What is sovereign AI? Strategic control of your AI future

Ask IT leaders what sovereign AI is, and you’ll get a wide range of answers. Some will even struggle to define the term.

Sovereign AI is an emerging concept focusing on giving organizations — or countries —control over how they develop, deploy, and govern the technology, often using in-house talent, data, and infrastructure.

But only 13% of respondents in a survey from AI platform provider Cohere and IDC say sovereign AI is widely understood across their organizations, and one in three IT leaders had difficulty describing sovereign AI in their own words.

It’s important for IT leaders to understand the concept, because it can help them control costs, keep internal data private, and avoid vendor lock-in, advocates say.

A solid sovereign AI plan can help organizations avoid disruptions caused by forces outside their control, says Joelle Pineau, chief AI officer at Cohere, which offers an AI platform that enables customers to host AI models on premises.

“Over the past year, enterprises and governments have confronted a hard truth: AI systems that rely on external infrastructure can be disrupted without warning by decisions and actions outside their control,” Cohere says in a recent report. “Recent model access restrictions and several high-profile cybersecurity incidents have become a global wake-up call, exposing how fragile technological dependencies can be.”

Sovereign AI is about giving organizations as much autonomy, choice, and control as possible as they deploy and run AI systems, Pineau says.

“The notion of sovereignty really is about giving users control over their tech stack, the ability to choose how it’s deployed, how it works, what data is fed into the system, and how employees are exposed to the technology,” she adds.

Pineau wasn’t particularly surprised about the lack of understanding about sovereign AI reflected in the survey. Cohere’s accompanying report is an attempt to bring more clarity to the issue, she says.

Many goals under one umbrella

Confusion about sovereign AI in part reflects practitioners’ varying goals. Some users want to maximize their AI model options, some want better control over data ingested into AI systems, some want data to reside within country borders, some want to control costs, and others may want to run AI models optimized to their native language or culture.

For Berk Yilmaz, co-founder and CTO at AI integrated development environment provider Noah Labs, sovereign AI encompasses five characteristics: data sovereignty, legal jurisdiction, model provenance, operational control, and supply chain independence.

“Fulfilling one of those does not mean fulfilling all the others, so two executives can agree with sovereign AI and have little in common,” he says.

Freedom of choice doesn’t always mean a company has to host an AI model on premises or data must reside within a certain country, advocates suggest. Sovereign AI is more about preserving options when something unexpected happens.

“The sovereignty model performs well even in a situation where the vendor breaks off the contract, your model is added to the list of models that are banned for exports, and the connection is off,” Yilmaz says. “Each of these three scenarios has already played out somewhere in the last year.”

Others have different definitions. Confusion over sovereign AI isn’t surprising because it is four separate concepts that were collapsed into one, says Jeet Pattanaik, founder and CTO of AI solutions provider Glokal AI. Those four concepts: where a company’s data physically sits, what country’s law can compel access to it, who controls the AI model, and whether a company could still operate if the relationship with the model provider ends.

“Vendors usually sell you the first and call it sovereignty, because data residency is easy to demonstrate and makes a good diagram,” says Pattanaik, author of the book Sovereign AI: The Enterprise Guide to AI That Is Private by Design, Compliant by Default, and Yours Forever. “The hard one is the second, and it’s a legal question rather than a technical one. A server in Frankfurt owned by a US company is still reachable under US law.”

While the concept is largely about control, few companies want full control of their AI stack, he notes.

“Building your own models is expensive and usually worse,” Pattanaik says. “What CIOs actually want is bounded dependency: knowing exactly what you depend on, what happens if it changes, and having an exit that doesn’t take three years.”

Future impact

But the benefits aren’t always immediate, Pattanaik notes.

“The value arrives in specific moments, not continuously — when a regulator asks who processes this data and under whose jurisdiction, or when a vendor changes terms at renewal,” he says. “Organizations that thought about sovereignty already have an answer. Everyone else discovers the question and the crisis at the same time.”

Still, Pattanaik sees momentum building for the concept, with regulated industries such as banking, healthcare, and the public sector paving the way, treating it as a requirement.

“Most others are still at the stage of asking during vendor selection and accepting whatever answer comes back,” he says. “From where I sit it’s moved from philosophy to a procurement line item over roughly the last 18 months, but unevenly.”

David Wang, COO at enterprise AI gateway provider Tetrate, sees similar adoption trends with regulated industries and defense contractors leading the charge.

Other organizations should focus on a handful of questions to decide whether to investigate sovereign AI, he recommends. Companies that can most benefit include those with more than one regulator or legal entity, including recent acquisitions; those with a huge developer population running coding agents; and those with one AI model vendor that commands more than half of their AI spending.

“At that size, a supplier price change becomes a budget event,” Wang says.

Like Pattanaik, Wang suggests that sovereign AI is part of a long-game strategy rather than immediate gains. A good plan enables organizations to quickly shift to open AI models when a frontier model gets too expensive, he says.

“This work mostly prevents a loss rather than creating a gain, which is why it rarely wins the budget on its own,” he adds.

Cohere’s Pineau sees benefits for a broad range of organizations. With token costs a major concern for many companies, a sovereign AI plan can explore alternatives to current AI vendors, she notes.

“A lot of companies care about it, but they care about different aspects,” she says. “In regulated sectors, they care about the compliance aspects, and in some sectors with tight profit margins, they care a lot about the cost control. The manufacturing, the telecoms, and the energy sectors care about the ability to control their costs.”