Machine learning in staffing and recruitment: Three key applications

It’s easy to be either afraid of or dubious about machine learning: if it doesn’t live up to the (considerable) hype, then it disappoints; if it’s as transformative as people say it is, then there will be inevitable worry about how it affects jobs.

Where recruitment is concerned, however, machine learning should be neither feared nor scorned: its benefits are already becoming clear. It has the potential to bring speed, simplicity, and cost-effectiveness to slow, complex, and costly processes.

In candidate relationship management, shortlisting, and placements, the industry is experiencing the advantages of machine learning most acutely. And why shouldn’t they? If automation speeds up processes and AI undertakes them in more intelligent ways, then machine learning has the potential to do both over time – continuously eliminating its mistakes and improving its ability to execute tasks.

Here are a few examples of how it’s going to change recruitment.

Candidate relationship management

Machine learning has had a dramatic effect on chatbots. They’re quickly becoming one of the more useful ways to communicate with candidates. By learning from common questions asked and answered, chatbots can provide an immediate response to their queries and concerns – recording specific details that make for better conversations with the recruiter.

It’s important to note that this isn’t a means of replacing one-to-one communication with recruiters – it simply helps to streamline some of the initial interactions. It can also help to rule out unsuitable or unqualified candidates before a recruiter even gets involved.

Shortlisting

Recruiters spend an inordinate amount of time sifting through CVs and LinkedIn profiles – spending almost as much time eliminating unsuitable candidates as they do interacting with suitable ones.

Another key advantage of machine learning is that it puts the ‘short’ back into ‘shortlisting’. By automatically filtering candidate skills, experience, employer information, and other important data from a variety of sources, it ensures that the recruiter only spends time considering viable, qualified candidates.

It also doesn’t discriminate according to gender, race, orientation, or any other secondary factor. The algorithm considers only merit and compatibility – unless specifically programmed to do otherwise. In CV screening, this can be invaluable. Truly meritocratic systems are hard to come by, influenced as they are by assorted human biases. Using machine learning, recruiters can filter out the best candidates according only to their accumulated experiences and abilities.

The human-to-human element is preserved, as recruiters take over the interviews process once shortlists are compiled. All machine learning does is reduce the amount of work it takes to get there.

Placement probability

Finally, machine learning provides an easy and effective way to gauge ‘placement probability’. It works simply: by using the data of past successful candidates, it can provide a window into the compatibility of current candidates and determine how likely they are to land the job.

As with automatic shortlisting, specific machine learning algorithms can be applied to candidate data – which is acquired from a number of sources, including social media, employee history, work experience, qualifications, and more. This allows the recruiter to trim the list of would-be hires down yet further: allowing them to find a close to perfect match for company and candidate alike.

Of course, ‘close’ is the operative word there. Machine learning algorithms aren’t perfect, and no technology really is. What really sets them apart is that imperfection is accounted for: the technology is designed to get better all the time. If a client or candidate isn’t fully satisfied with the experience an agency is providing, the recruiter can be assured that the next experience will be better. The algorithm will take stock of its flaws, correct them, and move on. Over time, this makes the technology, the recruiter, and the wider business much better.

Even now, recruitment businesses are benefiting from more effective communication, more convenient shortlisting, and more accurate assessments of candidate placement probability. With machine learning, you can be assured that this is only the beginning: the best is yet to come.

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