1. Introduction: The challenge of measuring national AI progress

Artificial intelligence is becoming an increasingly important component of national development strategies. Governments are investing in research and innovation, expanding digital infrastructure, developing AI policies, strengthening workforce capabilities, and participating in international AI initiatives.

However, announcing a national AI strategy or increasing investment in AI does not automatically demonstrate that a country’s AI ecosystem is developing effectively. Nor does it establish that AI is generating inclusive economic growth, improving public services, or strengthening institutional accountability.

For policymakers and Monitoring and Evaluation (M&E) professionals, this creates an important measurement challenge:

How can a country systematically monitor its AI ecosystem, identify institutional and policy gaps, and assess whether its AI-related policies are progressing toward their intended objectives?

The OECD.AI Index provides a useful starting point. It brings together AI-specific indicators and metrics to help policymakers assess national AI ecosystems, identify relative strengths and areas for improvement, and monitor progress in implementing the OECD Recommendation on AI.

This case study examines the Index through an M&E lens. It explains its structure, explores how its dimensions can inform national monitoring frameworks, and presents an illustrative approach that practitioners can adapt to different country contexts.

Case study at a glance

Scope: This is a country-neutral analytical learning case study. It does not present an assessment of a particular country’s performance.

2. Understanding the OECD.AI Index

The OECD.AI Index was developed in response to the growing demand for robust and internationally comparable metrics to inform AI policy decisions. It combines existing AI-specific indicators from the OECD.AI Policy Observatory with additional metrics to provide a structured view of national AI ecosystems.

Its five components are aligned with five policy recommendations in the OECD AI Principles. Each component is divided into sub-components, which capture measurable aspects of the relevant policy recommendation. Sub-components may contain one or more indicators.

The Index combines quantitative and qualitative metrics into a composite indicator. It is intended to support systematic comparisons and help policymakers identify relative strengths and areas for improvement in implementing the OECD AI Recommendation.

The OECD AI Principles were initially adopted in 2019 and updated in May 2024 to reflect technological and policy developments. The Index is anchored in these principles and is updated as new data become available.

Explore the official framework:
OECD.AI Index – Overview |
Index Components |
Country Profiles

3. Interpreting the five dimensions through an M&E lens

The OECD.AI Index provides a structure for examining national AI ecosystems. For M&E practitioners, its value lies in connecting these dimensions to questions about policy implementation, institutional capacity, measurable progress, and results.

Important: The indicators above are examples developed for this EvalCommunity learning exercise. They are not presented as the official OECD.AI Index indicators. Practitioners should consult the OECD component definitions and technical documentation before selecting or comparing indicators.

4. Translating AI policy into an M&E results framework

One of the most important contributions M&E professionals can make is to connect national AI policy objectives with a coherent results framework.

A national AI strategy may include investments in research, infrastructure, education, public-sector innovation, and international partnerships. However, monitoring the number of activities completed is not enough. Evaluation must also examine whether those activities contribute to meaningful changes.

Example: From an AI skills policy to measurable outcomes

Consider a hypothetical national initiative designed to strengthen AI capabilities among public-sector professionals.

This example illustrates why monitoring should go beyond counting training participants. An evaluation should investigate whether knowledge and skills are applied in professional practice and whether institutional conditions support responsible AI use.

5. Developing indicators for a national AI ecosystem

Indicators are useful only when they are clearly defined, measurable, relevant to the intended result, and supported by credible data. The same indicator can also produce misleading conclusions if its denominator, reporting period, or population is unclear.

An illustrative indicator specification

This example highlights an essential M&E principle: an indicator should be accompanied by a definition, calculation method, data source, reporting frequency, and limitations. Without these elements, comparisons across institutions or reporting periods can become unreliable.

6. Data sources, quality, and comparability

Monitoring a national AI ecosystem requires evidence from multiple sources. Depending on the indicator, useful sources may include administrative records, research databases, institutional surveys, policy documents, labour-market information, investment records, and international datasets.

Six dimensions of data quality to examine

1. Validity: Does the indicator measure the concept it is intended to measure?
2. Reliability: Can the same measurement process produce consistent results?
3. Completeness: Are important institutions, sectors, and population groups represented?
4. Timeliness: Is the information sufficiently current for policy decisions?
5. Comparability: Are definitions and methods consistent across time and contexts?
6. Transparency: Are sources, methods, assumptions, and limitations documented?

These considerations are especially important when using international benchmarks. Differences in statistical capacity, economic structure, institutional arrangements, and data coverage can affect how results should be interpreted.

7. Interpreting benchmarks and identifying gaps

Comparative indicators can help policymakers identify areas requiring attention. However, comparison should be treated as a starting point for analysis, not as a substitute for evaluation.

For example, a country may demonstrate substantial AI research activity but have limited capacity to translate research into public-sector applications. Another may have a strong policy environment but insufficient AI-related skills in its workforce.

These situations illustrate why a national AI ecosystem should be examined across all five dimensions rather than through a single indicator.

Questions M&E professionals should ask when interpreting results

• What explains the observed differences between countries or institutions?
• Are the underlying data sources sufficiently comparable?
• Does a high indicator value reflect actual implementation or simply the existence of a policy or programme?
• Are important groups, regions, or sectors missing from the data?
• What contextual factors may influence the results?
• What additional qualitative evidence is needed to explain the findings?

Year-to-year changes also require caution. Indicators and underlying data may change over time, so changes in Index results should not automatically be interpreted as changes in actual national performance.

8. From measurement to policy decisions

The purpose of an M&E framework is not simply to collect data or produce reports. It is to generate credible evidence that can inform decisions, improve implementation, and strengthen accountability.

Findings from a national AI ecosystem assessment could inform several types of action:

For M&E professionals, this means presenting findings in a way that connects evidence to decisions. Reports should identify not only where gaps exist, but also why they may exist, who is affected, what actions are feasible, and how progress will be monitored.

9. Limitations and critical considerations

The OECD.AI Index is a valuable measurement resource, but it should not be used as a substitute for a complete national AI evaluation. M&E professionals should recognize several limitations and analytical considerations.

1. Measuring policy implementation is not the same as measuring social impact

Evidence of AI policy development, research activity, or infrastructure investment does not automatically demonstrate improvements in public services, productivity, equality, or well-being. Additional outcome and impact evaluations are needed to examine these questions.

2. National averages can hide inequalities

National indicators may conceal differences between regions, population groups, institutions, sectors, and types of organizations. Where data permit, disaggregation should be considered to identify unequal access and distributional effects.

3. Data availability affects comparisons

Countries may differ in statistical systems, reporting practices, data coverage, and institutional capacity. These differences can affect the interpretation of cross-country results.

4. Indicators cannot replace qualitative analysis

Quantitative measures can reveal patterns, but interviews, institutional assessments, document reviews, and stakeholder consultations may be necessary to understand implementation barriers and explain why results differ.

5. AI ecosystems evolve rapidly

Technologies, labour-market demands, governance practices, and policy priorities change over time. Monitoring frameworks therefore need periodic review to ensure that indicators remain relevant and meaningful.

6. Responsible AI requires more than adoption metrics

AI ecosystem monitoring should consider human rights, fairness, privacy, transparency, safety, security, and accountability. Growth in AI activity should not be treated as sufficient evidence of responsible development or use.

EvalCommunity Practical Exercise

10. Build a national AI ecosystem monitoring framework

This exercise is designed for M&E practitioners, government evaluation units, development professionals, and AI policy researchers. It uses the five OECD.AI Index components as a starting structure for developing an adaptable monitoring framework.

Step 1. Define the monitoring purpose

Identify what the proposed framework should help decision-makers understand. For example, the objective may be to monitor progress in developing a responsible, inclusive, and sustainable national AI ecosystem.

Step 2. Organize the assessment around five dimensions

Use research and development, enabling infrastructure, policy environment, jobs and skills, and international cooperation as the initial analytical structure.

Step 3. Identify priority questions and indicators

For each dimension, define what progress means in the relevant context. Select indicators that are measurable, meaningful, and supported by credible evidence.

Step 4. Map data sources and responsibilities

Identify which institutions hold the required information, how frequently it can be collected, who will validate it, and how data quality will be assessed.

Step 5. Establish baselines and targets

Where reliable baseline data exist, establish a reference point. Set realistic targets with clear definitions, timeframes, and responsible institutions. Avoid setting targets before assessing data availability and feasibility.

Step 6. Analyze, report, and use the findings

Combine quantitative and qualitative evidence, identify gaps, explain contextual factors, and develop recommendations. Establish a process for reviewing progress and using findings to improve policy implementation.

11. Key lessons for Monitoring and Evaluation professionals

12. Conclusion: Measuring AI progress is only the beginning

The OECD.AI Index provides policymakers and researchers with a structured approach to examining national AI ecosystems across research and development, enabling infrastructure, policy environment, jobs and skills, and international cooperation.

For Monitoring and Evaluation professionals, its broader value is the opportunity to connect AI policy commitments with measurable evidence, institutional performance, and policy learning.

However, measuring ecosystem development is not the same as demonstrating that AI has improved people’s lives. That requires additional evaluation of outcomes, impacts, distributional effects, and the conditions under which AI initiatives deliver public value.

As AI becomes more integrated into public policy and development programmes, M&E professionals will have an increasingly important role in ensuring that progress is measured rigorously, interpreted responsibly, and translated into better decisions.

The objective is not simply to measure how much AI a country develops or adopts. It is to understand whether its AI ecosystem is becoming more capable, responsible, inclusive, and aligned with meaningful public value.

Original source and further reading

This case study is an independent EvalCommunity interpretation of the OECD.AI Index. The five components and their sub-components are based on the OECD’s published framework. The M&E questions, illustrative indicators, results chain, and practical exercise are proposed by EvalCommunity for learning and adaptation; they are not official OECD indicators or recommendations.

• OECD.AI Index – Overview
• OECD.AI Index – Components
• OECD.AI Index – Country Profiles
• OECD AI Principles

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