Transforming Corporate Administration through Artificial Intelligence: An Analysis of Strategic Decision-Making, Operational Efficiency, Ethical Governance, and Managerial Accountability
Aug 2026· International Journal of Modern Science and Research Technology· 0 citations· 12 references
TL;DR
A strong positive relationship was found between the use of AI and the four dimensions of corporate administration that were investigated, thereby suggesting that the proposed integrated framework could serve as a basis for developing a theory and organizational practice for the future.
Abstract
Background: Artificial Intelligence (AI) has become a pervasive part of
organisational activities, transforming it from a tool of the past into a
fundamental tool of the modern administration and forcing scholars to rethink
how AI is changing the nature of strategic decision-making, operational
efficiency, ethical governance and managerial accountability (Weismann, 2024;
Ouabouch & Yahyaoui, 2025). Research Problem: While there have been
individual studies on the impact of AI in specific functional aspects like HR,
marketing or finance, there is not yet a holistic understanding of the impact of AI
on all these four interdependent pillars of corporate administration. Objectives:
This study explores the effect of AI adoption on strategic decision-making,
operational efficiency, ethical governance and managerial accountability, and
suggests an integrated framework for AI-based Corporate Administration.
Design: The quantitative, cross sectional survey design was used. Data collected
were primary data, obtained by designing a structured questionnaire based on the
5-point Likert scale, which was then answered by 100 corporate managers,
executives, department heads and employees. The primary data were then
analysed by reliability test, descriptive analysis, correlation and regression
analysis. Key Findings: Good internal consistency of the constructs was
observed (Cronbach's alpha ranged from 0.90 to 0.92). Statistically significant
and strong positive relationships were found between AI adoption and outcomes
of strategic decision making, operational efficiency, moral governance, and
managerial accountability (all p < .001) and explained 82% to 88% of the
variance in each outcome. Practical Implications: The results indicate that
corporate decision makers, boards, and policy makers should view the use of AI
as an administrative strategy, not a mere technical upgrade, and implement
algorithmic governance measures to address algorithmic risk. Conclusion: The
findings indicate a strong positive relationship between the use of AI and the
four dimensions of corporate administration that were investigated, thereby
suggesting that the proposed integrated framework could serve as a basis for
developing a theory and organizational practice for the future.
Agentic AI can be viewed as a governance enhancing mechanism that enhances transparency, decreases information asymmetry and promotes adaptive, evidence-based board leadership.
Mahesh Agarwal· Journal of Intelligent Decis...· 0 citations
AI capability is a new strategic capability in the organization that goes beyond operational efficiency and can support the quality strategic decision-making, sustainable performance of an organization, and high decision quality. Though AI capability is evolving, current research remains disparate in how to transform an AI capability to a organizational value with the role of governance, leadership, and organizations capability. To solve this, in this study, a integrated conceptual framework grounded in the theory of resource-based view(RBV), dynamic capabilities theory(DCT) and the AI Governance literature is developed and empirically tested. In the model, the sequential relation between AI capability, AI governance, strategic decision quality, organizational agility, and organizational performance was proposed and the moderating role of digital leadership was examined. An explanatory sequential mixed-methods research design was used. The empirical analysis includes two phases. In the first phase, a cross-sectional survey of 446 senior executives and strategic decision makers of public and private organizations was conducted to empirically test the proposed integrated model using Partial Least Squares Structural Equation Modeling (PLS-SEM). In the second phase, qualitative data from 30 semi-structured interviews with senior executives was collected to gain a deep understanding of AI governance, digital leadership and organizational agility practices. Multi-group analysis further revealed differences in the proposed relationships for public and private organizations. Findings revealed that AI capability not only significantly strengthens the AI governance, and consequently the strategic decision quality, but it also improve the organizational agility, resulting in improved performance. Furthermore, digital leadership has a positive effect on reinforcing the association between AI governance and the strategic decision quality. Overall, this study integrates the technology capability, the organizational capability and the leadership capability to establish an AI-enabled strategic decision-making and performance management framework, and provides strategic insights for organizations that aim to realize greater value from their AI investments.
Dareen Alshamsi, Dr. Mohamed Manea Almansoori, Dalal S. Almansoori et al.· Journal of Intelligent Decis...· 0 citations
This study examines the role of Artificial Intelligence (AI) in enhancing public financial management and budgeting systems, with particular emphasis on its implications for efficiency, transparency, accountability, and fiscal governance. The increasing complexity of government financial operations and the growing demand for evidence-based decision-making have created the need for innovative technological solutions capable of improving budgeting processes and resource allocation. The study adopts a qualitative approach based on an extensive review of scholarly literature, policy documents, institutional reports, and empirical studies relating to AI applications in public finance. The theoretical foundation of the study is anchored on the Technology Acceptance Model (TAM) and Public Choice Theory. TAM explains the factors influencing the adoption of AI technologies by public officials, while Public Choice Theory highlights how AI can reduce inefficiencies, corruption, and waste through automated monitoring and transparent decision-making mechanisms. Findings reveal that AI significantly improves budget forecasting, expenditure monitoring, fraud detection, financial reporting, and policy evaluation through the use of machine learning, predictive analytics, and intelligent automation. The study also identifies challenges such as inadequate digital infrastructure, cybersecurity risks, poor data quality, limited technical expertise, and resistance to organisational change. The study concludes that AI possesses substantial potential to transform public financial management by promoting fiscal discipline, strengthening accountability, and enhancing the effectiveness of budgeting systems. It recommends increased investment in digital infrastructure, human capacity development, data governance frameworks, cybersecurity measures, and ethical AI policies to ensure successful implementation. The study contributes to the growing body of knowledge on digital governance and provides practical insights for policymakers, public administrators, and development practitioners seeking to leverage AI for improved financial management and sustainable public sector performance.
Ukpong Johnson, O. Duke, Bayo Lekara et al.· Science Journal of Business...· 0 citations
The artificial intelligence (AI) approach is rapidly influencing business development in supply chain - logistics by enhancing coordination, speed, and quality of decisions. Nevertheless, with the implementation of AI in logistics, governance issues are increasingly challenging to overlook, particularly those related to accountability, transparency, and safeguarding human dignity in the algorithmically mediated decisions. This paper adopts a qualitative, normative design and considers AI adoption in supply chain - logistics as an issue of governance, but not a technical or operational one. Based on the Antiqua et Nova and key principles of Catholic Social Teaching, the analysis addresses the gaps in governance that are apparent in logistics practices of AI and ASEAN level policy guidance, focusing on human control, moral responsibility, and algorithmic management. It constructs a normative framework of governance with the help of an illustrative case analysis and a governance gap assessment as a means to reposition human moral agency to the center of AI-enabled logistics operations. The framework highlights the importance of human responsibility, regulated openness, and reasonable human control as viable principles of ethically responsible adoption of AI and shows how dignity-based ethical thinking can be used to inform governance and legitimate sustainable business growth in supply chain - logistics.
Edwin P. Mercado· International Journal of Res...· 0 citations
The integration of Artificial Intelligence (AI) and digital platforms into public administration and private sectors is fundamentally reshaping governance structures worldwide. While these technologies offer substantial opportunities for efficiency and innovation, they simultaneously pose significant challenges to legal accountability, institutional legitimacy, social equity, and ecological sustainability. Despite a proliferation of policy initiatives, comprehensive evidence is lacking on how AI-driven governance concretely influences policy decision-making across diverse strategic sectors. This study employed a systematic literature review (SLR) methodology, following a structured screening and selection process. From an initial corpus, 45 peer-reviewed empirical and policy-oriented articles published between 2021 and 2026 were selected for in-depth analysis. A thematic synthesis approach was applied, categorizing the literature into four interconnected analytical pillars: (i) institutional transformation and regulatory governance, (ii) access, participation, and digital justice, (iii) innovation, competitiveness, and sustainable development, and (iv) ethics, risk mitigation, and policy legitimacy. The findings reveal that digital governance operates through four primary mechanisms: restructuring institutional procedures and oversight frameworks (law and public administration); enhancing access to resources, services, and citizen participation (agrarian and socio-cultural sectors); driving economic innovation and environmental sustainability (economic and resource management); and navigating ethical risks to maintain policy legitimacy (cross-cutting). A persistent gap was identified between technological adoption and the adaptive capacity of existing legal and regulatory institutions, particularly in addressing data governance, accountability, and inclusivity challenges. The SLR demonstrates that effective AI and digital platform governance demands a holistic, context-sensitive approach that actively balances efficiency with justice, innovation with accountability, and risk with public value. The study offers an integrative framework for policymakers and practitioners to navigate digital transformation complexities and establishes a robust foundation for future empirical research on policy decision-making in the digital era.
Examining how artificial intelligence (AI) governance supports sustainable decision-making across organizational contexts in Europe reveals that governance increasingly aligns with formal frameworks through policies, dedicated structures, human oversight and Environmental, Social and Governance oriented indicators, enhancing transparency and reliability.
Fernando Almeida· Journal of Ethics in Entrepr...· 0 citations
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