Growing Dependence on External Platforms Fuels Interest in Sovereign AI

As enterprises rush to deploy AI, a majority of them are discovering that relying entirely on external platforms can create new risks. Concerns about data residency, regulatory compliance, vendor lock-in, and service disruptions are pushing organizations to rethink how much control they have over the AI systems that are becoming increasingly important to business operations.

A new survey conducted by IDC on behalf of Cohere suggests that sovereign AI is becoming a strategic priority, particularly in highly regulated industries. However, the research also found a significant gap between interest and understanding, with many business leaders unable to clearly define what sovereign AI actually means or how to implement it.

Organizations are seeking a way to reduce AI dependencies

This report argues that recent cybersecurity incidents, regulatory pressures, and concerns about dependence on large technology providers are encouraging organizations to pursue greater control over their AI environments. According to IDC, sovereign AI gives organizations greater authority over how AI systems are designed, deployed, operated, governed, and maintained.

This study found that roughly one-third of leaders struggled to explain sovereign AI in their own words, while only 13% reported having broad awareness of the concept. IT leaders generally demonstrated a stronger understanding than business leaders and were more likely to associate sovereign AI with compliance and regulatory obligations.

Security and compliance concerns are driving adoption

Respondents identified compliance requirements and regulatory risks as the strongest motivators for sovereign AI investments across industries. Financial services, manufacturing, telecommunications, healthcare, and energy organizations ranked security and governance concerns among their top priorities.

This research also found that some organizations increasingly view sovereign AI as a competitive differentiator. Enterprises also see potential value in maintaining control over critical AI capabilities and reducing dependence on external providers.

The bigger question for IT leaders

The findings highlight that many organizations recognize the risks associated with AI dependence, but few have developed a clear strategy for achieving AI sovereignty. Uncertainty around ownership, governance, implementation responsibilities, and long-term operating models continues to slow adoption.

Keep in mind that building sovereign AI environments can improve control and resilience, but it may require additional investment, specialized expertise, and more complex infrastructure decisions compared with consuming AI solely as a cloud service.

What should organizations do next?

Organizations evaluating sovereign AI should begin by defining what sovereignty means within their specific regulatory and operational context. Security, compliance, data residency, and business continuity requirements should be documented before selecting AI platforms or deployment models.

The report also recommends establishing executive ownership, creating measurable governance objectives, and developing a long-term strategy that balances innovation with control. As AI becomes embedded in critical business processes, the ability to maintain operational independence may become just as important as the AI capabilities themselves.