Aug 2026· Corporate Board: role, duties and composition· 0 citations· 15 references
TL;DR
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.
Abstract
This study explored the impact of artificial intelligence (AI)-driven decision-making (data-backed insights, risk management, efficiency and speed, predictive analytics, bias mitigation, continuous learning, and auditability) on board accountability within commercial banks in Jordan. It utilized the expanding body of literature on AI governance and corporate accountability (Wirtz et al., 2020; Dwivedi et al., 2023). To achieve this aim, a quantitative methodology was employed, using a self-administered questionnaire completed by 72 individuals from 16 commercial banks in Jordan. Primary data were analyzed using the SPSS software, and it was determined that AI-driven decision-making has the ability to positively influence board accountability within commercial banks and the banking sector in general. Among the adopted sub-variables, 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. By adopting AI-driven decision-making, organizations will be in a better position to enhance their governance system, improve transparency, and improve the overall perception of stakeholders in the banking industry.
The study aims to examine the impact of artificial intelligence (AI) on the quality of financial decision-making of investors in Maharashtra, India. Specifically, it examines the impact of AI predictive analytics (AIPA), data processing speed (DPS), bias reduction (BR), and cost savings (CS) on financial decision-making quality (FDMQ) and the moderating effect of investor experience (IE). Data was gathered from 228 investors in Maharashtra through a closed-ended questionnaire administered through Google Forms and WhatsApp. Structural Equation Modeling (SEM) was employed using SmartPLS 4 to examine the hypothesized relations. The research finds that AIPA, DPS, BR, and CS considerably enhance FDMQ. Moreover, investor experience positively moderates these connections and strengthens the role of AI on decision quality. This research confirms the utility of incorporating AI into investment decisions aimed at enhanced precision, speed, and justice of decisions. Moreover, experience-led training and tactful usage of AI aids become necessary to benefit from various profiles of investors. This study contributes to the current body of research on AI in finance and provides actionable recommendations for investors, financial institutions, and policymakers with empirical evidence from one of India's leading financial hubs.
C. Anirvinna, Jyoti Ranjana, Babita Jha et al.· International Journal of Inn...· 0 citations
The study concludes that AI-driven analytics significantly enhances organizational performance through improved predictive analytics, decision automation, and data-driven strategic planning to maximize organizational benefits from AI technologies.
O. Enyinnaya, O. Onwuegbule, K. M. Amasiatu et al.· British journal of managemen...· 0 citations
Artificial Intelligence (AI) has become a game-changing technology in finance. It greatly affects how financial institutions and individuals make decisions. By integrating AI into banking, investment management, insurance, digital payments, and advisory services, organizations have improved decision-making in terms of accuracy, efficiency, and speed. AI technologies like machine learning, predictive analytics, robo-advisors, and fraud detection help institutions examine large amounts of data, spot patterns, assess risks, and offer tailored financial solutions. As AI advances, it's crucial to understand users' awareness, use, and thoughts about these technologies for the ongoing growth of the financial sector.
This study aims to look at awareness, use, benefits, challenges, and the future of Artificial Intelligence in making financial decisions. It uses a descriptive research design with both primary and secondary data. We collected primary data from 100 respondents through a structured questionnaire using convenience sampling. Secondary data came from books, research articles, journals, reports, and other trustworthy sources. We analyzed the gathered data using percentage analysis and presented it in tables and graphs to show respondents' views.
The findings show that most respondents are aware of AI applications in financial services and often use AI-enabled platforms for banking, investments, and digital transactions. Many believe that AI improves financial decision-making by increasing accuracy, reducing human errors, enabling quicker decisions, and enhancing customer experience. Investment management, fraud detection, and financial forecasting were highlighted as the main areas where AI has the most significant impact. However, respondents also raised concerns about data privacy, cybersecurity, ethical issues, and limited human judgment in AI-driven financial systems.
The study concludes saying that Artificial Intelligence is now a vital tool for modern financial decision-making and will continue to influence the financial industry’s future. The study suggests that financial institutions should strengthen cybersecurity, improve transparency in AI systems, promote responsible governance, and raise customer awareness to ensure ethical, secure, and sustainable use of AI-driven financial services.
Vithan A. H., Kiran Giddappagol· International Journal For Mu...· 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
The findings indicate that AI adoption for business decision-making is at a nascent stage, with 80% of respondents reporting either no or minimal usage, and AI has an overwhelmingly positive impact on decision quality and speed.
Patricia Chisanga, Dr. Felix Chibesa· International journal of res...· 0 citations
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