The National AI Index (NAII) has become an important mechanism for understanding the readiness of government entities in Saudi Arabia to adopt artificial intelligence technologies. Its strategic value extends beyond assessment itself. The index helps government organizations identify strengths and capability gaps, monitor progress over time, and translate assessment findings into clearer priorities and institutional improvement initiatives.

According to the Saudi Data and AI Authority (SDAIA), the index is designed to measure the readiness of government entities to adopt AI technologies, track their progress periodically, and provide tailored recommendations that strengthen each entity’s ability to develop and implement effective and sustainable AI solutions. The index also supports the broader objectives of Saudi Vision 2030 and the Kingdom’s efforts to accelerate responsible AI adoption across priority government sectors.

SDAIA’s official publications indicate that the framework consists of three main dimensions, seven sub-dimensions, and 23 measurement areas, providing a comprehensive view of government AI readiness.

Executive takeaway: NAII should not be treated simply as an assessment designed to produce a score. Its greater strategic value lies in helping government leaders understand their current position, identify readiness gaps, and develop a structured institutional roadmap for AI adoption and capability development.

What Is the National AI Index?

The National AI Index (NAII) is a national framework for assessing the readiness of Saudi government entities to adopt artificial intelligence technologies, tracking their development, identifying strengths and improvement areas, and supporting the development of institutional capabilities that enable effective and sustainable AI adoption.

It is important to distinguish between measuring readiness and achieving institutional maturity.

A government organization may already have AI applications, technical infrastructure, or pilot projects in place while still needing to strengthen its strategy, governance, data capabilities, workforce, risk management, or impact measurement.

For this reason, the assessment should be viewed as a starting point for understanding the organization’s institutional position—not as the final destination of its AI journey.

Why Is NAII Strategically Important for Government Entities?

As AI adoption expands, government leaders face questions that go well beyond selecting the right technology. They need to determine whether the organization actually possesses the capabilities required to adopt AI responsibly, effectively, and at scale.

The strategic value of NAII can be seen at several levels.

Understanding the Current State

Assessment enables government organizations to move beyond broad assumptions about digital or AI readiness and develop a more structured understanding of their capabilities.

Identifying Institutional Gaps

Assessment findings can highlight areas that require improvement across strategy, governance, data, technology, workforce capabilities, and other institutional dimensions.

Supporting Investment Priorities

Once capability gaps are clearly understood, investment decisions can be based on actual institutional needs rather than on acquiring new technologies without addressing foundational weaknesses.

Enabling Continuous Improvement

SDAIA positions the index as a mechanism for periodically monitoring progress, making measurement part of an ongoing institutional improvement cycle rather than a one-time exercise.

NAII can therefore be viewed as a link between assessment, improvement, readiness, and implementation.

What Does the National AI Index Measure?

According to SDAIA, the NAII framework includes three main dimensions, seven sub-dimensions, and 23 measurement areas.

This structure is important because it reflects the fact that AI readiness is a multidimensional institutional capability, rather than simply a technical capability.

From an executive perspective, these measurement areas should not be viewed as isolated requirements. Instead, leaders should consider how the different capabilities work together to enable sustainable AI adoption.

The central question is therefore:

Does the organization have the institutional ecosystem required to move from AI experimentation to sustainable, scalable, and measurable adoption?

AI Readiness vs. AI Maturity

A government entity may have advanced technology infrastructure and several AI initiatives while still operating at an early stage of institutional maturity.

This is because readiness depends on multiple capabilities working together:

• Strategy: Does the organization understand where and why AI should be used?
• Governance: Are responsibilities, controls, and decision-making mechanisms clearly defined?
• Data: Is the underlying data reliable, accessible, and governed?
• Technology: Can the infrastructure support current and future AI use cases?
• Workforce: Does the organization have the skills and capabilities required?
• Risk Management: Can AI-related risks be identified, assessed, and monitored?
• Measurement: Can the organization demonstrate the value generated by AI initiatives?

As a result, having more AI projects does not automatically mean having greater AI readiness or maturity.

True AI readiness is the organization’s ability to transform AI from isolated initiatives into a governed, scalable, measurable institutional capability.

How Should a Government Entity Prepare for an AI Readiness Assessment?

Preparation should not begin shortly before an assessment cycle. It should be embedded within a continuous institutional capability-building program.

1. Establish the Current State

Develop a comprehensive view of the organization’s current AI landscape, including existing initiatives, use cases, systems, data assets, workforce capabilities, organizational structures, and governance mechanisms.

2. Document Capabilities and Evidence

It is not enough for a capability to exist in practice. It must also be clearly documented and supported by evidence that can be evaluated against the relevant index and governance requirements.

This creates an important connection between institutional capability and supporting evidence.

3. Identify Gaps

Once the current state has been established, the organization should identify the gap between existing capabilities and the desired state.

This allows leadership to prioritize the most important gaps rather than treating every issue as equally significant.

4. Build an Improvement Plan

Gaps should be converted into defined initiatives with clear ownership, priorities, dependencies, timelines, and performance measures.

5. Track Progress

Readiness does not end when the assessment is complete. Findings should become part of a continuous cycle of measurement, review, and institutional improvement.

What Gaps Can an AI Readiness Assessment Reveal?

One of the most common misconceptions is that AI readiness gaps are primarily technical.

In reality, an assessment may reveal weaknesses across several institutional dimensions.

Strategy: AI initiatives exist without clear alignment to institutional priorities.

Governance: Decision rights, ownership, and accountability are unclear.

Data: Data quality, ownership, access, or governance practices are insufficient.

Enterprise Architecture: Systems and platforms operate in fragmented environments with limited integration.

Workforce: The organization lacks some of the skills needed to develop, operate, govern, or oversee AI.

Risk Management: There is no consistent approach for assessing AI-related risks.

Impact Measurement: AI solutions have been launched without clear indicators for evaluating their results.

These institutional gaps can be more significant than the number of AI projects currently underway.

How Can NAII Results Be Turned into an Institutional Roadmap?

The strategic value of the index begins after the assessment results are available.

Rather than treating the findings as a final report, organizations can convert them into an institutional AI roadmap through four core steps.

Establish Priorities

Not every capability gap requires the same level of investment. Gaps should be prioritized according to their strategic importance, institutional impact, and relationship to AI readiness.

Connect Gaps to Initiatives

Each major gap should be linked to a clear improvement initiative rather than remaining only as a finding in an assessment report.

Assign Accountability

Clear ownership should be established for every priority improvement area, supported by appropriate governance and decision-making mechanisms.

Measure Improvement

Performance indicators should be established to determine whether improvement initiatives are actually strengthening AI readiness and institutional capability.

This transforms NAII from a measurement mechanism into an input for a broader institutional improvement program.

The Relationship Between NAII and AI Governance

Sustainable AI readiness cannot be achieved without effective governance.

SDAIA emphasizes responsible and ethical AI adoption and provides principles and guidance for the ethical use of AI across the lifecycle of AI systems.

Government organizations should therefore view AI governance as a core component of readiness rather than as a separate compliance activity.

Depending on the nature of the use case, governance may include:

• Clear roles and decision rights
• AI risk assessment
• Data protection and privacy
• Model performance monitoring
• Change and decision documentation
• Incident management
• Human oversight
• Regulatory compliance

Effective governance helps government entities scale AI without compromising trust, accountability, or institutional control.

The Role of Data in Strengthening AI Readiness

AI readiness is closely tied to data maturity.

Poor-quality, fragmented, or weakly governed data can significantly limit the effectiveness of AI models, regardless of how advanced the underlying technology may be.

This creates a direct connection between the NAII journey and Data Governance, including data ownership, quality, classification, security, lifecycle management, and data-sharing practices.

SDAIA also evaluates data-management maturity across government entities through the National Data Index “NDE’” (NDA / NDE’ depending on the official English rendering), which supports the assessment and monitoring of government entities’ data-management practices and compliance with national data-management controls and specifications.

The broader relationship can therefore be understood as:

Data Maturity → AI Readiness → AI Adoption → AI Impact

The stronger an organization’s data foundation, the greater its ability to build trustworthy and scalable AI capabilities.

NAII Within Saudi Arabia’s National Data and AI Ecosystem

NAII does not operate in isolation from the Kingdom’s broader data and AI ecosystem.

SDAIA’s National Strategy for Data & AI (NSDAI) is designed to support national priorities, develop specialized capabilities, strengthen the national data and AI ecosystem, and accelerate practical adoption. The government sector is identified as a priority area for using data and AI to build a smarter, more effective, and more productive public sector.

The National Center for Artificial Intelligence (NCAI) also contributes by developing AI solutions and strengthening national capabilities that support decision-making and improve government efficiency.

NAII should therefore be understood within a broader institutional and national progression:

National Strategy → Readiness → Governance → Data → Infrastructure → Implementation → Impact Measurement

This perspective allows government leaders to see the index as part of a wider national and institutional transformation journey.

From Assessment to Execution: A Practical Framework for Government Entities

Government leadership can structure the journey into six stages:

Stage 1 — Diagnosis
Understand the current state and identify the organization’s readiness level and capability gaps.

Stage 2 — Assessment
Interpret the findings of the NAII assessment and determine the areas requiring institutional development.

Stage 3 — Prioritization
Rank gaps according to strategic impact, importance, dependencies, and resource requirements.

Stage 4 — Capability Building
Strengthen strategy, governance, data, infrastructure, workforce capabilities, and operating models.

Stage 5 — Implementation
Translate priorities into initiatives and programs linked directly to institutional outcomes.

Stage 6 — Measurement and Improvement
Monitor progress, reassess readiness, and continuously update the roadmap.

This approach ensures that assessment becomes part of a continuous institutional management cycle rather than an isolated exercise.

How EVC Approaches Government AI Readiness

Expert Vision Consulting (EVC) views AI readiness as an institutional challenge that extends beyond technology.

The approach begins by understanding the organization’s strategic context and current maturity level, followed by identifying capability gaps and linking them to a practical transformation roadmap.

Depending on the organization’s needs, the methodology may include:

• AI readiness and institutional maturity assessment
• Gap analysis
• Strategic alignment of AI initiatives
• AI governance framework development
• Integration between AI Governance and Data Governance
• Alignment with Enterprise Architecture
• AI risk identification and control design
• Initiative prioritization and portfolio management
• Performance and improvement measurement
• Long-term capability and institutional sustainability

The objective is to move from preparing for an assessment toward developing a sustainable institutional capability that can continue evolving as AI technologies, use cases, and regulatory requirements change.

Executive AI Readiness Checklist

Before responding to NAII findings or preparing for a new assessment cycle, government leaders can consider the following questions:

• Does the organization have a clearly defined AI strategy?
• Are AI use cases aligned with institutional priorities?
• Are responsibilities and decision rights clearly defined?
• Is there a structured AI governance framework?
• Is the underlying data reliable and governed?
• Can the technology environment support current and future AI use cases?
• Does the organization have the required workforce capabilities?
• Is there a consistent AI risk-management approach?
• Can the organization document and demonstrate relevant capabilities?
• Are assessment findings converted into improvement initiatives?
• Are the outcomes of those initiatives measured after implementation?

Where several of these questions remain unanswered, the priority may be to strengthen institutional readiness before expanding the scope of AI initiatives.

Conclusion

The National AI Index (NAII) is an important mechanism for Saudi government entities seeking to understand their readiness to adopt artificial intelligence, track progress, and identify areas for institutional improvement through a structured national assessment framework.

However, the strategic value of NAII does not lie in the assessment result alone. Its real value emerges when organizations use the findings to understand their gaps, establish priorities, strengthen governance, improve data and technology capabilities, develop workforce capacity, and translate these priorities into an actionable roadmap.

As Saudi Arabia continues to develop its national data and AI ecosystem, building sustainable institutional capability becomes increasingly important. The National Strategy for Data and AI reinforces this direction by emphasizing capabilities, ecosystem development, practical adoption, and responsible use of data and AI.

The most useful way for government leaders to view NAII is therefore not to ask, “What is our score?” but to ask a more strategic question: “What does our assessment tell us about our organization’s ability to use AI responsibly, effectively, and at scale?”

That question provides a practical starting point for assessing the current state, identifying priority gaps, and developing a roadmap that connects readiness with governance, execution, and measurable impact.