Addressing Managerial Challenges in Decision Making: A Strategic Framework for Mitigating Latency and Boosting Organizational Agility

Abstract

In today’s rapidly changing, data-rich environments, organizations face increasing complexity in their decision-making processes. They rely on operational and transactional decision-making, where slow, biased, or unclear decisions can hinder agility and competitiveness. This study introduces a Strategic Decision-Making Framework (SDMF), which creates an AI-enabled decision support structure incorporating real-time business intelligence, agile principles, and ethical governance. The SDMF addresses challenges such as decision-making delays, cognitive bias, information overload, and accountability. This qualitative study utilized a case study format over six months at a mid-sized manufacturing firm. The SDMF was developed using IoT-enabled data acquisition, predictive analytics, collaborative dashboards, and agile cycles. The findings revealed a 66% reduction in decision turnaround time, a 16.8% improvement in forecasting accuracy, an 80% decrease in defect detection, and full traceability of managerial decisions, with no compliance violations in ethical governance. The results demonstrate that when intelligent analytics, agile capabilities, and ethical governance are integrated, they enhance decision velocity and improve transparency, trust, and operational agility. The SDMF serves as a reproducible and scalable model for organizations navigating uncertainty while maintaining speed, accuracy, and ethical integrity.