A new generation of recruitment solutions powered by AI is gaining traction in Australia. Both investors and recruitment firms see future value in the technology.
While some solutions are more mature than others, they are already delivering results in terms of efficiencies and better outcomes. It is mainly driven by cost and accuracy. The main reason is that the cost of hiring people has become so expensive that employers cannot afford to let humans hire the wrong people.
Studies show that a wrong hiring decision generally equals three to six months’ salary and a significant opportunity cost in not getting the right person into the role. Then there is the impact on culture and morale of a workplace that experiences high turnover as a result.
Key to hiring right is the elimination of unconscious bias from the selection process. Essentially, we need to stop hiring based on gut feeling. Many intuitive hiring practices are now seen as managers reinforcing their personal choices through candidate selection and limiting workplace diversity.
Diversity is now the overarching goal for many HR teams. Research shows that diversity can improve performance. According to McKinsey & Company, gender diverse companies are up to 21% more likely to experience above-average profitability than less diverse counterparts, while the figure for culturally-diverse companies is a 33% likelihood of better performance.
Eradicating False Positives
One of the more established solutions in the Australian marketplace is PredictiveHire, which announced in February 2019 that it had secured major recruitment firm Hudson as a customer.
PredictiveHire's platform has so far been involved in the hiring of 8,000 people in Australia, the U.S. and the U.K. since it launched in 2013. It now has around 20 customers across these three markets.
PredictiveHire combines data and behavioral science and machine learning, but it does not use biographical data, CV’s or even names to identify candidates. Instead, candidates answer questions via text and the solution then creates a personality profile, which is matched against historical performance data from inside the hiring organization. This process acts as the first screening for job applications.
The advantage of this approach is that it can eliminate false positives, where people are interviewed or even hired because their CV’s look good and they present well in interviews.
While CV’s and interviews have long been the approach for recruitment, the downside risk is that it contains inherent biases. Many suitable candidates may not be considered because the managers hiring people are drawn to people similar to themselves or who conform to their predetermined images of suitable candidates.
Through reducing or even eliminating personal bias, the idea is to identify candidates who have the traits the employer is looking for. In this, PredictiveHire claims a success rate of 88%.
Also, in the same month, PredictiveHire announced a partnership with talent management software-as-a-provider PageUp. The latter will make PredictiveHire’s machine learning solution available through its platform, whose users are not only recruiters but employers in over 190 countries.
PageUp’s platform comprises around 100 HR tech providers who use the platform to offer solutions from video interviewing, background checking to time and attendance.
Discovering Hidden Potential
Another more recently launched solution in the AI recruitment space is a Shortlyster, which recently secured AUD 5 million in funding from investors including former recruitment executive and Shark Tank judge Andrew Banks.
Founded in 2015, Shortlyster is an AI-based recruitment platform which matches candidates to jobs based on their skills and personality. It compares tertiary qualifications against the skills requirements for a role and claims the ability to identify people who may not meet criteria on paper, but who are suitable candidates.
Once again, the key to the solution is the claim that Shortlyster eliminates bias. The company has plans to expand its technology to help the improvement and progression of existing staff. There are also plans to collaborate with other providers in areas such as psychometric testing and resume checking.
An even younger startup is Sydney based SaaS provider Curious Thing, which recently secured AUD 1.5 million in seed funding only six months after launching in 2018.
Curious Thing uses natural language processing to lead conversations and learn through questioning. The company claims that this approach will ultimately do a better job of interviewing candidates than a human.