Jul 2026· International Journal of Innovations in Science, Engineering And Management· pp. 6-12· 0 citations· 17 references
TL;DR
AI offers transformative opportunities for business decision-making, its long-term impact depends on responsible implementation, alignment with human values, and continuous adaptation to evolving business environments, according to the SLR.
Abstract
The rapid proliferation of Artificial Intelligence (AI) has significantly transformed business decision-making across diverse domains such as finance, marketing, supply chain, human resources, and strategic management. This study presents a Review of twelve peer-reviewed articles published between 2020 and 2025, sourced from international journals and conference proceedings. Using a structured coding framework, the review synthesizes insights on AI applications, methodologies, tools, opportunities, challenges, and future research directions in organizational decision-making.
Findings indicate that AI tools—including machine learning, deep learning, natural language processing, predictive analytics, robotic process automation, and intelligent decision-support systems—enable organizations to improve efficiency, accuracy, and foresight while reducing human bias. Across the studies, AI is shown to support enhanced financial forecasting, customer engagement, supply chain optimization, HR talent management, innovation, and strategic planning. Opportunities identified include real-time predictive insights, process automation, risk mitigation, and the creation of competitive advantages in dynamic markets.
However, challenges remain in the areas of data quality, algorithmic bias, interpretability, ethical concerns, integration with legacy systems, high implementation costs, and regulatory uncertainties. Several studies also emphasize the social and organizational risks of AI adoption, such as workforce displacement and trust deficits in automated decisions. The review highlights that successful adoption requires not only technological readiness but also ethical frameworks, transparent governance, and human–AI collaboration.
Future research directions proposed include developing sector-specific AI models, advancing explainable and ethical AI frameworks, investigating adoption in emerging markets, and exploring human–machine integration for responsible decision-making. Overall, the SLR underscores that while AI offers transformative opportunities for business decision-making, its long-term impact depends on responsible implementation, alignment with human values, and continuous adaptation to evolving business environments.
The study concludes that AI is not replacing managerial judgment but augmenting human decision-making through intelligent data-driven insights, and organizations that strategically embrace responsible AI adoption while investing in digital capabilities and ethical governance are likely to achieve sustainable competitive advantage.
Peter Stone· Research Journal in Business...· 0 citations
A systematic literature review of recent developments in AI-driven financial management and its impact on corporate financial decision-making suggests that AI is not replacing financial managers but augmenting their decision-making capabilities by providing intelligent recommendations based on large-scale data analysis.
Saddam Hussain· International Journal of Sci...· 0 citations
Abstract
Artificial intelligence (AI) has moved from a peripheral analytics tool to a core input in financial decision-making, shaping how institutions optimize portfolios, price risk, detect fraud, advise clients, and execute trades. This paper synthesizes recent academic literature, regulatory reports, and industry surveys (2020–2026) to examine the dual nature of AI adoption in finance: the strategic opportunities it creates and the risks and challenges it introduces. The review finds that AI materially improves predictive accuracy, operational efficiency, and access to financial services, with adoption accelerating sharply since the introduction of generative and agentic AI tools. At the same time, the literature converges on a consistent set of concerns: limited model explainability, algorithmic bias, cybersecurity and deepfake-enabled fraud, data privacy exposure, concentration and systemic risk from AI "monocultures," and a regulatory environment that has not kept pace with deployment. The paper presents a classification of AI application domains and associated risk categories, supported by quantitative adoption and market-growth data, and proposes a governance framework combining explainable AI, human oversight, and coordinated regulation. It concludes by identifying research gaps around long-term market stability effects, emerging-market adoption, and the governance of autonomous (agentic) financial AI.
Keywords: Artificial Intelligence, Financial Decision-Making, Risk Management, Algorithmic Trading, Explainable AI, Financial Regulation, Agentic AI
Abhishek Rajan· International Scientific Jou...· 0 citations
It is concluded that AI-driven business intelligence frameworks represent a transformative approach to enterprise management by enabling organizations to anticipate future challenges, optimize strategic decisions, improve resource utilization, and create resilient business ecosystems capable of adapting effectively to rapidly evolving economic and technological environments.
Shi-Hu Gan· International journal of com...· 0 citations
Background Artificial intelligence (AI) has emerged as a transformative force in the financial sector, reshaping traditional financial operations through advanced analytical capabilities, automation, and intelligent decision-support systems. While AI applications have expanded rapidly across banking, investment management, and financial services, evidence regarding their effectiveness, limitations, and broader implications remains fragmented. This review examines the evolving role of AI in finance by synthesizing empirical evidence on applications, benefits, challenges, and future research directions. Methods A systematic literature review was conducted on empirical studies, industry reports, and peer-reviewed publications examining AI applications in finance between 2010 and 2025. The review analyzed evidence across major AI domains, including machine learning, deep learning, natural language processing, and explainable artificial intelligence (XAI), focusing on their application in predictive analytics, credit risk assessment, fraud detection, algorithmic trading, portfolio management, regulatory compliance, and customer financial services. Results The findings indicate that AI significantly enhances financial decision-making by improving predictive accuracy, automating complex processes, strengthening risk assessment, and enabling personalized financial services. Machine learning and deep learning models demonstrate superior performance compared with conventional approaches, particularly in credit scoring, fraud detection, market prediction, and anomaly identification. However, the review highlights persistent challenges related to data quality, model interpretability, algorithmic bias, cybersecurity risks, regulatory uncertainty, and limited cross-context validation. Explainability and ethical AI governance emerge as critical requirements for the responsible deployment of AI in high-stakes financial applications. Furthermore, AI presents opportunities for advancing financial inclusion, sustainable finance, and real-time financial intelligence. Conclusion AI is fundamentally redefining the future of finance by enabling more efficient, adaptive, and data-driven financial ecosystems. However, realizing its full potential requires balancing technological innovation with transparency, accountability, regulatory alignment, and inclusive implementation. Future research should prioritize explainable, ethical, and context-aware AI frameworks capable of supporting resilient, equitable, and sustainable financial systems.
Nakayiso Eseza, M. Micheal· F1000Research· 0 citations
Artificial Intelligence (AI) has revolutionized the role of Management Information Systems (MIS) by bringing in a new era of intelligent, predictive, and automated decision-making support to help organizations make more informed choices. This review explores how MIS has been evolving to become more AI-powered and how this development is helping to streamline business operations, enhance customer interactions, and drive positive social and economic outcomes in various industries. It considers the evolution of MIS, the incorporation of AI technologies like machine learning, deep learning, natural language processing, and predictive analytics, and the transformation of enterprise decision-making. It also discusses the architecture of AI-driven MIS, optimization dimensions, methods to evaluate its performance, strategies for implementing it, its practical applications, and the governance requirements. The review points out the benefits of AI in ERP, CRM, DSS, and business intelligence systems, such as better process automation, resource management, customer personalization, and organizational responsiveness. AI-powered information systems are currently playing a significant role in various sectors such as manufacturing, healthcare, finance, government, retail, and smart city management, all of which rely on the power of AI to aid in the decision-making process and operational efficiency. The study further explores critical issues that arise with the use of AI, such as privacy, cybersecurity, explainability, adapting the workforce, ethical governance, and sustainability. In the broader context, AI-driven MIS is a paradigm shift that combines intelligent technologies with enterprise information systems to enhance organizational effectiveness, foster customer relations, and drive long-term socio-economic sustainability.
Md Shihab Rahman· American Journal of Technolo...· 0 citations
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