The research team argues that most current scholarship on artificial intelligence in the workplace has been captivated by performance metrics—productivity gains, cost reductions, service quality improvements—while treating the human cost as an afterthought. Their paper shifts the lens. By framing employee psychological wellbeing as a pillar of human sustainability, they contend that an organization cannot be truly sustainable if the mental health, motivation, and social connectedness of its workers deteriorate as algorithms take over more of the daily workload. The paradox emerges because the same adoption decisions that streamline operations can strip away precisely the experiences that make work psychologically nourishing.

To explain how this erosion happens, the authors build their framework on two well-established pillars of organizational psychology: self-determination theory and recovery theory. Self-determination theory holds that human flourishing at work depends on the satisfaction of three basic psychological needs—autonomy, competence, and relatedness. Autonomy is the sense of volition and control over one’s actions; competence is the feeling of effectiveness and mastery; relatedness is the experience of meaningful connection with others. Recovery theory, meanwhile, explains how workers replenish mental and emotional resources during and after demanding periods, and how chronic demands without adequate recovery produce strain, exhaustion, and psychological distress.

Applied to AI adoption, the theoretical machinery generates a sobering prediction. When intelligent systems automate decisions, standardize workflows, and monitor performance, employees may find their discretion shrinking—their sense of autonomy diminished as algorithmic recommendations dictate the pace and content of their work. Competence can suffer when the skills workers spent years honing are rendered obsolete or when success depends on opaque machine outputs they cannot fully understand. Relatedness may weaken as human interactions are replaced by automated transactions and as workers compete with, rather than collaborate alongside, machine agents. The cumulative result, according to the model, is unsatisfied psychological needs, which translate into psychological distress and a decline in what the authors term human sustainability.

Crucially, the framework does not treat this damaging trajectory as inevitable. The researchers identify two factors that shape how harshly AI adoption bears down on workers: employee AI literacy and the prevailing culture of AI within the organization. AI literacy—the knowledge and skills that allow workers to understand, evaluate, and work effectively with intelligent systems—can either amplify or buffer the paradox. Workers with low literacy may feel threatened, confused, and helpless in the face of AI, accelerating the erosion of competence and autonomy. But the relationship is not straightforward, and the authors emphasize that even high literacy alone does not guarantee positive outcomes if the surrounding organizational culture treats AI purely as a tool for surveillance and cost cutting.

This is where human-centered AI practices enter the model as moderators—variables that can soften or even neutralize the negative pathways. The study highlights four in particular. Transparency means that workers understand what the AI does, how it reaches recommendations, and what data it uses, reducing the anxiety born of opacity. Employee involvement means that staff participate in designing, selecting, and configuring AI systems, which preserves a sense of agency and ownership. AI literacy training equips workers with the competence to collaborate with intelligent tools rather than feel dominated by them. Human oversight ensures that meaningful decisions remain with people, keeping algorithmic systems in an advisory role rather than an authoritarian one.

When these practices are in place, the researchers suggest, the corrosive link between AI adoption and psychological need satisfaction can be tempered. An organization that deploys AI transparently, involves its employees, invests in their understanding, and retains human judgment at critical junctures can capture efficiency gains without paying the psychological price. Conversely, an organization that rolls out intelligent systems abruptly, opaquely, and without worker voice may find that short-term productivity is purchased with long-term burnout, disengagement, and turnover—a trade-off that ultimately defeats the goal of sustainability.

The practical implications the authors draw for managers are concrete. They call for participatory AI design processes in which employees help shape the systems that will share their workplaces. They urge organizations to preserve employee discretion, deliberately carving out spaces where human judgment remains authoritative. They recommend systematic assessment of employees’ psychological health and wellbeing as part of AI adoption programs, treating mental health indicators with the same seriousness as operational performance dashboards. And they argue that AI adoption strategies should be synchronized with the United Nations Sustainable Development Goals—specifically SDG 3 on good health and wellbeing, SDG 8 on decent work and economic growth, and SDG 9 on industry, innovation, and infrastructure—so that technological transformation aligns with, rather than contradicts, broader sustainability commitments.

The study also situates itself within the fast-growing literature on responsible AI, but with a distinctive reorientation. Whereas much of that literature concentrates on fairness, bias, privacy, and accountability of algorithms, this paper insists that the human experience of working alongside AI deserves equal analytical weight. Moving from an efficiency-focused lens to a human sustainability lens, the authors contend, is not merely an ethical nicety but a strategic necessity for organizations that intend to thrive over decades rather than quarters. Their conceptual model is explicitly designed as a foundation for empirical testing, and the researchers invite scholars to validate the framework across different industries, organizational types, and cultural settings—particularly relevant given the international composition of the team, which spans Gulf, Middle Eastern, and South Asian research institutions.

As artificial intelligence accelerates into every corner of the service economy, the message of this research lands with urgency. The paradox it identifies—efficiency gains that quietly hollow out the human core of work—will not resolve itself. Organizations that treat AI adoption as a purely technical project risk discovering, too late, that their most valuable asset, a psychologically healthy and motivated workforce, has been degraded in the process. The study’s framework offers a way forward: embed transparency, participation, literacy, and human oversight into the fabric of AI deployment, and measure success not only in output but in the wellbeing of the people doing the work. In an era when the race to automate is fierce, the research suggests that the true finish line is not the smartest machine, but the most sustainable human-technology partnership.

Subject of Research: The sustainability paradox of artificial intelligence adoption and its effects on workers' psychological wellbeing and human sustainability

Article Title: Sustainability paradox of artificial intelligence adoption for workers’ wellbeing

Article References: Sustainability paradox of artificial intelligence adoption for workers’ wellbeing. (n.d.). https://doi.org/10.1007/s43621-026-04671-y

Image Credits: AI Generated

DOI: 10.1007/s43621-026-04671-y

Keywords: artificial intelligence, sustainability paradox, workers wellbeing, self-determination theory, recovery theory, AI literacy, human-centered AI, psychological need satisfaction, human sustainability, responsible AI, employee autonomy, organizational culture