The New Recruiter: How Automation Is Changing The Hiring Game

How recruiters can work in tangent with new technologies

For the past 50 years, the talent sourcing process has followed the same model. Companies post their job advertisements in high-volume outlets, collect hundreds of resumes, and use recruiting and hiring managers to filter the pile down to a batch based on judgement and heuristics. However, with this approach, recruiters’ valuable time is wasted doing low-value work, important data is lost, and there’s no introspection on each match.
 

As technology advances, traditional recruiting continues to go through a fundamental change alongside the broader modernization of the HR function. While this transformation is still in its early days, we can now see automation and data-driven approaches chipping away at practices that have been historically dependent on manual human review and judgements. In fact, over $10 billion of venture capital dollars have been invested in HR tech over the past 5 years, with more than 800 start-ups tackling every aspect of the function.
 
And while much has been written about the role of the recruiter being made obsolete with this new wave of technologies, this is an unlikely outcome. In fact, the more probable trajectory for modern recruiters will be a move from tactical execution into a more strategic function. The human element will always remain, but now bolstered by data-driven insights to enhance decision making.
 
So, let’s take a look at the specific ways in which the recruiter’s role is set to change, and how it can work in tangent with these new game-changing technologies. 

A growing number of companies are allowing their recruiters to demo and review new technologies that can improve the quality of their hires, save recruiters’ time, and cut overall costs. And because the current landscape is highly fragmented with hundreds of companies offering niche solutions, HR departments prefer to test out small pilots and flexible plans than commit to big SaaS contracts.
 
As a result, a core competency of the new recruiter must be to clearly identify pain-points and areas of growth for their business, and develop a systematic way to test and experiment with new technology. This is a highly strategic capability, as it requires the recruiter to experiment structurally and review solutions against a set of goals and hypotheses -- particularly true in areas like sourcing, matching, filtering, assessing, and scheduling. The recruiter must be able to ensure that their chosen solution aligns with talent acquisition and recruiting operations, is within budget, along with comparing it to other technologies on a number of levels. 

A large part of the traditional recruiter’s job has been to match candidates to the exact description given to them by their hiring managers. This type of tactical execution would require them to sift through all of the applications to find the closest matches. But how often are these requirements actually put to test? New platforms like SquarePeg, Blind Data, and Mya are collecting alternative sources of data about candidates and the job matching process that can help discover which factors lead to optimal matches.

Whether it is personality or behavioral data, skills assessment, or environment fit, there are many more nuanced factors that should be measured and reviewed by recruiters when establishing whether or not a candidate is a good fit for a role. Too often a certain degree or type of experience is required, without any evidence that people with those backgrounds actually perform better, get promoted faster, or stay longer -- and these are qualities that companies need to pay attention to. With automation in job-matching combating the problem of piles of resumes by surfacing quality potential matches, recruiters can spend more time diving into what factors should be considered and what traditional assumptions should be challenged. 

With all of the hiring analytics being marketed to recruiters, it’s important to understand how the data being collected and used is actually helping to improve outcomes. And if there’s anything that a recruiter should be focusing on in the world of automation, it’s how insights at every stage of the hiring funnel and performance review can inform hiring strategies.

New technologies are collecting data from candidate sourcing, filtering, selection, onboarding, and review. As algorithmic matching and Machine Learning models make use of this data, the key for recruiters is how to make use of it to improve the funnel altogether. For example, if Sales Development Representative candidates from non-traditional schools that test high in perseverance and motivation are twice as likely to get promoted after one year, versus candidates with prior sales experience, this should inform the sourcing and filtering strategy for new candidates going forward.

Also important to consider for the new, technology-enhanced recruiter is the role of this pipeline in improving diversity in teams, as well as performance outcomes. Demographic data in matching can be used to train algorithms to never have preferences for one group over another. So, recruiters should be looking into the wealth of sourcing, filtering, and matching tools that are out there, so they can build the analytics that are top priority for their company.

Far from the view of many that technology is taking over recruiters’ jobs, these AI and ML-enhanced solutions are actually enhancing the role of the recruiter, getting rid of low-value tasks and allowing hiring staff to develop strategically as they test and analyze solutions, and interpret these insights to ensure their company is getting the best hire for the job. Not only this, the new performance recruiter should also be able to grapple with the data provided by these technologies, and use the insights to inform leadership on the key functions that are driving improvement and performance within the organization.