Losing top talent is a concern that all HR leaders have in common. By utilizing the appropriate resources, businesses can effectively safeguard themselves against disruptions caused by the unexpected departure of crucial staff members.
In the era of AI, predictive analytics plays a crucial role.
Recruit Holdings is leading the way in this shift. A new AI program has been launched by the Tokyo-based company to identify employees who may be at risk of leaving.
The recruit is testing out the new system for a limited time. The program apparently operates by gathering workforce data, including the work hours of current employees and the outcomes of their performance evaluations and personality assessments. This data is then compared with information from employees who have previously left the company, as reported by the Nikkei Asian Review.
Through the use of predictive analytics, it becomes possible to determine which employees are more inclined to leave their positions in the upcoming six months. According to reports, managers utilize the information to proactively engage with employees and attentively address any work-related concerns they may have.
The AI program is able to detect subtle indicators that may shed light on the reasons behind employee turnover, such as the amount of hours worked or recurring patterns identified in performance reviews by managers.
Predictive analytics, on the other hand, encompasses more than just identifying employees who may be at risk of leaving. Other companies have also started incorporating AI to handle talent management and analyze workforce trends.
IBM, for example, utilizes the exclusive Watson AI to not just monitor their employees’ present performance, but also to forecast future output or career trajectory.
Pymetrics, a data science startup, also utilizes AI technology. This time, to assist companies in selecting potential employees based on their performance and characteristics.
Applicants are required to complete a set of 12 online games that have been specifically designed to assess candidates’ cognitive and emotional characteristics. The data collected from these tasks are then compared to those of successful employees to assess the suitability of the candidates for the job.