The literature suggests that business analytics has evolved from a reporting tool into a strategic capability that supports evidence-based financial management, and organizations are likely to achieve greater value from business analytics when technological capabilities are combined with managerial expertise, sound governance, and a data-driven organizational culture.
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
The findings indicate that BI frameworks significantly enhance financial planning, governance, and strategic decision-making despite challenges related to data integration, organizational resistance, and implementation costs.
Ahmed Hassan, Fatima Noor· International Journal of Com...· 0 citations
The rapid adoption of Artificial Intelligence (AI) and business analytics is transforming organizational decision-making by enabling managers to utilize large volumes of data for timely and informed business decisions. This study examines the role of AI-driven business analytics in improving managerial decision-making and organizational performance. The study proposes an integrated framework in which AI-driven business analytics capability influences organizational performance through enhanced managerial decision-making effectiveness. The framework considers the ability of AI-enabled analytics to provide predictive insights, identify business patterns, support risk assessment, and improve the quality and speed of managerial decisions. A quantitative research approach is proposed, using a structured questionnaire to collect data from managers and executives working in organizations that utilize AI and business analytics. The collected data will be analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) to evaluate the proposed relationships and mediation effects. The study is expected to demonstrate that effective utilization of AI-driven analytics can strengthen managerial decision-making and contribute to improved organizational outcomes. The study contributes to the emerging literature on AI-enabled management by linking analytical capabilities, managerial decision-making, and organizational performance within a unified framework. The findings are expected to provide practical guidance for organizations seeking to develop data-driven and AI-enabled decision-making capabilities.
Lakshmi Vasanthi Jampani· World Journal of Advanced Re...· 0 citations
Business analytics has become an important component of strategic management today in banking, where large
volumes of customer, transaction, operational, and market data are generated. This study examines the role of
business analytics in strategic decision making with reference to HDFC Bank. It focuses on decision quality, risk
management, customer relationship management, operational effectiveness, predictive analytics, and business
intelligence. Primary information from employees was examined using percentage analysis. The findings indicate
that analytics improves decision quality, supports risk assessment, strengthens customer service, and assists
strategic planning. Overall, business analytics contributes to informed decisions, organizational performance,
competitiveness, and sustainable banking growth.
Keywords: HDFC Bank, Business Analytics, Business Intelligence, Strategic Decision Making, Data
Analytics
Banoth Julee, D. Lavanya· International Journal of Sci...· 0 citations
Finance 5.0 is a revolution in financial management that brings AI and intelligent analytics into the corporate decision-making process. This research paper focused on examining the impact of these digital technologies on financial decision-making in present organizations. A cross-sectional survey approach was used for the quantitative research design. Primary data were gathered from 420 finance professionals such as financial managers, accountants, analysts, banking officers, and finance executives of organizations that have implemented digital financial technologies (DFTs). The questionnaires measured Artificial Intelligence, Intelligent analytics, and financial decision-making on a 5-point scale in a structured manner. The collected data were analysed using descriptive statistics and multiple regression analysis in SPSS 29. The results showed a positive attitude toward all the Study variables. The highest mean (SD = 0.52) was found for financial decision making, followed by intelligent analytics (mean = 4.19, SD = 0.61), and artificial intelligence (mean = 4.27, SD = 0.56). The regression analysis revealed that artificial intelligence had a significant effect on financial decision making (β = 0.463, p < 0.001), as did intelligent analytics (β = 0.387, p < 0.001). The overall model accounted for 68.4 % of the variance explained (R² = 0.684) in financial decision making. The study found success in financial planning and forecasting, efficiency, and strategic decision making with the help of intelligent technologies in Finance 5.0. The result has important implications for financial institutions, corporate organizations, policy and decision makers, as well as technology developers who want to gain a competitive advantage and/or accelerate digital financial transformation by developing smart financial ecosystems.
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Sarmad Bin Saeed, Saba Mahmood, Abid Manzoor· Journal of Business Insight...· 0 citations
This study contributes to the strategic management literature by reconceptualizing financial modelling as a dynamic organizational capability, integrating previously fragmented theoretical perspectives, and establishing a conceptual foundation for future empirical research on AI-enabled financial decision capability.
Iman Sjamsu Rahardjo, Rida Justin Jacobalis, Idha Adhani et al.· Jurnal Minfo Polgan· 0 citations
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