By Anannya Devi, PhD candidate at University of Twente
This blog is produced as a part of the Reshaping Work Fellowship Programme. The opinions and views expressed in this publication are those of the author. They do not purport to reflect the opinions or views of Reshaping Work or organisations that have supported the programme.
Artificial Intelligence (AI) is often presented as a tool that will make work more manageable. It can summarise emails, automate scheduling, flag errors, and generate first drafts in seconds. In theory, AI tools promise greater efficiency and more time for meaningful or creative work. Yet across many workplaces, the lived experience of employees suggests something more complicated. Rather than reducing pressure, AI can sometimes intensify it. Understanding this tension requires looking more closely at how different groups of workers actually encounter AI in their daily work, and what it asks of them.
The Knowledge Worker Experience
For office-based and knowledge workers, AI tools are gradually reshaping expectations around productivity. When a task that once took an hour can now be completed in fifteen minutes, the time saved tends to lead to more tasks, faster deadlines, and higher expectations. Over time, this can contribute to a subtle erosion of boundaries between work and rest. AI-powered communication tools, smart scheduling assistants, and always-available digital platforms make it increasingly difficult to define where the working day actually ends. In many organisations, rapid responsiveness has become normalised, even if it is rarely stated explicitly. This dynamic is not entirely new. Digital technologies have long blurred the distinction between professional and personal time. What AI appears to do, however, is accelerate the pace at which these expectations develop. Efficiency quickly becomes the baseline against which future performance is measured. Research on digital transformation and employee well-being suggests that when new technologies reshape work practices without a corresponding shift in workload or workplace culture, it is employees who absorb the cost. Motivation and well-being depend, partly, on feeling a sense of autonomy, competence, and connection at work. When AI accelerates the pace of work without supporting those needs, disengagement and exhaustion tend to follow.
The Platform Worker Experience
For platform and gig workers, the impact of AI takes on a different form, but it is no less demanding. In this context, AI goes beyond simply being a productivity assistant; it takes on the role of the manager. It plays a crucial role in assigning tasks, monitoring performance, setting earnings, and determining access to future work. This leads to a structural, continuous pressure on these workers that is seldom seen in traditional employment scenarios.
Unlike a human manager, the algorithm does not clock off or offer informal recognition, and there is no channel through which a worker can explain context or contest an evaluation. Research on emotional responses to algorithmic management points to recurring feelings of anxiety and a loss of agency, particularly when workers do not understand how the system scores them or why their access to work fluctuates from one day to the next. A delivery driver, for example, may not know why fewer jobs are appearing on a particular day, or why their rating has changed. For platform workers, then, burnout does not emerge from an excess of digital tools but from the absence of any meaningful buffer between themselves and the system that governs their livelihood.
Where AI Can Actually Help
Despite these tensions, the impact of AI on work is more complex than a straightforward narrative of harm. AI tools, when introduced in the right manner, do show genuine potential to relieve certain sources of workplace stress. Repetitive cognitive tasks such as data entry, routine reporting, and basic customer queries can be meaningfully automated, freeing up mental energy for more engaging and creative work. Some organisations are also exploring AI-powered well-being tools that help employees monitor stress levels, flag early signs of overload, or access mental health resources more easily.
What seems to distinguish AI that supports workers from AI that burdens them is less about the technology itself and more about the conditions under which it is introduced. When employees have some say in which tools are adopted, how performance is measured, and what the expected change in workload looks like, the experience tends to be considerably more positive. When AI is deployed primarily as an efficiency measure, with little attention paid to its impact on people, the gains tend to be captured by the organisation while the human cost is carried quietly by the workforce.
Conclusion
The question worth asking is how AI reshapes the pressures and expectations that workers already navigate in different forms of employment. For knowledge workers, the issue may not be AI itself so much as the assumption that every efficiency gain should translate into greater output. When tasks become faster to complete, expectations often shift alongside them, leaving workers with less room to disconnect, reflect, or recover. For platform workers, the pressures take a different form. Here, AI systems increasingly shape access to work, evaluate performance, and structure daily routines in ways that are often difficult to understand or challenge.
What remains less clear is how these dynamics will evolve with time. Will workers adapt to the accelerated pace of AI-enabled work, or will the expectation of constant responsiveness deepen existing forms of exhaustion and disengagement? As algorithmic systems become more embedded in workplaces, questions of transparency, autonomy, and accountability are also likely to become more difficult to separate from broader questions about job quality and worker well-being. Research is still catching up with the speed at which AI is reshaping work across different sectors. What already seems apparent, however, is that the success of AI adoption cannot be measured through productivity alone. The future of work will depend just as much on how these systems shape people’s everyday experiences of work, control, flexibility, and belonging.