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Journal of Corporate Finance Research / New Research Vol. 18 | № 2 | 2024
Higher School of Economics17
e Impact of Articial Intelligence on
Corporate Governance
Göktürk Kalkan
Doctor of business administration, assistant professor, Department of Business Administration, Gaziantep University
İslahiye Faculty of Economics and Administrative Sciences, Gaziantep, Turkey,
gkalkan@gantep.edu.tr, ORCID
Abstract
e advent of articial intelligence (AI) marks a pivotal shi in the landscape of corporate governance, catalyzing a reeval-
uation of traditional frameworks and necessitating a forward-looking approach to decision-making, risk management,
and ethical considerations. is study explores the multifaceted impact of AI on corporate governance, oering a nuanced
analysis of how AI technologies are transforming the operational, strategic, and ethical dimensions of organizations. e
research underscores the potential of AI to enhance decision-making processes, optimize operational eciencies, 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 regula-
tory frameworks to mitigate risks associated with AI deployment. e article advocates for a proactive stance in redening
corporate governance models to accommodate the disruptive nature of AI, emphasizing the integration of ethical consid-
erations and transparency in AI applications. It calls for a collaborative eort among corporate leaders, policymakers, and
stakeholders to develop governance structures that not only leverage AI’s potential but also safeguard against its inherent
risks. e study’s recommendations include the establishment of ethical AI guidelines, the adoption of transparent AI prac-
tices, 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 ndings across dierent sectors and corporate governance frame-
works may be limited. Additionally, the rapidly evolving nature of AI technologies poses inherent challenges to keeping up
with emerging trends and potential risks.
Keywords: corporate governance, articial intelligence, digital transformation, decision-making, transparency, ethical con-
siderations, legal and regulatory challenges
For citation: Kalkan G. (2024) e Impact of Articial Intelligence on Corporate Governance. Journal of Corporate Finance
Research. 18(2): 17-25. https://doi.org/10.17323/j. jcfr.2073-0438.18.2.2024.17-25
e journal is an open access journal which means that everybody can read, download, copy, distribute, print, search, or link to the full texts of these
articles in accordance with CC Licence type: Attribution 4.0 International (CC BY 4.0 http://creativecommons.org/licenses/by/4.0/).
DOI: https://doi.org/10.17323/j.jcfr.2073-0438.18.2.2024.17-25
JEL classication: G32, G34
Journal of Corporate Finance Research / New Research Vol. 18 | № 2 | 2024
Higher School of Economics18
Introduction
Articial intelligence (AI) is emerging as an eminent
force of transformation fundamentally shaking the busi-
ness landscape to its roots and posing a powerful chal-
lenge to traditionally held convictions about corporate
governance [1]. e growing acceptability of AI in the
dierent modes of organizational function has kindled a
debate about its possible implications for corporate gov-
ernance structures and decision-making processes as well
as its overall transparency.
Currently, the contemporary business environment is wit-
nessing the integration of articial intelligence at an un-
precedented scale [2–4]. From predictive analytics to the
implications of machine learning algorithms, AI technol-
ogies are sparking an era of innovation like never before,
promising improvements in the eciency of operations
and data-driven decisions [5]. e impact of this tech-
nological transition on corporate governance frameworks
bears signicant potential for scholarly investigation. As
articial 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 opera-
tions, AI is a development aecting the very core of or-
ganizational functionalities [1]. Perhaps more importantly,
AI fundamentally alters the game with its ability to pro-
cess large datasets, detect patterns, and generate business
actions and insights, even in real time, thus totally trans-
forming 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 bounda-
ries 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 dy-
namics inherent in this transformation [1]. e increased
dependence on AI-driven tools translates into questions of
changing dynamics of leadership, accountability, and the
distribution of decision-making authority [8], while pro-
voking a reappraisal of the principles underpinning histor-
ically correct governance in the corporation.
Given the broad scope of AI applications in the corpo-
rate 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]. e 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 benets of AI while retaining the
integrity of their decision-making processes.
irdly, this study attempts to analyze the bigger picture
with regard to transparency initiatives embraced by corpo-
rate entities upon integrating AI [13; 14]. A key foundation
of eective corporate governance, transparency is arguably
one of the most critical challenges on the path of integrat-
ing 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 re-
vealing AI-driven processes in their decision-making [15].
is study deepens the discourse on corporate governance
in the era of AI by throwing light on the all-around pic-
ture of challenges and opportunities companies face in this
transformational era through a consideration of the specif-
ic impacts of AI on governance structures, decision-mak-
ing processes, and transparency initiatives [16]. In view of
corporate eorts to navigate the complex terrain of tech-
nological advancement, the present research tries to foster
responsible and eective corporate governance in the age
of AI [17].
e rest of this article is structured as follows. Second
section “AI and Decision-Making Processes”, examines
the transformative role of AI in enhancing decision-mak-
ing capabilities within corporate governance. ird sec-
tion “Board Dynamics in the Era of AI”, explores how
AI inuences boardroom interactions and governance
structures. Fourth section “AI and Risk Management”,
delves into the utilization of AI for identifying, assess-
ing, and mitigating corporate risks. Fih section “Cor-
porate Transparency and Stakeholder Engagement”,
discusses the impact of AI on improving transparency
and fostering engagement with stakeholders. Sixth sec-
tion “Challenges of AI Integration in Corporate Govern-
ance”, addresses the obstacles and ethical considerations
of incorporating AI into governance practices. Finally,
seventh section concludes the article by summarizing
the ndings, discussing the implications for corporate
governance, and suggesting avenues for future research.
e following sections dive deeply into the academic lit-
erature, 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 redenes the
landscape of decision-making within corporate govern-
ance. is section explores two dominant domains in
which AI can be a game-changer: data-driven and algo-
rithmic decision-making. Amalgamating the insights from
diverse studies, we show the multifaceted role of AI in in-
uencing strategic decisions and providing executive deci-
sion support.
Journal of Corporate Finance Research / New Research Vol. 18 | № 2 | 2024
Higher School of Economics19
Data-Driven Decision-Making: The Role of
AI in Processing Large Datasets
e management of large data sets has undergone revolu-
tionary changes, allowing organizations to gain strategic
insights for informed decision-making [1]. Due to its bulk
data processing capabilities, articial intelligence enables
companies to navigate complex business environments by
providing strategic insights [18]. In doing so, AI becomes
eective in formulating governance strategies in the public
interest while skillfully addressing emerging challenges [7].
e impact of AI on corporate governance involves a lot
more than simply facilitating data analysis. Articial in-
telligence increases the precision and eciency of deci-
sion-making processes [19]. Using advanced algorithms,
AI allows companies to predict future values of their shares
in markets and reduce potential business risks [3]. is
forward-looking approach to internal decision-making re-
quires organizations to quickly adapt to changing business
environments. B. Kaya highlights the central role played by
articial intelligence in driving this revolutionary change
in corporate governance practices, which calls for constant
adaptation [20].
However, the use of articial intelligence in corporate gov-
ernance structures requires taking ethical issues into ac-
count and aligning eciency 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 empha-
sizes the application of articial intelligence technologies
to protecting corporate governance rights and interests. If
articial intelligence technologies are used correctly and in
accordance with ethical rules, they can act as guardians in
internal decision-making processes, ensuring the protec-
tion of rights and achieving operational eciency [16].
In summary, while data-driven decision-making revolu-
tionizes corporate governance practices [1; 7], it also gen-
erates ethical challenges that organizations must overcome
[19; 20]. e sweeping impact of AI on corporate govern-
ance is both transformative and challenging, requiring a
balanced approach that prioritizes strategic insights, e-
ciency and ethical responsibility.
Algorithmic Decision-Making: Implications
for Executive Decision Support
AI algorithms play a very important role in executive deci-
sion 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 algo-
rithms [17]. M. Ashoori and J. Weisz also state that trust
is a vital component in AI-driven decision-making pro-
cesses [10]. e reliability of AI algorithms signicantly
aects 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
articial intelligence algorithms to produce a stronger and
more eective decision-making process [23].
A. Nassar and M. Kamal argue that ethical considerations
should cast the foundations of AI-based decision-mak-
ing. ere 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 articial intel-
ligence. Additionally, it is critical to understand and align
the preferences and expectations of articial intelligence
system users [11].
is is consistent with the ndings 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 considera-
tions. AI demonstrates its importance in shaping the fu-
ture of corporate governance by facilitating strategic de-
cision-making and executive decision support systems.
Aligning AI-based decisions with human judgment is cru-
cial for eective governance [10; 23].
Board Dynamics in the Era of AI
As AI continues to transform industries, its impact on cor-
porate governance is becoming increasingly signicant.
is section explores the evolving dynamics of corporate
boards in the era of AI, with a specic focus on board com-
position and expertise, as well as the inuence of AI on
board decision-making processes.
Board Composition and Expertise
e incorporation of AI into corporate governance re-
quires company boards to develop new skills and exper-
tise. Traditionally, boards consisted of members with ex-
perience in nance, law, and business. However, given the
growing signicance of AI, board members are now ex-
pected to possess knowledge of technology, data analysis,
and AI algorithms. Without tech-savvy members, boards
will struggle to comprehend the impact of AI on organi-
zations [1]. erefore, it is essential to include individuals
who can “decode the algorithm” on company boards.
Board composition has an important role in eective AI
governance. e complexity of AI issues requires im-
proving the representation of non-executive members on
boards. For example, gender diversity has been shown to
improve decision-making and innovation, which are cen-
tral in the age of articial 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 ar-
ticial intelligence algorithms [26].
In summary, technological expertise and diversity play
essential roles in eective AI governance. Technologically
Journal of Corporate Finance Research / New Research Vol. 18 | № 2 | 2024
Higher School of Economics20
astute board members contribute to grasping the nuanc-
es of AI, while diverse boards oer improved scrutiny and
fairness in decision-making processes [1; 26].
AI-Assisted Board Decision-Making
Integrating AI tools into the board of directors can sig-
nicantly 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]. is capabil-
ity gives boards the ability to eectively manage strategic
planning, risk management and nancial forecasting. R.
Rajendran et al. nd that such analytical capabilities help
boards adopt more data-driven decision-making process-
es, reducing reliance on intuition and gut instinct [27].
AI-powered tools also contribute to eective board pro-
cesses. Articial intelligence can automate routine tasks
such as document analysis and compliance checks, allow-
ing managers to focus on more strategic issues [28]. is
not only saves time but also reduces the risk of human
error in manual tasks. Moreover, articial intelligence can
enable boards to act quickly when faced with new challeng-
es and opportunities by providing real-time information
and predictive analytics [29]. In today’s world, the speed
of decision-making is increasingly important, and articial
intelligence increases the board’s ability to adapt.
e growth of AI oers both opportunities and challeng-
es for companies. To harness AI’s potential, boards need
more tech-savvy members. AI can make board decisions
more ecient and eective. 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 nd its way into a multitude of sectors,
and its applications in risk management are crucial. is
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 articial intelligence
and especially machine learning algorithms, plays a cru-
cial role in risk management by analyzing large data sets
to uncover patterns and generate accurate predictions,
while traditional risk assessment models oen struggle to
achieve this amid the complexity of contemporary busi-
ness environments [31]. S. Aziz and M. Dowling show how
machine learning and articial intelligence improve risk
management through more accurate predictions. By ana-
lyzing historical data, AI can pre-emptively identify trends
and potential risks that cannot be easily spotted through
traditional methods [32].
In the ntech sector, predictive analytics is increasingly
used to manage risk. N. Bussmann et al. explore the role
played in ntech risk management by explainable articial
intelligence (XAI), which refers to articial intelligence
models in which the decision-making process is transpar-
ent and understandable. is transparency is critical in
highly regulated industries such as nancial services [33].
I. Ivashkovskaya and I. Ivaninskiy emphasize the impor-
tance of ensuring that AI algorithms are explainable to
stakeholders, especially in sectors such as nancial services
where regulatory compliance is vital [19].
Discussing the challenges of AI in nance, P. Giudici em-
phasizes that AI’s real strength lies in providing real-time
risk monitoring and adaptive responses to ever-shiing
market conditions [34].
To summarize, predictive analytics is reshaping risk man-
agement by:
• Identifying Potential Risks. Leveraging machine
learning algorithms to detect patterns and trends in
large datasets [32].
• Ensuring Regulatory Compliance. Providing
transparency through Explainable AI models,
particularly in highly regulated sectors [19; 33].
• Adapting to Market Conditions. Oering real-time
risk monitoring and adaptive responses [34].
ese insights demonstrate how predictive analytics, com-
bined with regulatory compliance measures, can signi-
cantly enhance risk management strategies in the AI era.
Addressing Cybersecurity
Challenges with AI
e exponential growth of digitalization has brought about
an increase in cybersecurity threats. However, articial
intelligence presents both challenges and opportunities
in the eld of cybersecurity within corporate governance.
In this context, J. Schuett discusses the implications of the
Articial 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 articial
intelligence and machine learning are revolutionizing cy-
bersecurity practices and how these technologies are being
used to address a wide range of ever-evolving threats, simi-
lar to broader applications in risk management [36].
In the context of risk management and AI governance, ex-
plainable articial intelligence (XAI) plays an important
role in identifying vulnerabilities and supporting compli-
ance [34]. XAI models make the decision-making process
transparent and understandable, which is especially impor-
tant in highly regulated industries such as nancial services.
In summary, AI plays a game-changing role in risk man-
agement for corporate governance in several dierent 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].
Journal of Corporate Finance Research / New Research Vol. 18 | № 2 | 2024
Higher School of Economics21
• Dynamic cybersecurity response oers 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 pre-
dictive analytics and cybersecurity becomes increasingly
important for organizations navigating the complexities
of the digital age. Studies cited throughout this article un-
derscore the growing importance of responsible AI gov-
ernance, 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 prin-
ciples and how the infusion of AI into corporate practices
may further enhance transparency and engagement with
stakeholders, drawing on recent scholarly work that ex-
plores how AI impacts corporate reporting, disclosure, and
communication with stakeholders.
Automated Reporting and Disclosure
e 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, empha-
sizing 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. ar-
gue that embedding transparency in AI systems promotes
openness and accountability while ensuring that compa-
nies 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 ecient and eective communi-
cation and thus promote informed decision-making [14].
M. Hosain et al. argue that meaningful disclosures made
with the help of articial intelligence facilitate stakeholder
communication beyond transparency [13]. C. Zehir et al.
argue that transparency should be seen as a corporate re-
quirement that involves stakeholders in the decision-mak-
ing 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 report-
ing and communication fundamentally transforms trans-
parency, accountability, and stakeholder engagement. e
academic research presented here emphasizes the impor-
tance 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 account-
able and sustainable corporate governance. e proactive
adoption of these technologies not only addresses immedi-
ate business needs but also fosters a more collaborative and
informed relationship with stakeholders.
Challenges of AI Integration in
Corporate Governance
e processes for integrating articial intelligence into cor-
porate governance are extensive, ranging from improving
decision-making and operational eciency to fostering in-
novation. While the integration of AI into corporate gov-
ernance is associated with numerous benets, it also pre-
sents challenges [40]. ere 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 eective decision-making. e need for
board members to continuously educate themselves about
AI developments and implications is critical. is requires
a commitment to a culture of continuous learning and ad-
aptation in the boardroom [42].
is section delves into the key challenges facing AI inte-
grated 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 deci-
sion-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 align-
ment between AI applications and social responsibility and
ethical norms, noting the risks of unfettered AI use in cor-
porate decision-making [44]. L. Xue and Z. Pang argue for
an integrated analytical framework for governing ethical
AI applications. ey stress that transparency, fairness and
accountability are all essential to AI decision-making in
the corporate governance landscape to address ethical con-
cerns [45]. J. Mökander et al. explore the ethical challenges
and best practices of AI governance in the biopharmaceuti-
cal industry. is 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
Journal of Corporate Finance Research / New Research Vol. 18 | № 2 | 2024
Higher School of Economics22
issues as they emerge, acknowledging that these issues are
dynamic and evolve with the development of AI and its ap-
plications, necessitating adaptive organizational strategies
[46]. In a novel twist on integrating responsible AI into
governance, G. Baloğlu and K. Çakalı question whether ar-
ticial intelligence poses a new threat to academic ethics
and emphasize the importance of considering the ethical
consequences of articial intelligence in corporate govern-
ance [47].
Legal and Regulatory Challenges
e rapid advancement of AI technologies demands a le-
gal and conceptual framework distinct from convention-
al 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 inuenced by
AI technology [49]. J. omas discusses the potential le-
gal 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. Tal-
larita 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
oen render traditional laws obsolete [51]. E. Papagiannid-
is et al. recognize the legal hurdles to AI governance and
suggest the best practices for overcoming these challenges,
underscoring the importance of organizations contribut-
ing to the development of eective legal frameworks for AI
governance [52].
In summary, the challenges of integrating AI into corpo-
rate governance extend beyond technical considerations
and incorporate ethical, legal, and regulatory dimen-
sions. Organizations adopting AI must fully understand
the implications of AI-driven decisions on ethics, soci-
etal values, and legal compliance. Drawing on academic
research, proactive measures can ensure that corporate
governance structures manage the transformative poten-
tial of AI eectively, while respecting foundational values
and norms.
Conclusion
roughout this exploration of the intersection between AI
and corporate governance, a comprehensive understand-
ing of the multifaceted implications of AI technologies on
decision-making processes, transparency, stakeholder en-
gagement, and ethical considerations has emerged. Draw-
ing insights from a range of academic sources, the follow-
ing key ndings encapsulate the transformative eects of
AI on corporate governance:
• Enhanced Decision-Making. e integration of
AI into corporate governance processes contributes
to enhanced decision-making eciency and
eectiveness [1; 29; 22].
• Improved Transparency. AI facilitates real-time
and accurate disclosure, promoting transparency in
corporate reporting [13; 15].
• Stakeholder Engagement. AI serves as a powerful
tool for stakeholder engagement by facilitating
ecient communication channels and providing
meaningful explanations for AI-driven decisions
[14; 39].
• Ethical Considerations. e ethical dimensions of
AI governance underscore the need for aligning AI
applications with social responsibilities and ethical
norms [44–47].
• Legal and Regulatory Challenges. e 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 cor-
porate 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. e 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 eective communication
strategies tailored to diverse stakeholder expectations
[14; 39].
• Ethical and Legal Frameworks. e 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 [44–47].
In conclusion, the integration of AI into corporate gov-
ernance is an ongoing journey marked by transformative
impacts and evolving challenges. Organizations that pro-
actively address ethical considerations, enhance trans-
parency, and navigate legal landscapes will be better po-
sitioned 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 eective corporate practices.
e integration of AI into corporate governance has ush-
ered in a new era by transforming decision-making pro-
cesses, stakeholder relationships and ethical considera-
tions. With insights from academic sources, an intriguing
call for future trends research in the eld of AI and corpo-
Journal of Corporate Finance Research / New Research Vol. 18 | № 2 | 2024
Higher School of Economics23
rate governance emerges. M. Hilb, P. Cihon et al., M. Fen-
wick and E. Vermeulen, and others have shed light on the
multifaceted eects of AI [1; 2; 7].
Looking ahead, predicting and minutely examining future
trends shaping the intersection of articial intelligence and
corporate governance will greatly contribute to develop-
ment in this eld.
1. 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 ex-
plore how AI will continue to redene roles and responsi-
bilities, ensuring a harmonious integration that leverages
the strengths of both human and machine decision-mak-
ing processes [20].
Specic recommendations:
Organizational Hierarchies. Investigating how AI inu-
ences hierarchical decision-making structures and wheth-
er it necessitates atter hierarchies.
Human-AI Collaboration. Examining the interplay be-
tween human intuition and AI analytics, developing
frameworks to maximize their combined potential.
Governance Strategies. Exploring the strategic implica-
tions of AI-driven decision-making, particularly in diver-
sifying board composition and expertise.
AI Literacy Training. Advocating for AI literacy training
at all levels of corporate leadership to ensure informed de-
cision-making.
2. 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 gov-
ernance, contributing to the establishment of robust guide-
lines for responsible and eective AI use [4; 11; 44; 45].
Specic 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.
3. Intersection of AI and Stakeholder Relations
e intersection of AI and stakeholder relations, as exam-
ined 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.
Specic recommendations:
• Stakeholder Engagement Models. Creating models
that enhance stakeholder engagement through AI-
driven 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 articial intel-
ligence on corporate governance is an ever-evolving eld.
In the future, exploring the eects of articial intelligence
on corporate governance with the specic recommenda-
tions provided above will oer valuable contributions to
academics, practitioners, and policymakers. is endeavor
not only enhances our understanding of the role of arti-
cial intelligence but also holds promise for guiding organ-
izations toward ethical, responsible, and eective govern-
ance in an AI-driven future.
Acknowledgements
I am grateful to the anonymous reviewers for their insight-
ful comments and recommendations, which signicantly
improved the quality of this article.
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e article was submitted 06.03.2024; approved aer reviewing 08.04.2024; accepted for publication 29.04.2024.
