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Research Report
The AI Transformation 100: Concrete Ideas for Fixing How We Work
Enterprises with stronger control over their AI infrastructure, governance policies and data environments report significantly higher returns from generative and agentic AI initiatives, according to an MIT Technology Review survey.
The report, based on responses from more than 2,000 senior executives across 13 countries, found that companies described as “deeply committed” to AI and data sovereignty reported roughly 5x higher ROI from generative AI and agentic AI deployments than organizations with weaker governance and infrastructure controls.
Nearly all respondents — 95% — said they plan to establish their own AI and data platforms within the next three years.
Security and resilience ranked as the top drivers behind sovereignty efforts, cited by 85% of respondents, followed by data localization requirements and concerns around ownership and operational control.
Table of Contents
- Why Is Sovereign AI a Strategic Priority?
- Hybrid Infrastructure Becomes the Default Model
- Governance & Compliance Remain the Biggest Obstacles
- Sovereignty Is Central to Enterprise AI Strategy
Why Is Sovereign AI a Strategic Priority?
Companies are getting pushed toward sovereign AI by a combination of regulatory requirements, security concerns and the growing role AI now plays inside core business operations, according to Karyn Price, industry director, AI program manager at Frost & Sullivan.
“As AI becomes embedded in enterprise applications, companies are no longer willing to treat data location and control as secondary considerations."
According to her company's 2025 global study of AI decision-makers, 53% of organizations said becoming more AI-enabled and data-centric was now a crucial or very important business objective. At the same time, data sovereignty requirements increasingly shape infrastructure decisions:
- 31% choose on-premises infrastructure specifically for compliance and sovereignty reasons
- 31% select colocation environments for similar governance concerns
- 39% use edge deployments to satisfy sovereignty and compliance requirements
Organizations increasingly view sovereignty as a form of digital self-determination, said Massimiliano Claps, research director for worldwide national government platforms and technologies at IDC Government Insights. “In essence, this means giving data owners total control over how and where their data is managed, stored, and processed by service providers."
The pressure extends beyond traditional cloud governance. AI sovereignty increasingly includes control over model training, inference environments, fine-tuning processes and the broader AI supply chain. “Organizations with stronger sovereignty maintain exclusive control over intellectual property, training data and technology decisions,” said Claps.
Related Article: Cohere & Aleph Alpha Merge for $600M Sovereign AI Push
Hybrid Infrastructure Becomes the Default Model
Most enterprises are not attempting to achieve sovereignty by abandoning cloud infrastructure entirely. Instead, according to Price, they're pursuing sovereign AI through combinations of hybrid cloud, distributed edge infrastructure and multi-partner ecosystems rather than a single standardized architecture.
Key sovereign AI deployment strategies now include:
- Hybrid and multi-cloud architectures
- On-premises environments for sensitive workloads
- Edge deployments for localized processing and compliance
- Workload-specific deployment models based on data sensitivity
- Increased reliance on systems integrators and governance partners
Edge deployments are also becoming more important as organizations attempt to maintain tighter control over how AI systems process sensitive information close to where data is generated.
Claps describes sovereign infrastructure as a continuum rather than a single deployment model, noting organizations increasingly evaluate multiple “sovereign deployment archetypes." These range from sovereign public clouds and government clouds to hosted private infrastructure, air-gapped environments and localized hybrid stacks.
Governance & Compliance Remain the Biggest Obstacles
The largest barriers to sovereign AI are increasingly organizational rather than technical.
Price said governance, privacy, security and compliance complexity continue to create major operational challenges for enterprises attempting to scale sovereign AI environments; Frost & Sullivan found 71% of organizations view data privacy, security and governance as a challenge.
Claps added that enterprises face growing complexity integrating sovereign AI strategies into existing heterogeneous IT environments.
Several major barriers identified by IDC include:
- High implementation complexity
- Premium pricing for sovereign infrastructure
- Lack of internal expertise and specialized talent
- Integration challenges across existing systems
One emerging concern is what Claps described as “sovereign washing” — vendors marketing simple data localization features as comprehensive sovereignty solutions. “Organizations must carefully evaluate hardware and software supply chain dependencies,” he noted.
The issue is becoming more urgent as governments and enterprises alike increasingly view AI infrastructure as strategically important. Policymakers globally are pushing both public and private organizations to strengthen sovereign controls over AI systems, governance and infrastructure because of growing geopolitical uncertainty and concerns around long-term technological dependence, according to Claps.
Related Article: AI Governance Isn’t Slowing You Down — It’s How You Win
Sovereignty Is Central to Enterprise AI Strategy
The broader enterprise conversation around AI sovereignty increasingly centers on balancing innovation with operational independence. Organizations still want access to hyperscale cloud platforms and frontier AI capabilities, but many no longer want complete dependency on external providers for mission-critical AI systems and sensitive data environments.
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According to Price, data suggests sovereign AI is evolving into a foundational requirement for enterprise AI adoption rather than a niche regulatory issue. “Organizations are already reshaping their architectures toward hybrid, multi-cloud and distributed models to balance innovation with control."
The long-term goal for many enterprises and governments is not isolation from global technology ecosystems, but greater strategic flexibility and resilience, said Claps. “Their vision is not to lock data away so tightly that no one can innovate, but to find a balance.