Digital technology allows collaboration on scales unimaginable in the past. This has led to a reimagining of how humans can organise themselves to solve problems.

Future Horizons:

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5-yearhorizon

CI approaches gain traction

CI-based thinking becomes more ingrained in everyday life, with many tools that originated in CI emerging to help people deal with a range of issues, such as getting support for specific medical conditions, DIY home repairs and business or personal mentoring. Increasingly powerful language and vision AI gradually displaces humans in many crowd-sourcing and citizen science applications.

10-yearhorizon

CI applications grow

Tools from CI start to be applied in mainstream applications, as well as helping large-scale approaches to tackling big global challenges such as climate change, access to water and pandemic response. Organisations embrace new tools that combine AI and CI to better organise scientific knowledge and bring in perspectives from more diverse groups. The complicated process of redesigning institutions to better harness CI begins, although organisational inertia and resistance from vested interests holds up progress.

25-yearhorizon

Deliberative democracy becomes widespread

Democratic assemblies around the world embrace the CI tools of deliberative democracy as standard practice, helping to involve people far beyond the elected representatives in political decision-making. Companies' use of open innovation to solve problems becomes mainstream. Advanced AI agents become ubiquitous, requiring a shift in focus to enhancing the CI of human-AI groups.

Crowdsourcing information or small chunks of work from large numbers of people can tackle challenges as varied as training AI to predicting floods.⁸ Citizen-science projects engage the general public to help scientists collect and analyse data. Open innovation platforms like Kaggle and InnoCentive let companies outsource engineering challenges to independent experts. Deliberative democracy is involving everyday citizens in political decision-making in countries.⁹

New tools for gathering and visualising data, and open-source repositories of information and tools, are helping enhance the CI of large groups. AI is also playing a growing role in facilitating CI by filtering and summarising complex data, organising human knowledge, helping to connect experts and optimising deliberative processes.¹⁰ Powerful large language models (LLMs) are opening up the prospect of AI acting as a facilitator to coordinate group discussions at massive scale.¹¹ The advent of AI agents that can operate autonomously is driving efforts to understand and exploit the CI of human-AI groups.¹²

While CI has shown promise for generating novel solutions, existing decision-making processes are often too slow and inflexible to take full advantage. This is leading to a growing focus on designing organisations and institutions that promote and harness CI to help with multiple aspects of, and approaches to, thought at scale — including observation, interpretation, memory, prediction and creativity.¹³ AI could help here too, suggesting the best organisational set-up for specific problems.¹⁴ Decentralisation technologies like blockchain and quadratic voting also open up avenues for enhancing CI by helping run organisations in a distributed and non-hierarchical fashion.¹⁵

Large-scale collaboration - Anticipation Scores

The Anticipation Potential of a research field is determined by the capacity for impactful action in the present, considering possible future transformative breakthroughs in a field over a 25-year outlook. A field with a high Anticipation Potential, therefore, combines the potential range of future transformative possibilities engendered by a research area with a wide field of opportunities for action in the present. We asked researchers in the field to anticipate:

The uncertainty related to future science breakthroughs in the field
The transformative effect anticipated breakthroughs may have on research and society
The scope for action in the present in relation to anticipated breakthroughs.

This chart represents a summary of their responses to each of these elements, which when combined, provide the Anticipation Potential for the topic. See methodology for more information.