Aug 2026· International journal of computer information systems and industrial management applications· Vol 18, pp. 1753-1762· 0 citations
TL;DR
This review aims to review the literature on how AI can be used to assist in administrative decision making in the past five years using key academic databases and underscores the importance of using AI to assist – not replace – the human administrator, and to ensure decisions are responsible, accountable and ethical.
Abstract
The use of Artificial Intelligence (AI) is becoming more and more a tool for administrative decision making and is helping to improve the efficiency of the organization, analyse data and make decisions based on evidence, whether in the public or private sector. While it has been embraced and promoted, algorithmic bias, data privacy, transparency and accountability issues remain to be addressed to ensure responsible use. This narrative review aims to review the literature on how AI can be used to assist in administrative decision making in the past five years (2020–2025) using key academic databases. The review covers the applications, benefits, challenges and governance implications of AI, and the necessity for the role of human oversight in administrative processes. While the results reveal a range of benefits, including enhanced decision-making speed, operational efficiency, predictive capabilities, and service delivery, ethical and legal considerations, alongside organizational challenges, need to be addressed to ensure these advantages are effectively managed. This review is an integrated synthesis of the current evidence and underscores the importance of using AI to assist – not replace – the human administrator, and to ensure decisions are responsible, accountable and ethical.
It is argued that AI should be viewed as a supportive tool that enhances human administrative capabilities rather than replacing human decision-making to ensure that AI contributes to transparent, efficient, and inclusive governance.
Laxmi· Journal of Frontiers in Mult...· 0 citations
Purpose – This systematic review examines how artificial intelligence (AI) can support strategic decision-making in private universities, the organizational conditions shaping its value, and the governance and implementation risks that constrain responsible use.Methodology – Searches were conducted in the Web of Science Core Collection, Scopus, and Google Scholar between March and May 2026, with the final update on May 31, 2026. After duplicate removal, screening, full-text assessment, and evidence appraisal, 46 substantive sources published between 1955 and 2025 were included in this review. Four additional methodological references supported the review reporting and synthesis. Because the evidence base was heterogeneous, narrative thematic synthesis was applied while distinguishing direct private university evidence from evidence transferred from general higher education, organizational decision research, and AI governance.Findings – The synthesis identifies four interconnected roles of AI: environmental intelligence, decision augmentation, strategic execution, and governance infrastructure. AI can strengthen institutional sensing, the comparison of strategic alternatives, implementation coordination, and decision traceability. However, direct empirical evidence specific to private universities is limited. Strategic value depends on data quality, organizational learning, analytical capability, decision ownership, auditability, governance capacity, strategic fit, and alignment with the institutional mission. Therefore, AI is best understood as a human-led decision-support capability rather than a substitute for institutional judgment.Research limitations – The heterogeneous corpus prevents statistical estimation of a common institutional effect, while the review is restricted to English-language sources from three search platforms. Therefore, the four-part architecture should be treated as an evidence-organizing framework rather than a validated causal model.Originality – This review integrates higher education, organizational decision-making, strategic management, and AI governance evidence into an institution-level capability architecture for responsible AI-supported strategic decision-making.
Yun-Dong Wu, Wei-Jian Kong· Artificial Intelligence in E...· 0 citations
Artificial intelligence (AI) is increasingly transforming human resource management through applications in recruitment, employee evaluation, workforce analytics, talent management, and organizational decision-making. However, the growing use of AI in employment-related processes has also raised concerns regarding algorithmic bias, transparency, privacy, accountability, and regulatory compliance. This review aims to examine the future of HR governance by integrating AI ethics, risk management, and regulatory compliance within the context of global organizations. A structured literature review was conducted using relevant publications obtained from major academic databases, including Scopus, Web of Science, IEEE Xplore, ScienceDirect, and Google Scholar. The review focused on research addressing AI governance, responsible AI, HR risk management, algorithmic accountability, human oversight, and regulatory frameworks. The findings indicate that effective AI-driven HR governance requires an integrated approach combining ethical principles, lifecycle-based risk assessment, transparent decision-making, human oversight, data protection, and continuous compliance monitoring. The review also identifies fragmented governance practices and a lack of HR-specific frameworks as important research gaps. In conclusion, responsible AI governance should become a strategic component of modern HR management, enabling global organizations to balance technological innovation with employee rights, organizational accountability, and sustainable regulatory compliance.
Jaganathan Balaji· International Journal of Inn...· 0 citations
Artificial intelligence (AI) is increasingly being applied in human resource management, from recruitment, assisting with HR issues and scheduling to training, performance evaluation, and workforce planning. This article compiles 19 documents that have been checked for publication information and relevance to the research content. The results show that AI can help HR departments process tasks faster, implement more consistent processes, and support the personalization of certain activities. However, the effectiveness of implementation depends on the intended use, data quality, suitability of the assessment methodology, user capabilities, and accountability mechanisms. Key risks include algorithmic bias, uninterpretable results, negative reactions from candidates and employees, personal data breaches, vendor dependence, and a tendency to over-rely on system-generated results. Based on this, the paper proposes a framework for responsible AI deployment consisting of six components: defining purpose and risk levels; data governance; relevance and fairness assessment; ensuring human oversight and decision-making; transparency and review; and continuous monitoring and capacity building. This framework defines AI as a supporting tool, not a replacement for the responsibilities of managers
H. Nguyen· International Journal of Sci...· 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
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
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
