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The Future of Recruiting with Joanna Riley of Censia

Image by raphaelsilva from Pixabay

In today’s labor market, recruitment companies have become an essential tool that helps companies tackle their toughest recruiting challenges, be it finding talent for new, in-demand digital talent, or recruiting at scale.  

Talent Intelligence, the use of AI-powered software to optimize the recruiting process, helps companies avoid costly hiring mistakes, saves an average of 90% of the time on manual recruiting tasks such as resumé review and passive sourcing, and helps them find the best talent available, be it inside or outside their organization. 

Recruiting has seen major changes in the past few years, most notably marked by the increase in largely irrelevant applications (up to 88% of applications tend to be unsuitable for a role) and the need to hire digital transformation talent which is still very hard to find. At the same time, job applicants have also come to expect a great employer brand and candidate experience, placing significant pressure on the recruiters. 

Moving forward, we can expect the recruitment industry to continue to morph. Joanna Riley founded AI startup Censia to help companies meet those demands. “Nearly three-quarters of firms have difficulty sourcing top talent,” says Riley. “Even in flooded labor markets. There is simply too much talent and too many factors to evaluate. We built Censia to enable intelligent talent discovery, modeling our AI approach to align with future trends, and to help talent teams find the best talent, whether they are applying, working at other companies, or even working at yours.” 

Here’s what else you can expect from the future of recruiting: 

Artificial Intelligence

Artificial intelligence has become a powerful tool in recruiting and will continue to shape the future of the industry. Artificial intelligence employs problem-solving and machine learning to identify the best candidates for a position. Essentially, it intelligently automates the first part of the recruiting work workflow, eliminating the repetitive, high-volume tasks that are prevalent in recruitment. As a matter of fact, a Harvard study of more than 250,000 applications found that the algorithms used by this technology do a better job at predicting employee success. 

Applicant tracking and discovering is just one tool in the AI arsenal. In high-volume hiring, artificial intelligence becomes the first line of defense. For instance, strong recruitment databases contain complex word flows, keywords, and many other data points that AI can instantly organize and analyze. It also speeds up candidate selection by modeling hundreds of characteristics of successful employees, and then applying this search model to talent searches, revealing in-depth insights about stability, loyalty, learning ability and an individual’s desire for autonomy. 

The great thing about this technology is that it harvests data from thousands of sources, including social media profiles, alumnae groups, and professional platforms like LinkedIn. It then cleans up the data and presents it in an easy-to-understand and detailed profile, saving recruiters and hiring managers tens of hours of work and eliminating hiring bias by treating all candidates fairly.  A 2018 CareerBuilder Survey found that 70% of employers are using social media to screen candidates, and artificial intelligence will help them do it in a fairer and more meaningful way. 

Factoring in the Learning Quotient

Many American companies realize that a resumé does not clearly or fully represent a candidate, and a report published by Adecco showed that companies have lowered their candidate requirements to account for the learning factor. 

Just two years ago, Jennifer Carpenter, Head of Global Recruiting at Accenture, coined this term. In recruitment, the learning quotient refers to a candidate’s ability to learn new information, conjure creative solutions, work hard, and quickly adapt to new roles. In the future, recruitment agencies will adopt or develop tests designed to capture a candidate’s learning quotient. Although it’s difficult to say when LQ tests will become wide-scale, it’s clear that more companies are considering potential during the hiring process. 

Less Applicant Biases

As humans, we are naturally hard-wired to have biases, both conscious and unconscious, and studies upon studies continue to highlight how pervasive this problem is. Unfortunately, some of those biases subtly prevent recruiters from making smart, fair hiring decisions. Certain demographics, like age, gender, race, and marital status should never be considered during the recruitment process, but often reveal themselves through unconscious bias. Censia completed a comprehensive review of the numerous monetary and operational benefits of having diverse teams, and the numbers speak for themselves. 

“Algorithm-based technology helps make equal opportunity a feasible goal,” says Riley. “For example, at Censia, we reveal the most important details about a candidate and leave out data points that could result in cognitive bias. Candidate pools created by Censia are three times more diverse as those created by recruiters.”  

These hidden biases can prevent great candidates from getting deserved positions, and growing companies from getting the best talent possible. This is especially true when diverse workplaces can have a positive impact on the culture, bottom line and success of a company. Some research has found that diverse groups are better at decision-making, and furthermore, tend to have higher financial returns than national industry medians.