Sponsored by MightyRecruiter, the all-in-one recruiting software that provides the tools you need to find the candidates you want.
Artificial intelligence (AI) and machine learning are changing the face of recruiting, making it easier and quicker for recruiters and hiring managers to identify appropriate applicants, even for the most complicated roles. That’s a good thing, since polls suggest that 2017 is going to be a very busy year for recruiters.
Fifty-six percent of companies indicate that their hiring volumes will increase this year, but only 35 percent of businesses anticipate adding recruiting staff to manage the extra workload. That means efficiency in the form of AI and machine learning is going to be become even more important, as recruiters and hiring managers hustle to fill more vacant positions than ever in a 40-hour work week.
But how are AI and machine learning making recruiters more efficient at finding and connecting with the right candidates? Here are a few examples:
1. Skip scheduling
Many companies are employing bots to handle their scheduling needs to eliminate the often tiresome back and forth that happens when recruiters are trying to schedule interviews with multiple candidates. Products like Amy, an AI-powered personal assistant, can scan emails from candidates and correspond with them to schedule meetings, shaving off hours of time that recruiters used to be spent on tedious tasks.
2. Makes sense of semantics
AI and machine learning tools in recruiting are also helping recruiters deal with the very real problem of the semantics involved in resume examination. When hiring managers are looking to fill a Marketing Manager position, for example, the easiest route is to recruit only those people whose resumes reflect a past marketing manager role. However, since a variety of titles can mean the same thing in marketing, that system would eliminate qualified candidates whose current job title might be “marketing coordinator” or “marketing specialist.” Further, within those titles, not all candidates will describe their job function exactly as it is worded in your job description, which can leave even more qualified candidates out in the cold using a standard ATS.
This issue of language and semantics is another area that AI and machine learning are helping to address through the use of conceptual search tools that understand a recruiter’s intent and don’t require a precisely worded query to work. Instead, recruiters and hiring managers are able to choose a few keywords about the role, and the technology forms conclusions about applicants’ suitability by scanning their resumes. This allows qualified applicants who may have worded their resumes differently than the job post to rise to the top of the applicant pool while eliminating candidates who might have the right title but the wrong experience. The best applicant tracking systems have figured it out.
3. Smarter sourcing
Another area where AI is having a huge impact is in candidate searches and ranking.
One such product is MightyRecruiter, which has developed a new vector space matching technology that automatically sorts candidates based on their relevance to a job description. The technology analyzes applicants’ resumes for clusters of words or phrases from which it can draw key inferences, like how many years of experience a candidate may have or how proficient a candidate may be with certain software. The technology will do this for every piece of information in a resume – and then draw a conclusion about how well it matches the job description requirements, narrowing down the candidate pool as it goes. Hiring managers are then presented with a list of applicants that is ranked in order of suitability.
Ann Barzman, MightyRecruiter Product Lead, says:
The standard Applicant Tracking System (ATS) will never be able to trump vector space matching because it isn’t able to make inferences about the applicant the way humans do when they review a resume. Our technology does.
For example, an ATS may present a recruiter looking to hire a Systems Engineer with resumes of Restaurant Servers simply because of the mention of the word “server.
Our vector space matching, on the other hand, can draw certain conclusions about the resume of the Restaurant Server as a whole and, based on those inferences, will drop it to the bottom of the applicant pool as a bad fit.
Since some studies estimate that up to 75 percent of applicants aren’t qualified for the role they apply for, the use of these time-saving AI and machine learning tools will save time while helping recruiters to do their due diligence.
4. Leaves the human elements to the humans
With the time saved through the use of AI and machine learning, recruiters and hiring managers have the opportunity to focus on the parts of their jobs that technology can never fill: the human elements.
Recruiting and hiring is about much more than sorting resumes and scheduling interviews; it’s about human interaction – engaging with candidates and connecting with applicants to make the best hire. The time AI and machine learning save in finding a pool of candidates can now be spent developing relationships with your tops picks, which is sure to make your hiring and recruiting efforts more successful than ever.
For more information on how MightyRecruiter’s vector space matching algorithm can save you time by bringing the most relevant applicants to the top of your hiring list, take advantage of a free trial today!
