The introduction of talent acquisition automation brings change to a recruiter’s role and potential responsibilities.

To some, this can be a daunting prospect. This is because there are a lot of presumptions about how recruitment technology will affect or even replace the recruiter’s role. Something that is intensified by a continuous stream of news surrounding the ‘technology takeover’. Whilst these headlines are great at attracting attention, it’s important to differentiate between recruitment technologies, their capabilities and instead look towards the prospects automation will bring.

 

3 changes in recruitment with talent acquisition automation 

 

1. Sourcing techniques

Candidate sourcing can become much more efficient with the addition of automated recruitment software. Recruiters often source for new candidates by looking outside of their current candidate pool. Backed by the intention to bring in new talent and potential applicants for a specific role.

But what about the talent already existing within the ATS database? Consisting of historic applicants who may have been previously unsuccessful but are still interested in future opportunities?

In some cases, this is often avoided because recruiters simply do not have the time to manually search the database.

 

The solution: automated candidate discovery

For this, automated candidate discovery technology is highly effective in automatically scanning a database for matching candidates against any new vacancy added. In order to rediscover existing talent and maximise opportunities from candidates who have previously expressed interest in the organisation.

 

glasses with text overlay talent acquisition automation

 

2. Decision making

A vital shift will occur following the incorporation of hiring automation to contribute towards data-backed decisions. This is because automated solutions consider every single applicant and present results of best fit determined by a candidate’s skills and experience.

Therefore it’s far more difficult to ignore or even bypass an applicant who meets the job requirements. Squashing any bias rationales or seeping unconscious bias that will affect the hiring decision.

 

How? Automated CV screening technology

Review, screen and sift every single job application automatically with automation.

A method that is designed to assess candidate-job fit by scanning for keywords surrounding experience, job titles, skills and even position level.

In doing so, automated technology pays little attention to candidate attributes that serve no purpose to the job at stake during this crucial early hiring stage. Instead, it provides the recruiter with consistent bias-free hiring results, where each application has been considered equally and fairly. A great starting point for any hiring team!

Talent acquisition automation presents the recruiter results of best fit. It is then up to the recruiter to determine which applicants to progress further. Therefore instead of quickly scanning CVs (six seconds per each resume!), the recruiter can look in more detail at the closely matching applicants suggested as good candidates via automation.

CV screening technology alters a recruiter’s job by freeing up time they have previously spent on screening candidates during the initial hiring stage. This type of hiring technology makes getting to the decision-making stage much quicker than previous manual methods.

 

3. Analytics

Data is crucial in understanding the performance of different recruitment metrics. When dealing with high volume recruitment it can be particularly difficult to measure and track performance without the assistance of technology.

Automated hiring processes accumulate a lot of data. Data which is incredibly valuable for any recruitment team looking to improve and develop their future hiring strategy. Including information such as application rates, hiring times and candidate drop out.

For recruiters and employers being able to interpret this data is a must. A successful recruiter will be able to spot patterns and trends from the obtained data, to detect areas that have worked successfully or in some cases those that did not require improvement.

 

 

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