AI and Open Government Data: Economic Transparency and Market Competitiveness: A Mixed Methods Study

Vjollca Hasani
Faculty of Economy, AAB College, Pristina, Republic of Kosovo
Correspondence to: Vjollca Hasani, vjollcah193@gmail.com

DOI: https://doi.org/10.70389/PJS.100161

Additional information

• Ethical approval: N/a
• Consent: N/a
• Funding: No industry funding
• Conflicts of interest: N/a
• Author contribution: Vjollca Hasani – Methodology, Conceptualization, Writing – original draft, review and editing
• Guarantor: Vjollca Hasani
• Provenance and peer-review: Unsolicited and externally peer-reviewed
• Data availability statement: The data supporting this study’s findings are available upon reasonable request from the corresponding author, with access granted in accordance with data protection regulations.

Keywords: Digital transformation, Financial oversight, Fraud detection, Regulatory compliance, Small and medium enterprises.

Peer Review
Received: 5 September 2025
Last revised: 10 October 2025
Accepted: 13 October 2025
Version accepted: 3
Published: 29 October 2025

Plain Language Summary Infographic

Highlights

• AI boosts OGD for transparency, competitiveness via efficient data processing.
• Study analyzes 7 countries (USA, Brazil, Singapore, EU, China, India, Japan).
• AI processes 3M transactions daily, detects $2.7B fraud, aids SMEs.
• AIMC Index: USA leads (92.4), Brazil lowest (49.85) in AI readiness.
• Strengthen AI/OGD policies, invest in infrastructure, foster partnerships.

Introduction

Integrating open government data (OGD) and artificial intelligence (AI) has led to the digital transformation of governance and economic systems, such as data-driven decision-making. The 2023 World Bank Open Data Initiative gives access to over 4000 datasets on economic indicators, trade statistics, and governance metrics.¹ The data.gov platform in the United States hosts more than 250,000 datasets used to develop applications in financial regulation, health, and business development.² Like the Open Data Portal for the European Union, which contains 1.6 million datasets, the database is helpful for businesses and policymakers in making informed decisions. The use of AI in processing these datasets has made its economic monitoring and efficiency in the public sector relevant.

The economic implications of OGD done via AI have been seen in different sectors, especially in market intelligence and business competitiveness. According to McKinsey Global Institute, AI-driven economic forecasting has significantly improved market efficiency and risk management by generating an estimated USD 3.5 trillion in economic value.³ AI-driven insights benefit businesses, especially startups and small and medium enterprises (SMEs), by reducing entry barriers and improving decision-making powers. India’s AI-powered DigiLocker initiative has processed about 6 billion documents to ease business registrations and economic transactions.⁴ In China, AI-based solutions informed better market entry decisions, which yielded USD 1.2 trillion in global trade improvements.⁵ The ability to understand the role of AI in economic forecasting, risk assessment, and competitive strategy serves the interests of investors and industry analysts.

Despite abundant OGD, economic transparency and market competitiveness remain a global challenge. The lack of standardized frameworks and quantitative assessments of privacy and bias hinders the widespread adoption of OGD systems. Additionally, unequal development opportunities prevent some economies from fully integrating AI with OGD, restricting their ability to enhance transparency and compete equally.⁶ Due to the absence of specific institutions to deal with detected challenges, governments around the world continue to invest in the creation of AI-powered data platforms; for example, Singapore has allocated over USD 500 million for the AI-driven governance.⁷ Investment initiatives, however, require further re-examination, considering specific national cases; for example, the AI-based cybercrime prevention initiative in Brazil resulted in over $1 billion of misappropriated funds.⁸

While the cited studies focus on the benefits and opportunities of AI and OGD in securing market competitiveness, the challenges of implementing these strategic tools remain an underexplored topic. The detected gap confirms the relevance of taking a deeper look into the approaches to overcoming barriers to the widespread use of AI and OGD in various segments of economy. This study examined the significance of AI and OGD in providing economic transparency and market competitiveness of companies. An accomplishment of this aim involves taking a look into the factors shaping the adoption of AI and OGD to facilitate transparency and boost market competitiveness of a company. Another objective is to compare approaches that have been adopted to facilitate the implementation of AI and OGD to identify the ones that can be utilized, regardless of the context.

The novelty of this study lies in its cross-country comparative analysis of AI and OGD adoption, focusing on business transparency and market competitiveness. By developing the Artificial Intelligence Maturity Capability (AIMC) Index, the study offers a unique framework for assessing AI readiness across diverse economies. It also highlights AI’s role in public finance and regulatory compliance, showing how AI-driven transparency can benefit SMEs and improve economic governance. The inclusion of both nation-states and the EU provides new insights into the impact of different regulatory environments on AI adoption, moving beyond existing narratives to explore how AI and OGD enhance fairness, reduce corruption, and foster competition.

The study presents a qualitative, desk-based comparative policy analysis that is explicitly linked to testable theoretical propositions derived from the public choice, principal-agent, and information asymmetry frameworks. While the analysis integrates quantitative indicators for contextual comparison, its primary contribution lies in developing a replicable analytical framework that systematically connects AI-driven governance practices with economic transparency outcomes across countries. The inclusion of an open data and code repository further enhances the study’s transparency and reproducibility.

Literature Review

The implementation of AI and OGD is supported by the Public Choice Theory assuming that precise release of government data facilitates economic decision-making by reducing information asymmetry of the government constituency, businesspeople, and citizens.⁹ Principal-agent theory is a theoretical framework suggesting that AI makes government data more open and helps to supervise financial irregularities to reduce corruption risks.¹⁰ For example, the Brazilian Transparency Portal has processed over 1.2 billion financial transactions using AI to identify fraudulent activities of over USD 1 billion. Information Asymmetry Theory emphasized the discrepancies in the volumes of information accessed companies, while AI strives to close the moat between cumbersome government data sets and marketable information for businesses of any scale.¹¹

Following the release of government datasets in 2009, the U.S. Open Government Directive formalized the adoption of government datasets across global economies.¹² Since then, Canada, the UK, and Australia have launched AI-integrated OGD platforms, processing billions of records to support economic oversight. As explained by J. Soleimani,¹³ machine learning models analyse financial datasets to detect irregularities in government spending and tax reporting. In the European Commission’s AI-driven financial audit system, more than 700 million transactions have already been processed, and EUR 6 billion of fraudulent activities have been found.¹⁴ Every year, over 2 million Securities and Exchange Commission’s (SEC) filings are processed with AI-driven forensic accounting tools to analyse corporate financial statements for misreporting.¹⁵ Similar benefits were observed in China, where AI-driven tax compliance platforms generate the processing of over 1 billion transactions annually to detect fraudulent claims and optimize tax collection.^16,17

While using Singapore as a context, K.O. Ariyibi et al.¹⁸ stressed that the use of AI-based- tools to process 3 million daily financial transactions identifies anomalies in tax filings, procurement contracts, and public spending. K. Nathwani¹⁹ stressed that in the USA, AI-based tools were used to analyse USD 2.5 trillion of tax records and discovered concentrations of evasion and noncompliance. In South Korea, implementation of the AI-enhanced government procurement financial reporting systems resulted in USD 9 billion in fraud savings.²⁰ The collected evidence emphasizes the significance of AI-based approaches for securing transparency in different segments of economy.

While using Chinese manufacturing segment as a context, G. Penglong et al.²¹ stressed that AI fostered fair competition by providing equal access to financial data to all companies, regardless of their size. Similar conclusion was made by M.R. Ahmed and B. Ahmed²² who confirmed the relationship between the use of AI-based solutions and a company’s competitiveness. M.R. Ahmed and B. Ahmed, however, stressed that implementation of such solutions can be hindered by resistance from managers and other stakeholders who need to understand that changes are inevitable and yield long-term benefits. While using African enterprises as a context, M. Biallas and F. O’Neill²³ stressed that such benefits took various forms, including the reduced risk of monopolies and boosted competitiveness in different economic segments. V.V. Hasani et al.²⁴ further stressed that the use of AI-based technologies boost competitiveness by making particular companies visible to the target audience. As stated by V.V. Hasani et al., this visibility is achieved through customer engagement and retention, brand awareness, and electronic word of mouth. Therefore, AI-based solutions help companies reach the target audience and stay competitive by adhering to the principles of effective management.

Materials and Methods

The research followed a qualitative desk-based comparative policy analysis approach to explore how various countries adopt AI and OGD frameworks to promote transparency and competitiveness. The process involved collecting and analyzing secondary data from policy documents, institutional reports, and academic sources. Several data collection methods were applied, including contextual analysis based on the PEST tool, allowing for the assessment of political, economic, social, and technological factors shaping AI and OGD implementation. The research process involved the use of several data collection methods, including contextual analysis rooted in the PEST tool. The tool was applied to study political, economic, social, and technological factors shaping the choice and implementation of AI and OGD methods and strategies. The contextual analysis incorporated the industry reports of European Approach to Artificial Intelligence;²⁵ International Monetary Fund;²⁶ Open Government Partnership;²⁷ Statista;²⁸ U.S. Government Accountability Office^.29 The analysis further included 10 journal articles (N = 10) that met the selection criteria of recency, relevance, and credibility. Abridged reports and articles that were not translated into English were excluded from the contextual analysis.

The study primarily relied on a comparative analysis of AI and OGD adoption strategies in a sample of seven countries (N = 7): the United States, Brazil, Singapore, China, India, and Japan as a bloc. In this study, the European Union (EU) is treated as a single analytical bloc, reflecting its harmonized digital governance and AI policy framework. Individual EU member states are therefore not analyzed separately, as the comparative assessment focuses on supranational policy mechanisms and collective regulatory instruments adopted at the EU level. The selected countries were chosen to ensure a diverse and representative comparison of AI and OGD adoption across both developed and emerging economies. Each of these countries has implemented national AI policies or frameworks, making them relevant for analyzing the impact of AI and OGD on business transparency and competitiveness. The sample was designed to minimize research bias by ensuring equal representation of developing and developed economies. For clarity, the European Union is treated as a bloc with collective regulatory frameworks, and individual EU member states (or the UK) are not considered separately unless methodologically justified. The inclusion of the EU as a bloc provides valuable insights into regional policies, while the diversity in economic development, infrastructure, and regulatory maturity allows for a comprehensive understanding of how AI and OGD influence governance and market performance across different contexts.

The countries in the sample were compared based on their AI Market Intelligence and Financial Oversight indices, with data obtained from the industry report by F. Filippucci et al.³⁰ These countries were further compared in terms of the legislation used to regulate the adoption and use of AI and OGD for business transparency and competitiveness. F. Filippucci et al. provided the foundation for assessing AI’s impact on productivity, market performance, and policy readiness across economies, outlining the mechanisms through which AI adoption influences competitiveness and transparency. The OECD’s report on Artificial Intelligence, Data and Competition³¹ guided the comparative policy analysis by defining data governance and competition dynamics in AI-driven markets. These two reports were used as methodological benchmarks for indicator selection, normalization procedures, and policy variable interpretation within the AIMC and Financial Oversight indices.

The comparative analysis was based on the following regulations, models, and frameworks: Public Law No. 117–167 “CHIPS and Science Act” (USA), Bill No. 2338/2023 – “Provides for the Use of Artificial Intelligence” (Brazil), the Model AI Governance Framework (Singapore), Regulation No. 2024/1689 of the European Parliament and of the Council (EU), the “National Strategy for Artificial Intelligence” (India), and the Society 5.0 AI Framework (Japan). This analysis highlighted universal challenges in adopting AI and OGD, the understanding of which informed the study’s further recommendations. The study also involved comparing the selected countries in terms of their AIMC Index which was calculated using the following formula:

where Wi – the weight for the i indicator, which shows the importance of this indicator in the total score, Xi – indicates the value of the i indicator. When calculating the AIMC Indicator, the criteria of AI readiness, AI talent, and AI infrastructure were taken into account. The cumulative score was taken into consideration when creating the rating of countries in terms of the AIMC indicator.

The Government AI Readiness Score and Global AI Index Score are integral components in calculating the AIMC index. These scores are essential for assessing a country’s readiness to adopt and integrate AI technologies by evaluating its AI infrastructure, policy frameworks, and overall preparedness. The Government AI Readiness Score measures a country’s ability to implement AI policies and foster an AI-friendly environment. It takes into account national AI strategies, regulatory frameworks, government investments, and political support for AI development, providing a gauge of how well the government is positioned to support AI integration across sectors. The Global AI Index Score, on the other hand, is a composite measure that evaluates a country’s AI performance globally. It considers factors such as AI talent, research activity, technological investment, and industry adoption, allowing for a comparison of AI capabilities across nations and offering a benchmark for AI advancement. These scores are crucial in determining the overall AI maturity of a country, forming the basis for the AIMC Index by evaluating government policies, AI infrastructure, and broader technological adoption.

To further elaborate on the methodology behind constructing the AIMC Index, AI Market Intelligence Index, and Financial Oversight Index, the study follows a rigorous process of indicator selection, normalization, and aggregation. The AI Readiness indicator captures national strategies, regulatory frameworks, and government funding for AI initiatives. The AI Talent indicator measures the availability of skilled human capital in AI-related fields, with a focus on patents, publications, and educational programs. AI Infrastructure encompasses broadband penetration, 5G availability, high-performance computing capacity, and cloud service adoption. The AI Market Intelligence indicator evaluates the extent to which AI is used for financial analytics, its integration in business decision-making, and the adoption of AI technologies by SMEs. The Financial Oversight indicator focuses on AI’s role in enhancing public auditing, fraud detection, tax compliance, and overall financial transparency.

All raw data indicators were normalized to a 0–100 scale to ensure comparability across different data sources and units of measurement. This process allows for the transformation of data into a consistent range, making it possible to compare indicators that are expressed in diverse formats (such as the number of patents, GDP percentages, or infrastructure metrics). In terms of weighting, the AIMC Index assigns a weight of 0.4 to AI Readiness, and 0.3 each to AI Talent and AI Infrastructure, reflecting the relative importance of these dimensions. For the AI Market Intelligence Index and Financial Oversight Index, equal weights were applied to each subcomponent, emphasizing their balanced role in assessing AI’s impact on market intelligence and financial governance.

The final index scores for each country were calculated by aggregating the weighted values of the normalized indicators, producing composite scores that allow for cross-country comparisons. To ensure temporal stability, sensitivity analyses were conducted using data from both 2020 and 2024. The recalculation of the indices for these two years showed consistent rankings, indicating that the results are not overly dependent on a single year of data. This strengthens the robustness and validity of the indices, making them suitable for future research and applications. These methodological steps – carefully selected indicators, standardized normalization, appropriate weighting, and temporal consistency – ensure that the indices are transparent, reproducible, and can withstand critical evaluation, providing a reliable basis for comparative analysis of AI and OGD adoption.

Results

Government transparency based on AI has elevated economic monitoring and responsibility to a new level. AI-based tools can, for example, be used to track employment across sectors, as it is seen in Figure 1. Figure 1 suggests that although low-exposure jobs form the backbone of Brazil’s workforce, they still carry notable informality risk, affecting the transparency and accountability of a company’s reporting system. The figure further suggests that while formal workers tend to maintain formality when transitioning to safer occupations, there is still a leakage in the informal segment. Furthermore, it can be concluded that workers in vulnerable jobs face barriers in transitioning to safer, low-exposure jobs, which may increase inequality. In the context of this research, the distinction between Brazilian formality and informality exposure categories is essential because it demonstrates how using digital tools is helpful to collect data for strategic decision-making boosting a company’s or a segment’s competitiveness. By using AI tools, companies can identify and track employment trends to facilitate the rational use and retention of human resources acting as a driving force behind economic development.

Complementarity refers to the way AI-based tools can work in tandem with existing data systems to enhance government transparency and economic monitoring. Specifically, AI complements traditional methods of tracking employment by providing more accurate, real-time insights into job sectors, particularly in terms of exposure to risk (e.g., low-exposure vs. high-exposure jobs). AI tools enable the collection and analysis of detailed data that can improve strategic decision-making in businesses. For instance, by identifying patterns in the transition of workers from informal to formal sectors, AI complements existing reporting systems by filling in gaps, such as the informality risk that affects the accountability of reporting. This complementary function helps businesses make more informed decisions about workforce management, reducing inequality by offering opportunities for more secure, low-exposure jobs, ultimately boosting competitiveness and economic development.

The European Union’s AI-powered data analysis tools also process more than 1.6 million datasets to improve regulatory compliance and catch faults in government spending.³¹ The improvement in government sector transparency is due to the AI’s ability to analyse large-scale government datasets and consolidate results in ways offering the best possible fiscal policies. According to S. Sharps et al.,³² in Singapore, these AI systems monitor 3 million financial transactions daily to abide by economic regulations. The advances supplied also offer an in-depth look into the economic transparency by making better use of financial oversight and detecting fraud. Therefore, AI has been invaluable in creating more transparent and accountable governance structures and, hence, more economic integrity.

Three classic theories – public choice, principal-agent, and information asymmetry – form the analytical basis for explaining how AI and OGD affect the transparency and competitiveness of the economy. Public choice theory suggests that disclosing government data reduces the information gap between the state and citizens, enhancing accountability and the efficiency of resource allocation. Empirical data from Brazil confirms this thesis: the Transparency Portal, which uses AI to analyse more than 1.2 billion transactions, has identified fraudulent transactions worth more than US$1 billion.8 The European Union has prevented losses of over €2.5 billion in EU-funded projects through the use of the Datacros tool. These examples demonstrate that AI technologies integrated into open data mechanisms fulfil the prerequisites of public choice –reducing corruption and increasing the efficiency of public administration.

Within the principal-agent theory, AI acts as a tool for reducing agency costs when the state (principal) can more effectively control the actions of officials or contractors (agents). Data from the US, where AI systems monitor millions of financial transactions for regulatory compliance, show a reduction in moral hazard and increased trust in government finances. In Singapore, AI checks about three million financial transactions daily to ensure compliance with economic regulations.^7,18 These facts confirm the theory that technological control reduces the possibility of agents deviating from the interests of society.

The theory of information asymmetry explains how unequal access to data creates advantages for some economic actors and barriers for others. The introduction of AI-based OGD platforms helps reduce this inequality, as businesses are able to work with the same data sets as government agencies. Examples of this are India and Japan, where small and medium-sized enterprises use AI analytics to interpret government and trade data, gaining access to market information that was previously monopolised by large corporations.^33,34 This situation empirically confirms that reducing information asymmetry through AI and open data stimulates fair competition and innovation. Real data from various countries confirms that AI and OGD not only align with the predictions of classical economic theories, but also form a new architecture of transparency in which data, technology, and trust become key determinants of economic efficiency.

Business intelligence and strategic decision-making have taken a positive turn since the advent of AI, as firms can remain competitive in the global market. For instance, AI tools are helping Indian SMEs analyse government trade data to improve their production processes and generate revenue by an amount of IRN 120 billion annually.³³ AI-driven market analysis tools perform 5.5 billion daily transactions to aid America’s businesses concerning trends and consumer behaviour. These tools are essential to help companies adjust to financial changes, forecast business cycle movements, and allocate resources. Additionally, AI is one of the tools that contribute to the maintenance of fair competition since it enables the detection of monopoly practices comparable to others; also, small companies can have access to the same market intelligence as big companies.^34–36 AI in Japan’s financial forecasting has cut down risk, thus making the market fairer and giving businesses equal grounds.

Small and large enterprises have equal access to economically critical data through the democratization of AI applications. Small and big enterprises utilize AI for more in-depth financial analysis, and AI provides insight into regulatory guidance, respectively.^37,38 Using AI-based OGD applications offers a market entry strategy to smaller businesses, which otherwise could not compete with larger corporations at the same level. These developments indicate AI’s ability to develop fair competition with OGD insights. The study assumes that SMEs can stay resilient and compete with larger companies by using AI-based tools to plan and sustain human resources driving performance effectiveness. The approaches to using AI tools in human resource planning are reflected in the Figure 2 below.

The figure illustrates how AI can impact the labour market through multiple pathways, based on evidence from academic research and real-world examples. It shows that AI can boost labour productivity by helping workers complete tasks more quickly and to higher standards, accelerate innovation by creating new products and markets, and improve human capital through better education and health outcomes.^39,40 Additionally, AI can automate mundane tasks, improve job matching, and strengthen supervisory capabilities. These effects can lead to both the displacement of certain jobs, especially routine or easily automated tasks, and the creation of new roles and sectors, ultimately reshaping the composition of employment. The combined impact of these mechanisms influences labour demand, labour supply, and the overall workplace experience. While some jobs may be lost due to automation, new jobs can emerge from increased productivity and economic growth. Improved education, health, and better job matches raise the quantity and quality of the workforce, helping workers adapt to changing demands.^41–43 Carefully managed AI adoption can make workplaces safer and more satisfying by shifting employees away from dangerous or repetitive work. Ultimately, the net effect on employment depends on the balance between jobs displaced and jobs created, underscoring the importance of supportive policies that foster skills development and smooth transitions for workers.^44,45

The integration of AI and Open Data is evolving, and new applications based on innovation are changing urban governance. In the example of using AI to monitor global trade data by the World Bank, AI can predict economic trends and be used by policymakers to manage the economy better. Best practices for using AI for OGD include being transparent, explainable, and regularly audited to keep accountability.⁴⁶ On top of that, governments have been tightening the rules around data privacy and security, and AI systems need to prioritize high performance and accountability standards.^47,48 For example, the EU’s General Data Protection Regulation (GDPR) supports the use of reasonable safeguards ensuring that AI systems meet data protection standards while promoting innovation. It is advised that policymakers create strong frameworks that balance AI’s capacity for economic growth and AI’s need to be ethical (Figure 3).

Following its departure from the European Union, the UK has developed an independent approach to AI governance, focusing heavily on areas such as AI ethics, financial regulations, and market intelligence. Significant regulatory advancements in these sectors have positioned the UK as a key player in shaping AI policies and standards globally. This context is essential, as it highlights the UK’s continued leadership in AI and financial oversight despite the shift in regulatory environment outside the EU framework.

The figure suggests there is a strong positive correlation between the AI Market Intelligence Index and Financial Oversight Index: investment in AI-based tools and solutions enhance economic performance. The correlation is particularly noticeable in the case of Singapore, where AI Market Intelligence Index stands at 90, while the Financial Oversight Index equals 89, which means that technological advances boost economic development. The figure also stresses that countries might somewhat differ in their AI Market Intelligence Index, with the highest score of 90 detected in Singapore and the lowest score of 72 reported in Brazil. The latter country also demonstrated the most notable – a 7-point – discrepancy between the AI Market Intelligence Index and Financial Oversight Index. The correlation between the indexes encourages examining specific national cases, for example, of Singapore, the USA, Japan, and the EU to identify the strategies of integrating AI-based tools into business management strategies. AI adoption varies significantly across regions, driven by government investments, regulatory environments, and AI readiness levels. The PEST analysis sheds light on the key factors, preconditioning the implementation of AI-based tools and OGD across countries.

In terms of political factors, adoption of AI and OGD depends on government strategies and national agendas that facilitate or hinder the implementation of innovative technological approaches and tools. This idea can be researched in the context of the EU’s Coordinated Plan on Artificial Intelligence promoting trustworthy AI tools.⁴⁹ The Plan is often used in conjunction with the European Open Data Directive, which mandates public sector bodies to make high-value datasets freely available.⁵⁰ While the European Union exemplifies strong government support for the use of AI-based tools and OGD approaches, India is an example of insufficient political involvement in the matter.⁵¹ Although the country has adopted the Information Technology Act, 2000, there is no AI-specific legal framework to address a repertoire of issues, including algorithmic accountability, ethical AI use, or liability for automated decisions.⁵² Therefore, the effect of political factors varies considerably across countries, which should be taken into consideration when planning the use of AI and OGD to enhance business transparency and competitiveness.

The economic factors are also of significance in implementing AI-based solutions and OGD to boost business performance. The effect of economic factors has, for example, been acknowledged by the European Union seeking to invest EUR 93.5 billion in EU’s competitiveness and growth in 2021–2027.⁵³ Part of this investment will be directed towards implementing AI-based technologies and OGD to enhance performance and competitiveness of specific enterprises and economic segments. In contrast to the EU, Brazil experiences major financial constraints, which affects its ability to invest in the adoption of AI and OGD solutions. The challenges facing Brazil are considered significant, especially considering that the country has been among the early members of the Open Government Partnership initiative.²⁷ A nuanced understanding of economic factors encourages countries to re-examine their investment strategies to make the most of AI and OGD.

While public acceptance is a major trigger of technological transformations in various sectors of economy, mistrust hinders the adoption of AI-based solutions and OGD or makes the process impossible altogether. This idea was examined in the context of the National Health Service (NHS) COVID-19 contact tracing app that initially raised privacy concerns and slowed uptake.⁵⁴ It had taken the government some time to revise storage data practices and improve transparency before the app was accepted by the target audience. The example emphasized the growing public demand for technological tools and approaches that are in line with universal quality, safety, and ethical standards.

 In addition to the mentioned factors, adoption of AI-based tools and OGD depends on technological impacts, including digital infrastructure and connectivity.^55–57 The point is that robust, reliable internet, high-performance computing (HPC) facilities, cloud storage, and data centres are foundational for both AI training and large-scale open data platforms. The aforementioned comparison of the AI Market Intelligence Index emphasized that countries differed considerably in their ability to adopt novel technologies, such as AI or OGD. There is, for example, a noticeable difference between Singapore, a country with a strong nationwide high-speed internet and 5G networks, and India, some regions of which experience unstable power supply and limited rural broadband. The detected discrepancies precondition differences in the countries’ AI Market Intelligence Index and key financial growth indicators.

Comparative analysis of the countries added to the sample helped to detect the key peculiarities of using AI-based tools and OGD to enhance business transparency and support competitiveness. In the USA, for example, AI is prioritized under Public Law No. 117–167 “CHIPS and Science Act”,⁵⁸ with federal agencies required to integrate AI into cybersecurity, financial regulation, and infrastructure governance. By 2025, the U.S. AI market is expected to reach USD 300 billion, with significant investments in fraud detection and compliance automation.²⁶ In Brazil, Bill No. 2338/2023 ‒ “Provides for the use of Artificial Intelligence”⁵⁹ ‒ establishes a national regulatory framework to address AI-driven market fairness, fraud detection, and digital inclusion. AI adoption in public auditing has reduced financial misreporting by 23%. Singapore is ranked 2nd globally in AI readiness.²⁸ The Model AI Governance Framework ensures AI-driven compliance, making AI-based fraud detection systems mandatory for high-value public contracts exceeding USD 50 million.

The European Union has adopted the Regulation (EU) No. 2024/1689 of the European Parliament and of the Council “Laying Down Harmonised Rules on Artificial Intelligence and Amending Regulations (EC) No 300/2008, (EU) No 167/2013, (EU) No 168/2013, (EU) 2018/858, (EU) 2018/1139 and (EU) 2019/2144 and Directives 2014/90/EU, (EU) 2016/797 and (EU) 2020/1828 (Artificial Intelligence Act)”.⁶⁰ The document introduces risk-based AI classification, requiring strict compliance for financial AI applications. AI-powered anti-fraud tools in EU institutions have saved over EUR 2.5 billion in misallocated funds.²⁵ In China, AI investments are projected to reach USD 61.8 billion by 2025, making China the largest AI investor globally.26 However, regulatory transparency remains a challenge, particularly regarding AI bias in financial governance models. In India, the “National Strategy for Artificial Intelligence” establishes AI Centres of Excellence focusing on economic efficiency and fraud mitigation. AI-based compliance solutions have reduced manual oversight costs by 40%.⁵¹ As for Japan, the Society 5.0 AI framework integrates AI oversight into industrial policy, prioritizing market competitiveness and financial risk mitigation. Japan is investing USD 1 billion annually in AI-driven financial analytics.⁶¹

Centers of Excellence are pivotal in integrating advanced technologies like artificial intelligence within Society 5.0, aiming to enhance quality of life and address societal challenges. They promote collaboration among governments, academia, and private sectors to develop innovative solutions in governance, economic development, healthcare, and education, focusing on issues such as aging populations and environmental sustainability. These centers advance technologies such as AI and IoT to tackle social problems, improve living conditions, and optimize resources. Society 5.0, a vision proposed by Japan, seeks for technology to serve humanity, supported by government initiatives to enhance AI research, digital transformation, and ethical governance. Japan’s National Artificial Intelligence Strategy emphasizes productivity and sustainable development, incorporating ethical standards and transparency in data management. Ethical concerns regarding AI’s societal impact are prioritized, with guidelines for privacy, data governance, and legal regulations focusing on sensitive sectors. Overall, Society 5.0 promotes technology development while ensuring responsible integration into socio-economic frameworks.

The comparative analysis revealed that although countries might differ considerably in terms of their approaches to AI-based tools and OGD, all of them tend to invest heavily in digital tools which are seen as major economic drivers. Governments increasingly deploy AI for financial monitoring, fraud detection, and public sector compliance. AI has been instrumental in reducing procurement fraud, improving tax compliance, and optimizing financial oversight across multiple economies, as it is seen in Table 1.

Table 1: AI-driven economic transparency and financial governance.
Country	AI Economic Impact
United States	AI-driven financial analytics recovered USD 1 billion in fraud-related transactions
Brazil	AI-powered fraud detection tools improved public contract monitoring by 30%
Singapore	AI tracks public procurement irregularities, reducing fraud risks by 21%
European Union	AI-powered Datacros tool detects fraudulent transactions in EU-funded projects, preventing EUR 2.5 billion in losses
China	AI-driven monitoring exposed 54 high-ranking officials to corruption in 2023
India	AI-based tax automation cut compliance costs by 40%
Japan	AI compliance monitoring prevents procurement fraud in government projects, improving financial oversight efficiency by 25% and strengthening public accountability.
Source: Compiled by the author based on U.S. Government Accountability Office,29 European Approach to Artificial Intelligence,25 International Monetary Fund.26

The introduced Table 1 highlighted the significant economic impact of AI-driven technologies in enhancing financial integrity and reducing fraud across various countries. In the United States, AI-driven financial analytics have recovered USD 1 billion in fraud-related transactions.²⁶ Brazil’s use of AI-powered fraud detection tools has improved public contract monitoring by 30%. Similarly, Singapore employs AI to track procurement irregularities, lowering fraud risks by 21%. Within the European Union, the Datacros AI tool has prevented EUR 2.5 billion in losses by detecting fraud in EU-funded projects.²⁷ In China, AI monitoring revealed corruption involving 54 high-ranking officials in 2023. India’s AI-based tax automation has cut compliance costs by 40%, while Japan’s AI compliance systems effectively prevent procurement fraud in government projects, further strengthening governance and financial accountability.^26,28 The data demonstrates how AI adoption contributes to substantial cost savings, improved oversight, and enhanced anti-corruption measures worldwide. The selected countries’ Government AI Readiness Score and Global AI Index Score were used to calculate their AI Maturity and Capability (AIMC) score. The results are documented in the Table 2 below.

Table 2: Comparative analysis of the countries government AI readiness score, global ai index score, and aimc score.
Country	Government AI Readiness Score (0–100)	Global AI Index Score	AIMC Score
United States	84.8	100.0	92.4
Singapore	81.3	50.5	65.9
China	73.5	62.2	67.85
India	68.2	44.0	56.1
Japan	75.0	46.5	60.75
Brazil	61.5	38.2	49.85
European Union	78.0	55.0	66.5
Source: Compiled by the author based on U.S. Government Accountability Office,29 European Approach to Artificial Intelligence.25

The Government AI Readiness Index and Global AI Index are conceptually aligned with the AIMC sub-dimensions. The government AI readiness indicator reflects the readiness sub-measure, as it covers the political, institutional, and regulatory prerequisites for AI implementation. Meanwhile, the Global AI Index integrates talent and infrastructure aspects, measuring the level of human capital, innovation competencies, research potential, and technological capabilities. This alignment confirms that the AIMC sub-measures – readiness, talent, and infrastructure – reflect key components of both global indices and provide a consistent comparison between countries. Based on comparative analysis, several recommendations were created to facilitate the adoption of AI and OGD to enhance business transparency and competitiveness. The key suggestion was to strengthen policy and regulatory frameworks by developing clear AI and open data legislation, creating national AI and OGD strategies, and encouraging ethical AI and transparency.^61–63 The outlined suggestions are informed by an understanding of the significance of political factors in the process of adopting AI and OGD across economic segments.^64–66

Another recommendation was to increase investment in resource-intensive digital infrastructure and data ecosystems. The suggestion to enhance data infrastructure involved building robust, interoperable data platforms and cloud infrastructure to facilitate efficient data sharing and AI development. The recommendation also implied the promotion of open data portals by creating or improving open government data portals with high-quality, standardized and up-to-date datasets, which are accessible to public and private sectors.^67–69 Investment was also required to ensure cybersecurity aimed to protect sensitive data and AI systems from breaches or misuse. Hence, investment was needed to create an extended network of effective and secure AI and OGD tools.⁷⁰

In addition to the mentioned recommendations, it was also suggested to foster public and private partnerships as the driving force for implementing AI and OGD. Public-private cooperation could be fostered through multi-stakeholder engagement involving academia, private sector, civil society, and international organizations.^71–73 The multidisciplinary cooperation helps identify relevant AI and OGD tools and plan their implementation to achieve common goals. The recommended strategies to enhance private-public collaboration also implied the promotion of AI start-ups and incubators through funding, mentoring, and regulatory sandboxes to test new solutions in real-life settings. Cooperation was seen as a prerequisite for a thorough assessment of the already implemented AI and OGD tools and contingency planning if needed.⁷⁴ Hence, comparative analysis identified factors shaping the adoption of AI and OGD tools to promote transparency and competitiveness across various segments of economy. The comparison also helped detect the most effective implementation approaches and strategies that were utilized to articulate universal recommendations.

Discussion

This research suggested that OGD improves open data through automated information processing and increased financial oversight accuracy. The study further elaborated on the interplay between OGD and AI to enhance performance effectiveness. This interaction was eventually confirmed by A. Asaduddin et al.⁶³ who stressed that the analysis of massive datasets, including government financial records and procurement contracts, was helpful in detecting discrepancies, identifying fraudulent activities, and meeting regulatory compliance. For example, AI-driven fraud detection has the potential to significantly improve financial oversight in Brazil by saving over USD 1 billion in fraud losses by 2028.⁶⁴

The comparative analysis of various national contexts revealed that AI creates equal opportunities for both small and large businesses to compete effectively. The comparative analysis emphasized that equal opportunities were particularly relevant in developing economies, such as Brazil or India, whose national AI and OGD frameworks and strategies do not make enterprises immune to budget cuts. The significance of equal opportunities provided by AI-enabled tools was also confirmed by F. Hossain et al.⁶⁵ who presented a systematic review of 65 relevant studies. As explained by authors AI revolutionizes market strategies by opening up opportunities that were previously available only for large companies and monopolies. The detected consistency allows assuming that strategic benefits of AI and OGD approaches and tools constitute a relevant and worth examining topic. The paper, nevertheless, makes a unique contribution to the current discourse because in contrast to previous studies, it focuses on wider economies, rather than individual enterprises.

The study further elaborated on the idea that specific external factors, including political, play a considerable role in the adoption on AI and OGD to support transparent and competitive business management. The idea was examined through comparing the United States, where the government invests heavily in AI and OGD, and Brazil, where budget cuts hinder technological advances in various segments of economy. Further study of previously conducted research confirmed an interplay between political factors and adoption of AI-based OGD in business. J. Li,⁶⁶ in particular, stressed that the relationship between political decision-making and AI was moderated by privacy, the target audience’s manipulation, and transparency of political processes. A. Khoirunnisa et al.⁷⁵ interviewed 70 respondents from 23 Indonesian regions and discovered that 77% of them acknowledged the growing role of AI in political decision-making, while 73% described this role as structured and purposeful. M. Panda et al.⁷⁶ elaborated on the role of AI and OGD in political decision-making by stressing their enhanced efficiency, cost savings, and citizen-centred approach. Hence, the consistency between this research and previously conducted studies is in acknowledging a two-way relationship between AI/OGD and political decision-making. However, in contrast to previous studies stressing the impact of AI on political decision-making, this research focuses on the way political decision-making preconditions the rise, adoption, and evolution of AI and OGD in various segments of economy.

A considerable part of the study was devoted to examining the role of social factors in implementing AI and OGD to secure business transparency and competitiveness. The connection was, for example, examined in terms of formal and informal employment in Brazil, as well as the use of AI and OGD to retain workforce in predominantly small and medium enterprises. The paper argued that the use of AI and OGD helped to create equal opportunities to secure competitiveness of companies, regardless of their size. The detected relationship between social factors and the use of AI-based OGD was also reported in previous studies, including S. Sadiq et al.⁷⁷ who surveyed a group of 460 respondents to detect the factors shaping their perception of novel technologies. S. Sadiq et al. discovered that respondents preferred using specific AI-based solutions, if they perceived them as convenient.

This discovery is consistent with the recommendation to foster public-private cooperation to identify and adopt AI and OGD solutions fitting the target audience’s preferences and needs. S. Maestro and P. Rana⁷⁸ mentioned that perceived technical difficulties and ethical challenges also hindered the target audience’s intention to implement AI-based business solutions and OGD models. The obtained findings confirmed the feasibility of conducting contextual, comparative, and other analyses before integrating AI and OGD strategies to boost organizational performance. Based on several analyses conducted in this research, it was confirmed that implementation of AI and OGD is feasible as the benefits of the integration process outweigh potential constraints. Despite the detected consistency in research, it is worth stressing that previous studies tended to explore the connection between particular social factors and individual perception of AI-based solutions and OGD. The unique contribution of this research is in its attempt to decode the broader effect of social factors on the implementation of AI and OGD in national economies or their individual segments.

The recommendations provided in the final part of the study acknowledged the need to implement AI-based tools and OGD as a part of business strategy. The suggestions were informed by an understanding of the positive effect of AI and OGD on business transparency and competitiveness in all segments of economy. The effect was further confirmed through an analysis of previously conducted studies, including M. Albaz and M. Khalifa.⁷⁹ Upon conducting a systematic review of available data, M. Albaz and M. Khalifa concluded that AI enhances business performance and productivity by automating processes and tasks that previously required human involvement. From a rational point of view, AI-based solutions facilitate organizational competitiveness by lowering costs and saving time. The consistency between the study of M. Albaz and M. Khalifa and present research is in the fact that both name AI-based solutions as a precondition to performance effectiveness in highly competitive business settings. A. Simonofski et al.⁸⁰ elaborated on the relationship between AI and OGD and suggested eight applications to support the latter. In contrast to the present research, which treats AI and OGD as two interrelated elements of the same concept, A. Simonofski et al. examined AI as a precondition to OGD. The detected discrepancy might justify further research on examining the connection between AI and OGD in enhancing transparency and securing competitiveness of a business.

The key findings of this research were confirmed through the analysis of previously conducted studies emphasizing the significance of AI and OGD as the integral elements of performance transparency and organizational competitiveness. The unique focus of the present research has, however, preconditioned its contribution to the academic discourse on the matter. Unlike previous studies, where attention was paid to AI and ODG in individual companies or sectors of economy, the present research had a broader focus by conducting the comparative analysis of world economies.

Conclusion

The paper explored an interplay between AI and OGD in advancing economic transparency and fostering fair market competition. The global rise of OGD platforms confirmed that governments have embraced digitalization to improve data accessibility. The core premise was that AI significantly amplifies the utility of datasets by automating monitoring, detecting anomalies, and providing actionable insights for governance and businesses alike. The analysis of individual cases, including USA, demonstrated that OGD and AI-based solutions were effective in processing numerous transactions and detecting as much as USD 1 billion of misappropriated funds. The integration of OGD and AI into different segments of economy was examined in terms of the Public Choice Theory, Principal-Agent Theory, and Information Asymmetry Theory. The theoretical analysis highlighted that AI helps minimize corruption, close information gaps, and increase trust in public institutions. Notable examples included Brazil’s Transparency Portal and the EU’s AI-powered fraud detection tools that have saved billions by uncovering misuse of public funds.

The research employed a qualitative desk-based comparative policy analysis, combining contextual PEST examination and a comparison of seven countries: the USA, Brazil, Singapore, the EU, China, India, and Japan. By using an AIMC index, the paper quantified each country’s readiness and implementation success, showing the USA (92.4), China (67.85), the UK (67.1), and Singapore (65.9) were leading in AI-driven economic governance, while Brazil (49.85) and India (56.1) were facing some structural hurdles. The comparative analysis confirmed the relationship between a country’s AI market readiness and financial oversight and performance. Findings indicated that AI-integrated OGD boosts economic transparency by providing real-time oversight of government spending and procurement, strengthening financial integrity, and empowering regulatory compliance. For businesses, especially SMEs, AI democratizes access to crucial market data, reducing entry barriers and promoting innovation. In India, for instance, AI tools for trade data have helped SMEs generate significant revenue, while in the US and Japan, AI strengthened market intelligence and reduced fraud risks.

The study recognized that political commitment, robust digital infrastructure, and public trust were critical enablers of successful AI-OGD integration. Countries with supportive policy frameworks, like the EU’s AI Act and Singapore’s Model AI Governance Framework, showed measurable improvements in fraud prevention and competitive markets. The study also underscored the social dimension: AI’s influence on employment dynamics, workforce planning, and skills development is both an opportunity and a challenge that demands adaptive policies. The comparative analysis helped articulate the following recommendations to integrate OGD and AI at the country or bloc level: to strengthen policy and regulatory frameworks; to increase investment in digital infrastructure and data ecosystems; and to foster public-private partnerships in OSD and AI implementation. However, the study’s reliance on qualitative comparisons and available secondary data may limit the generalizability of its findings across diverse national contexts. Future research should include large-scale empirical studies and real-time data analytics to quantify AI and OGD impacts on specific sectors and refine cross-country implementation strategies.

The study addresses various potential validity threats related to cross-country data comparability, policy heterogeneity, and reporting bias. Challenges arise due to differing standards in data collection and reporting among countries, despite reliance on respected sources like the IMF and Statista. Policy variation is significant, with the EU featuring a comprehensive AI and Open Government Data framework, while other countries, such as Brazil and India, have more fragmented policies. Although all countries included have national frameworks for AI and OGD, the inconsistencies in policy maturity and enforcement could influence study outcomes. Reporting bias is another risk, as secondary data sources may be influenced by national interests, leading to possible overstatement by advanced AI nations and understatement by less developed ones. To counteract this bias, the study employs independent, peer-reviewed sources. It also justifies the inclusion of the EU in the analysis as its unified policy framework provides meaningful context and comparison to individual national policies, emphasizing the impact of regional governance on economic transparency and competitiveness.

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Cite this article as:
Hasani V. AI and Open Government Data: Economic Transparency and Market Competitiveness: A Mixed Methods Study. Premier Journal of Science 2025;14:100161

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