In a groundbreaking study published in Research Policy, researchers from ESMT Berlin have unveiled how artificial intelligence (AI) can transform the way large-scale research projects are managed, ushering in a new era of enhanced productivity and efficiency in the scientific realm.

The research, conducted by Maximilian Koehler, Ph.D. candidate at ESMT, and Henry Sauermann, professor of strategy at ESMT, explores the role of AI not as a tool for performing specific research tasks but as an intelligent "manager" overseeing and coordinating human workers engaged in data collection, analysis, and other research activities.

This concept, known as algorithmic management (AM), suggests a significant shift in how scientific research projects are conducted, enabling them to operate at an unprecedented scale and efficiency level.

"The capabilities of artificial intelligence have reached a point where AI can now significantly enhance the scope and efficiency of scientific research by managing complex, large-scale projects," states Koehler.

AI as a Comprehensive Research Manager Through extensive investigations involving online document reviews, interviews with project organizers, AI developers, and participants, as well as firsthand participation in some projects, Koehler and Sauermann identified several key managerial functions that AI can effectively perform:

1. Task division and allocation
2. Direction and coordination
3. Motivation
4. Facilitating learning and knowledge sharing

The researchers found that AI's instantaneous, comprehensive, and interactive capabilities enable it to not only replicate but potentially surpass human managers in carrying out these critical functions.

Enabling Large-Scale, Efficient Research Projects As the complexity and scope of scientific research continue to grow rapidly, the ability to leverage AI's managerial prowess could prove pivotal in improving overall research productivity. The study's quantitative analysis revealed that AM-enabled projects tend to be larger in scale compared to those without AI management and are often associated with platforms providing shared AI tools.

This finding suggests that while AM may enable projects to operate at a grander scale, it also requires robust technical infrastructures that standalone projects may find challenging to develop independently.

Implications for Research Organizations and Funding The proliferation of AI-managed research projects points towards a potential shift in the sources of competitive advantage in the scientific research landscape. This development could have significant implications for research funders, digital research platforms, universities, corporate R&D labs, and other major research organizations.

While AI's ascendance in research management does not necessarily render human managers obsolete, it does open up opportunities for them to focus on more strategic and social aspects of leadership, such as identifying high-value research targets, securing funding, and cultivating an effective organizational culture.

"If AI can take over some of the more algorithmic and mundane functions of management, human leaders could shift their attention to more strategic and social tasks such as identifying high-value research targets, raising funding, or building an effective organizational culture," notes Sauermann.

As the adoption of algorithmic management in scientific research continues to gain momentum, the study by Koehler and Sauermann provides valuable insights into the transformative potential of AI in this domain, paving the way for a future where human ingenuity and machine intelligence converge to drive scientific breakthroughs at an unprecedented pace and scale.