Impact of AI Adoption on SME Operational Resilience and Performance

Thesis Abstract

The rapid proliferation of artificial intelligence (AI) technologies among small and medium-sized enterprises (SMEs) has reshaped competitive dynamics by enhancing operational capabilities, yet the extent to which AI adoption fortifies resilience and drives performance remains underexplored in empirical settings. This study addresses the gap by examining how AI-driven process automation, analytics, and decision-support systems influence SME operational resilience and overall performance, particularly in the face of supply chain disruptions, demand volatility, and informational instability. The aim is to elucidate the mechanisms through which AI adoption translates into robust operational routines and financial outcomes, and to identify contextual factors that condition its effectiveness. Specifically, the study seeks to (1) quantify the relationship between AI adoption levels and operational resilience across SMEs in manufacturing and services sectors; (2) assess the impact of AI-enabled capability on key performance indicators such as throughput, lead time, inventory turns, and profitability; (3) investigate mediating roles of digital capability, data quality, and data governance; and (4) examine moderating effects of firm size, sectoral characteristics, and market dynamism on these relationships. A sequential explanatory mixed-methods design is employed. The quantitative phase collects cross-sectional survey data from 420 SMEs in a nationally representative sample, supplemented by archival performance data for a subset of 180 firms from financial statements and operational dashboards. AI adoption is measured through a multi-item scale capturing automation extent, analytics sophistication, and autonomous decision-support usage. Operational resilience is assessed via three dimensions recovery speed, recovery completeness, and continuity of critical operations during recent supply-and-demand shocks. Performance outcomes comprise revenue growth, gross margin, and inventory turnover. Validated instruments are adapted from prior literature and refined through a pilot test. Structural equation modeling (SEM) using AMOS 28 tests the hypothesized relationships and mediation effects, while hierarchical regression examines robustness. The qualitative phase conducts 28 in-depth interviews with SME managers and 6 case-study sites exhibiting high and low AI maturity to capture contextual narratives, using thematic analysis to triangulate and enrich the quantitative findings. The expected findings indicate that higher levels of AI adoption are positively associated with enhanced operational resilience, manifested in faster disruption recovery, more complete restoration of critical processes, and improved continuity during shocks. AI-enabled analytics and automation are anticipated to positively influence throughput, reducing cycle times and increasing inventory turnover, with spillover effects on profitability and cash flow. Mediation analysis is expected to reveal that data quality and organizational data governance partially transmit the effects of AI adoption on resilience and performance. Moderating analyses are likely to show stronger effects in medium-sized firms and in sectors facing greater volatility, while markets characterized by rapid demand shifts amplify AI's benefits. The qualitative insights are expected to reveal organizational routines, leadership, and culture as critical enablers or barriers to realizing AI-driven resilience gains. The study contributes to knowledge by integrating resilience and performance perspectives under AI-enabled operations in SMEs, advancing the theoretical linkage between dynamic capabilities and digital transformation. It applies and extends theoretical constructs from the Dynamic Capabilities Framework and the Resource-Based View by detailing how AI-driven processes convert data assets into resilient operational routines and superior performance. Practical implications include concrete guidance for SMEs on prioritizing AI investments, data governance, and capability building, as well as policy relevance for SME-support programs emphasizing digital upskilling and resilience planning. Limitations include cross-sectional data for the quantitative component and potential response biases in self-reported measures, which are mitigated by triangulation with archival data and case studies. The study concludes that strategic AI adoption, paired with robust data governance and adaptive organizational practices, substantially enhances SME resilience and financial performance, recommending phased implementation, continuous capability development, and sector-specific benchmarking as key managerial actions.