Industry 4.0 has made the career paths of employees unpredictable and diverse. Artificial Intelligence (AI) is becoming more and more integrated in the workplace which is bringing a shift in the traditional patterns of employee movement through large scale layoffs, occupational transitions and increased uncertainty in careers.
It is projected to increase involuntary turnover across industries as machines continue replacing human tasks, especially in routine roles like administration and manufacturing. However, the impact of voluntary turnover is also important.
Employees may leave organizations by escape motives, so where the stress and fear of being replaced results in burnout and quitting due to stress. But some employees may be motivated and may proactively seek new roles to ensure that their career is sustainable in the long-term in a tech-driven market.
In order to effectively understand the preferences of employees to stay or leave along with their perceived control of that decision, organizations can apply the Proximal Withdrawal States Theory (PWST). Moon and Mitropoulos (2026) used PWST to categorise employees into 4 states depending on their preference to stay and their perceived control over that decision:
- Enthusiastic Stayers: Employees who want to stay and feel that they can do so.
- Reluctant Stayers: Employees who want to leave but feel that they can’t do so.
- Enthusiastic Leavers: Employees who want to leave and that have the control to do so.
- Reluctant Leavers: Employees who want to stay but are forced to leave.
AI automation might disrupt these states. For example: An enthusiastic stayer may become a reluctant stayer or an enthusiastic leaver if they feel that AI is reducing their control over their own career.
Hence, Moon and Mitropoulos (2026) suggested that organizations can benefit by using Human Resource Development (HRD) to target specific motivations of employees instead of using a single method for retention by:
- Addressing Fear: This is concerned with employees who want to leave because they fear being replaced by AI. So here, AI literacy training might be helpful to understand AI, reduce anxiety and to gain a bit of sense of control over their career again.
- Addressing Growth: This is for employees who might leave to find better opportunities in other organizations. Here, AI skills training may allow them to use AI as a collaborative tool at their current organization.
- Building a Social Contract: This includes holistic career development for employees with access to mentorship and soft skills training. It shows the commitment and trust organizations have on their employees which makes employees want to stay at their current company, even in industries that are vulnerable to automation.
All in all, by reframing AI as a resource instead of it being a threat for replacement, organizations may be able to develop a resilient workplace environment that is able to adapt and thrive with new advancements in technology like AI.
Food for thought: If companies invest in AI literacy and career development, will employees feel more secure staying in their roles? Or will these new skills simply make it easier for them to leave for better opportunities?
References
- Moon, Y., & Mitropoulos, T. (2026). Predicting and Preventing Turnover in Industry 4.0: Understanding the impact of artificial intelligence adoption on employee turnover. Human Resource Development Quarterly. https://doi.org/10.1002/hrdq.70018