Jul 2026· British journal of management and marketing studies· 0 citations· 5 references
TL;DR
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.
Abstract
This study examined the impact of artificial intelligence (AI)-driven analytics on organizational performance. Specifically, the study investigated the influence of predictive analytics, decision automation, and data-driven strategic planning on organizational performance. The study adopted a survey research design. Data were collected from 320 managers and senior staff of selected manufacturing, banking, telecommunications, and service organizations in Nigeria. A sample size of 178 respondents was determined using Taro Yamane's formula, while data were analyzed using descriptive statistics and multiple regression analysis. Findings revealed that predictive analytics significantly improves organizational profitability (β = 0.381, p < 0.05), decision automation positively influences organizational productivity (β = 0.294, p < 0.05), and data-driven strategic planning has a significant positive impact on organizational efficiency (β = 0.427, p < 0.05). The study concludes that AI-driven analytics significantly enhances organizational performance through improved predictive analytics, decision automation, and data-driven strategic planning. The study recommends increased investment in AI infrastructure, employee training, and data governance systems to maximize organizational benefits from AI technologies.
The study concludes that AI adoption serves as a strategic organizational capability that significantly enhances strategic planning effectiveness and suggests that organizations leveraging AI technologies are more likely to develop effective strategies, improve decision quality, enhance forecasting accuracy, and strengthen organizational adaptability.
Mark Ian C. Abrias, Nerissa M. Revilla· World Journal of Advanced Re...· 0 citations
The article examines the potential of artificial intelligence to optimize management processes and improve decision-making in large organizations. The study is conducted within the framework of a business engineering approach. The analysis is based on data from the Stanford AI Index 2026 database. It evaluates the level of implementation of artificial intelligence–based decision support systems across different industries and business functions. The results reveal statistically significant differences in AI adoption between industries and functional areas (Friedman criterion, p < 0.05). The highest level of integration is observed in the technology sector, particularly in IT, software development, marketing, and sales. In contrast, manufacturing and supply chain/inventory management remain the least digitized areas. The study proposes a conceptual AI-based management framework. The framework integrates data sources, analytical models, and decision support systems into a unified adaptive cycle with a feedback mechanism. The findings confirm that the effectiveness of artificial intelligence depends not only on the technological sophistication of the solutions. It also depends on the depth of their integration into management processes and on the development of effective human–AI collaboration models. Finally, the article provides practical recommendations for scaling AI-based solutions in large organizations.
Serhii Kubitskyi, Yevhen Kozlovskyi, K. Balabukha et al.· Human Resources Management a...· 0 citations
This study examines the role of Artificial Intelligence (AI) in enhancing supply chain project management and operational performance in a dynamic business environment. As supply chains become increasingly complex, data-intensive, and disruption-prone, organizations are adopting AI-driven tools to improve forecasting accuracy, optimize inventory, streamline logistics, and strengthen decision-making. The purpose of this research is to assess the level of AI adoption, identify key application areas, and examine the relationship between AI familiarity and AI adoption while considering the broader roles of organizational readiness and governance mechanisms. A quantitative research design was employed using a structured questionnaire administered to 42 respondents, including supply chain professionals, project managers, data/AI analysts, students, and other business or technology-related participants. Data were analyzed using descriptive statistics, correlation analysis, and regression techniques. The findings indicate that approximately 57% of respondents reported current AI adoption within their organizations, while the mean AI familiarity score was 3.6 on a five-point scale, reflecting moderate awareness. Correlation analysis revealed a positive relationship between AI familiarity and AI adoption (r = 0.61), suggesting that increased knowledge supports adoption behavior. The results also highlight the perceived importance of AI training, organizational preparedness, and governance frameworks in maximizing implementation benefits. This study contributes to business analytics, operations management, and decision sciences by providing empirical insight into AI-enabled supply chain transformation. The findings offer practical implications for managers, policymakers, and industry stakeholders seeking to strengthen AI readiness, improve operational efficiency, and promote responsible AI adoption for sustainable supply chain excellence.
Denise Nalini, Dr. S.Barathi, Dr. Rubidhadevi· The Journal of Theoretical A...· 0 citations
Artificial Intelligence (AI) has emerged as a transformative force in modern business, fundamentally reshaping how organizations collect, analyze, and utilize information for strategic and operational decision-making. The increasing availability of big data, advances in machine learning, natural language processing, predictive analytics, and generative AI have enabled firms to improve decision accuracy, optimize business processes, and respond more effectively to dynamic market conditions. This review critically examines the role of AI in the future of business decision-making by synthesizing contemporary literature on AI-driven decision support systems, strategic planning, operational efficiency, customer relationship management, financial forecasting, supply chain optimization, risk management, and organizational innovation. The study adopts a comprehensive narrative review methodology, drawing on recent peer-reviewed articles, industry reports, and scholarly publications to identify emerging trends, opportunities, challenges, and future research directions. The review reveals that AI significantly enhances decision quality by enabling real-time analytics, predictive insights, and automation of routine and complex business processes. However, widespread adoption is constrained by challenges including data quality issues, algorithmic bias, cybersecurity risks, ethical concerns, regulatory uncertainty, workforce skill gaps, and the need for transparent and explainable AI systems. The findings further indicate that successful AI integration depends on effective governance frameworks, human-AI collaboration, continuous organizational learning, and responsible AI practices. The study concludes that AI is not replacing managerial judgment but augmenting human decision-making through intelligent data-driven insights. As AI technologies continue to evolve, organizations that strategically embrace responsible AI adoption while investing in digital capabilities and ethical governance are likely to achieve sustainable competitive advantage. This review contributes to the growing body of knowledge by providing an integrated understanding of AI's evolving influence on business decision-making and offering practical insights for business leaders, researchers, and policymakers navigating the future of intelligent enterprises.
Peter Stone· Research Journal in Business...· 0 citations
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.
Borhan Omar Ahmad Al-Dalaien, Modafar Al-Hroub, Rahaf Al-Syouf et al.· Corporate Board: role, dutie...· 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