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Artificial intelligence (AI) is transforming corporate governance by reshaping decisionmaking, risk management, and strategic oversight in organizations worldwide. This study examines the global implementation of Al-driven corporate governance, analyzing its effectiveness in different regulatory and economic environments. Through a cross-country comparative analysis, we investigated Al-driven management practices in leading Al economies (USA, China, and the EU) and emerging markets (Kazakhstan, Southeast Asia, and Latin America), identifying key trends, commonalities, and divergences in Al integration. The findings suggest that Al adoption in corporate governance enhances decision-making efficiency, regulatory compliance, and strategic foresight. However, significant variations exist due to differences in technological infrastructure, regulatory environments, workforce capabilities, and cultural influences. Empirical research demonstrates Al's positive impact on corporate governance through its ability to automate compliance processes, reduce financial reporting errors, enhance transparency, and minimize human biases in decision-making. Nevertheless, challenges remain, particularly regarding ethical concerns, algorithmic accountability, and Al's potential to reinforce biases. This research provides critical insights for business leaders, policymakers, and technology stakeholders aiming to leverage Al for enhanced governance structures, strategic innovation, and improved economic performance. As Al continues to redefine business landscapes, fostering collaborative Al governance models, knowledge-sharing networks, and regulatory harmonization across economies will be essential to ensuring sustainable, ethical, and effective Al integration in corporate governance worldwide.
Artificial intelligence (AI) is transforming corporate governance by reshaping decisionmaking, risk management, and strategic oversight in organizations worldwide. This study examines the global implementation of Al-driven corporate governance, analyzing its effectiveness in different regulatory and economic environments. Through a cross-country comparative analysis, we investigated Al-driven management practices in leading Al economies (USA, China, and the EU) and emerging markets (Kazakhstan, Southeast Asia, and Latin America), identifying key trends, commonalities, and divergences in Al integration. The findings suggest that Al adoption in corporate governance enhances decision-making efficiency, regulatory compliance, and strategic foresight. However, significant variations exist due to differences in technological infrastructure, regulatory environments, workforce capabilities, and cultural influences. Empirical research demonstrates Al's positive impact on corporate governance through its ability to automate compliance processes, reduce financial reporting errors, enhance transparency, and minimize human biases in decision-making. Nevertheless, challenges remain, particularly regarding ethical concerns, algorithmic accountability, and Al's potential to reinforce biases. This research provides critical insights for business leaders, policymakers, and technology stakeholders aiming to leverage Al for enhanced governance structures, strategic innovation, and improved economic performance. As Al continues to redefine business landscapes, fostering collaborative Al governance models, knowledge-sharing networks, and regulatory harmonization across economies will be essential to ensuring sustainable, ethical, and effective Al integration in corporate governance worldwide.
Keywords: Al adoption, Al-enabled management, Al readiness indexes, Digital economy
1. INTRODUCTION
Artificial intelligence (AT) is transforming corporate governance by reshaping decision-making, risk management, and strategic oversight in organizations worldwide. Al-driven corporate governance integrates data analytics, machine learning algorithms, and automation to enhance regulatory compliance, financial forecasting, and operational efficiency.
As firms increasingly rely on Al to optimize governance structures, the role of human oversight and ethical considerations in AI decision-making becomes a critical area of study. Al-based governance systems process large volumes of structured and unstructured data to provide realtime insights, identify financial risks, and improve transparency in corporate decision-making (Papagiannidis & Truong, 2022). Companies leveraging AI for governance benefit from enhanced fraud detection, regulatory adherence, and predictive analytics, which contribute to more informed boardroom decisions.
However, the degree to which AI enhances governance effectiveness varies across industries and regulatory environments, particularly in emerging economies where digital infrastructure and AI adoption rates are still developing. Empirical studies highlight АГ $ potential to improve governance performance. A survey of major corporations utilizing Al for risk assessment and compliance found that firms employing Al-driven oversight mechanisms reduced financial reporting errors by 34% and improved regulatory compliance by 27% (Toner-Rodgers, 2024). Additionally, Al-powered corporate governance systems have been shown to enhance decisionmaking speed and accuracy while minimizing human biases, leading to a 22% increase in shareholder confidence (Filippucci et al., 2024). Despite these advantages, concerns remain regarding algorithmic accountability, transparency, and the unintended consequences of Al in high-stakes governance decisions.
A recent OECD report analyzing Al adoption in corporate governance across seven leading economies-Austria, Canada, France, Germany, Ireland, the United Kingdom, and the United States-indicates that 78% of executives believe Al enhances governance effectiveness (Lane, Williams, & Broecke, 2023). However, resistance to AI implementation persists, particularly in industries where regulatory frameworks lack clarity on Al-driven decision-making. Ethical challenges, including AT's potential to reinforce biases in hiring, investment, and strategic planning, raise further concerns about the unintended risks of AI in governance structures (Arroyabe et al., 2024). As Al continues to redefine corporate oversight, understanding its role in governance effectiveness, regulatory compliance, and ethical decision-making remains essential.
This study examines the global implementation of Al-driven corporate governance, analyzing its effectiveness in different regulatory and economic environments. By assessing АГ $ impact on governance transparency, risk management, and corporate accountability, this research contributes to the growing discourse on the intersection of artificial intelligence and corporate governance in a rapidly evolving digital economy.
2. ARTIFICIAL INTELLIGENCE AND CORPORATE GOVERNANCE: BIBLIOMETRIC ANALYSIS
The number of publications on the topic "artificial intelligence and corporate governance" has shown steady growth from 2010 to 2024. According to data from Dimensions.ai, fewer than 2,500 publications were recorded in 2010, while by 2024 this figure had surpassed 32,000. A particularly sharp increase is observed after 2017, reflecting the rising academic and practical interest in the intersection of Al technologies and corporate governance mechanisms.
The analysis of disciplinary distribution reveals that research on "artificial intelligence and corporate governance" is highly interdisciplinary. The majority of publications are concentrated in Commerce, Management, Tourism and Services (56,476), followed by Information and Computing Sciences (33,734), Human Society (28,073), and Economics (15,747), with notable contributions also from Law, Philosophy, Engineering, and Health Sciences.
The leading contributors to the field of "АТ and corporate governance" include scholars from diverse institutions and countries. Yogesh Kumar Dwivedi (Swansea University, UK) leads in publication count (71 papers, 17,095 citations), while Naveen Donthu (Georgia State University, USA) and Debmalya Mukherjee (University of Akron, USA) show the highest citation impact with 3,280 and 6,514 citations per paper, respectively. This demonstrates both sustained and high-impact scholarly engagement across regions.
Based on literature review, we identify that the AI is redefining corporate governance and strategic frameworks. Kalkan (2024) and Moro-Visconti (2025) emphasize how Al reshapes risk management, ethics, and governance structures. Büber & Seven (2025) explore ATs synergy with traditional strategy schools, such as Classical and Resource-Based View, enhancing analytics and responsiveness. Biloslavo et al. (2024) use the Cynefin framework to show how АТ supports strategic planning in volatile and uncertain (VUCA) environments. Al significantly enhances operational processes and performance metrics. Korapati (2025) shows how Al-powered predictive analytics in ERP systems improve financial forecasting, inventory optimization, and customer behavior analysis. Liotine (2019) highlights real-time decisionmaking improvements in pharmaceutical supply chains enabled by AI automation and analytics. Al strengthens organizational alignment and efficiency through integration into enterprise architecture. Abu Bakar et al. (2024) explore how АТ enhances frameworks such as ТОСАЕ and SOA, while identifying challenges in implementation, ethics, and data governance. Al improves interdepartmental coordination and collaborative decision-making. Holloway (2024) demonstrates how AI supports demand forecasting and strategy alignment between supply chain and marketing through data analytics and personalization. Rane (2023) examines the role of ChatGPT and other generative AI models in business management, supporting customer communication, financial analysis, and decision-making, while also addressing ethical concerns like bias and data privacy.
3. ARTIFICIAL READINESS INDICES
Research conducted between 2018 and 2022 in Canada, Singapore, the United Kingdom, and the United States shows that the share of job postings requiring Al skills varies significantly across sectors. The highest demand was observed in manufacturing, construction, and business services, indicating that Al is reshaping professional requirements and increasing demand for Al-related expertise. Sectors with high AT intensity also face notable talent shortages, particularly in Electrical Equipment, Computers and Electronics, Pharmaceuticals, Scientific R&D, and Chemical Manufacturing (Calvino et al., 2024). The АТ Index is an annual report published by the Stanford Institute for Human-Centered Artificial Intelligence (HAI). It is one of the most comprehensive and authoritative global resources tracking the progress and impact of artificial intelligence. The AI Index aims to provide rigorous, data-driven insights into Al development, use, and policy. It is designed for policymakers, researchers, industry leaders, and the public. The key dimensions of the global AI ecosystem:
1. Research and Development - Publications, patents, open-source contributions, and frontier models.
2. Technical Performance - Benchmark results of models on tasks like reasoning, coding, and language understanding.
3. Responsible AI - Ethics, transparency, and safety of Al systems.
4. Economy - Investment trends, labor market impacts, and AI adoption in industry.
5. Science and Medicine - ?? $ contribution to scientific discovery and healthcare.
6. Education - Trends in ?? education, workforce training, and talent flows.
7. Policy and Governance - Al-related legislation, regulatory bodies, and national strategies.
8. Diversity - Demographics in Al research and education.
9. Public Opinion - Global sentiment and awareness of Al technologies.
The AI Index is based on data from numerous sources, including CSET, Epoch, McKinsey, LinkedIn, GitHub, and national governments. It is updated annually and available to the public Al has surpassed human performance in specific tasks, demonstrating significant progress in video classification, visual reasoning, and language modeling. However, it still lags in tasks requiring competitive mathematics, complex logical reasoning, and planning. Research in Al is increasingly concentrated in the industrial sector, while the academic contribution is in decline.
In 2023, the industry produced 51 major machine learning models, whereas academia developed only 15, reflecting limited funding for academic research. Regulatory activity in the United States has intensified, with 25 new legislative acts related to АГ passed in 2023- compared to just one in 2016-indicating a sharp rise in governance efforts. According to an Ipsos survey, the share of individuals who view AI as having a significant impact on their lives increased from 60% to 66%. Simultaneously, concerns about Al rose from 38% in 2022 to 52% in 2023. These trends underscore the growing societal role of АТ, the urgency of robust regulation, and the onset of a new phase in technological advancement. Private investment in Al continued its steady growth in 2023, with the number of newly funded АТ startups increasing by 40.6% to 1,812. The average size of private Al investments also rose slightly-from $31.3 million to $32.4 million-demonstrating growing investor confidence and sustained momentum in the sector.
In 2023, the United States remained the undisputed leader in attracting private investment in AI, underscoring its significant influence on the global development of the sector. The $67.2 billion invested in the U.S. was 8.7 times more than in China and 17.8 times more than in the United Kingdom (Figure 3), reinforcing the country's position as a leading technological and financial hub in AL
In 2023, the United States was the clear leader in the number of newly funded AI companies, aligning with overall trends in private investment. A total of 897 new Al companies were established in the U.S. far surpassing other regions. China ranked second with 122 new companies, followed by the United Kingdom with 104 (Figure 4). This highlights the U.S.'s continued dominance as the central hub for Al innovation and investment, while other countries remain significantly behind.
The comparical staudy show that the leading economies such as the United States, China, and the European Union dominate global AI development through robust institutional governance, significant investment, and high-impact innovation.
The U.S. and EU have established comprehensive Al regulatory frameworks, while China maintains centralized, state-driven Al strategies. These countries lead in private investment- especially in generative Al-and are responsible for the majority of foundational model development and patenting activity. In contrast, emerging markets like Kazakhstan, Southeast Asia, and Latin America lack cohesive national Al strategies, with policy efforts often limited to general digital transformation. Investment capacity remains low, with most initiatives relying on international partnerships. These regions contribute little to AI patents or foundational models, focusing primarily on localized adaptation of existing technologies rather than original development (Table 3).
South and Central Asia exhibit the lowest levels of АТ readiness globally, primarily due to disparities in economic development, technological adoption, and governance structures (Figure 5). India and Turkey are regional leaders with relatively high global rankings in AI development but continue to lag in infrastructure and data readiness. Central Asian countries are increasingly seeking cooperation within the C5 format to harmonize Al policies. Turkey has launched an industrial and technology strategy aimed at fostering technological sovereignty and innovation, with a focus on data governance, content moderation, and competition policy. Central Asian nations are also advancing digital transformation: Tajikistan has adopted a national AI strategy, while Kazakhstan's Astana Hub, in partnership with Google for Startups, is supporting regional startup ecosystems.
The Government AI Readiness Index is a global benchmarking tool developed by Oxford Insights. It evaluates how prepared national governments are to implement artificial intelligence (АГ) in public services. The index is widely used by policymakers, researchers, and international organizations. The overall score is composed of three sub-indices, each reflecting a key dimension of Al readiness:
1. Government Sub-Index measures the government's commitment to Al through national strategies, public sector innovation capacity, regulatory frameworks, and ethical guidelines.
2. Technology Sector Sub-Inde assesses the maturity and competitiveness of the country's private tech sector, availability of skilled workforce, research output, and digital innovation ecosystem.
3. Data & Infrastructure Sub-Index evaluates data availability, quality of digital infrastructure, internet access, and cloud computing capabilities essential for Al deployment.
The index includes over 180 countries and is based on a mix of quantitative indicators and qualitative expert assessments. The AI Readiness Index assesses countries' preparedness to adopt and implement artificial intelligence. It incorporates indicators such as government policy, innovation capacity, data infrastructure, and the technological environment.
Kazakhstan ranks at a moderate level, indicating the need to strengthen its technological sector and human capital to advance AI development. Canada, France, South Korea, Germany, Japan, and the Netherlands complete the top ten, though significant differences remain across their technological sectors. For instance, South Korea scored 87.6 in government indicators but only 54.4 in the technology sector. Among middle-tier countries, the Croatia ranks 41th (61.7 points) and Turkey 47th (60.5 points), both demonstrating progress in digital governance but lagging in technological performance. Kazakhstan is ranked 72nd with an overall score of 48.6. While its digital governance score is relatively stable, the technology sector score is low at 31.0, pointing to the need for further innovation development. However, its data and infrastructure indicator is comparatively strong at 66.1. Following Kazakhstan are Azerbaijan (73rd, 48.2 points), Uzbekistan (87th, 43.8 points), and Kyrgyzstan (131st, 34.1 points), with Kyrgyzstan showing a particularly low technology sector score of 22.9.
Overall, the ranking highlights global disparities in digital development and underscores the need for Kazakhstan and neighboring countries to enhance technological capacity and digital transformation in public administration (Table 4).
4. CONCLUSION
Artificial Intelligence (AI) is fundamentally transforming corporate governance, strategic decision-making, and operational management across industries and countries. This study highlights the significant variations in Al adoption due to differences in technological infrastructure, regulatory environments, workforce capabilities, and cultural influences. Through a cross-country comparative analysis, we examined Al-driven management practices in leading AI economies (USA, China, and the EU) and emerging markets (Kazakhstan, Southeast Asia, and Latin America), identifying key trends, commonalities, and divergences in Al integration. The findings suggest that AI adoption in corporate governance enhances decision-making efficiency, regulatory compliance, and strategic foresight. In developed economies, Al implementation is supported by strong regulatory frameworks, high investment levels, and advanced technological ecosystems, resulting in more structured governance models and a higher degree of automation in risk management, financial oversight, and policy implementation. In contrast, emerging markets face significant barriers, including limited Al infrastructure, fragmented policies, and lower investment in Al-driven business transformation. Nevertheless, these regions show growing engagement in AI adoption, with increasing interest in international collaboration and policy alignment to facilitate AI integration. Empirical research demonstrates ATs positive impact on corporate governance through its ability to automate compliance processes, reduce financial reporting errors, enhance transparency, and minimize human biases in decision-making. However, challenges remain, particularly regarding ethical concerns, algorithmic accountability, and AT's potential to reinforce biases. The study also highlights disparities in AI investment, where developed economies allocate substantial resources to Al innovation and foundational model development, while emerging economies rely more on international partnerships and the adaptation of pre-existing Al solutions. This research provides critical insights for business leaders, policymakers, and technology stakeholders aiming to leverage Al for enhanced governance structures, strategic innovation, and improved economic performance. The study underscores the need for a balanced approach to АТ governance, integrating regulatory oversight, ethical considerations, and adaptive business strategies to maximize АГ $ potential while mitigating associated risks. Additionally, future research should explore longitudinal AI adoption trends, the effectiveness of Al-driven corporate policies, and the role of Al in shaping global regulatory frameworks. As AI continues to redefine business landscapes, fostering collaborative Al governance models, knowledge-sharing networks, and regulatory harmonization across economies will be essential to ensuring sustainable, ethical, and effective Al integration in corporate governance worldwide.
LITERATURE:
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