On our own: Companies with AI sovereignty see 70% better returns

A comprehensive study of over 2,000 executives from the world’s largest companies has revealed a striking pattern in artificial intelligence success. Organizations that maintain sovereignty over their AI systems and data infrastructure are 70% more likely to achieve exceptional returns on their AI investments compared to companies that rely on external platforms.

This finding emerges from research encompassing enterprises with combined revenues exceeding $48 trillion, spanning regions from the United States and Europe to Japan and the UAE. The results suggest that as AI becomes increasingly central to business operations, the question of who controls the underlying technology and data has become a critical competitive differentiator.

Understanding agentic AI and sovereignty

Agentic AI refers to artificial intelligence systems that can act independently to complete complex tasks, make decisions, and interact with multiple business systems without constant human oversight. Unlike traditional AI tools that require specific prompts for each task, agentic AI can understand broader objectives and determine the best sequence of actions to achieve them.

AI sovereignty, meanwhile, means maintaining direct control over your AI infrastructure, algorithms, and data rather than depending entirely on third-party platforms. This includes owning the servers where AI models run, controlling how data flows through systems, and maintaining the ability to modify AI behavior according to specific business requirements.

The distinction matters because agentic AI systems often handle sensitive business data, make autonomous decisions that affect operations, and integrate deeply with core business processes. Organizations that maintain sovereignty can ensure these systems align precisely with their security requirements, compliance needs, and strategic objectives.

The performance gap

The research reveals stark differences between organizations that prioritize AI sovereignty and those that don’t. Companies in the top-performing category demonstrate superior results across four key dimensions:

Breadth of implementation: Leading organizations have deployed agentic AI across an average of 11 business functions, while lower-performing companies typically limit implementation to just four areas. This broader adoption suggests that sovereign AI platforms provide the flexibility and security needed to scale across diverse business operations.

Depth of integration: Top performers embed AI 2.5 times more deeply into their operations than their counterparts, moving quickly from experimental pilots to full production deployment. This deeper integration becomes possible when organizations control their AI infrastructure and can customize systems to meet specific operational requirements.

Strategic commitment: High-performing companies treat AI sovereignty as mission-critical rather than experimental. They invest in building comprehensive AI and data platforms rather than relying on piecemeal solutions or external services for core AI capabilities.

Financial returns: For every dollar invested in AI initiatives, leading organizations see a 5:1 return compared to 2:1 for the next performance tier. This dramatic difference suggests that sovereignty enables more effective AI deployment and better alignment with business objectives.

Why sovereignty drives better outcomes

Several factors explain why maintaining control over AI systems produces superior business results. First, sovereign AI platforms enable organizations to break down data silos that typically limit AI effectiveness. When companies control their entire AI stack, they can ensure seamless data flow between systems and create more comprehensive AI models that understand the full business context.

Second, sovereignty provides security and compliance advantages that become increasingly important as AI systems handle more sensitive operations. Organizations can implement custom security protocols, maintain data residency requirements, and ensure AI decisions align with industry regulations without depending on external providers’ policies.

Third, sovereign platforms enable faster iteration and customization. Companies can modify AI behavior, adjust decision-making parameters, and integrate new capabilities without waiting for external vendors to implement changes or worrying about compatibility issues with third-party systems.

The urgency factor

The research suggests that organizations face a limited window to establish AI sovereignty before competitive disadvantages become entrenched. Within approximately 1,000 days, an estimated 90% of enterprises will experience what researchers term “data and AI gravity” – the point where AI systems become so central to operations that switching platforms or approaches becomes extremely difficult and expensive.

This gravity effect occurs because agentic AI systems learn from organizational data patterns, integrate with multiple business processes, and create dependencies that make migration increasingly complex over time. Organizations that establish sovereign AI platforms early can shape these systems according to their specific needs, while those that delay may find themselves locked into external platforms that don’t align perfectly with their requirements.

Building sovereign AI capabilities

Organizations pursuing AI sovereignty typically focus on three core capabilities. The first involves creating unified data infrastructure that eliminates silos between different business systems. This requires investing in data platforms that can securely aggregate information from multiple sources while maintaining appropriate access controls and compliance requirements.

The second capability centers on developing internal AI development and deployment processes. Leading organizations create what researchers term “AI factories” – standardized environments where business teams can build, test, and deploy AI applications using low-code and no-code tools. These factories enable subject matter experts across different functions to create AI solutions without requiring deep technical expertise.

The third capability involves establishing governance frameworks that ensure AI systems operate according to business requirements and regulatory standards. This includes creating processes for monitoring AI decisions, auditing system behavior, and maintaining human oversight of autonomous operations.

Technology infrastructure considerations

The cost barriers to establishing sovereign AI capabilities have decreased significantly in recent years. Advanced AI processing units that once required millions of dollars in investment are now available for approximately $4,000, enabling organizations to establish production-grade AI capabilities across multiple departments without massive capital expenditures.

Many leading organizations are standardizing on open-source technologies that provide enterprise-grade performance while avoiding vendor lock-in. According to the research, 81% of high-performing enterprises plan to build their future AI infrastructure on open-source foundations, citing flexibility, cost control, and customization capabilities as key advantages.

Implementation across industries

Sovereign AI strategies are proving effective across diverse industry sectors. In logistics, companies are deploying autonomous systems that manage supply chain operations while maintaining complete control over proprietary routing algorithms and customer data. Telecommunications providers are integrating AI into operational support systems while ensuring compliance with strict data residency requirements.

Customer service organizations are implementing AI copilots that can access comprehensive customer histories and make autonomous decisions about service escalation while maintaining complete control over interaction data and decision-making parameters.

Practical next steps

Organizations considering sovereign AI strategies should begin by assessing their current AI and data infrastructure to identify dependencies on external platforms and potential integration challenges. This assessment should include evaluating data flows between systems, understanding compliance requirements, and identifying business functions where AI could provide the greatest value.

The next step involves developing a platform strategy that prioritizes internal capability building over external service dependencies. This doesn’t necessarily mean avoiding all external AI services, but rather ensuring that core AI capabilities remain under organizational control while using external services for supplementary functions.

Finally, organizations should invest in internal AI literacy and development capabilities. This includes training business teams to work effectively with AI tools and establishing governance processes that ensure AI systems operate according to business requirements and ethical standards.

The competitive imperative

The research suggests that AI sovereignty represents more than a technical decision – it’s becoming a fundamental competitive requirement. As AI systems become more central to business operations and customer interactions, organizations that maintain control over these capabilities will have significant advantages in customization, security, compliance, and strategic flexibility.

The 70% performance advantage demonstrated by sovereignty-focused organizations indicates that this isn’t merely a marginal improvement but a substantial competitive differentiator. For organizations serious about AI-driven transformation, establishing sovereignty over AI and data infrastructure appears to be not just beneficial but essential for long-term success.