We face accelerating, interconnected crises – ecological, financial, technological, and social – yet our institutions remain rooted in outdated, top-down models.
Designed for a world where centralized governance shaped social outcomes, they now struggle with fragmentation, siloed knowledge, and limited adaptability in an increasingly unpredictable landscape. Governments, despite their differences in resources and political structures, share a common challenge: responding to complex, fast-moving crises with governance models built for stability. Traditional reliance on law and finance often proves insufficient in addressing climate change, economic instability, and declining public trust.
New ideas such as Generative Shared Intelligence (GSI) offer a different approach. By integrating diverse forms of intelligence—real-time data, AI, citizen insights, and lived experiences—GSI fosters adaptability, collaboration, and systemic resilience. It shifts governance from knowledge hoarding to intelligence-sharing, enabling institutions to regenerate economic, social, and political systems.
To remain effective in an era of uncertainty, governments must embrace this shift – not as an add-on, but as a foundational principle of governance.
The Challenge
Institutions today have access to more data and expertise than ever before, yet they struggle to translate this into meaningful intelligence which can be used when planning and taking actions.
The core challenge lies in a lack of mechanisms for shared synthesis and recognition of diversity of knowledge. In short, to gather the right knowledge and channel them into relevant conclusions. As a result, institutional responses to systemic challenges often remain reactive and slow.
Governments and public institutions frequently overlook or remain disconnected from the full spectrum of available knowledge—grassroots insights, indigenous wisdom, statistics, and scientific research often exist in parallel but rarely converge. This disconnect creates blind spots, limiting understanding of complex issues, sidelining marginalized and rural communities, and unintentionally widening inequalities.
Recognizing and accessing knowledge is only the first step: what matters is creating spaces for shared sense-making, synthesis, and application. To not only gather knowledge but to use them. Without adaptive learning mechanisms grasping the whole spectrum of knowledge, governments remain ill-equipped to navigate uncertainty and long-term risks.
A resilient institution prioritizes continuous learning over rigidity, ensuring they can evolve and respond to emerging challenges.
Pathways for Institutional Innovation
Some organizations are redefining how multiple inputs of information can be synthesized for sense-making and shape decision-making:
- Dragonfly Thinking leverages AI to equip leaders with the clarity and foresight needed to navigate complexity. Drawing inspiration from the dragonfly’s ability to perceive multiple perspectives simultaneously, the company transforms fragmented insights into coherent, strategic action.
- A similar approach is employed by the Shared Intelligence Lab, a collaborative hub offering workshops, training, and research partnerships for organizations in development and foreign affairs. By harnessing collective intelligence and advanced AI tools, the lab fosters integrative thinking, enabling institutions to tackle intricate challenges with agility and depth.
- The Development Intelligence Lab is reshaping how Australia engages with international development by closing the gap between policy and real-world impact. Unlike traditional think tanks, it delivers timely, actionable intelligence—identifying blind spots and equipping decision-makers with insights tailored to the complexities of the Indo-Pacific. Through interdisciplinary collaboration and human-centered design, the Lab ensures that policy is informed by those on the ground, not just bureaucratic frameworks. In an era of rapid geopolitical shifts, it offers a faster, more responsive approach to shaping effective development strategies.
To harness the power of shared intelligence, governance should prioritize real-time collective sensemaking, adaptive experimentation, and knowledge as a public good. – as evidenced by many examples shared around IID.
- Initiatives like ICARUS use real-time ecological data from animal-borne sensors to connect researchers, satellite operators, and sensor developers, enabling open access to novel biodiversity data.
- Petabencana, an open-source platform from Indonesia, enhances disaster response by crowdsourcing real-time flood data via community reports and AI mapping, bridging citizen insights with government action.
- The Māori Data Governance Model embeds Māori values into governance, empowering Indigenous communities to shape data collection, sharing, and use in New Zealand.
Generative Shared Intelligence (GSI) aligns with existing views on data, AI, digital transformation, missions, and participatory governance but offers a broader, more integrated approach.
It argues that AI and data must be embedded within a richer understanding of intelligence—encompassing human collective knowledge and tacit experience—to avoid governance failures.
While digital tools improve accessibility, human oversight remains essential. How can enhance GSI in the existing institutions or create new ones with GSI at its core?
