From Automation to Aggravation: AI’s Unintended Consequences on Work–Life Conflict ^†

Abstract

In a time of pandemic interruptions, work arrangements and flexible work environments are becoming more and more crucial in service firms. While this issue is central to the ethics and effectiveness of human–AI interaction, it has received limited focused attention in both research and practice. As businesses increasingly deploy AI to enhance productivity and efficiency, concerns are emerging about its potential impact on employee well-being resulting specifically in work–life conflict. This study investigates how AI implementation can simultaneously drive performance and contribute to burnout, drawing on an empirical framework. Using a quantitative research design, data will be collected from employees at a university in Kuwait actively integrating AI technologies into their workflows. Guided by the IMPACT model and grounded in the Conservation of Resources (COR) theory and the Social Cognitive Theory (SCT), this study explores how organizational investment in AI influences employees’ experiences of work–life conflict. The findings will highlight AI’s dual role as a productivity enhancer and a potential stressor within a Kuwaiti institution. The study underscores the importance of balanced digital strategies—aligning technological advancement with leadership empathy, robust support systems, and employee well-being initiatives. By contextualizing global research within Kuwait’s evolving digital landscape, this study contributes region-specific insights and practical recommendations for fostering human-centered, sustainable AI integration. Ultimately, it aims to guide organizations in designing AI policies that enhance productivity without compromising employee health, advancing the responsible and ethical management of AI in the workplace.

1. Objectives

The purpose of this study is to assess how artificial intelligence (AI) is currently being used, with an emphasis on its effect on productivity, corporate culture, and work–life conflict. The study will emphasize the circumstances in which AI improves performance without endangering mental health and identify best practices in AI adoption through survey data analysis. It will especially look at how AI is being incorporated into day-to-day operations, evaluate how workers feel about changes in task design, organizational support, and autonomy brought about by AI, and accordingly analyze organizational tactics for preventing stress and burnout. Additionally, the study will investigate how support networks, leadership, and digital preparedness may help create a positive balance between worker well-being and technology efficiency. The final objective is to offer doable suggestions for the adoption of AI in a sustainable, human-centered manner that is in line with Kuwait’s Vision 2035 (available online: https://www.mofa.gov.kw/en/pages/kuwait-vision-2035, accessed on 18 December 2025) and its more general objectives of innovation-driven national growth.

2. Literature Review

Workplaces are changing into technology-driven settings that transform how people interact, cooperate, and carry out their duties as digital technologies become essential to organizational performance, innovation, and competitiveness. People’s experiences at work and their perceptions of their positions inside organizations are greatly impacted by this technological transformation [1,2,3]. Digital technologies are intended to foster sustainability, resilience, and employee well-being as essential components of advancement in addition to increasing efficiency and innovation [4]. Artificial intelligence (AI) is one of these technologies that is transforming business models, goods, services, and organizational processes [5]. Consequently, the adoption of AI alters job design, autonomy, and employee experience [2,6], even though it can increase productivity and strategic decision-making [7]. Therefore, organizations must reevaluate the alignment, coordination, and integration of human and AI roles considering this shift [8,9]. However, worries regarding possible detrimental effects on worker wellness, including stress, burnout, and job insecurity, are also raised by the growing reliance on AI [10]. In many organizations, AI systems are increasingly embedded into daily operations such as recruitment, performance monitoring, communication, scheduling, and decision-making processes. While these technologies can streamline workflows and improve organizational responsiveness, they may also reshape workplace relationships and alter employees’ sense of control and professional identity. Employees are therefore required not only to adapt to new technologies but also to navigate changing expectations, evolving skill requirements, and continuous digital connectivity. This transformation highlights the growing importance of balancing technological advancement with ethical considerations and employee-centered practices to ensure that innovation contributes positively to both organizational outcomes and workforce well-being. Furthermore, organizations that fail to adequately support employees during technological transitions may face challenges related to reduced morale, increased resistance to change, and lower levels of psychological well-being.

This is why the effects of AI on well-being should not be seen separately but rather as a dynamic interaction that results in what is referred to as the IMPACT model. The Interaction of Modality-Person-Area-Culture-Transparency categories, or IMPACT for short, identifies pertinent areas that must be comprehended in order to forecast an individual’s well-being when engaging with AI or considering the AI–well-being complex [11]. The IMPACT model emphasizes that employees’ reactions to AI are shaped by multiple interconnected dimensions, including the way technologies are introduced, the cultural environment of the organization, individual characteristics, and the transparency of AI-driven decisions. By acknowledging these interacting factors, the model provides a more comprehensive framework for understanding the complexity of AI-related well-being outcomes in modern workplaces. It also suggests that organizations should adopt more human-centered approaches to AI implementation that prioritize trust, fairness, communication, and employee participation. In doing so, organizations may be better positioned to minimize negative psychological consequences while maximizing the potential benefits of AI for both employees and organizational performance.

3. Methodology

A quantitative approach will be used to measure how AI affects employee work–life conflict in a Kuwaiti institution. SPSS Version 29 and SmartPLS Version 4. will be used for statistical analysis, and responses will be collected through online surveys distributed to employees at a university in Kuwait. The survey will incorporate validated scales that measure perceptions of AI, AI adoption, digital mindfulness, job autonomy, organizational support, and work–life conflict. A quantitative methodology is considered appropriate for this study because it allows for the systematic examination of relationships among variables and enables the researcher to analyze patterns, trends, and associations within a larger population. By using structured survey instruments, the study aims to obtain reliable and measurable data regarding employees’ experiences with AI technologies and their impact on the balance between professional and personal responsibilities. The use of online surveys also provides participants with flexibility and accessibility, which may improve response rates and facilitate efficient data collection within the institutional setting.

Descriptive and reliability testing (Cronbach’s alpha) will be performed using SPSS to evaluate the consistency and distribution of the collected data. Descriptive statistics such as means, standard deviations, frequencies, and percentages will be used to summarize participant demographics and key study variables. Additionally, SmartPLS (PLS-SEM) will be used to examine the hypothesized relationships among constructs and assess the overall model fit. The analysis will incorporate tests for convergent and discriminant validity, as well as the significance of direct and indirect effects through bootstrapping procedures. Furthermore, the study will assess the predictive relevance and explanatory power of the proposed research model to determine how AI-related factors influence employees’ work–life conflict. The use of Partial Least Squares Structural Equation Modeling is particularly suitable for this research because it enables the simultaneous analysis of multiple relationships among latent variables and supports exploratory research models involving mediation and complex interactions. Through these statistical procedures, the study seeks to provide empirically grounded insights into the influence of AI adoption on employee well-being within higher education institutions in Kuwait.

4. Results

The suggested findings should demonstrate that, although AI boosts productivity and performance, it may also contribute to a rise in stress and burnout if it is not accompanied by effective leadership and wellness programs. In line with the model of psychological well-being [12], which highlights purpose, autonomy, and meaningful relationships as essential components of human flourishing, it is expected that a balanced, human-centered AI adoption will improve both productivity and employee well-being. Employees are more likely to perceive AI positively when they feel that technology supports rather than replaces their professional roles and when they maintain a sense of autonomy and control over their work. Furthermore, organizational environments that encourage collaboration, trust, and employee participation in technological change may strengthen psychological well-being and reduce resistance toward AI adoption. These results underline the necessity of long-term digital transformation strategies that balance employee psychological well-being with technology development. They also emphasize the importance of designing AI policies and organizational practices that prioritize human needs alongside operational efficiency, ensuring that technological innovation contributes to sustainable organizational growth and healthier work environments.

5. Implications

The study is anticipated to yield important information for HR strategies, organizational planning, and digital transformation projects in Kuwaiti workplaces. In order to link AI adoption with goals related to productivity, job design, and employee well-being, it will provide managers, legislators, and agencies with evidence-based assistance. The results can help build human-centered AI policies, focused training programs, and organizational support systems that foster resilient and sustainable workforce performance by emphasizing the healthy balance between technological efficiency and psychological well-being. Furthermore, the findings may encourage organizations to adopt more inclusive and transparent approaches when implementing AI technologies in workplace environments. The study may also contribute to future academic research by providing a foundation for examining the long-term effects of AI adoption on employee well-being and organizational sustainability in the Gulf region.

6. Contribution and Originality

This study offers fresh perspectives on how AI affects workers’ work–life conflict. It provides a useful framework for striking a balance between psychological well-being and technological efficiency, promoting the application of human-centered AI and long-term digital transformation in line with Kuwait’s Vision 2035. By offering a cooperative framework to direct the implementation of AI in organizations, this study helps to maximize employee-technology interactions while boosting output. It increases knowledge of how integrating AI affects workers’ social and mental health and provides practical advice for developing human-centered, encouraging, and sustainable work practices [13]. The study’s conclusions will offer fresh perspectives on how businesses and individuals might manage work–life conflicts, especially for younger workers who are now a major influence on social and economic dynamics [14]. Organizations must in fact recognize this distinct generational shift and respond to its demands, particularly in how workers handle work–life conflicts [15]. In addition, this study may help organizations better understand how evolving digital expectations are reshaping employee attitudes toward flexibility, availability, and the boundaries between work and personal life. It may also provide a basis for future policy development aimed at improving employee resilience and ensuring that AI-driven transformation aligns with both organizational goals and workforce sustainability.