AI sovereignty is shifting from an impossible dream of total self-sufficiency to a realistic strategy of maintaining meaningful control and choice within global interdependence. Rather than requiring countries to build every component of artificial intelligence systems domestically, a new approach emphasizes three interconnected pillars: agency (the ability to make autonomous choices), interoperability (technical systems that work together), and openness (lower barriers to entry and innovation). This reframing offers governments a practical roadmap for shaping their technological futures without denying the realities of global supply chains.

Why Is the Traditional View of AI Sovereignty Failing?

For years, policymakers have pursued AI sovereignty through the lens of the Westphalian model, which emphasizes complete territorial control and independence. Transposed to artificial intelligence, this logic translates into a pursuit of "full-stack autonomy," meaning countries would need to domestically produce every layer of AI technology, from raw data and software to computational capacity and energy infrastructure. However, this ambition collides with the realities of how AI systems are actually built.

The AI value chain is defined by extreme specialization, immense capital requirements, and powerful network effects. Self-sufficiency is logistically impossible for most countries. A single AI model can cost hundreds of millions of dollars to train, and no nation can realistically replicate every specialized component. This mismatch between aspiration and reality has left many governments with abstract sovereignty goals but no concrete operational plan.

What Does Strategic AI Sovereignty Actually Look Like?

The Carnegie Endowment for International Peace, building on four years of research through the CyberBRICS project, proposes operationalizing AI sovereignty through three interdependent pillars. The first pillar, agency, means retaining the substantive capacity to exercise autonomous choice within a state of interdependence. Concretely, this involves being able to understand how AI systems function, access critical technologies on fair terms, adapt systems to local linguistic or cultural needs, and exert influence over global governance standards in accordance with one's own values.

"AI sovereignty should be operationalized as a strategic capacity built through three interdependent pillars: agency, interoperability, and openness. Grounded in these pillars, the KASE framework offers a blueprint for states to navigate global dependencies while retaining the power to shape their own technological futures," explained Luca Belli in the Carnegie analysis.

Luca Belli, Carnegie Endowment for International Peace

Under this framework, sovereignty is better understood as agency under constraint. The relevant question is not whether a state controls every layer of the technology stack, but whether it retains the capacity to make consequential choices: to switch providers, impose conditions, adapt technologies, and freely engage in national or international rule-making processes.

How Can Countries Build Interoperability and Openness Into Their AI Systems?

The second pillar, interoperability, serves as the primary technical enabler of agency. In a fragmented digital landscape, the lack of interoperability acts as a mechanism of "vendor lock," confining applications into proprietary walled gardens that prevent the ability to switch providers and stifle localized innovations. Conversely, when digital architecture is interoperable, it lowers switching costs and catalyzes competitive dynamics.

• Data Interoperability: Common standards and compatible systems that permit the secure exchange of datasets and prevent technical silos from forming around single vendors.
• Software and Infrastructure Interoperability: Open architecture that ensures sovereign cloud solutions are not tethered to a single vendor's product roadmap or business decisions.
• Policy-Level Interoperability: Mandates for open standards and data portability that prevent dominant external actors from unilaterally imposing rules on smaller economies.

However, for interoperability to succeed, countries must have alternative options to interoperate with. This means pursuing interoperability is as much a political and industrial choice as a technical one. Industrial policies that facilitate the development of alternatives are essential to ensure that interoperability is more than a normative obligation.

The third pillar, openness, lowers barriers to entry and enables distributed experimentation and rapid scaling. Yet openness is not a neutral benefit. Policies that ensure standards are developed through inclusive, multistakeholder processes are central to the broader pursuit of digital autonomy. This approach recognizes that cooperation across countries does not diminish sovereignty; rather, strategic partnerships often provide the leverage required to maintain autonomy, allowing countries to diversify and manage their dependencies on favorable terms.

How Are Regions Translating AI Sovereignty Into Practice?

The framework is already being applied in real-world policy contexts. In the Arab States region, the Ooredoo Group and the United Nations Development Programme (UNDP) launched a High-Level Digital and AI Policy Leaders Forum in early September 2026, bringing together senior government officials, regulators, and technology leaders to advance more inclusive and sustainable digital economies.

The forum is centered on the Digital Policy Framework (DPF), an evidence-based tool developed to support governments in designing digital policies that promote inclusion, economic growth, and human development. The framework focuses on three interconnected areas critical to building competitive digital economies: networks and connectivity, data and digital public infrastructure, and intelligent systems and applications, including the responsible use of AI across sectors such as health, education, agriculture, and public services.

"Digital transformation can create enormous economic and social value for our region, but technology alone is not enough. It requires the right policies, regulatory environment and collaboration between the public and private sectors. The priority now is to move from framework to implementation," stated Hilal Mohammed Al-Khulaifi, Ooredoo Group Chief Legal and Regulatory Officer.

Hilal Mohammed Al-Khulaifi, Group Chief Legal and Regulatory Officer, Ooredoo

The economic stakes are significant. Evidence referenced in the Digital Policy Framework indicates that a 10 percent increase in fixed broadband penetration can increase GDP by up to 1.5 percent in developing countries. Additionally, AI could contribute an estimated $21 billion to $35 billion annually to Gulf Cooperation Council (GCC) GDP when deployed through inclusive and accountable policy frameworks.

What Trade Barriers Are Blocking AI-Intensive Services in the Indo-Pacific?

Beyond regional frameworks, the Indo-Pacific region presents both opportunities and challenges for AI governance. Canadian AI-intensive services, which include information services, computer services, research and development, financial services, and professional services, face varying levels of market access across the region.

According to a recent analysis of digital trade rules for the AI era, Canadian AI-intensive firms have better access to Australia, Japan, New Zealand, and Singapore, thanks to the Comprehensive and Progressive Agreement for Trans-Pacific Partnership (CPTPP) and these economies' overall moderate applied services restrictions. However, they face high barriers in certain Indo-Pacific markets, especially China, India, Indonesia, the Philippines, South Korea, and Thailand, which maintain high multilateral restrictions and are not subject to CPTPP liberalization commitments.

Across all Indo-Pacific markets, the least liberalized AI-related services sectors include legal services, broader professional services, and financial services. In financial services, data processing restrictions, licensing requirements, and regulatory approvals can affect fintech and AI-enabled financial applications. In computer and related services, the most liberalized AI-relevant sector, restrictions vary significantly by market, with China, Indonesia, and Vietnam maintaining particularly high restrictions related to data localization, cloud licensing, and foreign participation.

This fragmented landscape underscores why the three-pillar approach to AI sovereignty matters. Without interoperability standards and openness commitments negotiated through trade agreements, countries risk creating isolated digital economies that cannot benefit from global AI innovation or compete effectively in international markets. The challenge for policymakers is to balance the desire for technological autonomy with the practical need for global integration and access to cutting-edge AI capabilities.