Machine Learning in HR can streamline and give more precision to talent management processes. From attracting professionals, going through digital onboarding to retaining people, there are countless applications where technology can lend a hand to HR managers.
Even before the rapid acceleration of the digitization of processes in 2020, Deloitte’s 2019 Global Trends in Human Capital report already announced the growth in the use of this type of tool in people management.
According to the study, around 80% of respondents predicted the expansion of cognitive technologies, which include machine learning, also known as machine learning.
Agile organizations value the combination of human and machine talent at all levels as an essential element of their business and strategic planning. Getting this mix right can significantly affect productivity, competitiveness and positioning.
What is Machine Learning
Machine learning is a subfield of artificial intelligence (AI), defined as the ability of a machine to mimic intelligent human behavior. It gives computers the ability to learn without being programmed.
In other words, machine learning is an evolved artificial intelligence that has taught itself to mimic human behavior.
Here we can draw a parallel between machine learning and the performance of babies. When they are born their hard drive is almost empty, they need stimulation (programming) from their parents and others around them to fill their repertoire. Gradually, it is possible to see the evolution of babies who start to imitate behaviors.
In machine learning, the programmers’ codes and other information that users end up leaving in the interaction with the machine are the stimuli for the robot to learn to deal with situations of human behavior.
With the emergence of Big Data, the Internet of Things (IoT) and ubiquitous computing, machine learning has become essential for solving problems in several areas, such as:
- Computer finance (credit score, algorithmic trading);
- Computer vision (facial recognition, motion tracking, object detection);
- Computational biology (DNA sequencing, brain tumor detection, drug discovery);
- Automotive, aerospace and manufacturing (predictive maintenance);
- Natural language processing (voice recognition).
See the difference between machine learning and artificial intelligence
As we have already seen in the previous topic, artificial intelligence and machine learning are elements of computer science. Both technologies are quite popular at the moment for helping the human being in complex activities.
Before comparing these technologies, how about knowing a little more about the concept of AI?
What Is Artificial Intelligence?
Artificial intelligence is the set of technologies used to build intelligent systems capable of simulating human intelligence. Simply put, it is a technology that combines computer science and datasets to enable problem solving.
AI is integrated into our lives through personal assistants like Apple’s Siri and Amazon‘s Alexa.
What Makes Artificial Intelligence So Different From Machine Learning?
Looking from above, the technologies seem similar, but there are some important differences between them, starting with the objective of each one.
The purpose of artificial intelligence is to create computer programs that can mimic human intelligence. The goal of machine learning is to teach machines to learn automatically from data they understand, without programming.
While AI is based on features of human intelligence, machine learning is based on the probability system.
Whereas artificial intelligence systems are concerned with maximizing the chances of success, machine learning systems are concerned with accuracy and standards.
4 benefits of using machine learning in HR
By sifting through massive volumes of data to identify patterns and make predictions about future events, machine learning for HR increases efficiency and eliminates many previously manual tasks in people management.
According to the IBM report “The Business Case for AI in HR” , the main reasons for using machine learning in people management are:
- Solve urgent business challenges;
- Improve the employee experience;
- Provide support (data) for decision making in talent management;
- Optimize the HR budget.
Decreased Repetitive Tasks
One of the most challenging tasks for recruiters is selecting candidates. Sometimes a lot of time is spent conducting the first round of interviews to assess the capabilities of potential candidates.
Now this work can be done by a chatbot based on artificial intelligence and machine learning, which can accept or reject a resume, conduct the necessary qualification questions and analyze the answers, allowing the manager to make a decision in the second stage of the process, the interview.
Deep Analysis
In addition to analyzing the professional qualities of a potential candidate, it is necessary to take into account their personal characteristics.
Machine learning-based human resource algorithms allow you to examine a candidate’s social media accounts to identify their interests, hobbies and activity and determine communication skills and characteristics of interacting with others.
This helps to understand if the candidate will fit into the organization.
Automated Integration
Finding and hiring the right employee is just the tip of the iceberg. In many ways, the success of cooperating with a new employee depends on their first week or month at the company.
This is a time-consuming part of leaders, but onboarding can also be automated by a chatbot with machine learning application. The technology will guide the employee through company policies and answer common questions.
24/7 remote support saves managers time and allows the employee to get used to the new rules and obtain the necessary information without additional involvement from the human resources department.
Helps Increase Employee Motivation
Recently, many concerns have been associated with the entry of new generations into the labor market. The rapid change in young people’s values requires more engaging solutions from human resources to keep employees motivated.
Analytical HR machine learning algorithms allow you to determine an employee’s individual needs, potential and interest.
This helps managers select training programs, make predictions and predict employee behavior patterns.
Applications of technology in the day to day of HR
In short, machine learning can help talent management in many ways, from employee attraction to employee retention. Here are some of the most common uses of technology in HR:
Recruitment And Selection
Machine learning can help map resumes and skills to job openings and sort CVs at a much faster pace than when done manually.
According to a survey by consultancy Gartner, 25% of candidates apply for more than ten jobs; In addition, the average number of applications received for a single position increased by 39% between 2012 and 2018.
Although this data is outdated, it is possible to see a steady growth in the number of applications, which means more work in the first stage of the recruitment funnel.
But with the right tool powered by machine learning, recruiters can spend less time scrolling through resumes and more time getting to know candidates with good potential for the job.
Employee Retention
When it comes to employee retention and motivation, organizations can monitor employee engagement and satisfaction using machine learning to analyze employee feedback.
The technology can examine structured content from weather surveys as well as unstructured social media conversations and come to a personalized conclusion per contributor.
Continuous Development
It can also play a role in employee continuous learning, technology can make highly relevant recommendations for related content on a learning platform, in the same way we find product or movie recommendations in our consumer lives.
