswork.

By Betsy Vereckey, Contributor

Sifting through piles and piles of resumes may be a task of days past. By employing new technologies to find candidates that are a better fit, companies may be able to cut down on turnover costs and save their HR departments invaluable time.

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For years, employers relied on the standard resume and cover letter to find the best candidates. Sometimes, even a hunch was all it took to offer someone the gig. Today, however, artificial intelligence (AI) leverages data and personality assessments to eliminate some of the guesswork for companies, and, in turn, helps workers find jobs that better align with their sense of self.

These technological advances are arriving to the market as studies continue to show that employees want jobs that deliver much more than a paycheck. In fact, survey results published in Harvard Business Review revealed that nine out of 10 Americans said they would trade nearly a quarter of their lifetime earnings for more meaningful work.

Is it time to say “goodbye” to occupational guesswork and “hello” to the perfect match?

How AI Helps Employers Choose A+ Candidates

A resume can only reveal so much about a candidate, but by incorporating AI, employers can learn about applicants’ emotional intelligence, among other key traits.

“Most companies are analytical in every other part of their business, but when it comes to hiring, guesswork and intuition are still quite prevalent.”

—Claire McTaggart, founder and CEO, SquarePeg

“Most companies are analytical in every other part of their business, but when it comes to hiring, guesswork and intuition are still quite prevalent,” says Claire McTaggart, founder and CEO of SquarePeg, an HR tech platform that uses data to source and match job seekers and employers.

While working in management consulting, McTaggart looked at an overwhelming number of resumes. “I was astounded that such important decisions were being made by personal judgment, without sufficient data or analytics around the decision-making process.”

SquarePeg considers a wide range of attributes when it comes to matching and ranking. Every job candidate takes SquarePeg’s psychometric assessment, which evaluates 19 workplace-specific traits, such as detail orientation, perseverance, intellectual curiosity, and adaptability.

“Psychometric assessments allow for considerably more insight in matching a candidate to a role than resume data alone, as they offer an appraisal of how someone tends to behave in workplace situations rather than just a historical snapshot,” McTaggart says.

When job seekers create a profile on SquarePeg, they upload a resume that is automatically parsed and analyzed. Users are asked for information about their skills, work history, preferences, and career priorities, and then take the psychometric assessment. Once they have a profile, SquarePeg starts matching them with open positions and reaches out directly with ideal opportunities.

Candidates receive curated job matches based on a range of criteria, including qualifications, experience, behavioral traits, preferences and interests. Job matches are sent for approval, and once a candidate approves of a job match, their profile goes directly to a hiring manager.

For candidates who have painstakingly submitted hundreds of perfectly crafted cover letters and resumes, the idea of having a machine do all the work can be enticing. On SquarePeg’s part, the company says that candidates can take its assessments in less than 15 minutes.

On the employer side, companies using SquarePeg upload a job description or create a posting, answer questions about their requirements for the job, and then receive highly curated candidate applications within a few days. Employers can specify which soft skills or behavioral traits are most critical for the job, or SquarePeg can pre-fill that data based on job title, function, and seniority.

Instead of looking through piles of unqualified resumes, employers using SquarePeg have detailed match profiles on a highly qualified group of applicants. Each applicant is scored and ranked, making comparison much easier than using resumes. As employers move candidates through their hiring funnel, SquarePeg provides analytics on diversity, selection patterns, hiring inconsistencies between recruiters, teams, or offices, and more.

SquarePeg treats each position differently, weighing certain behavioral traits more heavily than others depending on the demands of the job. Negotiation skills, for instance, may be more important for a client services position, whereas technical skills could be more valued for a software or data analytics role. “What an algorithm can do is align a hiring team on what matters for the role, and then standardize the way candidates are matched and ranked so that you remove bias,” McTaggert says.

Why Employers Like Using AI to Source Talent

“The efficiency is incredible,” says Ryne Sherman, chief science officer for Hogan Assessments, a B2B-maker of workplace personality tests.

Sherman notes that AI has a great return on investment for companies. Finding employees who are a good fit can also mean greater job satisfaction and increased productivity, which then translates to a better bottom line. One study conducted by BetterUp, a career coaching startup, and published in Harvard Business Review, estimates that employees who do work they consider highly meaningful will generate an additional $9,078 per worker, per year. The report spanned 26 industries and 2,285 American professionals across different pay levels, company sizes, and demographics.

Furthermore, hiring employees who are a good match can also translate to increased retention rates. Employees who find work highly meaningful—where they are able to develop their inner selves, grow professionally, and help others—are 69 percent less likely to plan on quitting their jobs within the next six months, and their job tenures are 7.4 months longer, on average, when compared to employees who find their work less meaningful, the BetterUp report showed.

Beware of Unintended Algorithmic Bias

One drawback of machine learning is that it’s only as smart as the data it’s fed by humans, which means there’s potential for algorithmic bias to create unfair outcomes. This may defeat the purpose of moving to AI to combat the bias hiring managers can have, even if unintended.

“Standardized assessments, whether they be IQ, personality, or work sample tasks, are surely the fairest and most reliable methods for predicting workplace performance.”

—Ryne Sherman, chief science officer, Hogan Assessments

“The real risk is that there’s bias we have as humans that we don’t recognize, and the algorithm picks it up and emphasizes it like crazy,” Sherman says. This could be “weird social quirks we haven’t thought of being biased against,” or degrees from certain universities or certain parts of the country, he says, noting that the algorithm itself isn’t biased—it’s just that the computer is trying to replicate what the humans are doing. “You might not realize you’re biased in some ways,” he says. “Often by the time you realize it’s happening, it’s too late.”

Yet despite the potential downside of algorithmic bias, personality testing still provides plenty of upsides. It can offer deep insight on a job candidate’s interests and motivation, transform the hiring process into an easier, faster, and more fair one, create more diverse workplaces, and help companies build the right teams.

“Standardized assessments, whether they be IQ, personality, or work sample tasks, are surely the fairest and most reliable methods for predicting workplace performance,” Sherman says.