Many sectors have started to embrace artificial intelligence (AI). Human resources is one industry that has seen what AI and machine learning (ML) can do.
Artificial intelligence can aid in finding qualified candidates, automate interview scheduling and create a smooth onboarding experience for the hiring manager and new worker. It can also help HR teams in other areas, including predicting employee burnout.
An employee can experience burnout for several reasons. One of the most common causes is if the worker has too many tasks to complete. Burnout can start to creep in if they feel they have too much to do and too little time to do it in.
On the other hand, staff can experience this if most of their day involves tedious and time-consuming manual duties. People who perform the same repetitive tasks every day can quickly experience burnout.
Many other factors also play a role, such as too little compensation for the amount of work, long hours, stress and poor work culture. A micromanaging supervisor could cause employees to feel overwhelmed.
According to Zippia’s research, 89% of workers experienced burnout in 2022, and 40% of staff quit their jobs because of it. Zippia’s findings state it is the No. 1 reason people leave their employment in the United States.
The world is still experiencing the “great resignation” — where millions globally left their employment. For many organizations, this sparked the need to increase worker retention.
Many companies are hiring from within, which helps with worker morale and engagement. About 45% of people say they would stay at a company longer if it invested in their learning and development. Other businesses have implemented solutions that take staff’s mental health seriously, such as employee-assisted programs (EAP).
These programs can aid in reducing job pressure and stress. Zippia’s extensive research has also shown that people are 70% more likely to experience burnout if they don’t feel supported at work.
Machine learning is a part of artificial intelligence. ML generally refers to a machine using algorithms to scan data to form predictions or conclusions. The more information the algorithm can access, the more accurate the assumptions are.
Machine learning has helped HR with various tasks. This makes life easier and allows workers to focus on other essential operations. ML can do the same in terms of predicting staff burnout.
AI technology can scan employee data and form predictions. HR teams can use ML’s conclusions to help workers feel less stressed and overwhelmed. This technology enables management to help employees better manage the pressure of the job. This allows staff to get their mental health back on track before burning out.
ML also helps identify workers likely to burn out and bring the issue to HR’s attention. In many cases, even if employees experience intense burnout, they don’t always notify their employers and seek the help they need. If management is unaware of the problem, it can’t implement measures to alleviate the burden.
Machine learning can play a vital role in identifying and predicting employee burnout. AI technology can scan relevant worker data to determine who will most likely experience it. For example, ML can analyze employee emails to discover patterns and show HR who is overwhelmed and stressed.
The concept of utilizing machine learning for detecting stressors is not a new one. A study from 2018 has shown how an organization using ML this way could determine with more than 80% accuracy which workers were likely to experience burnout.
Currently, more tools are becoming available that can help predict this, including Erudit. It can analyze real-time data gathered from text and video apps workers utilize daily. Erudit can calculate employee burnout risk, engagement levels and job satisfaction.
Uplevel is another AI tool showing exceptional promise. It is specifically designed to help with engineering effectiveness. It scans different data from messages, calendar entries and code repositories to create a deeper analysis. From here, Uplevel can determine if someone is at risk of feeling overwhelmed and experiencing burnout.
Machine learning benefits HR teams by identifying workers most likely to suffer from burnout. It brings the situation to the surface where management can implement solutions to help employees.
Human resource professionals have many vital tasks requiring their attention. They can’t rectify the situation if they are unaware of a pressing issue and the worker does not report it.
However, with ML, HR teams are notified of individuals who could suffer from this condition. They can provide them with mental health resources, decrease the workload and implement employee-assisted programs to improve their well-being. Because they know about the situation, they can help employees effectively deal with and alleviate burnout.
In addition to predicting burnout rates, machine learning can prove valuable to help reduce it. Here are three ways ML can aid with this.
HR must know what areas influence burnout. Machine learning can look for patterns that overwhelm or stress employees, such as long hours or excessive workloads. Identifying these factors can help management address the issues by encouraging breaks and allowing people to spend time outdoors.
While implementing employee-assisted programs is a great way to prioritize staff’s mental health, many organizations can’t afford it. In these cases, it could be more beneficial to utilize AI coaches. These chatbots could gather employee data and present it to HR anonymously.
Management can use this information to implement changes and help ease the burden on staff. These tools can aid workers in prioritizing their mental health and getting it back on track.
Probably the most significant benefit AI technology can offer is automating tedious operations. Employees who perform the same repetitive tasks every day could start experiencing burnout. HR should consider what duties can benefit from AI automation to help reduce workloads.
It is clear HR teams and employees have much to gain when incorporating machine learning. It can determine patterns of burnout and automate tedious and overwhelming tasks. Management embracing this technology can help workers effectively manage their mental health while working toward company goals.