By 2027, 35% of countries will be locked into region-specific AI platforms using proprietary contextual data, according to Gartner, Inc., a business and technology insights company. Gartner also predicts that platform lock-in will rise from 5% to 35% by 2027.
“Countries with digital sovereignty goals are increasing investment in domestic AI stacks as they look for alternatives to the closed U.S. model, including computing power, data centers, infrastructure and models aligned with local laws, culture and region,” said Mr. Gaurav Gupta, VP Analyst at Gartner. “Trust and cultural fit are emerging as key criteria. Decision makers are prioritizing AI platforms that align with local values, regulatory frameworks, and user expectations over those with the largest training datasets.”
Localized models deliver more contextual value; regional LLMs outperform global models in applications such as education, legal compliance, and public services, especially in non-English languages.
Nations Will Need to Invest 1% of GDP in AI Sovereignty by 2029
With non-Western customers changing alignment due to concerns of overly Western influence, AI sovereignty will lead to reduced collaboration and duplication of effort. Because of this, Gartner predicts that nations establishing a sovereign AI stack will need to spend at least 1% of their GDP on AI infrastructure by 2029.
AI sovereignty refers to the ability of a nation or organization to independently control how AI is developed, deployed, and used related to its geographical boundaries.
Regulatory pressure, geopolitics, cloud localization, national AI missions, corporate risks and national security concerns are driving governments and corporations to accelerate investments in sovereign AI. A fear of falling behind in the technological AI race will also push nations and companies to innovate rapidly and invest in an attempt to achieve self-sufficiency in all aspects of the AI stack.
“Data centers and AI factory infrastructure form the critical backbone of the AI stack that enables AI sovereignty, ” said Gupta. “As a result, data centers and AI factory infrastructure will see explosive build-up and investment going forward, propelling a few companies that control the AI stack to achieve double-digit, trillion-dollar valuations.”
- Design model agnostic workflows using orchestration layers that enable switching between LLMs across regions and different vendors.
- Ensure AI governance, data residency, and model tuning practices can meet country-specific legal, cultural, and linguistic requirements.
- Establish relationships with national cloud providers, regional LLM vendors, and sovereign AI stack leaders in priority markets and build a vetted list of partners.
- Monitor AI legislation, data sovereignty rules, and emerging standards that may affect where and how they can deploy AI models and process users data.
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