Abstract

Small and Medium-sized Enterprises (SMEs) are vital contributors to global economic stability, innovation, and job creation, yet they are disproportionately vulnerable to economic disruptions such as pandemics, inflationary shocks, and geopolitical instability. In this context, the ability to adapt swiftly and make informed decisions becomes crucial to business survival and growth. This paper explores the role of Adaptive Business Intelligence (Adaptive BI) in enhancing SME agility during economic disruptions. Adaptive BI is characterized by its real-time data processing, dynamic analytics capabilities, and ability to evolve with changing operational conditions. Unlike traditional BI systems, Adaptive BI empowers SMEs with tools to anticipate changes, respond to market volatility, and recalibrate strategies rapidly. This study introduces a model that integrates Adaptive BI into core SME functions such as supply chain management, customer relationship management, and financial forecasting. The model emphasizes iterative learning, scenario-based analysis, and predictive modeling to support proactive decision-making. Through case-based evidence and cross-industry analysis, the paper demonstrates how Adaptive BI can reduce response times, enhance customer satisfaction, and improve operational resilience. Particular attention is paid to cloud-based platforms and embedded AI tools that support real-time insights and contextual analytics. Furthermore, the research discusses key implementation enablers—including digital literacy, scalable IT infrastructure, and leadership alignment—and identifies common adoption barriers such as high upfront costs, resistance to change, and data quality concerns. The proposed approach aligns with the U.S. Small Business Administration's digital innovation goals and complements global sustainability frameworks by fostering agile, data-informed practices in underserved business communities. This paper contributes to the evolving discourse on digital transformation by offering practical pathways for SMEs to institutionalize agility through Adaptive BI. It advocates for a shift from static reporting to dynamic, intelligence-driven operations, emphasizing the urgency of resilience amid continuous uncertainty. The findings provide actionable insights for policymakers, SME owners, and technology providers aiming to future-proof small businesses through intelligent analytics systems.