Enterprise Search and Analytics Platforms for Scalable AI-Driven Decision Making

Abstract

The growing volume, velocity, and heterogeneity of enterprise data have intensified the need for platforms that can support timely, accurate, and scalable decision making. This study examines enterprise search and analytics platforms as integrated, AI-enabled infrastructures for decision intelligence, focusing on their architectural design, analytical capabilities, and organizational impact. Using a mixed-method approach that combines system-level experimentation with decision-oriented evaluation, the research assesses how semantic search, machine learning–driven analytics, and governance mechanisms interact within a unified platform. The results demonstrate that AI-enabled integration significantly improves system performance, search relevance, analytical sensitivity, and decision effectiveness across operational, analytical, and strategic roles. Multidimensional capability profiling and workflow clustering further reveal that balanced scalability, explainability, and governance are essential for sustainable enterprise adoption. The study contributes a structured framework for designing and evaluating enterprise search and analytics platforms that transform complex data ecosystems into actionable, trustworthy insights for AI-driven decision making.