metadata: authors: 0: author: Mandon,Pierre Jean-Claude
count: World
docna: 0: docna: Beyond the AI Divide : A Simple Approach to
            Identifying Global and Local Overperformers in AI Preparedness
docty: Policy Research Working Paper
abstracts: cdata!: This paper examines global disparities
            in artificial intelligence preparedness, using the 2023
            Artificial Intelligence Preparedness Index developed by the
            International Monetary Fund alongside the multidimensional
            Economic Complexity Index. The proposed methodology
            identifies both global and local overperformers by comparing
            actual artificial intelligence readiness scores to
            predictions based on economic complexity, offering a
            comprehensive assessment of national artificial intelligence
            capabilities. The findings highlight the varying
            significance of regulation and ethics frameworks, digital
            infrastructure, as well as human capital and labor market
            development in driving artificial intelligence
            overperformance across different income levels. Through case
            studies, including Singapore, Northern Europe, Malaysia,
            Kazakhstan, Ghana, Rwanda, and emerging demographic giants
            like China and India, the analysis illustrates how even
            resource-constrained nations can achieve substantial
            artificial intelligence advancements through strategic
            investments and coherent policies. The study underscores the
            need for offering actionable insights to foster peer
            learning and knowledge-sharing among countries. It concludes
            with recommendations for improving artificial intelligence
            preparedness metrics and calls for future research to
            incorporate cognitive and cultural dimensions into readiness frameworks.
display_title: Beyond the AI Divide : A Simple
            Approach to Identifying Global and Local Overperformers in
            AI Preparedness
listing_relative_url: /research/2025/02/34459301/beyond-ai-divide-simple-approach-identifying-global-local-overperformers-ai-preparedness
topicv3: ICT Innovation and Transformation m1326189 2399,ICT Innovation Policy m1326393 2350,Digital Economy Strategy m1327584 2345,Innovation, Technology and
            Entrepreneurship m1327691 376
publishtoextweb_dt: 2025-02-24T00:00:00Z
dois: 10.1596/1813-9450-11073
origu: Off of Sr VP Dev Econ/Chief Econ (DECVP)
no_of_pages: 31
author_keys: 0: author_key: 1492862
model_extractions: Policy Research Working Paper 11073

# Beyond the AI Divide

A Simple Approach to Identifying Global and Local Overperformers in AI Preparedness _Pierre Mandon_ Economic Policy Global Department February 2025
Policy Research Working Paper 11073

# **Abstract**

This paper examines global disparities in artificial intelligence preparedness, using the 2023 Artificial Intelligence Preparedness Index developed by the International Monetary Fund alongside the multidimensional Economic Complexity Index. The proposed methodology identifies both global and local overperformers by comparing actual artificial intelligence readiness scores to predictions based on economic complexity, offering a comprehensive assessment of national artificial intelligence capabilities. The findings highlight the varying significance of regulation and ethics frameworks, digital infrastructure, as well as human capital and labor market development in driving artificial intelligence overperformance across different income levels. Through case studies, including Singapore, Northern Europe, Malaysia, Kazakhstan, Ghana, Rwanda, and emerging demographic giants like China and India, the analysis illustrates how even resource-constrained nations can achieve substantial artificial intelligence advancements through strategic investments and coherent policies. The study underscores the need for offering actionable insights to foster peer learning and knowledge-sharing among countries. It concludes with recommendations for improving artificial intelligence preparedness metrics and calls for future research to incorporate cognitive and cultural dimensions into readiness frameworks.

This paper is a product of the Economic Policy Global Department. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The author may be contacted at pmandon@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
Beyond the AI Divide: A Simple Approach to Identifying Global and Local Overperformers in AI Preparedness Pierre Mandon (World Bank Group)<sup>1</sup> Keywords: AI Preparedness; Economic Complexity; Peer Learning; Policy Overperformance. JEL codes: F63; H11; O33; O38.

> 1 <u>pmandon@worldbank.org. The replication material is available here.</u>
# I- Introduction

global competitiveness (Brynjolfsson and Unger, 2023), making its adoption a critical priority for nations worldwide.<sup>2</sup> The most prominent accounting and consulting firms claim that AI is projected to contribute significantly to the global economy, with estimates ranging from US$2.6 trillion to US$4.4 trillion annually (Chui et al., 2023), to US$15.7 trillion by 2030, driven by productivity and consumption gains (PwC, 2017). Some recent NBER publications<sup>3</sup> highlight that AI has the potential to significantly boost productivity and economic growth by automating tasks and driving innovation. However, the realization of these gains depends on complementary policies, institutions, and societal adaptation to ensure equitable and sustainable outcomes (Aghion, Jones, and Jones, 2019; Trammell and Korinek, 2023; Acemoglu, 2024). To summarize, AI, and particularly generative AI, has the potential to boost economic growth and productivity but poses risks of labor market disruptions, including premature de-professionalization—where skilled jobs are replaced or diminished before workers can fully capitalize on their expertise—and challenges for developing economies, such as reduced opportunities for high-skill job creation, increased inequality, and difficulties in integrating AI technologies into less-developed industrial and educational systems (Liu, 2024).

Such substantial socio-economic impacts underscore the critical importance for countries to adopt and integrate AI technologies to remain competitive and keep a sovereign public policy.<sup>4</sup> However, the benefits of AI are not uniformly distributed. The 2023 Government AI Preparedness Index (AIPI) from the International Monetary Fund (IMF) reveals significant disparities in AI preparedness and readiness among nations, highlighting a growing divide between those equipped to leverage AI and those at risk of falling behind (Cazzaniga et al., 2024).<sup>5</sup> Related to this, Liu and Wang (2024) highlight that middle-income countries exhibit unexpectedly high engagement with generative AI tools relative to their economic scale, while low-income economies are notably underrepresented. In this context, it becomes imperative to identify effective strategies for enhancing a country's AI readiness. This paper aims to explore such strategies, providing a framework for nations to efficiently bolster their AI capabilities and ensure inclusive growth in the evolving digital landscape.

The methodology outlined in this paper is straightforward: it evaluates AI preparedness at the country level using the AIPI and compares it to each country's assessed level of economic complexity. This approach identifies countries that outperform in AI preparedness and readiness relative to their

> 2 The UN Secretary-General’s High-level Advisory Body on Artificial Intelligence (HLAB-AI) has published the final report “ _Governing AI for Humanity_ ,” advocating for the establishment of a globally inclusive and distributed AI institutional framework rooted in international cooperation. The report emphasizes the need for collaboration among governments and stakeholders to ensure AI promotes development while safeguarding human rights (UN HLAB-AI, 2024).

> 3 National Bureau of Economic Research (NBER) publications, including working papers, are highly regarded as they are produced by leading economists, often serve as precursors to top peer-reviewed journal articles, and are widely cited in academic and policy discussions.

> 4 “The late 2022 launch of GPT-3.5, OpenAI’s generative Artificial Intelligence (AI) chatbot, sparked a media frenzy about the onrushing evolution of AI and what it portends for the human race and the planet at large. During the recently concluded Global Digital Summit, World Bank President Ajay Banga observed that ‘even people who are relatively naive about AI are rushing toward AI, because of the fear of being left behind.’” [https://accountability.worldbank.org/en/news/2024/Developing-AI-for-development].

> 5 See https://www.imf.org/external/datamapper/datasets/AIPI.

2
economic complexity for a given income level, aiming to uncover the factors driving their overperformance. Economic complexity refers to the knowledge intensity of an economy, captured by the diversity and sophistication of the goods and services it produces. The Economic Complexity Index (ECI), initially developed by Hidalgo and Hausmann (2009), measures this complexity by analyzing a country's export mix, reflecting its capabilities to produce and export diverse, high-value products. More recently, the ECI expanded to technology, based on patents data, and research, based on research papers (Stojkoski, Koch, and Hidalgo, 2023), since trade data can miss key information about innovative activities.

Using this methodology, we categorize distinct groups of AI overperformers based on their Economic Complexity Index (ECI) and their preparedness and readiness levels. Notably, overperformers are not simply the countries with the highest AIPI scores but those that (i) meet a minimum AIPI threshold and (ii) outperform their predicted scores given their economic complexity. Among high-income economies, global overperformers include Australia, Japan, the Netherlands, New Zealand, three of the four Asian Tigers (Hong Kong SAR, China; Singapore; and the Republic of Korea), and Northern European countries such as Denmark, Finland, and Norway—aligning with findings on the long-term determinants of AIPI detailed later. Upper-middle-income local overperformers comprise Albania, China, Costa Rica, Indonesia, Kazakhstan, Malaysia, and Ukraine. Finally, lower-middle- and lowincome local overperformers include Ghana, India, Morocco, Rwanda, Sri Lanka, Tunisia, and Viet Nam.

From this list of AI overperformers, we observe that AI regulation and ethics consistently emerge as top drivers of AI preparedness across income groups, emphasizing the universal importance of institutions in managing AI integration. In low- and lower-middle-income countries (LICs/LMICs), regulation and workforce development dominate, reflecting foundational priorities amid resource constraints, while digital infrastructure and innovation receive less emphasis. For upper-middleincome countries (UMICs), regulation and human capital remain critical, but digital infrastructure becomes increasingly significant as technological capacities expand. High-income countries (HICs) prioritize regulation alongside well-established digital infrastructure and innovation systems, supported by advanced workforce readiness strategies.

This progression illustrates how AI preparedness strategies align with the unique capacities and priorities of each income group, offering tailored policy pathways for countries aiming to enhance their AI readiness. By understanding these patterns, policy makers can adopt fitted strategies to develop their countries’ AI ecosystems based on their specific economic and institutional contexts and might effectively bridge the gap in the global AI divide.

The rest of the paper is organized as follows: Section II outlines the data and methodology, Section III presents the main empirical findings, Section IV examines how different categories of countries overperform in AI preparedness, and Section V concludes and opens avenue for future research on AI preparedness.

3
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<u>Source: IMF, AIPI data mapper [https://www.imf.org/external/datamapper/AI_PI@AIPI/ADVEC/EME/LIC]. Notes: The map is checked and</u> validated by the World Bank Cartography Unit as of February 02, 2025.

A methodological decision in this study is the choice of the recent AIPI from the IMF over the AI Readiness Index developed by the private UK-based consulting firm Oxford Insights.<sup>9</sup> This decision is primarily driven by the IMF’s reputation as a globally recognized multilateral institution and supposed to be accountable, which ensures greater scrutiny and transparency in the development of its products. In contrast, the outputs of private entities like Oxford Insights, though valuable, do not operate within the same level of oversight or accountability. Furthermore, the AI Readiness Index is explicitly designed to assess the extent to which governments are prepared to deploy AI in public service delivery.<sup>10</sup> In contrast, the AIPI aims to focus on a more comprehensive evaluation of how countries can harness AI capabilities effectively. That said, the choice of index does not significantly impact the broader contribution of this study, as the proposed methodology remains adaptable and replicable.

Employing Bayesian Model Averaging (BMA), we analyze a comprehensive set of 67 historical, political, and economic indicators from growth determinants literature to identify robust predictors of national AIPI. The BMA framework,<sup>11</sup> as implemented here, enables us to isolate the most robust determinants of AI preparedness from a wide range of potential predictors, as defined and built by Sala-i-Martin, Doppelhofer, and Miller (2004).<sup>12</sup> Figure 2 illustrates the inclusion probabilities of various covariates in the top 5,000 models. The results highlight that initial conditions—such as life expectancy, higher education levels, and economic size in 1960—along with cultural and geographic influences, including Confucian heritage and an East Asian regional dummy, are positively correlated with AIPI. Conversely, the indicator political instability, captured by the frequency of revolutions and military coups, shows a negative association with AIPI.<sup>13</sup>

**Figure 2** : Model Inclusion in BMA on AIPI [hyper- / prior; random model prior] gg nn

> 9 See https://staging2.oxfordinsights.com/ai-readiness/. 10 Ibid.

> 11 BMA addresses the challenge of model uncertainty by allowing for the evaluation of multiple competing models rather than relying on a single model specification. In the context of this study, BMA provides a systematic way to assess the relative importance of various predictors of AI preparedness and readiness, through the AIPI, by calculating each variable’s Posterior Inclusion Probability (PIP). 12 Their database is available on the website of the American Economic Association (AEA): <u>https://www.aeaweb.org/articles?id=10.1257/0002828042002570.</u>

> 13 Furthermore, historical trade openness emerges as a significant driver of AI adoption. However, this correlation should be interpreted cautiously, as one of the AIPI pillars (pillar iii: innovation and economic integration) directly captures aspects of trade openness and liberalization. The exact definitions of variables and their sources are available in (Sala-i-Martin, Doppelhofer, and Miller, 2004, p. 820-821).

5
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where A A ho indicates the relevant average and median AIPI score for country for relevant income categories. ii,tthe oo mm

# III-

# _III.aGlobal overperformers_

The analysis of the two charts below, one unweighted and the other weighted by population size, reveals important insights about the relationship between the AIPI and the ECI trade, research composite obtained from 125 matching countries at a global level (Figure 3).<sup>18</sup> A strong relationship emerges between AIPI and ECI, with values of 0.79 (unweighted) and 0.70 (weighted), indicating that approximately three-quarters of the variance in AIPI is explained by the variance in ECI, and vice RR<sup>2</sup> versa. The partial correlations ( ) of 0.89 (unweighted) and 0.84 (weighted) further indicate a very high positive linear correlation, and it is statically significant at 0.1 percent. These results highlight the oo strong (and expected) link between economic complexity and AI preparedness, demonstrating that greater economic sophistication—reflected in the diversity and complexity of exports and research output—significantly enhances a country's capacity to adopt and integrate AI technologies effectively, irrespective of population weighting.

**Figure 3:** Global relationship between AIPI and ECI

<!-- Start of picture text --> a) Unweighted analysis b) Population-weighted analysis<br>.6.5.4.3.8.7 r = 0.89N = 125 R2 = 0.79 mli yem bfatgosdnginlbypngtjk azemngcoddzacivmdgirqmmr cogmozcmruzbngabwalaokazbolvengabecubgdhndmarpakzwe sen benkhmlkanamrwatzadomzmbomngha vnmeth arm ugamdaperalbtun blrbihkwtukr egy mkdbgrqatjor ind idnkencri arekgzurygtmpirnrchny russaujam phl mys pan ltusrbcoltharousvkgeolbnchlhrvgrcarg za f prt po svnturl b r a twnkormexauscze hkg nzl sgp hun nor esp dnkjpn itafin fra israutbelcani r l nld swedeu gbrcheusa .3.4.5.6.7.8 r = 0.84N = 125 R2 = 0.70 ml yemi bfatgosdnginlbypngtjk azemngdzacodcivmdgirqmmr cogmozcmruzbbwalaongakazbolvengabecubgdhndmarzwepak sen benkhmnamlkarwadomtzaomnzmbgha vnm arm ethmdaugaperalbtun blrbihkwtukrmkdbgrqat egy jor idnicri nd kenarekgzurypgtmrirny saujamchnrus pan mys ph l ltusrbcoltha svk rougeolbnchlhrvgrcarg zaf prt po svnturl b rtwnkor a mexauscze hkg nzl sgp hun nor esp dnk jpnfinitafisr ra autibelcan r l nld swedeu gbrche usa<br>.2 .2<br>afg afg<br>.1 .1<br>0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1<br>Economic Complexity Index [Trade-Research PCA] (OEC, 2021-22) Economic Complexity Index [Trade-Research PCA] (OEC, 2021-22)<br>AIPI≥0.7 AIPI≥0.7<br>Other Countries Other Countries<br>Overperformers Overperformers<br>Linear Fit Line Linear Fit Line [weighted pop. size]<br>AI Prepardness Index (IMF, 2023) AI Prepardness Index (IMF, 2023)<br><!-- End of picture text --> <u>Source: The AIPI score for 2023 is obtained from the IMF website [https://www.imf.org/external/datamapper/datasets/AIPI] and the ECI on</u> trade and research for 2021-22 are obtained from the OEC website [https://oec.world/en/rankings/eci/hs6/hs96?tab=ranking]. Notes: The first principal component (PCA) of ECI trade and ECI research, with an eigenvalue exceeding 50 percent, is used to capture the multidimensional aspects of national economic complexity. Population size for 2023, used for the weighted analysis, is sourced from the “wbopendata” module in Stata (Azevedo, 2020), with data for Taiwan Province of China obtained from the Statista website. All countries with an observed AIPI score exceeding 0.7 (the weighted average and median score for HICs) are highlighted in the grey quadrant. Among these, only countries with AIPI scores surpassing their predicted values (above the linear fit line) are marked in yellow and identified as global overperformers.

Both charts highlight the top performers in AI preparedness, concentrated in the northeast quadrants. Consistent with previous BMA findings and the critical role of initial conditions in AI readiness, these top performers are exclusively HICs. To identify global overperformers within this

> 18

8
group, we focus on countries with an observed AIPI score exceeding 0.7—corresponding to the population-weighted average and median AIPI scores for HICs (rounded to one decimal)—and surpassing their predicted AIPI score (i.e., above the linear fit line) given their level of trade and research economic complexity. These 10 overperformers, marked in yellow, include Australia, Japan, the Netherlands, New Zealand, three of the four Asian Tigers (Hong Kong SAR, China; Singapore; and Korea), as well as Northern European countries such as Denmark, Finland, and Norway.<sup>19</sup> Their observed AIPI scores are statistically significantly above their predicted scores (at 0.1 percent threshold). The relative prominence of East Asian economies, particularly the three recently developed Asian Tigers and particularly Singapore—ranked #1 in observed AIPI score for 2023, much above its predicted score—aligns with prior BMA findings, which underscore the significant influence of “Confucian cultural values” on AI preparedness. By contrast, other HICs with strong AIPI scores, such as the United States and Switzerland, do not qualify as overperformers. This is because their observed AIPI scores align with the predictions based on their high levels of economic complexity rather than exceeding them. Additionally, the largest economies, like the United States, may face challenges in scaling top-level AI implementation uniformly across regions (Hunt, Cockburn, and Bessen, 2024).

# _III.bLocal overperformers_

Figure 4, weighted by population size, demonstrates a positive but more moderate (yet still economically significant) correlation between AIPI and multidimensional economic complexity for the subgroup of 69 LMICs and UMICs, with an value of 0.45 and of 0.67 (statically significant at 0.1 percent). This suggests that while economic complexity likely plays a role in shaping AI RR<sup>2</sup> oo preparedness for these countries, policy decisions appear to have a more substantial independent impact on AIPI performance. A likely explanation, explored further in the next section, is that some of these countries, especially among UMICs, have greater macro-fiscal capacity than LICs (Choudhary, Ruch, and Skrok, 2024), enabling them to implement ambitious digital and AI-specific policy agendas. However, these countries often lack a fully developed infrastructure, and specifically digital infrastructure (pillar i of AIPI) and a sufficiently skilled workforce (pillar ii of AIPI), which are still in the process of being built, especially in comparison to HICs (World Bank, 2021; 2024c).

> 19 With an impressive AIPI score of 0.76 in 2023 and an ambitious digital agenda, Estonia has the potential to be considered among the AI preparedness overperformers. However, a lack of data on its multidimensional economic complexity limits such classification. Estonia's third national AI Strategy (2024–26) prioritizes advancing AI skills and competencies through education and training reforms, including the introduction of new Master's and PhD programs, elective AI courses, and vocational education. The strategy also emphasizes society-wide upskilling initiatives, such as a digital academy and public sector training programs that aim to train 10 percent of employees annually. Additionally, the Ministry of Education and Research offers specialized AI training for teachers, students, and public sector managers, while the Ministry of Economic Affairs and Communications targets 80 percent AI literacy across society and widespread AI adoption— 100 percent in the public sector and 75 percent in the private sector—by 2030 (Digital FOSS, 2024).

9
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standardizing the AI sector (PRC MIIT, 2024).<sup>22</sup> Furthermore, in December 2024, China's Ministry of Industry and Information Technology (MIIT) established an artificial intelligence standardization technical committee. This committee is tasked with developing industry standards in areas such as large language models (LLMs) and AI risk assessment. Lastly, DeepSeek-R1, an open-source reasoning model developed by the Chinese AI startup DeepSeek (or 深度求索 ) was publicly released on January 20, 2025. A notable aspect of this LLM is its cost-effectiveness. The model was developed with an investment of under US$6 million, a fraction of the expenditure—estimated to be multiple billions—reportedly associated with training models like OpenAI's o1. It also relies on inferior chips.<sup>23</sup> By contrast, certain UMICs with relatively high observed AIPI scores, such as Brazil, Mexico and Türkiye, appear to underperform relative to their multidimensional ECI.

Last but not least, Figure 5, also weighted by population size, highlights the relationship between AIPI and multidimensional economic complexity for the subgroup of 43 LICs and LMICs, with an value of 0.70 and of 0.83 (statically significant at 0.1 percent). This stronger correlation aligns with the RR<sup>2</sup> earlier explanation regarding fiscal buffers. Unlike certain UMICs, these countries, particularly LICs, oo face severe fiscal constraints, including notably low levels of tax mobilization, which significantly limit their policy options (Choudhary, Ruch, and Skrok, 2024). As a result, they are compelled to prioritize cost-effective digital and AI-specific strategies whenever feasible. This also highlights that, similar to HICs at the opposite end of the spectrum, their AI preparedness is on average very closely linked to their trade and research complexity. However, this relationship functions at a much lower equilibrium, constrained by their critical multidimensional development challenges (UNSD, 2024).

> 22 These guidelines propose the development of over 50 national and industry-wide AI standards by 2026 and contributions to more than 20 international AI standards, focusing on key technologies and practical applications. This comprehensive approach underscores China's strategic commitment to AI dominance.

> 23 “In terms of performance, DeepSeek-R1 has demonstrated capabilities comparable to leading models like OpenAI's o1. For instance, on the MATH-500 benchmark, which assesses high-school-level mathematical problem-solving, DeepSeek-R1 achieved a 97.3 percent accuracy rate, slightly outperforming OpenAI o1's 96.4 percent. In coding tasks, it scored 49.2 percent on the SWE-bench Verified benchmark, edging out OpenAI o1's 48.9 percent.” <u>[https://venturebeat.com/ai/calm-down-deepseek-r1-is-great-but-chatgpts-product-advantage-is-farfrom-over].</u>

11
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Saharan Africa/SSA models as detailed hereafter). This highlights the critical role of institutions in building trust and establishing frameworks for AI deployment, even amid considerable structural challenges. Following closely is the focus on human capital and labor markets (25.3 percent, Indian model), reflecting the pressing need to develop skills and prepare the workforce for a technologydriven future. However, digital infrastructure (22.0 percent) and innovation and integration (22.6 percent) are less emphasized, likely constrained by limited fiscal resources and technological investment capacity.

For upper-middle-income overperformers, the emphasis shifts, with digital infrastructure taking the lead (28.8 percent, Chinese model), reflecting the growing technological capabilities and infrastructure investments of these nations. Regulation and ethics remain essential at 24.5 percent (Malaysian model), underscoring the importance of robust institutional framework in fostering AI readiness. Human capital and labor markets (24.0 percent, Malaysian and Kazakh model) also maintain a strong focus as these nations work to enhance workforce preparedness. Innovation and integration, however, remain the least prioritized pillar at 22.7 percent, possibly due to structural constraints, though this requires careful interpretation. For instance, China's status may be underestimated in this metric due to the "economic integration" component, which accounts for tariff and non-tariff barriers, as well as restrictions on capital and labor movement. This underscores that robust domestic innovation can thrive even with lower scores in economic integration measures.

lead at 27.9 percent (Singapore and North Europe model), emphasizing the importance of institutional frameworks in managing advanced AI integration. Digital infrastructure follows at 24.7% (Singapore model), reflecting the continuous effort to maintain a technological edge. Innovation and integration (24.3 percent) gain slightly more prominence compared to lower-income groups, while human capital and labor markets (23.1 percent) play a supporting role, as these countries already benefit from well-established systems for education and workforce development.

Across all income levels, regulation and ethics consistently top the list, but the relative emphasis on digital infrastructure, workforce development, and innovation adapts to the specific needs and capacities of each economic group.

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# _IV.bSelected country cases_

**Figure 7:** Selected HICs (global) overperformers

- a) Singapore b) Northern Europe (Denmark, Finland, Norway)

<!-- Start of picture text --> 0.25 0.25<br>0.20 0.20<br>0 .15 0 .15<br>0.10 0.10<br>0.05 0.05<br>0.00 0.00<br>Other Non-ov. (i) HICs Other Non-ov. (i) HICs<br>Other Non-ov. (ii) ECA-NAC HICs Other Non-ov. (ii) ECA-NAC HICs<br>Overperformer (SGP) Overperformers (Northern Eur.)<br>Digital Infr. Digital Infr.<br>Human K. & Labor Mkt Human K. & Labor Mkt<br>Reg. & Ethics Reg. & Ethics<br>Innov. & Integr. Innov. & Integr.<br><!-- End of picture text --> <u>Source: The AIPI score and pillars for 2023 are obtained from the IMF website [https://www.imf.org/external/datamapper/datasets/AIPI]. Notes: Population size for 2023, used for the weighted analysis, is sourced from the “wbopendata” module in Stata (Azevedo, 2020).</u> Weighted average AIPI scores per group in parenthesis. Dotted yellow shows the weighted average pillar scores for non-overperformer HICs in Europe, Central Asia, or North America, while dotted red shows the scores for all other non-overperformer HICs. The “spider” package is taken from Naqvi (2024a).

Singapore’s AI readiness strategy strongly emphasizes all the AIPI pillars detailed above, and particularly regulation and ethics, as well as the development of robust digital infrastructure and solid human capital and labor market policies to enable responsible and efficient AI adoption (Figure 7a).<sup>25</sup> Singapore’s “Model AI Governance Framework,” developed by the Infocomm Media Development Authority (IMDA) and the Personal Data Protection Commission (PDPC), is a cornerstone of its AI strategy.<sup>26</sup> This framework provides practical guidelines for deploying AI ethically, with a focus on principles such as transparency, fairness, and accountability. To ensure these principles are actionable, Singapore launched AI Verify, a world-first AI testing framework and toolkit that enables organizations to validate the trustworthiness of their AI systems against regulation standards. These efforts reflect Singapore’s commitment to balancing innovation with public trust, ensuring AI systems are both ethical and effective.

the release of GPT-3.5 by OpenAI, Singapore emphasized heavy investments in digital infrastructure

> 25 Singapore also excels in AI human capital and labor market development, particularly in scaling up AI talent across creators, practitioners, and users to meet the growing demands of AI adoption. Its latest national strategy (Smart Nation Singapore, 2023) prioritizes the cultivation of top-tier AI researchers and engineers through tailored support, redesigned training programs, and global talent attraction initiatives. Programs like the AI Apprenticeship Program and sector-specific upskilling efforts under the Industry Transformation Maps (ITMs) play a central role in reskilling the workforce for AI integration. By leveraging targeted interventions and fostering industry-academia collaborations, Singapore aims to enhance workforce readiness, ensuring that its citizens can thrive in an AI-driven economy.

> 26 See https://www.pdpc.gov.sg/help-and-resources/2020/01/model-ai-governance-framework.

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to create a foundation for widespread AI adoption. Key initiatives included expanding high-speed connectivity networks and building state-of-the-art data centers to support AI applications requiring significant computational and data-sharing capabilities. The government also supported the integration of AI across nine priority sectors such as health care, transport, and finance<sup>27</sup> by creating sector-specific data ecosystems and AI solutions. The orientations are reaffirmed in the NAIS 2.0 (Smart Nation Singapore, 2023). This infrastructure ensures Singapore can effectively deploy AI at scale while maintaining seamless interoperability and efficiency.

Northern Europe (i.e., Denmark, Finland, and Norway), has developed comprehensive AI readiness strategies that prioritize regulation and ethics to ensure responsible AI development and deployment (Figure 7b).

Denmark has established ethical principles for AI, as part of its national strategy to maintain high levels of trust in technology (Danish Gvt, 2019).<sup>28</sup> These principles reflect “Danish values” and are based on recommendations from an official expert group on data ethics, as well as draft EU ethical guidelines for AI. The framework is designed to guide both public authorities and businesses in the responsible use of AI, ensuring that ethical considerations are integrated into AI development and application.

as central pillars in its AI strategy (MEAEE, 2022). The program emphasizes the importance of balancing digital and green transitions, underpinned by principles of sustainable development and technological sovereignty. By prioritizing ethics and regulation, Finland seeks to establish trust in AI technologies, ensure responsible deployment, and position itself as a frontrunner in creating fair and sustainable AI-driven solutions for global markets. The approach includes collaboration involving public, private, and research sectors to align digital innovation with societal and environmental goals.

The Norwegian “Digitalization Strategy 2024-2030” emphasizes a holistic approach to policy making and ethics in the context of AI readiness and digital transformation (MDPG, 2024). The regulatory framework includes ensuring robust privacy protection, secure digital infrastructure, and transparent use of AI technologies aligned with ethical standards. It also prioritizes fostering public trust through inclusive digital practices and safeguarding against digital vulnerabilities such as fraud and misinformation. The strategy integrates AI into broader societal and economic objectives, reinforcing oversight structures to create equitable opportunities while addressing societal challenges like security, environmental sustainability, and the ethical use of advanced technologies.

> 27 “We have identified nine such sectors: Transport & Logistics, Manufacturing, Finance, Safety & Security, Cybersecurity, Smart Cities & Estates, Healthcare, Education and Government.” (Smart Nation Singapore, 2019, p.16) 28 There are six ethical principles to guide responsible AI development and use: (i) self-determination ensures citizens can make informed, independent choices when interacting with AI; (ii) human dignity emphasizes respecting human rights and diversity; (iii) equality and justice ensure fairness and non-discrimination in AI systems; (iv) responsibility promotes accountability in AI development and deployment; (v) explainability advocates for transparency to build trust by clarifying how AI decisions are made; and (vi) ethically responsible development mandates that AI aligns with societal values and ethical standards. These principles aim to collectively foster trust and ensure that AI benefits society.

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**Figure 8:** Selected UMICs overperformers

<!-- Start of picture text --> a) China b) Malaysia<br>0.25 0.25<br>0.20 0.20<br>0.15 0.15<br>0.10 0.10<br>0.05 0.05<br>0.00 0.00<br>Other non-overperformers (i) LMICs Other non-overperformers (i) LMICs<br>Other non-overperformers (ii) UMICs Other non-overperformers (ii) UMICs<br>Overperformer (CHN) Overperformer (MYS)<br>c) Kazakhstan<br>0.25<br>0.20<br>0.15<br>0.10<br>0.05<br>0.00<br>Other non-overperformers (i) LMICs<br>Other non-overperformers (ii) UMICs<br>Overperformer (KAZ)<br>Digital Infr. Digital Infr.<br>Digital Infr.<br>Human K. & Labor Mkt Human K. & Labor Mkt<br>Human K. & Labor Mkt<br>Reg. & Ethics Reg. & Ethics<br>Reg. & Ethics<br>Innov. & Integr. Innov. & Integr.<br>Innov. & Integr.<br><!-- End of picture text --> <u>Source: The AIPI score and pillars for 2023 are obtained from the IMF website [https://www.imf.org/external/datamapper/datasets/AIPI]. Notes: Population size for 2023, used for the weighted analysis, is sourced from the “wbopendata” module in Stata (Azevedo, 2020).</u> Weighted average AIPI scores per group in parenthesis. Dotted yellow shows the weighted average pillar scores for non-overperforming LMICs and dotted red shows them for non-overperforming UMICs. The “spider” package is taken from Naqvi (2024a).

Spiderweb charts illustrate that China and Malaysia excel in all AIPI pillars relative to the average of non-overperformers within their income group. Notably, China demonstrates exceptional proficiency in digital infrastructure (Figure 8a), whereas Malaysia shows significant strengths in regulation and ethics, as well as human capital and labor market policies (Figure 8b).

As already underlined, China's AI readiness strategy heavily emphasizes the development of robust digital infrastructure as a cornerstone for its technological ambitions (de Seta, 2023). Central to this effort is the “New Generation Artificial Intelligence Development Plan” or AIDP (China State Council, 2017), which outlines a roadmap to position China as a global AI leader by 2030, underscoring the 17
critical role of advanced infrastructure.<sup>29</sup> largest 5G network and an extensive fiber-optic broadband system, enabling high-speed connectivity and the Internet of Things (IoT) essential for AI applications across industries. Complementing this, initiatives such as the Digital Silk Road under the Belt and Road Initiative aim to enhance global digital connectivity, extending China's AI influence internationally. To meet its domestic AI computing needs, China’s MIIT is advancing plans to bolster computational power and develop indigenous AI technologies by 2027.<sup>30</sup> Furthermore, China's smart city initiatives, exemplified by projects like City Brain, leverage AI and advanced infrastructure to optimize urban management and services (Zhang et al., 2019). While these efforts have positioned China as a leader in digital infrastructure, challenges such as regional disparities and regulatory complexities remain, prompting continuous efforts to ensure balanced and sustainable development,<sup>31</sup> as also observed in the United States, for instance (Hunt, Cockburn, and Bessen, 2024).

Malaysia has implemented robust frameworks to ensure responsible AI adoption, aligning with ethical principles. Its “AI Roadmap,” or AI-Rmap (MOSTI, 2021) establishes guidelines to safeguard data privacy, promote transparency, and mitigate risks such as cybersecurity threats. Institutional frameworks like the AI-Catalyst coordinate implementation across government, industry, and academia. These efforts are reinforced by the “National Fourth Industrial (4IR) Policy” (EPU, 2021), which integrates anticipatory and agile regulatory approaches, ensuring that legislation adapts to evolving technological needs.

In September 2024, the country introduced its “National Guidelines on AI Governance & Ethics,” or AIGE (MOSTI, 2024). These guidelines provide a comprehensive framework for stakeholders— including end users, policy makers, and developers—to adopt AI technologies in a safe, transparent, and ethical manner. The AIGE outlines seven fundamental principles designed to promote responsible AI practices across various sectors (e.g., finance, health care, manufacturing).<sup>32</sup> To further strengthen its AI framework, Malaysia launched the National AI Office (NAIO) in December 2024. The NAIO serves as a centralized agency dedicated to formulating AI policies, addressing regulatory issues, and coordinating strategic planning and research.

Malaysia also emphasizes the development of a skilled workforce to drive AI adoption. The AI-Rmap (MOSTI, 2021) identifies fostering AI talents as a core strategy, focusing on reskilling and upskilling programs and partnerships with universities to cultivate industry-ready talent. Additionally, the National 4IR Policy (EPU, 2021) supports initiatives to match talent pipelines with future economic needs and reduce disparities in access to 4IR opportunities.

> 29 An unofficial translation of the AIDP is available on Stanford's DigiChina website: https://digichina.stanford.edu/work/full-translation- <u>chinas-new-generation-artifcial-intelligence-development-plan-2017/.</u> 30 See https://www.theregister.com/2024/01/31/china_homegrown_ai_infrastucture. 31 See https://www.reuters.com/markets/asia/china-tackle-risks-new-infrastructure-push-2024-09-04.

> 32 These include (i) fairness, to prevent bias and ensure equitable access; (ii) reliability and safety, to guarantee secure and robust systems; (iii) privacy and security, to protect personal data and ensure informed consent; (iv) inclusiveness, to promote accessibility and diversity; (v) transparency, to make AI decision-making clear and understandable; (vi) accountability, ensuring developers and operators take responsibility for AI outcomes; and (vii) the pursuit of human benefit, prioritizing societal progress and well-being.

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The Human Resources Ministry (KESUMA) recently conducted a comprehensive study on the impact of AI, digitalization, and the green economy across 10 strategic sectors.<sup>33</sup> The findings, released in September 2024, identified over 600,000 jobs requiring skill enhancements and highlighted 60 emerging roles, such as AI engineers and sustainability specialists, essential for advancing Malaysia's digital and green economy.<sup>34</sup> Finally, Kazakhstan’s “ National Artificial Intelligence Development Concept for 2024–2029” (MDDIAI, 2024)<sup>35</sup> outlines a comprehensive strategy to position the country as a regional leader in AI, with a strong emphasis on human capital and labor market policies (Figure 8c). The strategy prioritizes human capital development through AI-focused education, reskilling programs, and widespread AI literacy campaigns, aiming to train 1 million citizens in AI skills. It emphasizes fostering research and innovation, including the development of a LLM to preserve Kazakh cultural and linguistic heritage, alongside investments in robust digital infrastructure. By integrating AI into traditional industries like agriculture and mining and promoting economic modernization, Kazakhstan aims to enhance productivity and global competitiveness while addressing labor market adaptation to technological advancements.

**Figure 9:** Selected LICs/LMICs overperformers

<!-- Start of picture text --> a) India b) SSA (Ghana, Rwanda)<br>0.25 0.25<br>0.20 0.20<br>0.15 0.15<br>0. 1 0 0. 1 0<br>0.05 0.05<br>0.00 0.00<br>Other non-overperformers (i) LICs Other non-overperformers (i) LICs<br>Other non-overperformers (ii) LMICs Other non-overperformers (ii) LMICs<br>Overperformer (IND) Overperformer (SSA: GHA, RWA)<br>Digital Infr. Digital Infr.<br>Human K. & Labor Mkt Human K. & Labor Mkt<br>Reg. & Ethics Reg. & Ethics<br>Innov. & Integr. Innov. & Integr.<br><!-- End of picture text --> <u>Source: The AIPI score and pillars for 2023 are obtained from the IMF website [https://www.imf.org/external/datamapper/datasets/AIPI]. Notes: Population size for 2023, used for the weighted analysis, is sourced from the “wbopendata” module in Stata (Azevedo, 2020).</u> Weighted average AIPI scores per group in parenthesis. Dotted yellow shows the weighted average pillar scores for non-overperforming LICs and dotted red shows them for non-overperforming LMICs. The “spider” package is taken from Naqvi (2024a).

> 33 See <u>https://www.mida.gov.my/mida-news/ai-digitalisation-and-green-economy-to-reshape-malaysian-job-market-sim.  “The report</u> highlighted 10 key sectors in the first phase of the upskilling initiative – aerospace, chemicals, electrical and electronics, energy and power, food manufacturing and services, global business services, information and communications technology, medical devices, pharmaceutical manufacturing, and wholesale and retail trade. 34 To support workforce upskilling and reskilling, KESUMA has allocated a RM3 billion fund (approximately US$670 million) to provide support through levies, credits, scholarships, and matching grants. These initiatives will be facilitated via the MyMAHIR platform and the forthcoming Akademi KESUMA. See <u>https://hr.asia/asean/malaysia-to-study-impact-of-ai-digitalisation-and-green-economy-onworkforce.</u> 35 The Concept is available online and has been translated into English using Google’s automatic translation tool: <u>https://adilet.zan.kz/rus/docs/P2400000592.</u>

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an ethical framework to support its vision of becoming a leader in AI innovation (Figure 9a). On the human capital and labor market front, the IndiaAI Expert Group Report (MeitY, 2023) outlines a comprehensive approach that includes establishing Centers of Excellence (CoEs) for AI research and education, fostering industry-academic partnerships, and promoting entrepreneurship. The report emphasizes large-scale upskilling initiatives, such as the FutureSkills Program, which aims to build a robust AI talent pool through specialized curricula, faculty training, and career pathways for roles like data scientists and AI ethicists.<sup>36</sup> These interventions target not only IT professionals but also school students, graduates, and non-IT sectors, ensuring widespread AI literacy. Similarly, the National Strategy for Artificial Intelligence (NITI Aayog, 2018) highlights the importance of democratizing AI education through public-private partnerships and certification programs with market value, aiming to position India as an “AI Garage" capable of serving global AI needs.<sup>37</sup> In terms of regulation and ethics, both documents prioritize the establishment of a robust framework to ensure responsible AI deployment. The NITI Aayog Strategy (NITI Aayog, 2018) advocates for “Responsible AI for All," emphasizing fairness, accountability, and transparency (FAT) as guiding principles. It proposes the creation of Ethics Councils within Centers of Research Excellence (COREs) to monitor compliance with ethical standards.<sup>38</sup> Data privacy and protection frameworks aligned with international norms are highlighted to mitigate risks related to bias, privacy violations, and security breaches. The IndiaAI Expert Group Report (MeitY, 2023) reinforces this by proposing a secure and transparent India Dataset Platform (IDP) to support ethical data usage while ensuring public trust through anonymization and secure protocols.<sup>39</sup> Ghana and Rwanda are two SSA countries with notable performance in the regulation and ethics aspect of their AI preparedness strategies, indicating their efforts to develop responsible and inclusive AI ecosystems (Figure 9b). In Ghana’s National Artificial Intelligence Strategy (MOCD, 2022), the establishment of a Responsible AI Office (RAI Office) is a key component to coordinate stakeholders, monitor policy implementation, and promote trustworthy AI development.<sup>40</sup> The strategy emphasizes data privacy, transparency, and the creation of a regulatory framework to mitigate algorithmic bias and ensure secure data sharing. Ghana’s approach includes enforcing a robust data framework through initiatives like the Ghana Open Data Initiative (GODI), aimed at

> 36 The FutureSkills Program, launched by Indian National Association of Software and Service Companies (NASSCOM) in partnership with the Ministry of Electronics and Information Technology (MeitY), aims to build digital competencies in emerging technologies such as AI, data analytics, and cybersecurity. It offers industry-recognized certifications, virtual labs, and government incentives to make skilling affordable, with over 1.8 million learners benefiting from its flexible online and classroom formats.

> 37 The AI Garage concept aims to position India as a global hub for AI innovation, where solutions developed for domestic challenges— such as health care, agriculture, and education—can be scaled and adapted for global markets, particularly for developing economies with similar needs.

> 38 COREs are envisioned as specialized hubs for AI research, fostering innovation through collaborations between academia, industry, and government. These centers aim to advance cutting-edge AI solutions, promote ethical AI development through dedicated ethics councils, and build a robust talent pipeline to support India's AI ecosystem.

> 39 The IDP is envisioned as a centralized, secure repository for high-quality datasets to support AI research and innovation. The platform aims to ensure responsible data sharing by incorporating robust privacy protections, data anonymization protocols, and frameworks that foster public trust and facilitate collaboration across academia, industry, and government.

> 40 The RAI Office is a coordinating body aiming to oversee the implementation of AI policies, ensure compliance with ethical guidelines, and foster collaboration between government, academia, and industry. Its role includes monitoring AI applications to prevent bias and promoting transparency and public trust in AI systems.

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supporting responsible AI adoption while safeguarding human rights.<sup>41</sup> Similarly, Rwanda’s National AI Policy (MINICT, 2023) underscores practical ethical guidelines to align AI solutions with human rights and societal values. The policy mandates an annual review of these guidelines by the Rwanda Utilities Regulatory Authority (RURA) and incorporates fairness, transparency, and accountability in public sector applications.<sup>42</sup> Rwanda also integrates AI ethics into national education and collaborates with global institutions to align with international standards. The establishment of a Responsible AI Office (RAIO) further reinforces oversight and promotes ethical practices.<sup>43</sup> Both countries’ strategies highlight a commitment to using frameworks to balance technological progress with social accountability, ensuring that AI advancements contribute to inclusive and sustainable development.

# V- Discussion

importance of tailored strategies that reflect the unique socio-economic and institutional realities of individual nations. The proposed methodology, which combines the AIPI with multidimensional ECI, offers a nuanced framework for assessing national AI readiness relative to economic complexity capacities. By identifying both global and local overperformers, this approach not only captures excellence at the highest level, but also elevates the achievements of countries that have outperformed their predicted potential despite economic constraints.

A recurring theme in global policy discussions on AI is the widening readiness gap between highincome nations and lower-income economies (Cazzaniga et al., 2024). While this AI divide highlights disparities in digital infrastructure, access to data, and human capital, it often overlooks the strides made by local "champions"—countries that, despite resource limitations, have implemented forward-thinking strategies to bolster their AI ecosystems. By shifting the focus from the divide itself to identifying and following the examples set by these overperformers, the proposed methodology reframes the narrative. It encourages policy makers to learn from the experiences of peer nations that have successfully leveraged regulatory coherence, ethical frameworks, and targeted investments to enhance their AI capacities.

For example, Rwanda and Ghana seem to demonstrate that robust regulation and ethics structures, combined with strategic partnerships and capacity-building initiatives, can compensate for limited economic complexity and fiscal constraints. Similarly, Kazakhstan’s seemingly ambitious AI literacy and upskilling programs illustrate how countries can focus on workforce transformation to align their economic potential with AI advancements. These examples emphasize the importance of fostering

> 41 The GODI is a national program aimed at enhancing data transparency and accessibility by making government datasets open to the public. It supports responsible AI adoption by providing high-quality data for research and innovation, while also ensuring data protection measures are in place to uphold privacy rights and prevent misuse.

> 42 RURA is an independent multi-sector regulatory body responsible for overseeing utilities, including ICT and AI framework. RURA is tasked with conducting annual reviews of AI ethical guidelines, ensuring compliance with national policies, and addressing concerns related to fairness, accountability, and transparency in AI deployments.

> 43 Rwanda’s RAIO is a dedicated office established to promote responsible AI practices and foster ethical innovation. The RAIO aims to coordinate policy implementation, support regulatory reforms, and monitor AI systems for alignment with national guidelines, ensuring that AI applications foster inclusive development while adhering to global best practices.

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innovation ecosystems that support sustainable AI adoption rather than merely seeking to close the divide.

countries facing similar structural challenges can exchange best practices and tailor their AI readiness strategies. By promoting knowledge-sharing and collaboration, the framework proposed here helps transcend income-based categorizations and instead fosters a more comprehensive AI development landscape. This peer-learning approach also supports policy experimentation, enabling nations to test scalable, cost-effective solutions before committing to large-scale deployments. By spotlighting overperformers across income groups, the methodology provides actionable insights into the pillars—whether regulatory, educational, or infrastructural—that drive AI overperformance.

In this study, the methodological decision to use the recent AIPI from the IMF instead of the AI Readiness Index developed by the private UK-based consulting firm Oxford Insights is based on several considerations. The IMF, as a globally recognized multilateral institution, offers a higher level of accountability, scrutiny, and transparency in the development of its products. Conversely, while the contributions of private entities like Oxford Insights are valuable, they do not operate under the same oversight or accountability. Moreover, the AI Readiness Index specifically assesses the preparedness of governments to deploy AI in public service delivery, whereas the AIPI provides a more comprehensive evaluation of how countries can effectively harness AI capabilities. That said, the choice of index does not significantly impact the broader contribution of this study, as the proposed methodology remains adaptable and replicable. The focus is on providing a transparent, data-driven approach that allows policy makers to assess their country’s AI preparedness relative to their economic complexity. By prioritizing a clear and simple framework, this study enables comparisons across different datasets and indices, reinforcing the robustness and utility of its findings regardless of the specific AI readiness index used.

Future research could also explore ways to improve the AIPI or similar indexes. For instance, our findings suggest that a significant number of East Asian countries—particularly those influenced by Confucian traditions—tend to overperform in AI preparedness regardless of their income levels, from Viet Nam to Singapore and China. This trend warrants further investigation to determine whether it reflects specific cultural norms and values that prioritize “social harmony" and collective design (Kung and Ma, 2014) that are appropriate for AI readiness, and/or if it is influenced by higher average cognitive abilities in these countries (Lynn and Meisenberg, 2010; Jones, 2011). Notably, collective cognitive abilities have been shown to positively impact economic performance (Madsen, 2016) and could be better captured in future iterations of the AIPI if they prove relevant for readiness in cuttingedge technologies like AI.

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