Addressing AI anxiety: workforce development strategies for an AI-driven era

C. Dhilipan (), A. S. Kannan () and Elamurugan B ()
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C. Dhilipan: Global Institute of Business Studies
A. S. Kannan: New Prince Shri Bhavani College of Engineering and Technology (Autonomous)
Elamurugan B: Saveetha Institute of Medical and Technical Sciences

International Journal of System Assurance Engineering and Management, 2025, vol. 16, issue 12, No 17, 4141 pages

Abstract: Abstract Artificial intelligence (AI) transmutes the work culture and workforce dynamics, creating opportunities and challenges for the employees and the organisation. This rise in the use of AI applications is bringing challenges for employees regarding their job security, which is termed “AI Anxiety”- majorly a sense of apprehension of losing jobs or facing career-related issues. This study investigates the origins, implications and coping strategies added to India’s workforce in specific sectors open to technological disruptions. A survey was done for 1606 professionals across diversified roles. Based on a quantitative survey, the findings reveal that AI anxiety significantly affects employee performance, well-being, and career prospects. The main highlights of the study provide some strategies like continuous learning, robust social support systems, and fostering a growth-oriented mindset that can help in reducing AI-related concerns. These strategies help the employees prepare for technological shifts. The insights supplied in this research offer practical guidance for educators, business leaders, and policymakers, to develop adaptive workforce strategies. This practice addresses AI anxiety and equips employees to survive in the emerging job market. By implementing this guidance, stakeholders can foster a resilient workforce that will be capable of leveraging AI-driven opportunities and managing the transformational changes in the organization.

Keywords: AI anxiety; Workforce development; AI adoption; Continuous learning; Coping strategies; J24; J28; O33 (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s13198-025-02916-z

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