The Impact of Artificial Intelligence on Corporate Governance

The advent of artificial intelligence (AI) marks a pivotal shift in the landscape of corporate governance, catalyzing a reevaluation of traditional frameworks and necessitating a forward-looking approach to decision-making, risk management, and ethical considerations. This study explores the multifaceted impact of AI on corporate governance, offering a nuanced analysis of how AI technologies are transforming the operational, strategic, and ethical dimensions of organizations. The research underscores the potential of AI to enhance decision-making processes, optimize operational efficiencies, and foster innovation by providing advanced analytical capabilities and predictive insights. However, it concurrently highlights the emergence of unprecedented challenges, including data privacy concerns, algorithmic bias, and the need for robust regulatory frameworks to mitigate risks associated with AI deployment. The article advocates for a proactive stance in redefining corporate governance models to accommodate the disruptive nature of AI, emphasizing the integration of ethical considerations and transparency in AI applications. It calls for a collaborative effort among corporate leaders, policymakers, and stakeholders to develop governance structures that not only leverage AI’s potential but also safeguard against its inherent risks. The study’s recommendations include the establishment of ethical AI guidelines, the adoption of transparent AI practices, and the continuous monitoring of AI systems to ensure their alignment with corporate governance objectives and societal values. However, it is important to note that the approach and methods used in this study are based on a qualitative literature review and, therefore, the generalization of the findings across different sectors and corporate governance frameworks may be limited. Additionally, the rapidly evolving nature of AI technologies poses inherent challenges to keeping up with emerging trends and potential risks.


Introduction
Artificial intelligence (AI) is emerging as an eminent force of transformation fundamentally shaking the business landscape to its roots and posing a powerful challenge to traditionally held convictions about corporate governance [1].The growing acceptability of AI in the different modes of organizational function has kindled a debate about its possible implications for corporate governance structures and decision-making processes as well as its overall transparency.
Currently, the contemporary business environment is witnessing the integration of artificial intelligence at an unprecedented scale [2][3][4].From predictive analytics to the implications of machine learning algorithms, AI technologies are sparking an era of innovation like never before, promising improvements in the efficiency of operations and data-driven decisions [5].The impact of this technological transition on corporate governance frameworks bears significant potential for scholarly investigation.As artificial intelligence permeates corporate environments, an increasing number of enterprises are embracing AI to adeptly maneuver through the intricacies of the digital era [6].
More than just a technical innovation in business operations, AI is a development affecting the very core of organizational functionalities [1].Perhaps more importantly, AI fundamentally alters the game with its ability to process large datasets, detect patterns, and generate business actions and insights, even in real time, thus totally transforming how -and with what architectures -decisions are made in organizations [4].As a result, boards of directors, C-suite executives, and stakeholders must plot a course through a governance landscape in which the infusion of AI into their organizations not only blurs existing boundaries but also creates new territories [7].
For a proper understanding of how AI impacts corporate governance, it is imperative to meticulously examine the evolving roles and responsibilities within organizations in response to these changes.Such scrutiny is crucial not only for gaining insight into the ways AI shapes corporate governance but also for elucidating the accountability dynamics inherent in this transformation [1].The increased dependence on AI-driven tools translates into questions of changing dynamics of leadership, accountability, and the distribution of decision-making authority [8], while provoking a reappraisal of the principles underpinning historically correct governance in the corporation.
Given the broad scope of AI applications in the corporate domain, this research seeks to accomplish three main objectives.First, it aims to study how AI impacts the structural elements of corporate governance [2; 9].As the roles played by boards of directors, executives, and stakeholders are changing, understanding these changes is essential for realizing governance frameworks in the digital age.
Secondly, it aims for an in-depth understanding of the impact of AI on the decision-making process within or-ganizations [10; 11].The integration of AI makes the decision-making process not only data-driven but also automatic and predictive in its essence [12].Unravelling the subtleties of these changes is critical for organizations seeking to harness the benefits of AI while retaining the integrity of their decision-making processes.
Thirdly, this study attempts to analyze the bigger picture with regard to transparency initiatives embraced by corporate entities upon integrating AI [13; 14].A key foundation of effective corporate governance, transparency is arguably one of the most critical challenges on the path of integrating AI into company operations.It includes the concept of "transparency by design", which, in turn, recognizes the explicit choices organizations make in the process of revealing AI-driven processes in their decision-making [15].
This study deepens the discourse on corporate governance in the era of AI by throwing light on the all-around picture of challenges and opportunities companies face in this transformational era through a consideration of the specific impacts of AI on governance structures, decision-making processes, and transparency initiatives [16].In view of corporate efforts to navigate the complex terrain of technological advancement, the present research tries to foster responsible and effective corporate governance in the age of AI [17].
The rest of this article is structured as follows.Second section "AI and Decision-Making Processes", examines the transformative role of AI in enhancing decision-making capabilities within corporate governance.Third section "Board Dynamics in the Era of AI", explores how AI influences boardroom interactions and governance structures.Fourth section "AI and Risk Management", delves into the utilization of AI for identifying, assessing, and mitigating corporate risks.Fifth section "Corporate Transparency and Stakeholder Engagement", discusses the impact of AI on improving transparency and fostering engagement with stakeholders.Sixth section "Challenges of AI Integration in Corporate Governance", addresses the obstacles and ethical considerations of incorporating AI into governance practices.Finally, seventh section concludes the article by summarizing the findings, discussing the implications for corporate governance, and suggesting avenues for future research.The following sections dive deeply into the academic literature, drawing from a rich array of sources that inform and buttress our research objectives.

AI and Decision-Making Processes
AI has emerged as an enabling force which redefines the landscape of decision-making within corporate governance.This section explores two dominant domains in which AI can be a game-changer: data-driven and algorithmic decision-making.Amalgamating the insights from diverse studies, we show the multifaceted role of AI in influencing strategic decisions and providing executive decision support.

Data-Driven Decision-Making: The Role of AI in Processing Large Datasets
The management of large data sets has undergone revolutionary changes, allowing organizations to gain strategic insights for informed decision-making [1].Due to its bulk data processing capabilities, artificial intelligence enables companies to navigate complex business environments by providing strategic insights [18].In doing so, AI becomes effective in formulating governance strategies in the public interest while skillfully addressing emerging challenges [7].
The impact of AI on corporate governance involves a lot more than simply facilitating data analysis.Artificial intelligence increases the precision and efficiency of decision-making processes [19].Using advanced algorithms, AI allows companies to predict future values of their shares in markets and reduce potential business risks [3].This forward-looking approach to internal decision-making requires organizations to quickly adapt to changing business environments.B. Kaya highlights the central role played by artificial intelligence in driving this revolutionary change in corporate governance practices, which calls for constant adaptation [20].
However, the use of artificial intelligence in corporate governance structures requires taking ethical issues into account and aligning efficiency with ethical responsibilities [19].
It is crucial to ensure that AI decisions are made ethically and in the best interest of stakeholders [4].W. Shen emphasizes the application of artificial intelligence technologies to protecting corporate governance rights and interests.If artificial intelligence technologies are used correctly and in accordance with ethical rules, they can act as guardians in internal decision-making processes, ensuring the protection of rights and achieving operational efficiency [16].
In summary, while data-driven decision-making revolutionizes corporate governance practices [1; 7], it also generates ethical challenges that organizations must overcome [19; 20].The sweeping impact of AI on corporate governance is both transformative and challenging, requiring a balanced approach that prioritizes strategic insights, efficiency and ethical responsibility.

Algorithmic Decision-Making: Implications for Executive Decision Support
AI algorithms play a very important role in executive decision support systems, especially in critical areas of business.Q. Yang et al. highlight the importance of incorporating AI into decision support systems to guarantee consistent interaction between human decision makers and AI algorithms [17].M. Ashoori and J. Weisz also state that trust is a vital component in AI-driven decision-making processes [10].The reliability of AI algorithms significantly affects managers' trust in AI-based recommendations and insights [21].
However, excessive reliance on AI-based advisory systems can hamper sound decision behavior, especially in critical areas such as research and development investments [22].
It is important to know the strengths and limitations of AI for proper decision-making.M. Jarrahi highlights the need for a symbiotic relationship between human reasoning and artificial intelligence algorithms to produce a stronger and more effective decision-making process [23].
A. Nassar and M. Kamal argue that ethical considerations should cast the foundations of AI-based decision-making.There is a continuing need to pay attention to ethical boundaries when processing large data sets and to address ethical issues arising from the application of artificial intelligence.Additionally, it is critical to understand and align the preferences and expectations of artificial intelligence system users [11].This is consistent with the findings by S. Sharma et al., who argue that AI systems should be designed to be attractive to end users, especially in autonomous decision-making scenarios involving retail customers [24].
In summary, the relationship between AI algorithms and executive decision support requires a balanced approach that integrates technical progress with ethical considerations.AI demonstrates its importance in shaping the future of corporate governance by facilitating strategic decision-making and executive decision support systems.Aligning AI-based decisions with human judgment is crucial for effective governance [10; 23].

Board Dynamics in the Era of AI
As AI continues to transform industries, its impact on corporate governance is becoming increasingly significant.This section explores the evolving dynamics of corporate boards in the era of AI, with a specific focus on board composition and expertise, as well as the influence of AI on board decision-making processes.

Board Composition and Expertise
The incorporation of AI into corporate governance requires company boards to develop new skills and expertise.Traditionally, boards consisted of members with experience in finance, law, and business.However, given the growing significance of AI, board members are now expected to possess knowledge of technology, data analysis, and AI algorithms.Without tech-savvy members, boards will struggle to comprehend the impact of AI on organizations [1].Therefore, it is essential to include individuals who can "decode the algorithm" on company boards.
Board composition has an important role in effective AI governance.The complexity of AI issues requires improving the representation of non-executive members on boards.For example, gender diversity has been shown to improve decision-making and innovation, which are central in the age of artificial intelligence [25].Additionally, boards with diverse memberships are better equipped to detect biases in AI systems.Diverse boards ensure fairness and prevent unintentional discrimination by reviewing artificial intelligence algorithms [26].
In summary, technological expertise and diversity play essential roles in effective AI governance.Technologically astute board members contribute to grasping the nuances of AI, while diverse boards offer improved scrutiny and fairness in decision-making processes [1; 26].

AI-Assisted Board Decision-Making
Integrating AI tools into the board of directors can significantly improve board decision-making processes."A machine can process large amounts of data to identify patterns and draw nonlinear conclusions, something far beyond the capabilities of any director" [3].This capability gives boards the ability to effectively manage strategic planning, risk management and financial forecasting.R. Rajendran et al. find that such analytical capabilities help boards adopt more data-driven decision-making processes, reducing reliance on intuition and gut instinct [27].
AI-powered tools also contribute to effective board processes.Artificial intelligence can automate routine tasks such as document analysis and compliance checks, allowing managers to focus on more strategic issues [28].This not only saves time but also reduces the risk of human error in manual tasks.Moreover, artificial intelligence can enable boards to act quickly when faced with new challenges and opportunities by providing real-time information and predictive analytics [29].In today's world, the speed of decision-making is increasingly important, and artificial intelligence increases the board's ability to adapt.
The growth of AI offers both opportunities and challenges for companies.To harness AI's potential, boards need more tech-savvy members.AI can make board decisions more efficient and effective.By embracing AI and tackling its ethical and governance issues, boards can thrive in the digital era [30].

AI and Risk Management
AI is continuing to find its way into a multitude of sectors, and its applications in risk management are crucial.This section examines two core facets: predictive analytics and cybersecurity.We will try to get a feel of how AI is used to forecast risks and shore up cybersecurity in corporate governance.

Predictive Analytics: Forecasting and Identifying Potential Risks
Predictive analytics, powered through artificial intelligence and especially machine learning algorithms, plays a crucial role in risk management by analyzing large data sets to uncover patterns and generate accurate predictions, while traditional risk assessment models often struggle to achieve this amid the complexity of contemporary business environments [31].S. Aziz and M. Dowling show how machine learning and artificial intelligence improve risk management through more accurate predictions.By analyzing historical data, AI can pre-emptively identify trends and potential risks that cannot be easily spotted through traditional methods [32].
In the fintech sector, predictive analytics is increasingly used to manage risk.N. Bussmann et al. explore the role played in fintech risk management by explainable artificial intelligence (XAI), which refers to artificial intelligence models in which the decision-making process is transparent and understandable.This transparency is critical in highly regulated industries such as financial services [33].I. Ivashkovskaya and I. Ivaninskiy emphasize the importance of ensuring that AI algorithms are explainable to stakeholders, especially in sectors such as financial services where regulatory compliance is vital [19].
Discussing the challenges of AI in finance, P. Giudici emphasizes that AI's real strength lies in providing real-time risk monitoring and adaptive responses to ever-shifting market conditions [34].
To summarize, predictive analytics is reshaping risk management by: • Identifying Potential Risks.Leveraging machine learning algorithms to detect patterns and trends in large datasets [32].
• Adapting to Market Conditions.Offering real-time risk monitoring and adaptive responses [34].
These insights demonstrate how predictive analytics, combined with regulatory compliance measures, can significantly enhance risk management strategies in the AI era.

Addressing Cybersecurity Challenges with AI
The exponential growth of digitalization has brought about an increase in cybersecurity threats.However, artificial intelligence presents both challenges and opportunities in the field of cybersecurity within corporate governance.In this context, J. Schuett discusses the implications of the Artificial Intelligence Act for risk management.He argues that a strong regulatory framework is vital to ensure that machine learning risk management practices remain safe and accountable [35].M. Gupta et al. review how artificial intelligence and machine learning are revolutionizing cybersecurity practices and how these technologies are being used to address a wide range of ever-evolving threats, similar to broader applications in risk management [36].
In the context of risk management and AI governance, explainable artificial intelligence (XAI) plays an important role in identifying vulnerabilities and supporting compliance [34].XAI models make the decision-making process transparent and understandable, which is especially important in highly regulated industries such as financial services.
In summary, AI plays a game-changing role in risk management for corporate governance in several different ways: • Predictive analytics leverages AI to provide a sophisticated methodology for identifying and forecasting potential risks [32].
• Financial risk management integrates AI for improved decision-making processes and real-time insights [33].
• Dynamic cybersecurity response offers a dynamic response to cybersecurity challenges by bolstering defenses, detecting vulnerabilities, and proactively responding to emerging risks [36].
As AI continues to advance, the interplay between predictive analytics and cybersecurity becomes increasingly important for organizations navigating the complexities of the digital age.Studies cited throughout this article underscore the growing importance of responsible AI governance, regulatory frameworks, and ongoing research to ensure the safe integration of AI into risk management practices [34; 35].

Corporate Transparency and Stakeholder Engagement
In the fast-evolving corporate governance landscape, the principles of transparency, stakeholder engagement and sustainable practices are essential building blocks of trust and accountability within organizations [37].
In this section, we consider the intersection of these principles and how the infusion of AI into corporate practices may further enhance transparency and engagement with stakeholders, drawing on recent scholarly work that explores how AI impacts corporate reporting, disclosure, and communication with stakeholders.

Automated Reporting and Disclosure
The integration of AI into corporate reporting promises to open the doors for real-time and accurate disclosure.
A. Karbekova et al. explore how AI and dataset automation can revolutionize corporate accounting and sustainability reporting within the framework of Industry 4.0, emphasizing the role of AI in improving reporting quality and management practices.With businesses increasingly using AI for reporting and disclosure, the idea of "transparency by design" is gaining traction [38].H. Felzmann et al. argue that embedding transparency in AI systems promotes openness and accountability while ensuring that companies meet legal standards [15].M. Hosain et al. also argue that AI systems should not only be transparent but also provide meaningful explanations to stakeholders [13].

Leveraging Stakeholder Communication: Enhancing Dialogue through AI
AI has the capacity not only to automate reporting but also to enrich the dialogue with stakeholders.H. Güngör examines the multi-stakeholder perspective of creating value with AI, delineating how AI may provide value for divergent stakeholders via efficient and effective communication and thus promote informed decision-making [14].
M. Hosain et al. argue that meaningful disclosures made with the help of artificial intelligence facilitate stakeholder communication beyond transparency [13].C. Zehir et al. argue that transparency should be seen as a corporate requirement that involves stakeholders in the decision-making process, emphasizing how participating stakeholders can help bridge the gap between transparency initiatives and corporate results [39].
In summary, the integration of AI into corporate reporting and communication fundamentally transforms transparency, accountability, and stakeholder engagement.The academic research presented here emphasizes the importance of transparency by design, meaningful explainability, and proactive stakeholder engagement in an AI-focused corporate environment.For businesses that navigate these complexities, leveraging AI to enhance transparency and stakeholder engagement is crucial for promoting accountable and sustainable corporate governance.The proactive adoption of these technologies not only addresses immediate business needs but also fosters a more collaborative and informed relationship with stakeholders.

Challenges of AI Integration in Corporate Governance
The processes for integrating artificial intelligence into corporate governance are extensive, ranging from improving decision-making and operational efficiency to fostering innovation.While the integration of AI into corporate governance is associated with numerous benefits, it also presents challenges [40].There are ethical issues surrounding the use of AI, while accountability and algorithm bias need to be addressed [41].Striking the right balance between human judgment and AI-driven insights is a good measure of responsible and effective decision-making.The need for board members to continuously educate themselves about AI developments and implications is critical.This requires a commitment to a culture of continuous learning and adaptation in the boardroom [42].
This section delves into the key challenges facing AI integrated corporate governance: ethical considerations, legal and regulatory challenges and the broader implications for organizational practices.

Ethical Considerations
Embedding AI into corporate governance processes raises profound ethical considerations [43].Such considerations require a thorough examination of the impact of AI decision-making on societal values and the ideas of corporate responsibility and accountability.Camilleri delves into the ethical dimensions of AI governance and calls for the alignment between AI applications and social responsibility and ethical norms, noting the risks of unfettered AI use in corporate decision-making [44].L. Xue and Z. Pang argue for an integrated analytical framework for governing ethical AI applications.They stress that transparency, fairness and accountability are all essential to AI decision-making in the corporate governance landscape to address ethical concerns [45].J. Mökander et al. explore the ethical challenges and best practices of AI governance in the biopharmaceutical industry.This sector provides a valuable case study due to its early adoption of AI and consistent examination of AI governance at the company level [40].B. Stahl et al. argue that organizations must be prepared to respond to ethical issues as they emerge, acknowledging that these issues are dynamic and evolve with the development of AI and its applications, necessitating adaptive organizational strategies [46].In a novel twist on integrating responsible AI into governance, G. Baloğlu and K. Çakalı question whether artificial intelligence poses a new threat to academic ethics and emphasize the importance of considering the ethical consequences of artificial intelligence in corporate governance [47].

Legal and Regulatory Challenges
The rapid advancement of AI technologies demands a legal and conceptual framework distinct from conventional systems [48].G. Schildge addresses AI and corporate governance issues, arguing for a solid legal construct and the proactive development of legal guidelines to adapt to the evolving nature of corporate governance influenced by AI technology [49].J. Thomas discusses the potential legal consequences of AI decision-making, highlighting the need for boards to evolve in order to address the emerging legal challenges associated with AI integration [50].R. Tallarita examines how AI governance is "testing the limits of corporate law", focusing on the importance of managing risk and adapting to fast-paced advancements in AI, which often render traditional laws obsolete [51].E. Papagiannidis et al. recognize the legal hurdles to AI governance and suggest the best practices for overcoming these challenges, underscoring the importance of organizations contributing to the development of effective legal frameworks for AI governance [52].
In summary, the challenges of integrating AI into corporate governance extend beyond technical considerations and incorporate ethical, legal, and regulatory dimensions.Organizations adopting AI must fully understand the implications of AI-driven decisions on ethics, societal values, and legal compliance.Drawing on academic research, proactive measures can ensure that corporate governance structures manage the transformative potential of AI effectively, while respecting foundational values and norms.

Conclusion
Throughout this exploration of the intersection between AI and corporate governance, a comprehensive understanding of the multifaceted implications of AI technologies on decision-making processes, transparency, stakeholder engagement, and ethical considerations has emerged.Drawing insights from a range of academic sources, the following key findings encapsulate the transformative effects of AI on corporate governance: • Enhanced Decision-Making.The integration of AI into corporate governance processes contributes to enhanced decision-making efficiency and effectiveness [1; 29; 22].
• Stakeholder Engagement.AI serves as a powerful tool for stakeholder engagement by facilitating efficient communication channels and providing meaningful explanations for AI-driven decisions [14; 39].
• Ethical Considerations.The ethical dimensions of AI governance underscore the need for aligning AI applications with social responsibilities and ethical norms [44][45][46][47].
• Legal and Regulatory Challenges.The rapid evolution of AI technologies has outpaced the development of comprehensive legal and regulatory frameworks, presenting challenges for corporations [49; 52].
Looking toward the future, several trends and challenges are anticipated in the ongoing integration of AI into corporate governance: • Advancements in Decision-Making.Continuous advancements in AI technologies will likely lead to further improvements in decision-making processes, enabling organizations to adapt to dynamic business environments [10; 23].
• Evolution of Transparency Standards.The concept of "transparency by design" is expected to evolve, with organizations placing even greater emphasis on intentional design choices that prioritize transparency and align with evolving ethical standards [3; 15].
• Deepened Stakeholder Engagement.AI will continue to play a pivotal role in stakeholder engagement by facilitating more meaningful explanations for AI-driven decisions.Organizations will need to focus on effective communication strategies tailored to diverse stakeholder expectations [14; 39].
• Ethical and Legal Frameworks.The development of ethical and legal frameworks for AI governance is likely to gain momentum, with regulators and organizations working collaboratively to address emerging challenges and ensure responsible AI practices [44][45][46][47].
In conclusion, the integration of AI into corporate governance is an ongoing journey marked by transformative impacts and evolving challenges.Organizations that proactively address ethical considerations, enhance transparency, and navigate legal landscapes will be better positioned to harness the full potential of AI in shaping the future of corporate governance [3; 17; 52].As AI continues to advance, a commitment to responsible governance and a proactive approach to emerging challenges will be essential for fostering sustainable and effective corporate practices.Looking ahead, predicting and minutely examining future trends shaping the intersection of artificial intelligence and corporate governance will greatly contribute to development in this field.

Long-term Implications of AI Adoption on Decision-Making Structures
Future research should focus on discerning the long-term implications of AI adoption on decision-making structures within organizations.B. Kaya emphasizes the need to explore how AI will continue to redefine roles and responsibilities, ensuring a harmonious integration that leverages the strengths of both human and machine decision-making processes [20].

Specific recommendations:
Organizational Hierarchies.Investigating how AI influences hierarchical decision-making structures and whether it necessitates flatter hierarchies.
Human-AI Collaboration.Examining the interplay between human intuition and AI analytics, developing frameworks to maximize their combined potential.
Governance Strategies.Exploring the strategic implications of AI-driven decision-making, particularly in diversifying board composition and expertise.

AI Literacy Training.
Advocating for AI literacy training at all levels of corporate leadership to ensure informed decision-making.

Evolving Ethical Governance Frameworks for AI
As AI continues to transform corporate governance, it generates new ethical challenges that require adaptive strategies.H. Han's exploration of AI and blockchain, A. Nassar and M. Kamal's study of large data-driven ethical considerations, along with papers by M. Camilleri and by L. Xue and Z. Pang underscore the importance of ethical governance frameworks.Future research should identify best practices, potential barriers, and outcomes in AI governance, contributing to the establishment of robust guidelines for responsible and effective AI use [4; 11; 44; 45].

Specific recommendations:
• Algorithmic Accountability.Developing metrics and guidelines to ensure that AI algorithms are accountable and transparent in decision-making.
• Ethical Auditing.Exploring methodologies for auditing AI systems to ensure adherence to ethical governance principles.
• Best Practice Frameworks.Developing comprehensive best-practice frameworks for ethical AI governance.
• Regulatory Compliance.Researching the implications of global regulatory standards for AI governance and how organizations can align with them.

Intersection of AI and Stakeholder Relations
The intersection of AI and stakeholder relations, as examined by H. Güngör and C. Zehir et al., presents a rich area for exploration [14; 39].Future trends research should aim to unravel the evolving dynamics between organizations, AI technologies, and stakeholders, ensuring transparency and accountability in this multifaceted relationship.

Specific recommendations:
• Stakeholder Engagement Models.Creating models that enhance stakeholder engagement through AIdriven communication tools.

• Transparency Standards.
Researching new standards for transparency in AI-enabled corporate reporting and stakeholder communication.
• Trust Building.Investigating approaches to build trust in AI systems among stakeholders, emphasizing meaningful explainability.
In conclusion, the transformative impact of artificial intelligence on corporate governance is an ever-evolving field.
In the future, exploring the effects of artificial intelligence on corporate governance with the specific recommendations provided above will offer valuable contributions to academics, practitioners, and policymakers.This endeavor not only enhances our understanding of the role of artificial intelligence but also holds promise for guiding organizations toward ethical, responsible, and effective governance in an AI-driven future.
The integration of AI into corporate governance has ushered in a new era by transforming decision-making processes, stakeholder relationships and ethical considerations.With insights from academic sources, an intriguing call for future trends research in the field of AI and corpo-