Human resources is seen to adopt new technology such as artificial intelligence (AI), machine learning (ML), and deep learning at a slower pace than other fields like marketing or communications. The slower adoption of technology and automation in human resources is caused by various factors such as talent gap, concern over privacy, ongoing maintenance, integration capabilities, and limited proven applications. And yet – the cost of adopting this automation might be justified with AI or ML application in human resources department.
EY research found that by adopting advanced automation for human resources job, you can actually reduce the amount of time HR professionals spend on administrative tasks, the burden or shared service centres, as well as help desks for routine queries. Automation can also help HR in terms of recruiting and retention, measuring ROI, and reducing bias in HR decision-making. These are some current application of ML in human resources.
Hiring process – During the recruitment process, HR often meets a mountain of resumes. They need to scans and shortlist those resumes to find matching candidates for a position. But with machine learning, HR can improve their process by creating patterns form applicants resumes, social media activity, and interview responses.
Employee attrition – Machine learning can be used in identifying early warning of employee attrition. By monitoring employee satisfaction survey results, you can see clearly if there is a drop in efficiency and absenteeism, making you aware of what’s really going on to your employees. Thusly, you can take action before problems such as dissatisfaction or lower motivation happen.
Employee engagement – Machine learning is also beneficial to collect and identify data about employee satisfaction. By the report of ML data, you can analyse and provide better engagement campaign which ensures employee retention and satisfaction in the longer run.
Likewise, in the future, we will constantly see a robust technology adoption of ML and other automation in our workplace, not to mention in the middle of human resources workload. While current trends are only about the recruitment process and talent engagement. In the future, machine learning can benefit more in terms of enterprise management and behaviour tracking.
Machine learning is here and it has helped HR department to be savvier in their job. Predictive analytics and big data management can help HR department in their approach to lead greater business success. Daniel Faggella mentioned that such data can produce insights that help company take wider and better actions. For example, an organisation can limit the number of interviews required for an applicant or better managing of maternity leave.
Monitoring your employees can be a daunting job, especially if you have hundreds of employees working in your office. However, the present of IoT wearable and baby cube can help you collect data companies. Then, the data can be used to help you measure behaviour of your employees or how much interaction happens within your organisation. Privacy, in this regard, might be a concern. These technologies, however, allow you to answer crucial questions that driving business such as “how does relationship between sales team and engineering team?” or “what is the current issue in engineering team that you might not aware of?”
“Machine learning will automate jobs that most people thought could only be done by people.” – Dave Waters
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