Drawing on 26 UNDP Artificial Intelligence Landscape Assessments (AILA) completed between 2024 and 2026, with 10 more underway, this report examines what AI readiness means once adoption is already underway. The countries analysed reflect the set of national AILA engagements completed at the time of writing, undertaken in response to government demand and UNDP country-level programming priorities across diverse regional, institutional, and digital maturity contexts. They are not presented as a statistically representative sample, and the report does not rank countries, aggregate readiness scores, or provide a country-by-country review. Instead, the report uses this evidence base to identify recurring patterns in how AI adoption is moving through public systems, markets, institutions, and wider ecosystems.

Infrastructure is in transition. No longer is it about how well a country builds and connects physical assets, but how intelligently those assets can sense, communicate, learn and act. Intelligent infrastructure is more than a technology investment – it is a determinant of national competitiveness. The aim of this report is to help policy-makers and business leaders steer this transformation in a structural system-wide way rather than as an isolated technology shift. Intelligent infrastructure is the ability to embed system-level intelligence and autonomy into the systems that power economies. It spans industrial production, energy systems, urban services, supply chains and national security. As such, it requires coordinated action across policy, investment and governance. This report, developed with global leaders, presents the DNA framework – a practical architecture comprising Devices, Network, Artificial intelligence, cyber resilience – to make the concept of intelligent infrastructure concrete. Grounded in real-world experience from expert communities including advanced manufacturing, energy and materials, cybersecurity and urban transformation, the framework maps intelligent infrastructure across four levels: Company, Cluster, City and Country. And it offers deployment pathways to translate potential into systemic value. Intelligent infrastructure is not a destination but an evolving capability: one whose strategic value will compound over time.

In preparing to launch its forthcoming artificial intelligence (AI) diplomacy strategy, the Netherlands would benefit from a better understanding of leading peers’ (international) strategies and AI governance structures. This Clingendael Report contributes to this objective by offering an exploratory analysis of key initiatives and governance structures of the United Kingdom, Denmark, France, and Singapore. These countries stand out for their strong AI policies or their leadership in sub-fields such as AI safety. The case studies show that the United Kingdom (UK) has adopted a laissezfaire attitude towards AI, aligning with the United States (US) and focusing on attracting foreign investors. While this comes with development gains, it also poses risks to data sovereignty. The UK does have a notable ‘AI Security Centre’ focused on research around emerging AI risks and AI auditing, but it has recently withdrawn from international consensus-building efforts.

Lifelong learning is not a new concept – but it has never been more important. In the face of rapid technological innovation, the green transition, demographic shifts and evolving patterns of globalization and work, the ability of individuals and societies to keep learning and adapting to new realities is a critical foundation for inclusion and resilience. This report argues that lifelong learning should be elevated as a strategic policy priority – not just as a tool to increase productivity and facilitate sustainable growth, but also as a systemic enabler of personal and societal advancement, equity and decent work in the twenty-first century. However, despite the growing policy focus on lifelong learning and skills development, there remains a striking gap in comprehensive, authoritative, up-to-date research that captures the full scope of lifelong learning.

Artificial intelligence (AI) is changing the rules of leadership. It can process vast amounts of information, detect patterns invisible to the human eye, and streamline operations — but it can't replace judgment or trust. Executives today face a dual challenge: leveraging AI’s capabilities while maintaining the human-centered leadership that drives culture and accountability.  Explore how leaders can navigate AI adoption responsibly and develop the skills needed for strategic decision-making in an AI-enabled world.

People expect their government to act quickly, adapt to change and respond effectively, putting pressure on public institutions to keep up. Digital technologies and data are integral to meeting these demands: they have become core infrastructure for governments to perform and address today's policy and service delivery challenges. This report presents results from the OECD Digital Government Index (DGI) and the Open, Useful and Re-usable Data Index (OURdata), illustrating how governments across 36 OECD countries and 8 accession countries have been improving coherent, effective and human-centred digital transformation in government in recent years.

The concept of artificial intelligence (AI) sovereignty has entered policy discussions as governments confront the strategic importance of AI infrastructure, data, and models amid rising dependence on a small number of firms and jurisdictions. This report defines AI sovereignty as a spectrum of strategies to enhance a country’s capacity to make independent decisions about critical AI infrastructure deployment, use, and adoption, rather than literal autarky. Motivations vary—from protecting national security and resilience and supporting economic competitiveness, to ensuring cultural and linguistic inclusion in model training and datasets and strengthening influence in global governance. These aims are often legitimate, but “sovereign AI” can also become a vehicle for protectionism, fragmented markets and standards, and duplicative or stranded public investment. The central finding is that full-stack AI sovereignty is structurally infeasible for almost any country because AI is a transnational stack with concentrated choke points across minerals, energy, compute hardware, networks, digital infrastructure, data assets, models, applications, and the crosscutting enablers of talent and governance. The practical alternative is “managed interdependence,” an approach that relies on strategic alliances and partnerships to reduce risks throughout the AI stack. Countries can operationalize managed interdependence by mapping dependencies by layer, prioritizing feasible interventions, diversifying suppliers and partners, and embedding interoperability and portability through technical standards, procurement, and governance. Done well, managed interdependence can strengthen resiliency and agency while preserving the benefits of open markets and cross-border collaboration.

As societies, economies, and public institutions become ever more dependent on digital systems, cybersecurity has become a core condition of public safety, economic continuity, and social resilience. Yet the rapid expansion of the cyber threat landscape has exposed a structural gap in the global cybersecurity ecosystem. Commercial providers remain essential, but market incentives, high costs, and a persistent talent shortage constrain them. Governments, meanwhile, are limited by jurisdiction, mandate, and political priorities. It is within this gap that non-profit cybersecurity organisations have emerged as critical actors, providing services that protect vulnerable communities, strengthen institutional resilience, and sustain critical parts of the digital environment that neither for-profit organisations nor the state can adequately secure on their own. In this context, this study examines the value provided by non-profit cybersecurity organisations as being both substantial and systematically under-recognised. Its central finding is that these organisations do useful work and perform functions on which the wider cyber ecosystem depends. Their contribution lies not only in direct service provision but also in reducing harm, raising baseline resilience, supporting collective cyber hygiene, and maintaining shared infrastructure and standards.

Global supply chains are being reshaped by multiple forces, but two have systemic reach: the rapid uptake of artificial intelligence (AI) and the expansion of environmental requirements linked to market access. Addressing both depends on a shared foundation – digitalised, interoperable and trusted trade systems. This report examines how trade facilitation policies and tools enabled by AI and broader digital technologies can enhance supply chain resilience, efficiency and environmental performance, while effectively responding to new data and verification needs. Reaping the benefits of AI requires a functioning paperless trade environment with regulatory and technical interoperability at its core. Preconditions for this are high levels of digitisation and data standardisation, legal certainty for electronic transactions, and openness to cross-border data flows. Co-ordinated border management and enhanced international co-operation are critical to deploying digital and AI-enabled solutions that preserve efficiency while advancing resilient and environmentally performing supply chains.

Trustworthy artificial intelligence (AI) is a global priority, as societies shift from broad ethical principles to the more challenging work of putting them into practice. Universities are emerging as key actors of responsible AI, embedding fairness, privacy and accountability through ethics-by-design methods and interdisciplinary governance. Collaboration and education in academia are shaping sustainable AI for the future, advancing innovation while safeguarding the social values that underpin stable economies and societies.