I like spending time with recruiters and sourcers, watching them do their work. As the CEO of a sourcing AI company that automates much of the process, it is important to me to understand how these professionals think. 

Recruiters have mastered the manual recruitment process, so AI will only streamline that process insofar as it speaks to the ways they already search for candidates.  To create an effective AI, we want to truly understand why recruiters select or pass over specific candidates.

After becoming very familiar with recruiters’ methods, I have noticed that despite their best efforts, they often miss many perfectly qualified candidates. Here is why:

Manual Searches Are a Time-Suck

The common strategy to source talent is to run a search on platforms such as LinkedIn. While the search may turn up hundreds or thousands of profiles, there is no way to immediately tell how many of them are actually qualified for a given role. This is because Boolean search capabilities are still limited.

Oftentimes, recruiters must sift through the listings of profiles and manually select the ones they like. They may select only one out of 10 profiles, then change the search and select more candidates from a new list, until they have enough candidates to contact.

Over the course of this long and tedious process, recruiters have only a few seconds to judge each individual profile. They simply do not have time to give every candidate a thorough look because of the sheer number of search results.

Recruiters’ brains are therefore trained to focus on profiles that look a certain way or with certain information, and more importantly, to skip everything else. Specifically, they are only interested in candidates who have enough information on their profiles to justify trying to reach them.

For example, if a recruiter searches for a specific skill, they want to see it not only in the skill section in LinkedIn but also in the candidate’s self-description or in the description of a specific job they’ve had.  When asked for the reason behind that approach, someone who bothers to mention a specific skill multiple times is more likely to have a significant knowledge of it. 

RippleMatch Fall 2022 Recruitment Checklist

This is what they want to ensure after all. If recruiters take the time to contact, schedule with and interview a candidate who turns out not to be qualified, they’ve lost valuable time and energy without anything to show for it.

LinkedIn is Limited

This approach is perfectly logical, but it excludes more qualified candidates than recruiters realize. 

It’s true that even qualified candidates, for a number of reasons, do not always fill their LinkedIn profiles with the type of skills and content that recruiters wish to see. For example, engineers may enter more relevant information on sites such GitHub and Stack Overflow than on LinkedIn.

There is another disadvantage to this strategy: small candidate pools. Because of the tedious work of finding perfectly qualified candidates, recruiters will typically only reach out to around 20-50 people per job.  Of those people, only a fraction will show interest in an interview and it may not even be possible to make a hire.

How Sourcing AI Can Help

AI provides a solution to both glaring problems. It quickly and efficiently finds hundreds of qualified candidates for each job by inferring skills from similar candidates’ background, experience and workplace. It then contacts each of these candidates simultaneously about the relevant opening.

Those who show interest are pushed back to the ATS for follow-up by recruiters. So, the time and effort taken by those long Boolean searches can now be put toward speaking with candidates and scheduling interviews.

 By eliminating the need to manually, time-consumingly sift through profiles and contact each individually, AI can put an end to recruiters’ Myopia. 


Authors
Gal Almog

Gal Almog is the Co-Founder & CEO at Talenya, a world leader in talent sourcing solutions. Gal spent the last 20 years inventing AI-powered products that disrupted the recruitment technology market. Prior to founding Talenya, Gal founded PandoLogic, a world leader in recruitment advertising technology.  In 2021 he was named one of the 100 most influential thought leaders in the Talent Acquisition technology market, by the Association For Talent Acquisition Solutions.