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With the exponential growth of data and computational power, organizations are increasingly adopting data-driven decision-making (DDDM) to enhance strategic agility, operational efficiency, and managerial precision. This study empirically investigates how the adoption of DDDM improves decision quality, performance metrics, and organizational outcomes across five key industry sectors, finance, healthcare, manufacturing, retail, and telecommunications, using a mixed-methods explanatory design. Based on structured surveys, multi-year panel data, and predictive model validation, it examines the combined effects AI utilization, data completeness, and managerial adaptability on decision performance. Findings reveal that decision quality, cost-effectiveness, and operational speed improve most significantly when high data quality is paired with employee readiness for AI. Sectoral comparisons show that industry-specific characteristics, particularly investment intensity and immediacy of benefits, moderate the rate and scale of benefit realization. The proposed Decision Optimization Score (DOS) integrates four key dimensions: AI exposure, data readiness, workforce adaptability, and decision-cycle speed. Empirical evidence indicates that balanced investment across these levers yields superior firm performance compared to focusing on a single factor. The study concludes that successful DDDM implementation depends not only on technological advancement but also on human and institutional readiness, offering pragmatic insights for leaders pursuing scalable and sustainable analytics transformation.

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AI-Driven Managerial Strategy and Institutional Readiness: Social Science Insights into Data-Centric Transformation

  • 会议论文

  • 发表日期：

    2026年

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  • Yahya Majeed Alsaad,
  • Faiza Abdulla Ali,
  • Baydaa Essam Abdulrahman Jasim,
  • Aqeel Mahmood Jawad,
  • Ali Alsaray,
  • Viktoriia Trofymchuk

摘要

With the exponential growth of data and computational power, organizations are increasingly adopting data-driven decision-making (DDDM) to enhance strategic agility, operational efficiency, and managerial precision. This study empirically investigates how the adoption of DDDM improves decision quality, performance metrics, and organizational outcomes across five key industry sectors, finance, healthcare, manufacturing, retail, and telecommunications, using a mixed-methods explanatory design. Based on structured surveys, multi-year panel data, and predictive model validation, it examines the combined effects AI utilization, data completeness, and managerial adaptability on decision performance. Findings reveal that decision quality, cost-effectiveness, and operational speed improve most significantly when high data quality is paired with employee readiness for AI. Sectoral comparisons show that industry-specific characteristics, particularly investment intensity and immediacy of benefits, moderate the rate and scale of benefit realization. The proposed Decision Optimization Score (DOS) integrates four key dimensions: AI exposure, data readiness, workforce adaptability, and decision-cycle speed. Empirical evidence indicates that balanced investment across these levers yields superior firm performance compared to focusing on a single factor. The study concludes that successful DDDM implementation depends not only on technological advancement but also on human and institutional readiness, offering pragmatic insights for leaders pursuing scalable and sustainable analytics transformation.