50
Finance, Accounting and Business Analysis
Volume 8 Issue 1, 2026
http://faba.bg/
ISSN 2603-5324
DOI:
https://doi.org/10.37075/FABA.2026.1.05
Digital Readiness and Growth: How Government AI Preparedness
shapes African Economies
Imane BENMIMOUN
Laboratory of Markets, Employment, Simulation and Legislation in the Maghreb Countries, University of
Ain Temouchent, Algeria
Info Articles
Abstract
History Article:
Submitted 15 September 2025
Revised 10 March 2026
Accepted 16 April 2026
Purpose: This study investigates the impact of government AI readiness on
economic growth, with a particular focus on the roles of government, the
technology sector, and data & infrastructure. It aims to provide a nuanced
analysis of the digital readiness-growth nexus and evaluate whether artificial
intelligence in government acts as a catalyst for or a constraint on African
economic growth.
Design/Methodology/Approach: The study employs an econometric analysis,
namely the GMM system. The analysis is conducted on a panel of 44 African
countries over the period 2020–2024, capturing the dynamic relation between
government AI readiness indicators and economic growth.
Findings: The main result is that economic growth in the African countries is
strongly dependent on its past values, while the direct contribution of
government, technology sector, and data & infrastructure does not emerge as
significant within the short timeframe examined.
Practical Implications: Policymakers should promote the application of AI in
African governments by strengthening governance through robust legal
frameworks, transparent digital strategies, and technology development through
the digitization of public services. As well as effective data management and
infrastructure improvement.
Originality/Value: This study provides new empirical evidence on the relation
between the digital readiness in government and growth in African economies,
considering multiple dimensions of government AI readiness and utilizing a
robust econometric approach (GMM system). It contributes to the debate on
digital readiness by distinguishing between government, the technology sector,
data, and infrastructure.
Paper Type: Research Paper.
Keywords:
Artificial intelligence;
Government; Digital
readiness; African
economies; GMM system.
JEL: O33; H11; O55; C23.
*
Address Correspondence:
E-mail:
imane.benmimoun@univ-temouchent.edu.dz
Imane Benmimoun / Finance, Accounting and Business Analysis, Volume 8, Issue 1, 2026
51
INTRODUCTION
Artificial intelligence (AI) has significantly enhanced human capabilities to carry out complicated
tasks from various domains such as infrastructure, data ecosystem, digital economy, healthcare,
environmental sustainability, and agriculture. Advancements in AI offer a chance to create better solutions
to solve current societal challenges and have long-term beneficial impacts (Kondo and Diwani 2023).
Recent years have witnessed a growing scholarly focus on “artificial intelligence” (AI) in public
administration, driven by the emergence of Big Data and the automation of work via “digital information
and communication technologies” (ICT) (Ingrams 2021, p. 390). Incorporating AI technologies into
government functions has huge benefits. One major benefit is that it makes administration more efficient,
which is a key part of providing good public services Alhosani and Alhashmi 2024. Especially since, over
the past few decades, the acceptance of AI in the public sector has been delayed than in the private sector.
As a result, interest in using AI in government is relatively new. (Desouza & al, 2020). AI methods and
digital transformation initiatives from the private sector cannot directly be translated to the public sector
because of the public sector’s obligation to maximize public benefit. Compared to the private sector, there is
less understanding of addressing AI concerns directly related to the public sector (Zuiderwijk et al. 2021).
AI can streamline processes, such as automating bureaucratic tasks, which improves response times and
reduces human error (Coelho and Silva 2024), and also Large-scale data processing enables better resource
management and informed policy-making, enhancing transparency (Söker 2024; Coelho and Silva 2024).
Furthermore, AI tools can facilitate improved communication with citizens, allowing for more targeted and
effective public policies (OECD 2022). A study on UK government services estimated that 143 million
complex transactions are highly automatable, potentially saving about 1200 person-years annually (Straub
et al. 2024).
Artificial Intelligence (AI) is transforming economies by enhancing productivity, efficiency, and cost-
effectiveness. Governments occupy a distinctive role regarding AI since they establish national strategic
aims, allocate public investments, and formulate regulations. Governments have acknowledged the
significance of AI and its potential applications across several sectors of the economy in the future. Over 60
nations are engaged in this endeavour (OECD 2022).
Artificial intelligence (AI) has demonstrated its value in a variety of fields and applications, making
it a potentially beneficial tool for the economy, society, and government. Even while AI is becoming more
and more accepted as a science or technology, intellectual and political elites have not yet fully
acknowledged it as a game-changing invention. As a result, supporting the public sector in defining the
scope of AI is hard (Alhosani and Alhashmi 2024). The numerous consequences of AI in government for
public governance are poorly understood by most countries. Given the rapid adoption of AI applications in
government worldwide, governance and AI thought leadership is shrinking. This information gap is a major
development hurdle as governments grapple with the social, economic, political, and ethical effects of AI
changes (Zuiderwijk et al. 2021). Artificial intelligence has been described as a "game changer" for public
administration, with the potential to "disruptive" or "transformative". It has two types of effects: technical
effects on technological progress, and effects on societal or ethical outcomes that characterize the quality of
government-citizen relations (Maciejewski 2016; Agarwal 2018).
Most national AI policies acknowledge the significance of implementing AI in the public sector.
Actually, governments are reinventing how they create and provide policies and services by utilizing AI
more and more for public sector innovation and change (OECD 2022). The EU's comprehensive assessment
shows that AI improves policy-making by detecting growing social challenges, overseeing implementation,
and reviewing policy effectiveness (Noordt and Misuraca 2022). As a result, African countries are making
national AI plans and setting up AI advisory groups to lead the way in using and developing technology
(Plantinga et al. 2024).
Despite the presence of research examining government readiness for artificial intelligence, empirical
findings on the causal direction of its relationship with economic growth remain inconclusive. This study
contributes to the ongoing debate on the relationship between digital readiness in government and economic
growth by addressing the following question: Does government readiness for artificial intelligence stimulate
economic growth in the African countries under study? Based on this research question, the study
hypothesizes that government AI readiness indicators have a positive impact on economic growth in the
African countries under study.
LITERATURE REVIEW
Previous literature has provided mixed results on the impact of government digital readiness on
economic growth. Although some studies indicate that high levels of readiness contribute positively to
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52
productivity, competitiveness, etc., some other research highlights the challenges facing the adoption of
artificial intelligence in developing governments. These studies have provided diverse opinions and
empirical findings that expand the scope of ongoing debate.
Some studies have adopted a descriptive analytical approach by analysing the specific factors for AI
readiness and the factors that may have a direct impact on government’s readiness for AI. Such as Montoya
and Rivas (2019) , this study compares indicators of AI technology development and use in governance,
infrastructure, technological skills, and public services to each country's economic metrics. It also examines
non-economic parameters that affect AI readiness and its implications on each country's population.
Similarly, Alalaq (2025) focused on Iraq, discussing the challenges the Iraqi government encountered in
implementing and using AI technology. The report analysed the economic, social, and political constraints
inhibiting this shift and presented clear recommendations to overcome these difficulties. This report
underlines the significance of investing in education, training, and capacity building to generate a trained
workforce, enabling governments to harness AI potential and enhance government service efficiency. Also,
Nzobonimpa and Savard (2023) this study explores the relationship between government AI preparedness
and accountability, and concludes that advancement in AI is not enough to provide responsible governance,
demonstrating the necessity for principles like privacy, transparency, and inclusion.
Study of Ahangama and Krishnan (2023) examines the mediating role of AI preparedness of a
government in affecting the e-participation implementations of a country and its people's well-being. The
empirical analysis was performed based on archival data for 72 nations. Results show that AI preparedness
strengthens the positive effect of e-participation implementation on the well-being of the people. In addition,
study of Seini et al. (2024) investigates the dynamic relationships between the maturity of the technology
sector, human capital, and African government AI readiness. Using the Technology-Organization
Environment paradigm and Partial Least Squares Structural Equation Modeling, the study analyses data
from the Government AI Readiness Index 2023 of 53 African countries. Results reveal that there is a high
positive correlation between government AI readiness and technology sector maturity, and between human
capital and technology sector maturity. However, the relationship between government AI readiness and
human capital is less strong. These findings suggest that whereas an advanced technology sector has a direct
positive influence on government AI readiness, its effect through the creation of human capital is less
important in the African context.
On the other hand, some other studies adopted the standard approach by using panel data such as
Amin Mohamed (2024); this study looks at how AI government readiness affects economic growth using
the Cobb-Douglas production function for 115 developing nations between 2020 and 2023. The study's
findings demonstrate that government preparedness for AI significantly and favourably affects economic
growth in developing nations. Previous studies have also linked artificial intelligence to economic growth
Sarker (2022), the study employed time series data to investigate the effects of AI on the labour market and
productivity. The data suggested that Bangladesh had not yet realized the projected economic merits, despite
the combined number of AI-induced industry robots was insignificant. A study of Acemoglu and Pascual
(2018) found that the AI has considerable effects on the labour market and employment. In the short run,
given that capital is fixed, it was found that automation negatively affects employment and reduces wages.
On the contrary, Wang et al. (2021) and Chih-Hai Yang (2022) found that AI technologies had a positive
effect on productivity and employment. In another context, the research of Yu (2025) contained two studies
that explored the influence of AI integration on economic growth in China. Study one assessed the mediating
function of workforce adaptation and the moderating effect of government policy in the link between AI
integration and economic growth. Study two enhanced this approach by studying technical infrastructure as
a joint moderator coupled with government policy. The results suggested that Study two provided more
hopeful outcomes than Study one, demonstrating a major breakthrough in the knowledge of AI integration’s
impact on economic growth when technology infrastructure was addressed alongside government policy.
The research conducted by Saba and Ngepah (2024) examines the impact of investment in AI on
growth and employment in BRICS nations during 2012-2022. Using The CS-ARDL model. The evidence
confirms the existence of long-run equilibrating relationship between the variables employed in the models
of job and growth. Causality test results for our variable of interest are mixed in the employment-growth
models. The findings suggest that BRICS policymakers and governments need to prioritize and scale up the
use of AI in governance systems to create jobs and spur growth both in the short and long term. In addition,
study of De Fegueiredo (2024) examined the role of digital readiness in shaping economic growth within
the European Union, drawing on the Digital Economy and Society Index (DESI) across a sample of 22
states. The analysis indicated that human capital development and the adoption of digital technologies were
key determinants of growth, whereas digital public services showed a slight negative relationship with GDP.
The results indicated that development of digital capabilities and extending company utilization of technical
tools were imperative to spur productivity and encourage innovation, highlighting the importance of targeted
investments in digital readiness as a foundation for sustained economic growth.
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According to previous studies, the research gap has been in the study of regions such as Africa, where
the economic impacts of adopting artificial intelligence are still emerging.
DATA AND METHODS
This study employs the GMM system, more precisely “the two-step difference GMM approach”, in
conjunction with the STATA 17 software in order to analyse the impact of Government AI Readiness on
economic growth across 44 African economies. The analysis spans a 5-year period (2020-2024), with the
temporal scope determined by data availability constraints for key government AI readiness indicators. The
study covers all African countries, excluding those with unavailable or missing data. Endogeneity,
autocorrelation, and heteroskedasticity issues are handled with the GMM estimator. This approach is
suitable, when the number of cross-sectional units (N) is larger than the duration (T) (Roodman 2009).
In order to accomplish the goals of the study, the following regression models are created:
(1)
Where:
Gross Domestic product (GDP) of country i(1,……,44) at year t(1,….5).
The constant parameter.
δ The speed of adjustment to equilibrium.
the one-period lagged GDP.
the coefficient parameters of the model.
the model error term.
Data sources
This study relies on panel data drawn from two main sources, the World Bank’s foundation (2024),
which provide data on GDP as a dependent variable, and the Oxford insights, which offers measures for 4-
keys independent variables, and the following table provides a detailed description of the variables used in
the analysis.
Table 1. Variables and Data collection sources
Variable
Source
Measurement
Economic growth
(GDP)
World Bank database
GDP (constant 2015 US$)
Government AI
Readiness
(GAIR)
Oxford insights Data
Composite measure derived from government,
technology, and data infrastructure dimensions,
using normalized and weighted indicators to
generate a final readiness score.
Countries are ranked on a 0–100 scale, where
higher scores indicate greater AI readiness
Government
(GOV)
Oxford insights Data
Measures the readiness of state institutions to
develop policies and strategies related to artificial
intelligence.
Includes sub-indicators such as: national vision and
strategy for AI, quality of institutions, regulatory
effectiveness, and transparency. Countries are
ranked on a 0–100 scale.
Technology sector
(TECH)
Oxford insights Data
Measures the technological infrastructure and
innovation ecosystem. Includes sub-indicators
such as: Internet penetration, R&D expenditure,
cybersecurity, availability of digital skills.
Countries are ranked on a 0–100 scale.
Data & Infrastructure
(DINF)
Oxford insights Data
Measures the Availability and quality of data and
supporting infrastructure. Includes sub-indicators
such as: Open data initiatives, data protection
frameworks, access to digital platforms, ICT
infrastructure. Countries are ranked on a 0–100
scale.
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RESULT AND DISCUSSION
Given the short time dimension of the panel dataset (5 years across 44 countries), traditional panel-
unit root tests such as Levin–Lin–Chu, Im–Pesaran–Shin, or Fisher-type ADF/PP suffer from low statistical
power and may produce unreliable results. In such cases, the concept of stationarity remains important, but
its empirical testing becomes less informative. To address this limitation, the analysis relies on transforming
variables into growth rates or first-differences (e.g., log-differences) whenever non-stationarity is
theoretically expected, while variables that are generally considered stationary in levels (such as ratios or
rates) are maintained in their original form. This approach reduces the risk of spurious regression and ensures
that the estimated relationships remain robust despite the short time span of the data.
Table 2. Descriptive Statistics
Variable
Mean
SD
Min
Max
GDP
6.23e+10
1.13e+11
1.42e+09
5.70e+11
GAIR
32.56101
7.450553
19.73651
55.62589
GOV
32.02502
12.46696
10.0215
71.4382
TECH
23.77138
5.855924
13.85716
42.12676
DINF
41.88663
8.336116
25.49187
72.22241
Source: Stata 17 software output (2025)
The descriptive statistics show that the average GDP across the sample was approximately 6.23e+10,
with a standard deviation of 1.13e+11, indicating a high degree of variation among countries. The minimum
GDP was 1.42e+09, while the maximum reached 5.70e+11. Government AI readiness (GAIR), the mean
value was 32.56 with a standard deviation of 7.45, suggesting moderate dispersion; values ranged between
19.73 and 55.62. The government indicator (GOV) recorded a mean of 32.03, a relatively large standard
deviation of 12.47, and a wide range (10.02 to 71.43), which reflects considerable heterogeneity in
governance. The technology index (TECH) had an average of 23.77 and a relatively lower variability (SD =
5.86), with values spanning from 13.86 to 42.13. Finally, the data & infrastructure index (DINF) exhibited
a mean of 41.89 and a standard deviation of 8.34, with values ranging between 25.49 and 72.22, suggesting
a relatively more balanced distribution compared to other.
Correlation Matrix
A correlation matrix is a tool employed in econometrics to examine the patterns of correlations
among variables. It signifies the presence of multicollinearity and offers a metric for the statistical
significance of the correlation among the study's variables.
The results of the correlation matrix for each of the study’s variables are shown in table 3, such as
GDP has a significant positive association with all independent variables. GAIR demonstrates a moderate
positive connection with GDP (0.5251). Similarly, GOV shows a weaker but still positive correlation with
GDP (0.4315), while TECH and DINF display moderate positive correlations with GDP, at 0.4804 and
0.4254 respectively. The results also highlight the presence of strong intercorrelations among the
independent variables, notably between GAIR and GOV (0.8620), GAIR and TECH (0.8277), and GAIR
and DINF (0.8108). Such high correlations raise concerns about multicollinearity, which may bias
regression estimates. To resolve this issue, it is important to rely on “the generalized method of moment
(GMM)”, to ensure the robustness of the empirical results.
Table 3. Correlation Matrix
GDP
GAIR
GOV
TECH
DINF
GDP
1.0000
GAIR
0.5251
1.0000
GOV
0.4315
0.8620
1.0000
TECH
0.4804
0.8277
0.5290
1.0000
DINF
0.4254
0.8108
0.4441
0.7257
1.0000
Source: Stata 17 software output (2025)
Regression Analysis
“Two-step system GMM (Arellano-Bover/Blundell-Bond)” estimator was applied to address
potential endogeneity and dynamic dependence in short panel data. The potential issues with “GMM
estimation” are identified in this study by use of two conventional diagnostic tests, which the autocorrelation
of residuals is measured by the AR1- AR2 tests, and the validity of instruments is measured by the “Sargan”
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test. In order to ensure that the instruments utilised are legitimate, “Sargan” is employed. That the tools
utilised are external is the premise around which this test is based. Therefore, a high p-value is necessary.
The autocorrelation at levels 1-2 can be detected by the Arellano and Bond autocorrelation tests (AR1-AR2).
This test's null hypothesis states that, at levels 1 and 2, there is no serial correlation between the error terms.
To accept the null hypothesis, p-value for AR1-AR2 tests must be greater.
Arellano-Bond autocorrelation test was employed to examine the presence of serial correlation in the
differenced residuals. The results indicate that AR (1) test is statistically insignificant (z = -1.55, p = 0.121),
suggesting the absence of first-order serial correlation. More importantly, the AR (2) test is also insignificant
(z = 0.09, p = 0.928), which confirms the absence of second-order serial correlation in the residuals. This
outcome satisfies a key requirement of the “System GMM” methodology, thereby validating the consistency
of the estimator and indicating that the instruments used are not affected by excessive serial correlation.
The validity of the instruments was further assessed through the “Sargan” and “Hansen” tests of over
identifying restrictions. The Sargan test yielded a statistically insignificant result (χ²(12) = 18.35, p = 0.106),
indicating that the null hypothesis of instrument validity cannot be rejected. However, it is important to note
that the Sargan test is not robust to heteroskedasticity, although it is not weakened by the limited number of
instruments in this model. By contrast, the “Hansen” test, which is robust and more reliable in the presence
of heteroskedasticity, also produced an insignificant result (χ² (12) = 15.34, p = 0.223). This confirms that
the instruments are valid and not correlated with the error term. Taken together, these results provide strong
evidence that the GMM instruments used in the estimation are appropriate and that there is no indication
of instrument weakness. In addition, the Difference-in-Hansen tests for subsets of instruments confirm their
validity and exogeneity, further improving the trustworthiness of the estimates.
Methodologically, a limited number of instruments (17) were adopted, which is relatively appropriate.
However, the variable of government AI readiness was excluded due to multicollinearity, and its three
dimensions were sufficient.
The results suggest that GDP exhibits a remarkable degree of persistence, as indicated by the very
significant and positive coefficient of the lagged dependent variable (GDP (-1) = 1.037, p = 0.000).
This result explains why economic growth in African countries has maintained a strong continuity,
which suggests that past economic performance is largely responsible for current economic performance.
This result may reflect structural characteristics of African economies, such as limited diversification and
institutional rigidity.
Due to the dominance of the dependent variable, the marginal explanatory power of other
independent variables is reduced, indicating that structural and historical determinants of growth hold
greater influence than emerging digital readiness factors in the short term. This conclusion illustrates the
path dependent character of growth in the sampled African economies.
By contrast, the estimated coefficients of GOV, TECH, and DINF were statistically insignificant,
implying that their direct impact on economic growth was not visible during the short research period. This
outcome may be attributable to the restricted time dimension of the dataset or to the existence of indirect or
non-linear correlations not captured by the model. Taking into account dynamic stability, these factors
cannot have an independent, measurable effect on growth within the specified model and time horizon.
This result can be attributed to several economic explanations, the first of which is that government
readiness for AI does not automatically translate into productive economic outcomes. In many African
countries, AI strategies and digital policies may exist at the institutional level, yet implementation capacity,
digital infrastructure, skilled human capital, and private sector uptake remain limited. Therefore,
institutional readiness may not yet have reached the minimum threshold required to achieve measurable
aggregate economic impacts.
African economies also continue to face structural challenges, including high informal sector rates,
limited industrialization, weak research and development ecosystems, and infrastructure gaps. These
constraints may weaken the transmission mechanism through which AI readiness can stimulate economic
growth. In such contexts, basic development factors may overshadow digital transformation initiatives.
Table 4. Dynamic panel-data estimation, two-step system GMM
Variable
Coefficient
Corrected
Std.err.
T
P- value
GDP L1.
1.037437
0.0072046
144.00
0.000
GOV
30,400,000
42,100,000
1.20
0.237
TECH
-273,000,000
320,000,000
-0.85
0.398
DINF
84,000,000
188,000,000
0.45
0.657
_Cons
1,340,000,000
2,820,000,000
0.48
0.636
N of obs 176
N of instrument 17
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N of groups 44
AR(1): z = -1.55 Pr > z = 0.121
AR(2): z = 0.09 Pr > z = 0.928
Sargan test: chi2(12) = 18.35 Prob > chi2 = 0.106
Hansen test: chi2(12) = 15.34 Prob > chi2 = 0.223
Difference-in-Hansen tests of exogeneity of instrument subsets:
GMM instruments for levels
Hansen test excluding group: chi2(8) = 13.93 Prob > chi2 = 0.083
Difference (null H = exogenous): chi2(4) = 1.41 Prob > chi2 = 0.843
gmm(L.GDP, collapse lag(2 .))
Hansen test excluding group: chi2(9) = 8.09 Prob > chi2 = 0.525
Difference (null H = exogenous): chi2(3) = 7.25 Prob > chi2 = 0.064
iv(GAIR, eq(level))
Hansen test excluding group: chi2(11) = 15.33 Prob > chi2 = 0.168
Difference (null H = exogenous): chi2(1) = 0.01 Prob > chi2 = 0.924
Source: Stata 17 software output (2025)
CONCLUSION
This study provides empirical evidence on the impact of government AI readiness on economic
growth in 44 African countries over the period 2020–2024 using the GMM system. The findings reveal that
economic growth in the countries under study exhibits a high degree of dependence on its past values,
reflecting path dependence. In contrast, none of the independent variables (government, technology sector,
data & infrastructure) demonstrated a significant impact on economic growth during the period under study.
This result is likely due to the short time series and the weak explanatory power of the variables in the short
run, or to the existence of indirect or nonlinear relationships between the studied indicators and growth.
From a theoretical perspective, the findings suggest that digital readiness may represent a necessary
but not sufficient condition for economic growth. Arguments that focus on absorptive capacity, institutional
quality, and structural transformation as mediating factors between technological preparedness and
macroeconomic performance are supported by the results. In emerging economies, the growth-enhancing
effects of artificial intelligence may depend on complementary reforms in education, infrastructure, financial
systems, and governance effectiveness.
Overall, Government readiness for artificial intelligence does not necessarily translate into
widespread practical implementation, immediate productivity gains, or a direct contribution to gross
domestic product. In many African countries, although digital strategies and policy frameworks have been
introduced, critical enabling conditions—such as digital infrastructure, skilled human capital, and adequate
financial resources—remain relatively limited. Therefore, the hypothesis proposing a positive and significant
impact of GAIR on economic growth is not empirically supported. Consequently, the economic impact of
AI readiness is more likely to emerge over the long term rather than in the short term captured by the
dynamic model used in this study.
Despite government digital transformation initiatives, many African countries still suffer from a
significant gap in digital infrastructure, such as internet access and information security. This limits the
ability of digital transformation to create a tangible economic impact in the short term.
The impact also needs sufficient time to appear, as investment in government digitization is usually
long-term, and its impact on growth appears years later by improving the efficiency of services, reducing
costs, and stimulating investment. In general, the nature of African economies, which rely heavily on
traditional sectors such as agriculture and natural resources, may weaken the direct role of digital readiness
in stimulating growth.
Based on the findings of this study, policymakers should promote the adoption of artificial intelligence
in African governments, which requires a multidimensional strategy. Strengthening governance through
robust legal frameworks, institutional capacity building, and transparent digital strategies is essential to
creating an enabling environment. Developing technology by supporting local innovation, digitizing public
services, and investing in digital skills will also promote sustainable innovation. Equally important is
effective data management, which includes building secure repositories, implementing open data policies,
and improving data quality standards to ensure its reliability. Finally, widespread AI deployment will rely
on improving infrastructure through broadband expansion, establishing local data centres, and enhancing
cybersecurity.
This study opens several directions for future research on the relationship between government AI
readiness and economic growth, particularly in developing regions. First, future studies may examine the
long-term effects of AI preparedness by using longer time horizons or alternative dynamic modelling
Imane Benmimoun / Finance, Accounting and Business Analysis, Volume 8, Issue 1, 2026
57
approaches that can better capture the delayed impact of technological adoption on economic performance.
Since digital transformation often requires a considerable period before generating observable
macroeconomic outcomes, longitudinal analyses may offer deeper insights into the actual economic benefits
associated with artificial intelligence readiness. Furthermore, future research could integrate additional
institutional and structural variables that may influence the relationship between AI readiness and economic
growth. Variables such as human capital development, the quality of digital infrastructure, innovation
capacity, research and development expenditure, and governance effectiveness may play a crucial role in
transforming policy readiness into tangible economic productivity. Incorporating these factors could help
clarify the mechanisms through which AI readiness contributes to economic growth.
Future studies may also benefit from adopting comparative or regional approaches. By comparing
African economies with other developing regions, it is possible to obtain valuable evidence to determine
whether the limited impact observed in this study is a result of structural characteristics specific to Africa or
broader patterns across developing economies.
Acknowledgments
Not applicable.
Funding
This research received no external funding.
Data Availability Statement
The data used in this study are publicly available and can be accessed from the following sources: The
Government AI Readiness Index provided by Oxford Insights, available at:
https://oxfordinsights.com/ai-readiness/government-ai-readiness-index-2025/
The World Bank database (https://data.worldbank.org/indicator/NY.GDP.MKTP.KD).
These sources provide open-access data used for analysis in this study.
Conflict of Interest
The author declares no conflict of interest.
AI Tools Statement
The author confirms that no AI tools were used in the preparation of this manuscript.
Author Contributions
The author is solely responsible for all aspects of this work.
REFERENCES
Acemoglu, D., and R. Pascual. 2018. The Race between Man and Machine: Implications of Technology for
Growth, Factor Shares, and Employment. American Economic Review, 108(6):1488–1542. doi:
https://doi.org/10.1257/aer.20160696
Agarwal, P. 2018. Public Administration Challenges in the World of AI and Bots. Public Administration
Review., 78(6): 917-921. doi:
https://doi.org/10.1111/puar.12979
Ahangama, S., and S. Krishnan. 2023. Impact of AI Readiness and E-Participation Implementation of a
Government on the Well-being of its Citizens. e-Service Journal, 15(1): 29-59. doi:
https://dx.doi.org/10.2979/esj.00002.
Alalaq, A. S. 2025. The Oxford Insights Government AI Readiness Index (GARI): An Analysis of its Data
and Overcoming Obstacles, with a Case Study of Iraq. arXiv preprint, 1-18. doi:
https://doi.org/10.48550/arXiv:2503.20833.
Alhosani, K., and S. Alhashmi. 2024. Opportunities, challenges, and benefits of AI innovation in
government services. Discover Artificial Intelligence, 4(18). Retrieved from
https://doi.org/10.1007/s44163-024-00111-w
Amin Mohamed, H. 2024. Evaluating the Role of Artificial Intelligence Government Readiness in
Economic Growth: An Empirical Cobb-Douglas Analysis for Developing Countries . JCES Journal,
15(4): 734-762. doi:
https:/doi.org/10.21608/jces.2024.412983
Chih-Hai Yang. 2022. How Artificial Intelligence Technology Affects Productivity and Employment: Firm-
level Evidence from Taiwan. Research Policy, 51(6).
doi:
https://doi.org/10.1016/j.respol.2022.104536
Coelho, M. R., and M. H. Silva. 2024. A inteligência artifical como ferramenta de eficiência na
administração pública. Ciências Sociais Aplicadas, 29(140). doi:
https://doi.org/10.69849/revistaft/ch10202411122213
Desouza, K. C., G.S. Dawson, and D. Chenok. 2020. Designing, developing, and deploying artificial
intelligence systems: Lessons from and for the public sector. Business Horizons, 63(2): 205-213. doi:
https://doi.org/10.1016/j.bushor.2019.11.004
De Fegueiredo, S.A. 2024. DIGITAL READINESS AND ECONOMIC GROWTH. ECONOMY AND
Imane Benmimoun / Finance, Accounting and Business Analysis, Volume 8, Issue 1, 2026
58
FINANCE, 11(3): 282-300. doi:
https://doi.org/10.33908/EF.2024.3.3
Ingrams, A. K. 2021. In AI we trust? Citizen perceptions of AI in government decision making. Policy &
Internet, 14(02): 390–409. doi:
https://doi.org/10.1002/poi3.276
Kondo, T. S., and S. A. Diwani. 2023. Artificial intelligence in Africa: a bibliometric analysis from 2013 to
2022. Discover Artificial Intelligence, 3(34). doi:
https://doi.org/10.1007/s44163-023-00084-2
Maciejewski, M. 2016. To do more, better, faster and more cheaply: using big data in public administration.
International Review of Administrative Sciences, 83(1): 120-135. doi:
https://doi.org/10.1177/0020852316640058
Montoya, L., and P. Rivas. 2019. Government AI Readiness Meta-Analysis for Latin America And The
Caribbean. IEEE International Symposium on Technology and Society (ISTAS), 1-8. doi:
https://doi.org/10.1109/ISTAS48451.2019.8937869.
Noordt, C. v., and G. Misuraca. 2022. Artificial intelligence for the public sector: results of landscaping the
use of AI in government across the European Union. Government Information Quarterly, 39(3). doi:
https://doi.org/10.1016/j.giq.2022.101714.
Nzobonimpa, S., and J. F. Savard. 2023. Ready but irresponsible? Analysis of the Government Artificial
Intelligence Readiness Index. Policy & Internet, 15: 397–414. doi:
https://doi.org/10.1002/poi3.351
OECD. 2022. The Strategic and Responsible Use of Artificial Intelligence in the Public Sector of Latin
America and the Caribbean . Paris: OECD Publishing,. doi:
https://doi.org/10.1787/1f334543-en.
Plantinga, P., K. Shilongo, O. Mudongo, A. Umubyeyi, M. Gastrow, and G. Razzano. 2024. Responsible
artificial intelligence in Africa: towards policy learning. Data & Policy, 6(e72). doi:
https://doi.org/10.1017/dap.2024.60
Roodman, D. 2009. How to do xtabond2: An introduction to difference and system GMM in Stata. The
stata journal, 9(1): 86-136. doi:
https://journals.sagepub.com/doi/pdf/10.1177/1536867x0900900106
Saba, C. S., and N.Ngepah. 2024.The impact of artificial intelligence (AI) on economic growth in BRICS:
Does the moderating role of governance Matter?. Research in Globalization., 8. doi:
https://doi.org/10.1016/j.resglo.2024.100213
Sarker, P. K. 2022. Macroeconomic effects of artificial intelligence on emerging economies: Insights from
Bangladesh. Economics, Management and Sustainability, 7(1): 59–69. doi:
https://doi.org/10.14254/jems.2022.7-1.5
Seini, A. B., M.Issah, and I.O.Adam. 2024. Examining the Effects of Technology Sector Maturity on
Government Ai Readiness in Africa. Does Human Capital Matter?.Available at SSRN 4979976. doi:
http://dx.doi.org/10.2139/ssrn.4979976
Söker, B. 2024. Leveraging Artificial Intelligence for Public Sector Decision-Making: Balancing
Accountability and Efficiency in Digital Public Services. Human Computer Interaction, 8(1). doi:
https://doi.org/10.62802/ejr09s21
Straub, V. J., Y. Hashem, J. Bright, S. Bhagwanani, D. Morgan, J. Francis, S. Esnaashari, H. Margetts.2024.
AI for bureaucratic productivity: Measuring the potential of AI to help automate 143 million UK
government transactions. arXiv preprint arXiv:2403.14712. Retrieved from
https://arxiv.org/pdf/2403.1471
Wang, L., P. Sarker, K. Alam, and S. Sumon. 2021. Artificial intelligence and economic growth: A
theoretical framework. Scientific Annals of Economics and Business, 68(4):421-443. doi:
https://doi.org/10.47743/saeb-2021-0027
Yu, S. 2025. Ai Integration and Economic Growth: A Quantitative Analysis of Workforce Adaptation,
Government Policy, and Technological Infrastructure. doi:
https://doi.org/10.2139/ssrn.5081994
Zuiderwijk, A., Y. Chen, F. Salem. 2021. Implications of the use of artificial intelligence in public
governance: A systematic literature review and a research agenda. Government Information Quarterly,
38(3). doi:
https://doi.org/10.1016/j.giq.2021.101577
