Artificial Intelligence In Business: Evaluating The Transformative Role Of AI In Managerial Decision-Making Efficiency

Artificial Intelligence (AI) is driving a transformative shift in the Indian Information Technology sector which demands organizations to improve their decision-making efficiency by tackling issues like data-driven uncertainty and automation complexity along with strategic adaptation needs. The investigation evaluates how AI-Powered Business Intelligence (ABI), AI-Enabled Process Automation (APA), AI-Driven Strategic Innovation (ASI), AI-Based Customer Experience Management (ACEM), and AI-Integrated Risk Management (AIRM) affect AI-Driven Decision-Making Efficiency (DDE). The study followed a descriptive-correlation design to collect data from 150 IT managers based in major Indian IT hubs while using validated measurement scales and Structural Equation Modeling (SEM) in AMOS for hypothesis testing. The study shows significant positive effects of ABI (β = 0.238, p < 0.001), APA (β = 0.124, p = 0.002), ASI (β = 0.289, p < 0.001), and AIRM (β = 0.212, p < 0.001) on DDE while ACEM (β = 0.019, p = 0.003) demonstrates a weaker impact. The research extends Resource-Based View (RBV) and Dynamic Capability Theory by examining how AI supports strategic flexibility and risk management. Real-world applications demonstrate that human-AI collaboration is essential for improving AI-driven decision-making processes. Future research needs to examine longitudinal AI adoption patterns and identify applications across different industries.

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Publication Details

Journal
International Journal of Environmental Sciences
Published
2025-06-05
DOI
https://doi.org/10.64252/1m5pd925
Citations
1
Primary Topic
Big Data and Business Intelligence
Type
article
Field-Weighted Citation Impact
2.28
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article

Artificial Intelligence In Business: Evaluating The Transformative Role Of AI In Managerial Decision-Making Efficiency

Digvijay Singh, V. P. Rameshkumaar, РАЛЫК Д.В. РАЛЫК Д.В., K. Prakash et al.
1 citations
International Journal of Environmental Sciences
Big Data and Business Intelligence
2.28
article

Artificial Intelligence In Business: Evaluating The Transformative Role Of AI In Managerial Decision-Making Efficiency

Digvijay Singh, V. P. Rameshkumaar, РАЛЫК Д.В. РАЛЫК Д.В., K. Prakash, Shanker Krishnan, Sivakumar. S
article en
1 citations

Abstract

Artificial Intelligence (AI) is driving a transformative shift in the Indian Information Technology sector which demands organizations to improve their decision-making efficiency by tackling issues like data-driven uncertainty and automation complexity along with strategic adaptation needs. The investigation evaluates how AI-Powered Business Intelligence (ABI), AI-Enabled Process Automation (APA), AI-Driven Strategic Innovation (ASI), AI-Based Customer Experience Management (ACEM), and AI-Integrated Risk Management (AIRM) affect AI-Driven Decision-Making Efficiency (DDE). The study followed a descriptive-correlation design to collect data from 150 IT managers based in major Indian IT hubs while using validated measurement scales and Structural Equation Modeling (SEM) in AMOS for hypothesis testing. The study shows significant positive effects of ABI (β = 0.238, p < 0.001), APA (β = 0.124, p = 0.002), ASI (β = 0.289, p < 0.001), and AIRM (β = 0.212, p < 0.001) on DDE while ACEM (β = 0.019, p = 0.003) demonstrates a weaker impact. The research extends Resource-Based View (RBV) and Dynamic Capability Theory by examining how AI supports strategic flexibility and risk management. Real-world applications demonstrate that human-AI collaboration is essential for improving AI-driven decision-making processes. Future research needs to examine longitudinal AI adoption patterns and identify applications across different industries.

International Journal of Environmental SciencesVol. 11(8s)
GS Engineering (United States) (US)
Openalex Percentile: Top 10%
Big Data and Business Intelligence
2.28
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Artificial Intelligence In Business: Evaluating The Transformative Role Of AI In Managerial Decision-Making Efficiency — Digvijay Singh, V. P. Rameshkumaar, et al. · International Journal of Environmental Sciences (2025) | TGRS Research Map | TGRS