Jul 2026· Journal of Intelligent Decision Making and Information Science· 0 citations· 36 references
TL;DR
Agentic AI can be viewed as a governance enhancing mechanism that enhances transparency, decreases information asymmetry and promotes adaptive, evidence-based board leadership.
Abstract
The study examined that how Agentic artificial intelligence (AI) can transform corporate governance by improving the quality of decision-making at the board level, governance effectiveness, and readiness of stakeholders. Its main goal is to examine the impact of autonomous intelligence and adaptive learning processes within Agentic AI systems on the strategy, risk management, compliance, and governance performance. The study used quantitative research design and used a structured questionnaire that was given to 200 corporate governance stakeholders, who are board directors, executives and compliance professionals. Analysis and interpretation of data was done by use of “descriptive statistics, correlation analysis, regression analysis, and paired sample t-tests” in SPSS. The findings indicate that Agentic AI capability has a statistically significant positive correlation with the quality of board-level decision-making, and Agentic AI capability should be considered a significant contributor to the accountability of the governance results. Additional results show that the degree of Agentic AI integration has a moderate and significant relationship with the overall corporate governance effectiveness. More so, the willingness of stakeholders to adopt Agentic AI is also high even though their exposure to practical experience is rather limited. The study concludes that Agentic AI can be viewed as a governance enhancing mechanism that enhances transparency, decreases information asymmetry and promotes adaptive, evidence-based board leadership.
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
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.
Tulika Dutta Roy· International Journal of Mod...· 0 citations
Artificial Intelligence (AI) has emerged as a transformative technology that enhances organizational decision-making by enabling institutions to analyze complex data, generate predictive insights, and support evidence-based governance. In higher education, AI offers significant opportunities to strengthen strategic planning, resource optimization, and institutional sustainability. This study examined the role of Artificial Intelligence-assisted strategic decision-making in promoting sustainable university governance among selected State Universities and Colleges (SUCs) in the Philippines. Specifically, it determined the level of implementation of AI-assisted strategic decision-making, assessed the level of sustainable university governance, examined the significant relationship between the two variables, and developed an Artificial Intelligence-Assisted Strategic Decision-Making Framework for sustainable university governance. The study employed a quantitative descriptive-correlational research design using a researcher-developed questionnaire administered to university administrators involved in institutional planning and governance. Data were analyzed using mean, standard deviation, and Pearson Product-Moment Correlation. The findings revealed that universities demonstrated a very high level of implementation of Artificial Intelligence-assisted strategic decision-making, with an overall mean of 4.21 (SD = 0.59). Among the dimensions, AI-driven data analytics obtained the highest rating, followed by intelligent resource management, while predictive decision support and automated strategic planning were rated high. Likewise, sustainable AI-driven data analytics university governance was assessed at a very high level with an overall mean of 4.27 (SD = 0.56). Transparency and accountability received the highest rating, followed by strategic leadership, operational efficiency, and institutional sustainability. Correlation analysis further revealed a strong positive and statistically significant relationship between Artificial Intelligence-assisted strategic decision-making and sustainable university governance (r = .821, p < .001), indicating that greater implementation of AI technologies is associated with stronger governance practices. Based on these findings, the study developed an Artificial Intelligence-Assisted Strategic Decision-Making Framework that integrates institutional data sources, AI technologies, intelligent decision support systems, and governance processes to facilitate evidence-based strategic planning, resource optimization, policy formulation, and institutional sustainability. The study concludes that Artificial Intelligence serves as a strategic enabler that enhances governance effectiveness by improving transparency, accountability, leadership, operational efficiency, and long-term institutional resilience. It is recommended that higher education institutions strengthen AI integration in governance, establish comprehensive AI governance policies, invest in digital infrastructure and capacity building, and pilot-test the proposed framework to support sustainable university governance and digital transformation initiatives.
Yasmini Rose E. Dongallo· Engineering and Technology J...· 0 citations
It was observed that AI-driven decision-making helps minimize bias if organizations use special AI algorithms for analyzing large amounts of data and producing conclusions from them.
Borhan Omar Ahmad Al-Dalaien, Modafar Al-Hroub, Rahaf Al-Syouf et al.· Corporate Board: role, dutie...· 0 citations
Agentic artificial intelligence is reshaping how organizations plan, coordinate activities, allocate resources, monitor risks, and make strategic decisions. Unlike conventional AI systems that primarily provide predictions or recommendations, agentic AI can interpret objectives, plan actions, use organizational tools, coordinate workflows, and execute tasks with varying levels of autonomy. This shift creates important implications for strategic leadership, particularly regarding executive authority, decision rights, accountability, and organizational control. This study examines how strategic leaders can redefine executive decision-making and governance structures in organizations adopting agentic AI systems. Drawing on dynamic capability’s theory, agency theory, organizational control theory, and socio-technical systems theory, the study develops a framework that links strategic leadership capability, AI governance maturity, executive oversight, agentic AI autonomy, accountability clarity, and organizational performance. A mixed-methods approach is proposed, combining survey evidence from executives and AI governance professionals with qualitative interviews involving senior leaders, digital transformation managers, and risk professionals. The study investigates how leadership capabilities, governance arrangements, and control mechanisms influence decision quality, trust in AI-enabled processes, and organizational resilience. The proposed framework emphasizes the importance of clearly defined decision boundaries, human approval thresholds, explainability, auditability, escalation procedures, and continuous governance review. The study contributes to strategic leadership and AI governance research by positioning executives not simply as final decision-makers, but as architects of human-AI decision systems. It offers practical guidance for organizations seeking to balance agentic AI autonomy with responsible executive control, ethical accountability, and long-term strategic value.
Satyasri Akula· International Journal of Eme...· 0 citations
The rapid integration of artificial intelligence (AI) into public governance systems has profoundly impacted how government agencies make administrative decisions, develop policies and deliver services in the context of democratic organizations. However, existing governance theories are unable to articulate how human cognition and machine intelligence cooperate in a co-decision making environment within government. A pervasive problem in existing governance literature and theories about AI and government is the lack of clarity or the fragmentation of theory regarding the interaction between human intelligence and machine intelligence in such hybrid governance systems particularly when concerning the accountability, the legitimacy, and the quality of decisions in an AI public administration setting. This study attempts to bridge this void. More specifically, this study proposes Cognitive Governance Systems (CGS) Theory as an original explanatory framework of the interaction between human intelligence and machine intelligence in a co-decision making context of democratic government. The research follows a theory building and theory validation-oriented design that is achieved via systematic literature review synthesis. Within this new CGS Theory, we present a cognitive system-based view of governance, conceptualized as a distributed cognition system that includes six elements: human intelligence, machine intelligence, human and machine learning cognition, the interface among those two intelligences (interaction mechanism), the framework on which governance is situated (democratic principles), and the capability of that governance system to perform, respond and evolve (resilience and adaptiveness). This new paradigm in cognitive governance theory explains why the interplay among these six elements influences the quality of decision making, the effectiveness of public policies, and the sustainability of public trust in the age of AI enabled governance systems. Our approach is built with a view to enable future empirical testing, with mixed-method techniques, such as the Delphi study method, survey instruments, and structural equation modeling (SEM).
Unknown authors· Journal of Advances in Socia...· 0 citations