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CEO Perception Survey on Sovereign AI Adoption

Purpose: To understand how Chief Executive Officers perceive the value, risks, and strategic direction for adopting Sovereign AI—AI systems governed by national or organizational control over data, infrastructure, and algorithms.

CEO Profile
Section 1: Strategic Awareness and Vision
Section 2: Decision-Making and Investment Readiness
Section 3: Organizational Capability and Partnership
Section 4: Governance and Outlook
Section 6: Sovereign AI Maturity Self-Assessment

Please assess your organization's current maturity level across the following Sovereign AI dimensions. For each sub-dimension, select the level that best describes your organization's current state.

Dimension 1: Infrastructure Sovereignty & Compute

Assess your organization's reliance on domestic vs. foreign infrastructure and compute resources.

Level 1 - Initial/Awareness
At this level, the organization relies almost entirely on foreign cloud providers or offshore data centers with no domestic compute capacity. There is minimal awareness of infrastructure sovereignty risks, and local regulatory or security considerations are not prioritized in technology decisions.
Level 2 - Developing
The organization has begun to consider domestic or regional infrastructure options. Some pilot projects or secondary workloads may run on local providers, but core systems remain on offshore platforms. Basic awareness exists around data residency and latency concerns, but no formal strategy is in place.
Level 3 - Operational
A hybrid approach is implemented with a mix of on-premises, domestic cloud, and international cloud resources. The organization actively evaluates local compute vendors and has policies requiring certain data or applications to remain within national borders. Infrastructure choices reflect both performance and sovereignty considerations.
Level 4 - Optimized
The majority of critical infrastructure runs on sovereign or nationally controlled platforms. The organization maintains strong relationships with domestic providers and contributes to or influences national infrastructure initiatives. Advanced capabilities such as edge computing or sovereign AI hardware accelerators are in use.
Level 5 - Leading
The organization is a recognized leader in infrastructure sovereignty, potentially operating its own data centers or co-investing in national AI infrastructure. It actively shapes policy and standards in this space and serves as a model for balancing global connectivity with local control.

Evaluate your organization's dependence on specific hardware vendors and diversification strategy.

Level 1 - Initial/Awareness
Hardware choices are made purely on cost and availability with no consideration for strategic independence. The organization is locked into a single vendor or architecture (e.g., entirely dependent on NVIDIA GPUs) with no diversification plan.
Level 2 - Developing
Leadership recognizes the risks of hardware lock-in and has begun exploring alternatives. Limited proof-of-concept work may be underway with alternative accelerators or chip architectures, but production workloads remain dependent on a single platform.
Level 3 - Operational
The organization actively diversifies its hardware portfolio, using a mix of GPUs, TPUs, and emerging accelerators. It evaluates chips from multiple vendors and geographies and has contingency plans for supply chain disruptions. Some workloads are optimized for specific hardware to reduce dependency.
Level 4 - Optimized
A well-defined hardware strategy includes partnerships with domestic or allied chipmakers and active participation in open-source hardware initiatives. The organization can adapt AI workloads across different hardware platforms with minimal friction and maintains strategic stockpiles or long-term contracts to ensure access.
Level 5 - Leading
The organization leads industry efforts to develop or procure sovereign AI hardware, potentially investing in domestic semiconductor R&D or co-designing chips. It influences hardware ecosystems and standards and is recognized as a pioneer in reducing reliance on concentrated supply chains.
Dimension 2: Data & Model Sovereignty

Assess your organization's data governance, residency policies, and cross-border data flow controls.

Level 1 - Initial/Awareness
Data governance is minimal, with limited visibility into where data is stored or processed. The organization may unknowingly store sensitive data offshore or rely on third-party processors with unclear jurisdictional status. Compliance is reactive, often triggered by audits or incidents.
Level 2 - Developing
Basic data residency policies are in place, and the organization can identify where sensitive data resides. Some data is intentionally kept within national borders, but enforcement is inconsistent. Data mapping and classification efforts are underway.
Level 3 - Operational
Comprehensive data localization policies are enforced for critical datasets. The organization uses tools to monitor and control cross-border data flows and maintains clear documentation of data processing locations. Privacy-enhancing technologies (e.g., encryption, anonymization) are deployed to mitigate risks.
Level 4 - Optimized
Data sovereignty is embedded in enterprise architecture. Automated controls ensure compliance with data residency requirements, and the organization uses federated learning or secure multi-party computation to collaborate internationally without data transfer. It contributes to national or industry data governance frameworks.
Level 5 - Leading
The organization is a thought leader in data sovereignty, pioneering new approaches to data control and sharing. It influences policy, participates in international standards bodies, and may operate data trusts or shared infrastructure that balance openness with sovereignty.

Evaluate your organization's control over AI models, their provenance, and transparency into model behavior.

Level 1 - Initial/Awareness
The organization relies entirely on black-box proprietary models from foreign vendors with no visibility into model training, data sources, or decision logic. There is no strategy for model ownership or internal AI capability.
Level 2 - Developing
The organization has begun to build or fine-tune some AI models in-house, though most production systems still use external models. There is growing awareness of the importance of model transparency and control, and initial experiments with open-source models are underway.
Level 3 - Operational
A balanced portfolio exists with some models developed internally, others fine-tuned from open-source foundations, and select use of commercial APIs for non-critical tasks. The organization documents model provenance and has policies on when external models are acceptable.
Level 4 - Optimized
Most strategic AI capabilities are built on models the organization owns or controls. There is deep expertise in model training, evaluation, and auditing. The organization contributes to open-source AI projects and has processes to ensure models align with national values and regulations.
Level 5 - Leading
The organization is a recognized leader in sovereign AI model development, potentially publishing research, releasing open models, or influencing AI standards. It has full transparency into model behavior and actively works to reduce dependence on foreign AI platforms.
Dimension 3: Governance, Ethics, & Compliance

Assess how well your organization's AI initiatives align with national AI policies and regulatory frameworks.

Level 1 - Initial/Awareness
The organization is unaware of or does not consider national AI policies, strategies, or regulatory frameworks in its AI initiatives. Compliance is reactive and often incomplete.
Level 2 - Developing
Basic awareness of national AI policies exists, and the organization monitors relevant regulations. Some efforts are made to align AI projects with national priorities, but there is no formal process or accountability.
Level 3 - Operational
The organization has policies and procedures to ensure AI initiatives align with national strategies and regulatory requirements. Regular compliance reviews are conducted, and leadership actively engages with government and industry bodies.
Level 4 - Optimized
AI governance is deeply integrated with national policy frameworks. The organization proactively shapes policy through participation in working groups, consultations, and public-private partnerships. It is recognized as a responsible AI leader domestically.
Level 5 - Leading
The organization is a key architect of national AI policy and standards, often advising government and setting industry benchmarks. It balances innovation with sovereignty and is a model for responsible AI governance globally.

Evaluate how your organization ensures AI systems align with societal values and ethical principles.

Level 1 - Initial/Awareness
Ethics and values are not explicitly considered in AI development. Models may reflect foreign cultural norms or biases, and there is no process to evaluate or mitigate ethical risks.
Level 2 - Developing
The organization has begun to define ethical AI principles and is aware of the need for value alignment. Some ad hoc reviews or bias assessments are conducted, but processes are not standardized.
Level 3 - Operational
Formal ethical AI guidelines are in place and regularly applied. The organization conducts bias audits, impact assessments, and stakeholder consultations to ensure AI systems align with societal values. Training on AI ethics is provided to staff.
Level 4 - Optimized
AI ethics is embedded across the AI lifecycle, from design to deployment. The organization uses diverse teams, participatory design methods, and ongoing monitoring to ensure value alignment. It publicly reports on ethical AI performance and contributes to national or industry ethics frameworks.
Level 5 - Leading
The organization is a global leader in ethical AI, pioneering methods for value alignment and cultural sensitivity. It influences international norms and standards and is recognized for balancing innovation with responsibility.
Dimension 4: Operational & Talent Sovereignty

Assess your organization's investment in developing and retaining domestic AI talent and expertise.

Level 1 - Initial/Awareness
The organization relies heavily on foreign AI expertise, outsourcing development to offshore teams with minimal local capability. There is no strategy to build or retain domestic AI talent.
Level 2 - Developing
Some investment in local AI talent is underway, including hiring, training, or partnerships with universities. However, critical AI roles are still often filled by foreign experts or contractors.
Level 3 - Operational
The organization has built a strong local AI team with diverse expertise. It invests in continuous learning, offers competitive compensation, and partners with educational institutions to develop the next generation of AI talent.
Level 4 - Optimized
The organization is a top employer for AI talent domestically and contributes to national talent development through scholarships, internships, and knowledge-sharing initiatives. It has deep bench strength and can execute complex AI projects entirely with local teams.
Level 5 - Leading
The organization is a nationally recognized AI talent hub, attracting and retaining top researchers and practitioners. It influences AI education policy, publishes cutting-edge research, and exports AI expertise internationally while maintaining strong domestic roots.

Evaluate your organization's AI supply chain resilience and vendor diversification strategy.

Level 1 - Initial/Awareness
AI supply chains are entirely dependent on foreign vendors with no visibility into dependencies or risks. There is no contingency planning for disruptions.
Level 2 - Developing
The organization is beginning to map AI supply chains and identify critical dependencies. Some efforts are made to diversify vendors or establish backup suppliers, but resilience is limited.
Level 3 - Operational
A comprehensive understanding of AI supply chains exists, including hardware, software, and services. The organization actively manages vendor risk, diversifies suppliers, and maintains business continuity plans for critical AI systems.
Level 4 - Optimized
AI supply chains are highly resilient with strong relationships with domestic or allied vendors. The organization participates in industry initiatives to strengthen sovereign supply chains and has redundancy for all critical components.
Level 5 - Leading
The organization leads efforts to build sovereign AI supply chains, potentially investing in domestic vendors or open-source alternatives. It shapes industry standards and is recognized as a model for resilient, independent AI operations.
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