A lot has been made of artificial intelligence’s impact on talent management, but when it comes to making good hiring decisions, it may be collective intelligence that has an outsized influence on talent acquisition success. AI is becoming more effective at interpreting data about a candidate’s history, work habits, skills and other factors, but at the end of the day technology still depends on the input of information to determine whether a candidate is right for a job.
As a talent management professional, your mandate is to deliver resources as quickly and cost-effectively as possible to ensure your business has access to the skills needed to succeed. Metrics around time to fill, hourly rates and other tangible performance indicators are important, but too often companies fail to examine factors that affect the quality of talent. Whether workers have sufficient experience and skills, that they can work independently or on a team, that they are truthful on their job application and other questions can be difficult to verify, but this type of information is exactly what hiring managers need when deciding on an applicant.
Yet compiling this type of information is time-consuming and potentially risky due to bias and other institutionalized shortcomings. For instance, when a candidate is interviewed by multiple hiring managers, a decision may be unconsciously swayed by “groupthink,” a phenomenon in which the outcome is reached not based on impartial findings but the group’s desire to minimize conflict over making the right selection. Furthermore, collecting and viewing multiple sources of background data can be laborious and resource-intensive, distracting staff from performing their regular duties.
Yves Lermusi, CEO of Checkster, an automated platform for supporting hiring decisions based on collective intelligence, explains that employers not only need a disciplined approach to conducting reference checks and interview debrief but also a way for processing and using the information collected to arrive at the best candidate selection. Obtaining this kind of collective intelligence provides a comprehensive picture of a prospective hire’s potential and helps protect companies from not just a bad hire but one that may put them at risk.
the value of diverse sources of information
Collective intelligence is unquestionably an important asset for hiring managers. With access to information collected from multiple sources – interviews, personal and professional references, criminal records, former employer testimony and other data – companies can unlock the most telling information about a hire. And because a compilation of many sources paints a more balanced picture of a job applicant’s potential, the employer can decide on an individual basis the qualities they value most in a job seeker.
Lermusi points out that for all of today’s hype over AI’s impact on automating the hiring process, technology still relies on the input of people and data. That’s why it’s important to capture every available bit of information for each candidate considered before a hiring decision is made. He points out one example in which a Checkster client was close to hiring an applicant whose background check initially showed no criminal offense. However, a more detailed search revealed he had been arrested for a criminal offense but his case had not yet been adjudicated in court at the time of his application. An arrest without a conviction might not have been enough to disqualify the candidate, but it turned out the individual falsified his references and the Checkster tool detected it, which was enough to knock him out of consideration.