Mental effort, not AI, drives meaningful work in hybrid workflows

If AI tools automate tasks to the point where workers no longer need to think deeply, they may inadvertently reduce meaningfulness. This risk becomes particularly relevant in roles centered on creativity, emotional labor, communication, problem-solving or knowledge work, domains where meaning is tied to decision-making and personal interpretation.

A new scientific analysis assesses how AI influences meaningful work, revealing that technology itself is not the determining factor; what matters is the degree of mental effort the worker invests once AI enters the task.

The study, “Collaboration between Individuals and AI: Fusing Mental Effort and AI for Work Meaningfulness,” published in AI & Society, examined how the presence of AI support in a writing task affects the perceived meaningfulness of the work, exploring the underlying cognitive and motivational dynamics that shape that experience. Their findings challenge common assumptions and offer new guidance for workplaces navigating the transition toward hybrid human–AI collaboration.

AI alone does not make work meaningful, mental effort does

The research begins from a growing debate: does AI make work more meaningful by making tasks easier, or does it erode meaning by replacing human contribution? The study’s results show that neither claim is accurate. Instead, meaningfulness depends on how much mental effort the individual puts into the task, regardless of whether AI is involved.

The authors tested three experimental conditions. Participants wrote a prosocial message, intended to benefit others, either with full AI support, with partial AI assistance, or with no AI at all. After completing the task, they evaluated the meaningfulness of the experience, how challenging it felt, how much mental effort it required, and how motivated they were to perform it.

The study found that meaningfulness increased alongside mental effort. When people invested more cognitive energy, they felt that their actions mattered, that they were contributing something of value, and that the task had purpose. The presence or absence of AI did not change meaningfulness on its own. Instead, AI shaped meaningfulness only through its effect on mental effort.

In tasks where AI reduced cognitive engagement, making the activity more passive or effortless, participants felt less meaning. Conversely, when the task still required them to think, act, and make decisions, meaningfulness was preserved or strengthened, even with AI involvement.

These results reinforce emerging theories on meaningful work, which suggest that people derive meaning not from whether a task is easy or optimized, but from whether they feel personally involved in creating the outcome. The authors note that the findings challenge the assumption that human–AI collaboration inherently threatens workplace purpose. Instead, the key risk arises when AI replaces too much of a worker’s cognitive role.

The strongest meaningfulness outcomes occurred in the condition without AI, where participants reported the highest levels of mental effort. However, partial AI assistance also maintained relatively strong engagement, suggesting that hybrid systems, which support but do not replace cognitive activity, may offer the best balance for meaningful work.

These findings also align with the psychological perspective that meaning is tied to difficulty. When workers must think, interpret, and decide, they are more likely to internalize the value of the task and see themselves as responsible for the outcome.

Social impact, motivation and challenge all play critical roles

The study also shows that meaningfulness is influenced by multiple psychological factors beyond mental effort. Among these, perceived social impact emerged as the most powerful predictor. Participants felt more meaning when they believed their work benefited someone else.

This finding confirms earlier theories that meaningful work is often anchored in a sense of contribution. Even when tasks are simple or repetitive, knowing that the output supports someone’s wellbeing or solves a problem helps people see significance in their actions. The prosocial nature of the writing task used in the experiment likely amplified this effect.

The results show that intrinsic motivation, the internal desire to perform a task out of interest or enjoyment, also increases meaningfulness. Participants who felt naturally motivated reported higher meaning regardless of whether AI was involved. This suggests that personal engagement and internal drive remain central to workplace purpose even in environments shaped by automation.

In addition, the study highlights the role of task challenge. When participants felt the task demanded skill or competence, they were more likely to see it as meaningful. This aligns with research suggesting that tasks perceived as overly simple or trivial tend to reduce engagement and undermine meaning, while tasks with moderate difficulty strengthen it.

Importantly, these factors, social impact, intrinsic motivation, and challenge, interacted with mental effort but did not replace its role. Rather, they worked together to shape participants’ overall experience, revealing a complex ecosystem of psychological factors underlying meaningful work.

This combination supports the theory that meaningfulness emerges from a blend of cognitive investment, emotional engagement, and perceived contribution. AI influences these factors indirectly, depending on how it alters the task’s mental and motivational structure.

The authors note that organizations designing AI systems should carefully consider these elements to prevent unintended reductions in workplace meaning. As AI tools grow more capable, the risk of disengagement increases if tasks become too automated or passive, disconnecting workers from the mental processes that create meaning.

Implications for the future of human–AI collaboration

The findings have broad implications for workplaces adopting AI. Rather than viewing AI as a threat to meaningfulness or as a pathway to enhanced purpose, organizations should recognize that technology influences meaning only when it affects personal cognitive involvement.

If AI tools automate tasks to the point where workers no longer need to think deeply, they may inadvertently reduce meaningfulness. This risk becomes particularly relevant in roles centered on creativity, emotional labor, communication, problem-solving or knowledge work, domains where meaning is tied to decision-making and personal interpretation.

But the study also shows that AI can support meaningful work under the right conditions. Hybrid systems that maintain worker agency, enforce cognitive participation and leave room for human judgment can help preserve or even enhance the sense of purpose. Such systems help workers focus on thoughtful engagement rather than repetitive or mechanical tasks.

The findings suggest that organizations should design AI tools not only for efficiency but also for preserving mental effort. Strategies may include prompting workers to evaluate outcomes, make decisions, apply judgment or elaborate on ideas generated by AI. In this way, AI can become a collaborator rather than a replacement.

The authors highlight that the public narrative around AI often oversimplifies its effects. Concerns that AI will strip work of meaning focus too heavily on automation and ignore the psychological mechanisms underlying meaningfulness. On the other hand, claims that AI will enrich work overlook the importance of challenge and personal involvement. The study provides empirical evidence that meaningfulness is neither lost nor gained by technology itself, but by the mental processes workers experience while interacting with it.

These findings also expand academic discussions around hybrid intelligence, showing that the human side of human–AI collaboration must remain central. The cognitive and motivational experience of workers cannot be treated as an afterthought in the design of AI-enabled tasks.

The study calls organizations to balance efficiency with engagement. As more AI tools enter workplaces, leaders must ensure that tasks still require human thinking and sustain a sense of contribution. This balance may determine whether workers thrive alongside AI or feel displaced by it.

The researchers call for further studies across more diverse contexts, job types, and cultural settings to deepen understanding of how AI and mental effort interact in real-world work environments. They note that meaningfulness is highly personal and may shift depending on the nature of the task or the individual’s background.