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How Machine Learning is changing the Hiring Process

Machines have always been prudent processing vast amounts of information quickly. In recent times, with the capabilities of machine learning growing exponentially, so has the ability for machines to provide greater assistance in decision making and in making recommendations.  So, logically speaking, the world only stands to gain from embracing machines and Artificial Intelligence being integrated into the hiring process, where both, the company and the candidate depend on a good fit. 

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A Rapidly Evolving World:

Before 1999, it would only be natural for a candidate looking for a new job to search for opportunities in their local newspaper, send in an application and wait to hear back from the company. By the end of 1999, it was already becoming a thing of the past. The reason? The job board Monster.com was founded. All of a sudden a candidate could send in a lot more applications, and reach out to a greater number of companies than before. From then on, for a little over a decade, a lot of changes continued to take place in this space, and applicant tracking systems (ATS) also came into lot wider use, to process the increased numbers of applications coming in.

ATS served the business world well, but only to an extent. While it was great at organising the huge volumes of applications, human intervention was still required to even so much as generate a list of shortlisted candidate to be interviewed. As a result, companies were compelled to spend large sums on hiring screeners, hiring managers, recruiters, and others to ultimately find a candidate worth the effort.

Enter Artificial Intelligence.

In a nutshell, AI models historical hiring patterns. There are a lot of factors one can choose to have weighed in while using AI to generate a pool of candidates to interview. In practical terms, this would mean that AI can help narrow down 600 applicants to the 50 best fits, and the recruiter can then proceed with the 50 without having to manually sift through all the 600. 

The Future and What It Holds:

It’s not 1999 anymore, and neither is it 2010. The new age comes with its own unique follies and perils. The biggest issue for recruiters currently is that they have massive networks but there isn’t really an effective way to leverage those connections without spending a significant amount of time and resources. Having 4000 connections on a platform such as LinkedIn doesn’t really do anything for a recruiter except allow some fleeting bragging rights. That’s where machine learning comes into play to transform these vast networks ad volumes of information into tangible results in the form of the ‘best-fit’ shortlists discussed above.

In conclusion, here is an example to illustrate the operation and scope of machine learning: it can determine that a certain developer has been at their job for two and a half years, and there is a 97% chance that they will leave their job in the next three months. This is a major insight that can be determined in almost no time using machine learning. In fact, machine learning may well be the recruiter’s best friend when it comes to the R.O.I for all their efforts. Time is money, and there has been little if any doubt about machine learning’s capabilities to save time for quite a while now.

Based in the City of London, Recruitment SMART is a HR tech startup on a mission to bring disruptive technology to the recruitment industry. They are developing Artificial Intelligence and Machine Learning based sourcing solutions. Team includes veterans from both the recruitment and tech industries and has client globally in UK, US and APAC. Feel Free to Write to us at info@recruitmentsmart.com

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