tap into collective intelligence for better hiring outcomes.

In an age of increasing automation, data input is more important than ever for proper candidate selection.

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.

checkster interface

For some employers, a criminal conviction may not be reason enough for rejecting a candidate, but collective intelligence provides additional insights that can support or disqualify an applicant. How a company uses this information should be strictly determined by its hiring standards. What’s important is that the data is presented in a clear, consistent and comprehensive format. 

For instance, Checkster’s platform provides reports of each candidate based on collective intelligence, offering an assessment of the individual’s skills and abilities, a review of her top accomplishments and even an overall summary of past performance and probability of rehire. Furthermore, the report offers detailed information about the source of the data, how “fresh” it is relative to the last contact with the candidate and the relationship of the reference to the job applicant (i.e., her manager, co-worker, etc.). 

Having such a complete view offer greater context around an applicant’s work history and is a more reliable predictor of future performance. The technology also helps accelerate selection when employers are trying to quickly fill roles. That means decisions can be made within hours instead of days.

checskter ranking matrix

Beyond the pre-hire support such a platform offers, Lermusi points out that it’s important for companies to also monitor outcomes post-hire. That means examining a number of factors such as retention, promotion, hiring manager and candidate satisfaction scores and other metrics, which are strong indicators of whether a company’s process is working. Unfortunately, many employers currently fail to track these important data points, which means they aren’t able to impartially determine whether improvements are needed and, if so, what actions are needed.

He advocates that employers that don’t do so seriously consider implementing a process for assessing hiring outcomes. The insights provided would significantly help companies acquire better talent and reduce other costs over time.

As organizations increasingly turn to AI and automation to enhance their recruitment and hiring process, it’s more important than ever to provide reliable and accurate data to the machines for optimized outcomes. This includes important background and reference checking information that can help predict on-the-job performance. Just as importantly, such insights should be presented in a comprehensive and easy-to-understand format to be meaningful and valuable.