Enhancing managerial decisions through strategic intelligence: The contribution of advanced information retrieval systems

This study explores the comparative impact of classic and advanced information retrieval sys- tems (IRS) on strategic intelligence and decision-making quality within organizations. Classic IRS, rooted in keyword-based searches and basic indexing, have supported decision-making by providing fast access to relevant data. However, their limitations in contextual depth, adaptabil- ity, and capacity to handle complex, unstructured information often restrict their effectiveness in today’s dynamic business environments. Advanced IRS, on the other hand, utilize techniques such as natural language processing, machine learning, and semantic analysis to deliver richer, contextually relevant insights. These systems enable a more comprehensive understanding of complex information landscapes, aligning with strategic goals and supporting proactive decision- making. The paper’s analysis highlights that while advanced IRS offer substantial advantages in data precision, strategic alignment, and responsiveness, they also entail higher costs and tech- nical requirements. The findings provide actionable insights for organizations in selecting and implementing the most suitable IRS to enhance decision quality, considering factors like orga- nizational goals, resources, and ethical considerations. This research contributes to the body of knowledge on strategic intelligence by examining the evolution of IRS and their implications for organizational success.