Study: AI Makes Work More Intense, Not Lighter
Contrary to the narrative of AI reducing workloads, a Harvard Business Review field study found it actually intensifies work. The 8-month study of a tech firm showed AI leads to task expansion, blurred work-life boundaries, and higher speed expectations as employees use the tools to take on tasks outside their core roles.
The study was conducted by University of California, Berkeley researchers Aruna Ranganathan and Xingqi Maggie Ye, who spent eight months at a 200-person tech firm. They observed that employees voluntarily worked faster and for longer hours, with AI making it easier to start tasks that might have previously been postponed or outsourced. This phenomenon of "workload creep" happens as employees use AI to fill knowledge gaps, leading them to take on tasks outside their traditional roles, such as product managers writing code. While this can feel empowering initially, it can lead to a new baseline of higher speed and responsiveness, eventually causing cognitive fatigue and burnout. The conversational nature of many AI tools contributes to the blurring of work-life boundaries. Employees reported sending prompts during lunch breaks or after hours because it felt more like a casual chat than formal work, eroding natural pauses in the day. However, the impact of AI on workload is not universally seen as negative. A survey by The Adecco Group found that AI saves workers an average of one hour per day, with many using that time for more creative and strategic work. Another survey from SAP suggests employees save nearly five hours a week on average with AI tools. Despite potential time savings, there's a clear divide in perception. One survey revealed that 45% of employees believe AI will increase workloads and burnout, while 38% think it will decrease them. Interestingly, leadership is more concerned, with 71% of C-suite executives anticipating that AI will lead to more intense workloads. This has led to the concept of the "productivity paradox," where AI adoption can initially lead to a temporary decline in productivity as companies adjust. For example, a study of U.S. manufacturing firms showed a short-term productivity drop after AI implementation due to the need for new data infrastructure, training, and workflow redesigns. To counter the negative effects of AI-driven work intensification, some experts recommend establishing formal "AI practices." This involves creating clear norms about when to use AI and implementing "intentional pauses" to assess workflows and prevent employee overload.
