Artificial intelligence is moving from research into daily use faster than institutions can coordinate responses. As Switzerland hosts key AI forums and assumes the OSCE Chairpersonship in 2026, the GESDA Science Breakthrough Radar® highlights why anticipation is becoming central to how governance takes shape — not through a single agreement, but through practice.
Feb. 5, 2026
Artificial intelligence is moving from research labs into daily use faster than many institutions can adapt. As systems based on advanced machine learning are deployed across finance, health care, education, and public administration, the question facing governments is no longer whether AI will reshape societies, but how coordination can keep pace with its spread.
That challenge will come into sharper focus in 2026, as Switzerland hosts a series of major meetings and assumes a central role in multilateral discussions on emerging technologies. From Geneva to Zurich and Davos, international organizations, researchers, and policymakers will confront a shared problem: scientific and technical capability is advancing faster than the systems designed to govern it.
The 2026 GESDA Science Breakthrough Radar® identifies advanced AI as one of the clearest examples of this pattern. Governance remains fragmented across institutions with different mandates, timelines, and tools. This imbalance framed discussions at the World Economic Forum Annual Meeting in Davos last month.
Davos as a signal, not a settlement
At Davos, artificial intelligence cut across every major policy discussion, from economic growth and labor markets to development and regulation. “AI is infrastructure,” NVIDIA founder and CEO Jensen Huang told the forum. “There’s not one country in the world I can’t imagine where it won’t be needed … You should have AI as part of your infrastructure.”
What did not emerge was a single approach to governing those effects across borders. Instead, discussions reflected a dispersed landscape. Economic institutions focused on productivity; technical bodies examined safety testing; and regional groupings explored approaches shaped by local legal contexts. Rather than pointing toward a comprehensive global framework, Davos revealed how governance is already forming through practice—standards and institutional routines developed in parallel, often well before formal rules are agreed.
What the Radar highlights
The Radar places AI within a trajectory that maps how scientific capability is likely to evolve over 5-, 10-, and 25-year horizons. It identifies several pressures arriving simultaneously: rapid improvements in model performance, falling barriers to deployment, and expanding use in decision-making systems. These trends increase expectations for oversight and accountability—often before institutions have agreed on shared standards.
The Radar repeatedly points to a familiar gap: detection and capability advance faster than institutional readiness. In AI, that gap shows up in uneven standards and differing access to data, compute, and expertise. More coordination does not automatically follow more information; governance depends on institutions being able to translate technical insight into shared practices.
Switzerland’s test case in 2026
Switzerland enters this year positioned at several points where these coordination questions converge. In Geneva, international organizations play central roles in standards-setting and diplomacy. In July, the AI for Good Global Summit will bring together governments and civil society to examine AI applications. While not a negotiating forum, the International Telecommunication Union’s platform has become a focal point for aligning technical insight with policy.
Beyond hosting, Switzerland is exercising political leadership. In January, it assumed the chair of the Organization for Security and Cooperation in Europe (OSCE), spanning 57 participating nations. One of Switzerland’s priorities is anticipating emerging technologies in relation to security and cooperation.
“When science and diplomacy work hand in hand, technology becomes a tool for our common advancement,” said Raphael Nägeli, Switzerland’s ambassador to the OSCE, the United Nations and other international organizations in Vienna. This brings AI into a forum designed for dialogue and trust-building, reflecting a growing recognition that technology governance is no longer confined to economic or scientific institutions alone.
Data, access, and trust
One emerging bottleneck is not innovation itself, but governance-grade information. AI systems rely on large volumes of data and opaque decision processes. For institutions, questions of traceability and access become central. The Radar highlights that without shared approaches to data governance and evaluation, coordination becomes harder. Transparency and trust depend on nations able to understand and compare systems developed in different contexts.
This is where Switzerland’s role as a convening space matters. By bringing together technical experts and diplomats repeatedly over time, coordination can develop incrementally, even in the absence of formal agreements.
What comes next
As 2026 unfolds, AI governance is likely to continue forming through practice across standards bodies, scientific panels, and regional forums. The question for the year ahead is whether those parallel efforts can begin to align before practices harden into rules by default.
As Switzerland hosts these discussions, the focus will be on whether early engagement can narrow the gap between what technology enables and what institutions are prepared to manage.
Where the science and diplomacy can lead us
The 2026 GESDA Science Breakthrough Radar®, distilling the insights of 2,390 leading researchers from 89 countries, indicates that AI represents a broader signal. When scientific capability accelerates, governance must learn to move earlier, not faster. The challenge is no longer theoretical.
Key Radar references:
→1.1 Artificial intelligence — AI aims to produce intelligence using algorithms and machines. This includes systems that perceive and analyze their environment, take decisions, communicate and learn. In the past decade, AI has reached major milestones and is poised to disrupt societal norms.
→1.1.2 AI for Science — Scientific discovery is currently constrained by the pace and cost of data generation, and AI can accelerate progress via automated experiment design and high-throughput simulation. AI will also democratize tools for scientific discovery, allowing for new digital experiments, hypothesis generation and global-scale simulations such as digital organisms and weather models.
→1.1.4 Governance, coordination, and oversight — Deep learning has dramatically accelerated progress on a wide range of AI problems. This has led to large swathes of the research community focusing their efforts on refining and scaling existing approaches to tackle increasingly complex tasks. But the underlying technology remains in its infancy and continued progress may require fundamental conceptual breakthroughs.
→Anticipating the Geopolitical Impact of Advanced AI — AI systems are being deployed on a massive scale throughout the world. While the technology is still developing, it is clear that it will have equally massive potential consequences for a wide variety of sectors — and diplomacy and international relations are no exception.
