AI-Driven Knowledge Management for Sme Competitive Advantage

Thesis Abstract

This study investigates how AI-driven knowledge management (KM) practices influence competitive advantage in small and medium-sized enterprises (SMEs) within dynamic market environments. The problem addressed is the persistent underutilization of collective organizational knowledge in SMEs due to limited KM capabilities, fragmented data sources, and insufficient integration of AI-enabled decision support. The aim is to assess how AI-powered KM platforms affect operational efficiency, innovation capability, and strategic positioning, with objectives to (1) identify AI-enabled KM practices adopted by SMEs, (2) examine the relationship between AI-driven KM and firm performance metrics, (3) analyze moderating effects of organizational learning culture and digital maturity, and (4) develop a practical framework linking KM capabilities to competitive advantage outcomes. The study integrates the Resource-Based View (RBV) and the Dynamic Capabilities theory to explain how AI-enhanced KM transforms tacit and explicit knowledge into sustainable advantages under environmental turbulence. A mixed-methods design is employed. In the quantitative strand, a cross-sectional survey targets 360 SME respondents across manufacturing, services, and technology-enabled sectors in a defined metropolitan region, with 310 valid responses (response rate 86%). Constructs include AI-enabled KM capability, knowledge quality, decision-support utilization, process efficiency, product/process innovation, and performance outcomes (profit margin, market share growth). Data collection uses a structured questionnaire validated through content validity expert review and pilot testing (n=30). Reliability is assessed via Cronbach’s alpha and composite reliability, while convergent and discriminant validity are established using confirmatory factor analysis (CFA). Hypotheses are tested with partial least squares structural equation modeling (PLS-SEM) to handle formative and reflective constructs, assess mediation effects (knowledge quality mediating AI KM and performance) and moderation effects (organizational learning culture, digital maturity). In the qualitative strand, 20 semi-structured interviews with SME managers and KM practitioners supplement the survey, analyzed using thematic analysis to identify contextual factors, implementation barriers, and best practices. Triangulation enhances construct validity and interpretive depth. The study anticipates finding that AI-enabled KM practices—such as automated knowledge capture from operations, natural language processing-based retrieval, intelligent recommendations for decision-making, and chat-based expert systems—significantly correlate with improved process efficiency and faster innovation cycles. It is expected that higher knowledge quality mediates the relationship between AI KM and performance outcomes, while organizational learning culture and higher digital maturity strengthen this effect. The research also anticipates identifying contingent factors such as firm size, industry sector, and data governance maturity that influence the strength of AI KM’s impact on competitive advantage. Contributions to knowledge include a) empirical evidence on the mechanisms by which AI-driven KM creates competitive advantages for SMEs, b) a validated measurement model for AI-enabled KM capabilities in SME contexts, and c) a context-specific framework integrating RBV and Dynamic Capabilities to guide SMEs in designing and deploying AI KM platforms. The study offers practical implications for SMEs, including a phased implementation roadmap, data governance guidelines, and metrics to monitor performance gains from AI KM investment. Policy-relevant insights relate to SME digital transformation support, incentive structures for data sharing, and standards for AI-assisted decision transparency. The main conclusion is that AI-driven KM capabilities can yield substantive competitive advantages for SMEs when aligned with a supportive organizational learning culture and appropriate digital maturity, enabling improved efficiency, faster innovation, and enhanced strategic positioning. Recommendations include prioritizing data quality initiatives, developing knowledge governance policies, investing in AI-enabled search and recommendation systems, fostering communities of practice to strengthen learning, and conducting periodic capability maturity assessments to sustain performance gains. Suggestions for future research include longitudinal studies to examine causal relationships over time, sector-specific analyses, and exploration of AI ethics and trust considerations in SME KM deployments.