The rise of Artificial Intelligence (AI) and Automation technologies has brought the promise of streamlining work processes, increasing productivity, and creating new job opportunities. However, a recent study by researchers in synthetic biology challenges the assumption that introducing these technologies will necessarily lead to such positive outcomes. This article examines the findings of this study and explores the challenges associated with implementing AI and automation technologies. Forbes discussed the study’s implications and the importance of recognizing unseen tasks that people perform in addition to the more visible work that is typically rewarded.
Amplification and Diversification of Tasks
The introduction of AI and automation into the laboratory did not always result in simplification and job cuts, as some might expect. Instead, the researchers found that automation often amplified and diversified the tasks needed to be performed and that scientists were not necessarily freed from the boring and mundane tasks they hoped to avoid. While automation can enable scientists to scale up their work and save time they can devote to other tasks, the reality is often more complex. For example, there was a significant increase in the number of experiments and hypotheses that had to be performed, with automation amplifying this increase. This led to much more data that must be checked, standardized and shared.
Training and Supervision of AI and Automation
The robots used in the laboratory required adequate training so that they could effectively perform the tasks required of them. Scientists also needed to be trained to work effectively alongside the robots. This involved learning how to prepare, repair, and supervise the machines. In addition, evaluating scientific work is often based on outcomes such as grants and peer-reviewed publications, which may not account for the time and effort invested in cleaning, troubleshooting, and supervising automated systems. These tasks are considered less valuable and may go unnoticed because managers are less involved in the laboratory work and unaware of the routine tasks. This can lead to scientists feeling overworked and undervalued, with no compensation or recognition for their extra tasks.
The Digitalization Paradox
The research highlights the need for careful implementation of AI and automation technologies. It is essential to recognize that introducing these technologies may not necessarily lead to labor savings, increased productivity, or free time for all involved or affected. Organizational and political efforts to automate and digitalize work must consider the unseen tasks people perform and the more visible position that is typically rewarded. This highlights the “digitalization paradox,” where the assumption that automation and digitalization lead to increased productivity and free time for all involved or affected is challenged by invisible work. We must examine our productivity metrics and acknowledge the unseen tasks people perform and the more visible position typically rewarded.
The Role of Human Capabilities in AI and Automation
Ensuring that technology supports human capabilities by effectively designing and managing these processes is also essential. The development and maintenance of digital infrastructure require a considerable amount of invisible work, often performed by “ghost workers” who are paid poorly to fine-tune and develop these tools for public use. This suggests that we must not unquestioningly accept claims of increased productivity gains but instead critically examine the impact of automation and digitalization on work processes.
