CI is an emerging field, drawing from a broad range of disciplines including biology, psychology, economics and computer science.¹ That said, CI methods are already widely used in various fields, such as citizen science, experiments in democracy, mobilising consumers for product design and predictions in finance.

The methods of CI make it possible to look more systematically at how teams, organisations and even entire communities think, observe, analyse, plan and create. Better understanding of these processes can then be used to help groups think more successfully.

Principles from CI are being applied in areas as varied as organisational design, citizen science and open democracy, and can help to improve everything from social-media moderation to predicting and responding to natural disasters. Harnessing CI could provide a new, more inclusive and more effective model for global governance,² and play a vital role in tackling climate change,³ the UN's Sustainable Development Goals⁴ and a host of other thorny societal problems.⁵

In the last decade, there has been significant growth in the use of technology to enhance the CI of groups, both large and small⁶. This ranges from web platforms designed to coordinate large-scale collaboration to the use of artificial intelligence to facilitate group discussions. The advent of AI chatbots has created a powerful new way for humans to interface with machines, making it increasingly important to consider how AI-human teams will operate in the future.

However, efforts to apply ideas from CI in the real world are piecemeal. The vast majority of organisations are failing to employ simple principles that could significantly enhance their effectiveness, suggesting a need for more translational research. And where there are attempts to harness CI, the practice is often well ahead of the theory. Significant research is required to improve our understanding of the fundamentals underpinning CI and how to design and apply new tools to enhance it. Current models of collective action have delivered innovation, but they themselves have remained remarkably unchanged over centuries; the field of collective intelligence seeks to challenge and alter these paradigms.⁷

KEY TAKEAWAYS

Solving the world's biggest challenges will require input from large numbers of people with diverse experience and expertise, all of whom must be able to work collaboratively. The field of Collective Intelligence (CI) seeks to understand the theory and practice of human cooperation to make this a reality. Thanks to digital technology, Large-scale collaboration can now be used to solve scientific challenges, innovate new ideas and even revitalise democracy. But organisations and institutions need to be redesigned to help put these ideas into action. Insights into group dynamics and new tools are helping create Smarter teams in a time when working practices are in flux. Increasingly powerful AI agents could soon play a vital supporting role and may eventually become team members themselves. But continuing progress is reliant on better models of Collective cognition — how groups “think” as a unit. The theoretical underpinnings of CI are fragmented, which is holding back efforts to understand and enhance these processes. Attempts to use technology to boost CI also require advances in Human-computer interaction research. Our understanding of how people perceive and use decision-making tools remains piecemeal and AI is a long way from exhibiting the kind of social intelligence required to operate seamlessly alongside human teammates.