Jul 2026· Acta Carolus Robertus· Vol 16, pp. 44-58· 0 citations· 22 references
TL;DR
The main contribution of the study lies in systematically synthesizing the findings of the relevant literature and identifying the key organizational, managerial, and regulatory factors that influence the effectiveness of AI-based decision support.
Abstract
The aim of this study is to identify the key factors of effective artificial intelligence-supported managerial decision support systems. Although the body of literature examining the role of artificial intelligence in decision-making is rapidly expanding, existing studies predominantly adopt a technological perspective, while integrated analyses of managerial and organizational implementation dimensions remain limited. The central research question of this study is which factors contribute, at the organizational level, to the successful implementation and sustainable use of AI-based decision supper systems. The research is based on a qualitative literature review methodology, which presents the key theoretical aspects of the relationship between artificial intelligence and managerial decision support systems and analyzes the relevant literature in the field. The main contribution of the study lies in systematically synthesizing the findings of the relevant literature and identifying the key organizational, managerial, and regulatory factors that influence the effectiveness of AI-based decision support. A limitation of the study is that it relies exclusively on qualitative literature analysis and does not include empirical investigation. Nevertheless, the findings provide a foundation for further empirical research, particularly regarding leader – AI interactions and the examination of long-term performance effects.
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
The entry of Artificial Intelligence (AI) in an organization decision-making has attained international popularity but the strategic use of Artificial Intelligence in educational leadership is less researched. Although literature emphasizes the ability of AI to automate administrative tasks, there is little empirical evidence of its impact on strategic judgments involving educational leadership and decisions associated with high stakes and associated risks, especially in developing countries. The current study fills this knowledge blank by analyzing the impact of AI in decision-making processes in higher education leadership. Under a mixed-methods design, the data was drawn using a validated survey instrument and quantitative data collected on 214 senior leaders of various universities. The relationship between adoption of AI, quality of decision and speed of decision were measured using Structural Equation Modeling (SEM). Complementary qualitative information was received with the use of 20 semi-structured interviews with institutional leaders as well as using thematic analysis with the help of NVivo software to allow identifying the subtleties of the context. The results indicate that AI enjoys a positive relationship with the quality and the efficiency of strategic decisions, upon the digital literacy of leaders and the institutional preparedness. Nonetheless, the ethical concerns, the question of reliability of the data and the risks of overestimating AI became the important moderating factors. The research is helpful to the disciplines of socio-technical decision-making and rational decision-making as well since it is shown that AI continuously becomes a strategic resource but not an operational one. In practice, the study provides practical suggestions on leadership training, policymaking and ethical implementation of AI in educational establishments.
Anwar Saeed, A. Rizvi· Aposta: Revista de Ciencias...· 0 citations
The article examines the potential of artificial intelligence to optimize management processes and improve decision-making in large organizations and proposes a conceptual AI-based management framework that integrates data sources, analytical models, and decision support systems into a unified adaptive cycle with a feedback mechanism.
Serhii Kubitskyi, Yevhen Kozlovskyi, K. Balabukha et al.· Human Resources Management a...· 0 citations
The transformation of artificial intelligence (AI) in organizational decision-making demands that managers understand, interpret, and strategically integrate algorithmic information to keep decisions accountable. This study aims to analyze the manager's strategic sensemaking process in AI-based decision-making, focusing on the relationship between data interpretation, human considerations, and the quality of organizational decisions. The study used a descriptive qualitative approach through semi-structured interviews with 18 managers from companies who had applied AI in managerial functions. The data were analyzed using thematic analysis to identify patterns of meaning, interpretation strategies, and implementation challenges. The results show that strategic sensemaking is formed through a combination of data literacy, managerial experience, cross-functional collaboration, and ethical evaluation of AI recommendations. In conclusion, this study confirms that AI does not replace the role of managers, but rather expands decision-making capacity through the integration of strategic reasoning, technology, and organizational responsibility
Junus Johanis Lunamasa· Formosa Journal of Science a...· 0 citations
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
Aim: The objective of this article was to systematically analyse the literature on AI-related competencies of project managers and assess how these competencies were addressed in the context of project management. Methodology: The study was conducted in accordance with the PRISMA 2020 guidelines, using bibliometric analysis (VOSviewer) and qualitative content analysis. The bibliometric analysis included 38 publications, and the qualitative analysis covered 26 works, coded into four competency categories: technological, cognitive, decision-making, and ethical-organisational. Results: The literature focuses primarily on the technological dimension of AI, while project managers' competencies are only marginally and inconsistently described. Technological and decision-making competencies dominate, cognitive and ethical-organisational competencies remain underdeveloped, and there is no consistent model of AI competencies for project managers. Implications and recommendations: Project management competency models should be expanded to include AI-related competencies, such as AI literacy, data work, algorithmic decision-making, and technology ethics. The DigComp 2.2 framework can support their systematisation. Originality/value: The article identifies the gap between the growing role of AI and current project management competency models and outlines directions for further research.
Katarzyna Marek-Kołodziej· Prace Naukowe Uniwersytetu E...· 0 citations
