A critical arm of artificial intelligence (AI), machine learning makes technology truly intelligent and capable of understanding human needs. We discuss its role in HR and people-centric transformation.
HR has been using analytics for years. What’s changed is the approach. Data collection, processing, and analysis were entirely manual in the past. This limited the amount of time HR could spend on interpreting the data.
With machine learning making strides into various areas of business, it is no surprise that HR teams are finally accepting the importance of machine learning and its transformative potential.
Unlike manual approaches, machine learning is a faster model that is more responsive to dynamic scenarios â€“ offering accurate, valuable, and actionable data points.
What Is Machine Learning?
Machine learning is a self-learning algorithm that uses data and statistical models to perform a task without being given specific instructions each time.
Instead, it finds patterns in data and learns from these patterns to make future predictions based on these patterns. The software powered by machine learning is fed algorithms to interpret data.
Machines learn when individuals react to the data it presents. And the goal is to become more accurate with each instance. For example, when you engage with a particular account on Instagram, the algorithm that powers Instagram’s machine learning feeds you more information from that specific account, and less from an account you probably do not engage with. So, the machine learns that you are more interested in a certain type of information/person.
Machine learning is one of the technologies that drives artificial intelligenceOpens a new window (AI), not AI itself. AI is making strides in every area of HR,
What Are the Applications of Machine Learning in HR?
As workplaces consistently grow and become more complex (for example, remote workers are a big part of the workforce in many organizations), machine learning helps manage the change in expectations from HR departments. The HR role has largely expanded into a driver of value, assisting the organization in meeting key enterprise objectives. HR must be in a position to strategize for recruitment, engagement, and retention.
This is the age of Big Data. Managing employees means gathering data in a host of areas â€“ employee attitudes and feelings, qualification verification, employee approach towards policies, compensation management, and addressing relevant external developments.
Here’s where machine learning comes in. It can effectively accept, store, process, and manage these enormous data volumes and offer smarter insights via simple analytics in the following areas:
1. Smarter candidate identification and applicant tracking
In HR, machine learning can be used to identify and define recruitment patterns. Say you want to recruit a person with a specific set of skills. You feed data about those skills to a machine learning-powered software. Machine learning uses that data to shortlist a set of resumes or candidate profiles. You accept certain profiles and reject others. The machine learns to give you more profiles similar to those you accepted and downgrade those that you did not.
This is an elementary example of how machine learning can be implemented for recruitment. As big data comes from various sources â€“ forums and social media â€“ machine learning can look at a variety of key criteria â€“ qualifications, experience, interests, professional connections, and memberships, among others â€“ and bring up profiles of candidates that are the best fit for the company.
All of this will effectively reduce manual efforts in candidate assessment and trackingOpens a new window .
2. Smart predictions about employee turnover
Machine learning can help predict key movements and their impact. HR teams can set clear parameters that map possible scenarios and can, therefore, assess how likely it is that an employee is ready to leave the company.
By predicting such situations, machine learning helps HR teams reduce the possibility of turnover. Â You can use this data to develop well-articulated, intelligent programs to engage such employees. You can find out what it is that is driving them to leave, and you can put their managers in charge of their learning and growth in the organization.
The success of these measures can easily be replicated to identify future patterns. As the algorithm learns how to predict flight-risk employees quicker, you can take preventive measures much before an employee realizes that they are on the path to their next job.
3. Smart predictions about job success
Data on a candidate’s credentials, attitudes, memberships, and performance can often effectively point to their possible success in a role. Machine learning can help when it is given access to historical data about the most successful employees in the organization. For example, you identify the top 10 employees and feed their history into the software. It identifies different parameters associated with the success of these employees â€“ right from their educational qualifications, their general attitude, their responsiveness to the company’s learning and development program, and their growth through the ranks.
The machine learning-enabled program can match that data with the available parameters of potential candidates for the company. With its predictive capabilities, it can then reveal which candidates may be most suited for success in the role you are hiring for. So, HR analyticsOpens a new window obtained through machine learning can guide your hiring decisions.
The Future of HR is Machine Learning and AI
There’s no doubt that machine learning is going to drive the HR industry to new heights. Companies like JP Morgan have used it to identify rogue employees, and LinkedIn uses it to help show more relevant jobs to job seekers. And while it can enhance the efficiency of HR to a point where the process can become entirely strategy-oriented, some basic privacy issues need to be discussed.
The ethics of AI in HROpens a new window and using employees’ personal data for business growth have been under question for a while now. So before machine learning solutions are implemented, companies must build a legal framework that guards employee data privacy within the organization to protect employee data.
In addition, machine learning predicts information based on past performance. This means your algorithms must be updated regularly if you want to ensure that they are giving you the best predictions.
Once done, implementing machine learning will enable companies to focus on innovation, technological advancements, and their game-changing impact on enterprise vision and goals.
Have you implemented any machine learning programs for HR? Let us know on FacebookOpens a new window ,Â LinkedInOpens a new window , orÂ TwitterOpens a new window and let’s take this conversation forward. We’re waiting to hear from you.