A new study published in Harvard Business Review challenges one of the most persistent assumptions about AI in the workplace: that it will free us from drudgery and create space for higher-value work. Instead, researchers found that generative AI intensifies work rather than reducing it, leading to expanded workloads, cognitive fatigue, and burnout.
As someone who has deployed AI tools across various enterprise settings here in the UAE, these findings resonate deeply. The promise of AI productivity gains is real, but so are the hidden costs that organizations often fail to anticipate.
The Study: Eight Months Inside an AI-Enabled Company
Researchers Aruna Ranganathan and Xingqi Maggie Ye from UC Berkeley's Haas School of Business conducted an eight-month ethnographic study at a US technology company with approximately 200 employees. Their methodology was rigorous: in-person observation twice weekly, tracking of internal communications, and over 40 in-depth interviews across engineering, product, design, research, and operations teams.
What they found contradicts the narrative that AI tools would allow employees to accomplish the same work in less time. Instead, workers experienced three distinct forms of work intensification that compounded over the study period.
Task Expansion: Doing More, Not Less
The first pattern the researchers identified was task expansion. When AI tools made certain tasks faster, employees did not use the time savings for rest or strategic thinking. Instead, they assumed responsibilities outside their traditional roles.
Product managers started writing code. Researchers handled engineering tasks. One engineer captured the dynamic perfectly: "You had thought that maybe you could work less. But then really, you don't work less."
This mirrors what I have observed in organizations across the Gulf region. When teams adopt AI coding assistants or content generation tools, the initial productivity boost is real. But within weeks, the scope of what is expected from each role expands to fill (and often exceed) the time saved. The efficiency gains get immediately reinvested into additional output expectations.
Blurred Boundaries: Work Becomes Ambient
The second form of intensification was the erosion of work-life boundaries. Employees began conducting work during breaks, lunch hours, and even during meetings. The researchers described work as becoming "ambient, something that could always be advanced a little further."
This is particularly concerning because recovery time is not optional for sustained performance. When work can happen in any spare moment (because AI tools make small tasks trivially easy), the psychological space for genuine rest disappears. The study found that downtime no longer provided the recovery it once did.
For organizations in the UAE, where work culture already trends toward high intensity and long hours, this pattern deserves serious attention. AI tools that enable "just one more quick task" during what should be personal time may be eroding the resilience of your workforce.
Cognitive Overload: Always Juggling
The third pattern was increased multitasking. Workers managed multiple concurrent threads simultaneously, creating what the researchers described as "cognitive load and a sense of always juggling."
AI introduced a new rhythm where workers managed several active threads at once. But the reality was continual attention switching, frequent checking of AI outputs, and a growing number of open tasks. This is not the focused deep work that produces breakthrough results. It is fragmented attention that leads to errors and exhaustion.
The Real Costs: Burnout and Turnover
The consequences of this work intensification were significant. The study documented cognitive fatigue, burnout, workload creep without explicit requests from management, weakened decision-making, lower quality outputs despite initial productivity gains, and increased employee turnover.
This last point is critical. If your AI investments are driving away your best people, the ROI calculation looks very different. The productivity gains visible in short-term metrics may be masking longer-term costs in talent retention and institutional knowledge.
What Leaders Can Do: The Practice of AI Practice
The researchers did not simply diagnose the problem. They observed that some employees developed what they call "AI practice," deliberate strategies for managing the intensity that AI tools introduce. Three practices stood out:
Intentional pauses: Structured moments for assessment and reflection, deliberately stepping back from the constant forward motion that AI enables.
Sequencing: Batching notifications and protecting focus windows, rather than allowing AI-generated outputs to create constant interruptions.
Human grounding: Institutionalizing dialogue and social connection, ensuring that human judgment and relationship-building remain central to work, not marginalized by efficiency-focused automation.
These practices were not mandated by management. They emerged organically among workers who recognized the unsustainability of unchecked AI-enabled work intensification.
Implications for the UAE and Gulf Region
For organizations in our region, this research offers a timely warning. The UAE's AI strategy is ambitious, and adoption is accelerating across government and private sectors. But adoption without attention to work sustainability risks undermining the very benefits we seek.
Leaders should consider auditing not just productivity metrics but also indicators of work intensity: hours logged, after-hours activity, task switching frequency, and employee wellbeing scores. The goal is not to slow AI adoption but to adopt it in ways that are sustainable over the long term.
Looking Forward
The HBR study does not argue against AI adoption. The researchers acknowledge that many employees welcomed the tools because they "enabled us to accomplish more, made our work feel more familiar, and made our work more rewarding." The problem is not the technology itself but the failure to manage its second-order effects on work intensity.
As we continue deploying AI across enterprises in the UAE and beyond, we need to move past naive assumptions that efficiency gains automatically translate to better outcomes for workers and organizations. The real work is designing systems, cultures, and practices that capture AI's benefits while protecting against its tendency to intensify everything it touches.
The question is not whether to use AI. The question is whether we are thoughtful enough to use it well.
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