What CEOs Need to Know About Sovereign AI

As multinational companies implement artificial intelligence workflows and look to adopt AI across their global operations, they are increasingly running up against country-specific regulations and policies that aim to govern AI and align its use with national priorities and local cultural norms. These regulations and policies — which fall under the umbrella of “sovereign AI” — govern where data is stored and processed, whose infrastructure is used for training and operating AI models, and how algorithmic decisions are reviewed and enforced in a given jurisdiction. Many markets are now developing their own sovereign AI frameworks to reduce dependence on the United States and China, where nearly 70% of leading AI models originated.

This creates a strategic dilemma for multinationals. Relying on global AI platforms maintains operational consistency but deepens exposure to geopolitical disruption and local market access risk. Localizing data, infrastructure, and models earns regulatory trust but incurs significant cost and complexity when a company operates across dozens of jurisdictions with differing requirements. The challenge is that policies vary significantly by country and are evolving rapidly, making a single global AI strategy untenable, and fully independent local systems impractical.

Most companies are responding defensively, treating sovereign AI as a compliance obligation managed by legal or IT teams. Our December 2025 survey of 1,928 executives across 28 countries reveals a striking gap: Sixty percent of respondents said that rising geopolitical risk makes them more likely to pursue sovereign technology solutions, yet only 15% have made AI sovereignty a CEO or board-level priority, and fewer than 13% see it as a growth driver rather than a cost.

In this article, we argue that sovereignty is better understood as a continuum of choices and that the companies best positioned to scale AI globally are those that treat those choices as a source of competitive advantage rather than a constraint to be minimized.

The Sovereign AI Landscape

The regulatory landscape governing AI has shifted significantly in recent years, from relatively narrow data residency rules to a far broader set of requirements governing models, infrastructure, and how algorithmic decisions are made and enforced. Most major markets are now developing their own sovereign AI frameworks, resulting in a patchwork of locally governed ecosystems, each with distinct rules, data standards, and expectations for responsible use.