AI is the Solution for Employee Turnover

Pravir Ishvarlal
April 20, 2023

AI is the Solution for Employee Turnover

Employers have noted staggering growths in employee turnover statistics in 2022. While the growth has been gradual over the years, reports suggest that there has been a rapid increase over the past five years. According to statistics published by ‘Shortlister’, at least one-third of new employees quit after six months on the job. This results in business disruption, loss of valuable talent and mounting costs on recruitment and training of new workers on a regular basis. Further to that, the other ripple effects of employee turnover include reduced productivity, loss of profitability and low employee morale.

Due to the gravity of the effects of employee turnover employers are constantly seeking solutions that can mitigate the cause. Artificial intelligence (AI) has been identified as a tool that can decrease employee turnover significantly, and subsequently increase retention. AI has been identified as a critical tool as it analyses large amounts of data to form patterns and trends that may not be immediately apparent to humans. By analysing these large amounts of data, AI can enable organisations to better understand and predict employee turnover. This can significantly impact the way companies approach employee turnover, and retention.

Building AI Models to Diagnose Employee Turnover

The first phase in the process of using AI as a predictive tool is data collation. As we have noted, AI is heavily dependent on large amounts of data supplied by the user. For the purpose of building an AI model to diagnose employee turnover, a company will need to gather data on factors that may influence an employee’s decision to leave the organisation. This data can include:

    • Demographics – employee’s age, gender, education level, and traits that may influence resignation.
    • Employment history – employee’s length of service, previous job roles and responsibilities, and promotions or advancements received.
    • Performance – feedback or evaluations received.
    • Engagement – employee’s level of participation in team meetings and willingness to take on additional responsibilities.
    • Salary and benefits – current earnings and changes in earnings
    • Exit Interview Data of Previous Employees

Once the process of collecting the data is complete, the ensuing phase is using the data to train machine learning algorithms. Dependent on the data, the machine learning algorithms used can be decision trees, random forests, support vector machines (SVMs), neural networks, and K-means clustering. These algorithms can analyse the data to identify the factors that are most likely to lead to employee departure and from these insights, companies can put proactive measures to retain ‘at risk’ employees.

AI Integration to Internal Processes for Employee Turnover

For an organisation to fully yield the returns of implementing AI for the purpose of addressing employee turnover, it must integrate AI into the standing organisational processes. The integration of AI into these processes, may include using AI to:

      • 1. Gather and analyse the necessary data on what may influence employee resignations.
      • 2. Train machine learning algorithms to analyse the data and identify the factors that are most likely to lead to employee departure.
      • 3. Improve the onboarding process. AI can be used to analyse data on successful and unsuccessful onboarding experiences. It can identify the factors that contribute to a successful onboarding experience and make recommendations on how to improve the process. This can help improve retention rates.
      • 4. Automate routine HR tasks thereby freeing up HR professionals to focus on more strategic and impactful work, such as employee development and retention.
      • 5. Gather data-driven insights to inform HR decisions such as identifying patterns and trends in employee turnover and making recommendations for retention strategies.


Overall, integrating AI into an organisation’s processes can improve HR processes and decision-making wholistically. By automating routine tasks and providing data-driven insights, AI can help HR professionals to focus on more strategic and impactful work.

AI is the ideal solution to address issues pertaining employee turnover. By addressing the issue of employee turnover, AI will contribute positively to the organisation’s efforts towards talent retention, employee morale and the byproduct of this will be continuous productivity, and thereby profitability, of the organisation.

AI is the response the market has been seeking to address employee turnover.