What place do AI tools have in the search for top talent at a global level? Should we believe the hype? We examine the ins and outs of AI in the recruitment process.
A university professor once accused me of being a Luddite as we debated the necessity of artificial intelligence technologies in the modern-day classroom. The image this conjures up for me is that of a 2001 Space Odyssey’s Hal 9000 telling a frantic Dave that he’s unable (or unwilling) to open a pod bay door and lead the protagonist to safety.
As we hand over more and more of our lives to artificial intelligence, its increasing presence across every working industry is clear.
So what place (if any) do AI tools have in the search for top talent at a global level?
1. Initial candidate engagement
We’re hard-pressed to visit any website nowadays without being greeted by a chatbot who asks us why we’re here and what we need help with. Many contingent recruitment agencies have adopted these AI-powered assistants, claiming to improve initial candidate engagement in the recruitment process by increasing the number of candidates who might apply for a position. They also use chatbots to answer basic questions about vacancy benefits or the overall package, such as paid time off, sick policy, and maternity leave.
These artificially-sentient tools do aid in addressing basic admin-level concerns for prospective candidates; however, this type of feature can never fully develop the intelligence to analyse the social nuances that can determine a candidate’s level of interest and engagement.
Active V Passive Talent
Additionally, this machine-learning structure would only be useful when dealing with active talent (job seekers) rather than passive; candidates who are not actively searching for employment are unlikely to find themselves on a recruitment website. At a global headhunting level, therefore, this type of machine learning technology has limited use when looking for top talent rather than low-hanging fruit thus affecting the overall candidate experience.
2. Candidate Sourcing and outreach
Recruiters are hoping to increase efficiency by using AI algorithms in the talent acquisition process that learn who would be best suited to a particular role among job seekers and subsequently reach out to these qualified candidates to engage in discussion. Sounds good in theory, doesn’t it?
The Dangers to Your Talent Acquisition Process
The danger lies in the programmer; since these self-learning algorithms will need to be taught which personal qualities and levels of experience should be considered or prioritised, the algorithms of these AI recruiting software are at risk of two casualties.
Firstly, overlooking other qualities that could be detected in personable interaction, and second, arriving at false conclusions of candidate fit based on an overly simplistic formula.
Missing Out On Talent
By employing formulas that might seek to favour harder skills over softer, talent managers face the danger of missing out on exactly what defines talent – a marriage of skillset and intrinsic traits centred around a candidate’s personality, critical thinking capacity, and ability to read social situations.
3. Interview stage
As all talent leaders will know, reducing or eliminating human bias in the hiring process entirely is an ongoing target for recruiters and organisations. Some argue for an AI-driven recruitment process, such as having the interviewee answer a series of bot-produced questions online. The thinking here is that by following this approach, social and visual cues that could unfairly benefit the interviewee are eliminated in the talent acquisition process. And overall it will benefit the screening process.
Face-to-Face Benefits in The Hiring Process
It seems counterintuitive, however, to avoid the benefits that an interviewer receives from a face-to-face interview, especially when conducting a recruitment process for executive hires that need executive level insights. A person’s social intelligence can be analysed through her conversational adaptability, body language, and response to stimuli, which are vital characteristics to consider when placing a senior-level candidate.
Recruiting teams should avoid assessing job seekers through a series of AI-driven questions from an AI-powered recruiting software and instead maintain a focus on the social and emotional benefits that can be gained through a face-to-face exchange; these could be further assessed through psychometric testing tools that Solutions Driven employ for senior-level hires.
4. Data analytics
Hiring teams eploying sophisticated machine learning and AI recruiting tools in their hiring process will allow for swathes of candidate data to be analysed in a matter of seconds. But will this reduce human error or will it reinforce existing human bias in the hiring process?
Talent acquisition professionals run the risk of their artificial intelligence tools arriving at quick or false conclusions based on innumerable data sets and machine learning algorithms that are being evaluated simultaneously.
Artificial intelligence technology bots might identify a technically-impressive candidate as being fit to lead a team due to their experience in the sector; however, as all hiring managers who have done countless candidate assessments will know, this doesn’t mean that a candidate can effectively manage a team.
When evaluating executive candidates in the hiring process, our in-house strategy is the “candidate fit scorecard.” This tool allows delivery teams to rank preferred qualities for all qualified potential candidates based on various levels of importance and means both cumulative and more targeted scores are assessed.
It might not be a bot with a fancy name or a string of coding all the way down your screen, but it’s allowed us to place hires correctly on the first try 97% of the time.
5. Ensuring best fit
In the age of “influencers”, any 20-something with an iPhone and a filter app can reach an audience of thousands to promote a cause, a brand, or simply his or her self.
Forbes argues that an individual’s involvement in social movements or overall online presence are important qualities when considering candidacy in the workplace, claiming that “overall fit” can be more accurately assessed when viewed in context of an individual’s life outside work.
While an artificial intelligence recruiting tool can be programmed to crawl potential candidates who are involved in social movements, it seems far-fetched to claim that any person with online presence or reach will make for a better fit.
Best fit occurs only when candidates match a business culturally, behaviourally, and technically – it is not based on their total count of Instagram followers.
A far better indicator is the 6Fs; specifically designed to ensure cultural and behavioural fit for the candidate as well as the organisation, for prospective candidates and recruiting teams as well as hiring managers, this process lends valuable insight and can quite accurately predict whether the individual will both perform well in the business, and fit in without a glitch.
More Suited to Contingent
No one wants to bear the accusation of being a Luddite, but it’s pretty clear that artificial intelligence and machine learning is better left to the standard contingent agencies who target low-hanging fruit and adopt the traditional 180 model.
The ethics and foundational structure of artificial intelligence and AI recruiting solutions are simply not fully developed yet to deal with the engagement, candidate experience, assessment, and selection of senior, C-suite, and business-critical hires.
What’s more, the presence of bias in artificial intelligence – powered tools means that any recruiter or agency considering its use should be exceedingly careful to ensure that systems have been thoroughly tested and are reliable AI recruitment solutions.
Solutions Driven’s unique model keeps the humanity and human intelligence in hiring by adopting a personalised and bespoke approach to each candidate search. After all, we’re in the business of people – not robots.
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