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How Machine Learning is Revolutionising HR

7th May 2021
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The digital transformation driven by businesses aiming to adapt to the COVID-19 pandemic has prompted a fundamental shift in how we understand HR departments. HR used to be about finding the right candidates for roles, managing assessments, offering jobs, managing employee careers and departures. However, today, technology has leveraged new expectations and capabilities of departments - modern HR needs to be able to predict attrition and candidate success before they even begin the onboarding process. 

Machine learning has emerged as a developing technology that’s set to revolutionise the role that HR departments play in the wake of the pandemic. Before, HR managed data in a manual and semi-automated manner. To create analytics, data had to be gathered, stored and processed by the users. All of this needed to be performed in a short space of time because the data may become dated as situations change and updates occur. 


(Image: Codetiburon)

As the illustration above shows, there are plenty of aspects of HR in which machine learning can come to the aid of workers. As we progress towards the age of the new normal, let’s take a deeper look into how machine learning can be implemented to revolutionise HR departments over the course of the decade: 

Recapturing The Human Element of HR

The notion of machine learning and the implementation of more AI technology making HR more human may seem a little counterintuitive, but the logic is sound. 

Particularly in the wake of the pandemic, HR teams are tasked with so many duties that are both monotonous and crucial to the functioning of a business. Things like data requests, repetitive answering of the same questions for different employees can weigh heavily on HR workers, and the ability to automate some of these processes by handing the duties to a bot or algorithm can actually help to position workers more prominently to deal with more complex issues while machines can emulate a more personal experience elsewhere. 

By using machine learning to automate monotonous tasks, employees can get a faster, more personalised and around the clock experience as machines work to answer their questions. This also helps to free up HR workers instantly, in turn making them more responsive. 

This means that implementing machines in the employment journey can offer more to workers, which subsequently creates a better working environment for both employees and job applicants alike. 

Screening Candidates

There’s a limit to the volume of CVs that an HR manager can see throughout the day. However, such limits don’t exist for machines. 

Looking through CVs isn’t the only way of screening candidates, there are other key methods - such as assessments and interviews to contend with. However, these processes can be even more time consuming overall. 

As a result, more companies are turning to machine learning as a means of automatically screening CVs, assessing candidates and even undergoing preliminary interviews. Through these tools, HR departments can save significant amounts of time, broaden the range of candidates they’re capable of vetting and eliminate unconscious bias in the hiring process. 


(Image: Agency Central)

As the table above shows, the vast majority of recruiters believe that unconscious bias not only exists when hiring staff, but as much as 36% identify it as a big problem. 

Recently, book retail chain Indigo decided to embrace artificial intelligence and machine learning solutions to effectively screen the huge volumes of candidates who were applying for roles with the company. With around 2,200 job applications being received on a weekly basis, HR managers looked to the technology to help onboard the right candidates during seasonal sales. 

In using Ideal’s AI and machine learning technology to vet their candidates, Indigo tripled the number of qualified candidates they onboarded whilst lowering the costs per hire by as much as 71% - indicating that machine learning can act as a significant time and money saver alike. 

Grasping Employee Engagement

Employee engagement has become something of a popular word in HR department glossaries. Many studies have shown that, at any given time, large numbers of employees are struggling to find themselves fully engaged in the tasks that they’re performing - leading to concerning losses of productivity and quality of output. 

However, machine learning can be on hand to process the data to measure and better understand engagement levels among employees - helping to provide deep insight into how businesses can boost their productivity and limit cases of staff turnover. 

Solutions have already been created by organisations like Glint and Workometry that are widely used by companies around the world. These software systems measure, analyse and report on employee engagement rates as well as studying the sentiment towards their workloads and tasks. Data is collected from a wide range of sources that would be extremely difficult for HR personnel to manually extract for analysis. 

Likewise, real-time machine learning platforms can help to keep employees safe from threats. One of the best examples of this new wave of HR technology may be found in Exabeam, a data and user monitoring platform that can detect and manage security threats as they unfold. 

In potentially listing Exabeam’s IPO, Freedom24 acknowledges that Exabeam has received nearly $200 million in fundraising for a company value of more than $800 million. The company’s decision to launch an IPO underlines the widespread confidence in the growth of machine learning-based HR solutions.

However, before taking part in an IPO, Maxim Manturov, Head of Investment Research at Freedom Finance Europe, says: “Research is always a top priority. First things first, one should pay attention to how the company's earnings rise and its potential TAM. It is also advisable to explore the company's products and what needs they cover, the company's financial condition, and liquidity. A healthy debt-to-assets ratio, renowned IPO underwriters, and suggested ways to use the raised funds are also worth paying attention to. The more information one can get, the deeper understanding one will have.”

With HR productivity tools already spotting their chance to go public without fear of a lack of investor interest, we can see that confidence is high for digital transformation in workplaces over the coming years. 

Although machine learning will carry a significant impact on the world of human resources, it may also contribute to a wholly more ‘human’, helpful and data-driven experience, to the benefit of companies, employees, and HR departments alike.

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