AI-Driven Knowledge Management for SMEs Competitive Advantage

Thesis Abstract

The rapid digitization of business processes and the growing volume of organizational data pose a strategic challenge for small and medium-sized enterprises (SMEs) seeking sustainable competitive advantage; existing knowledge management (KM) practices often fail to scale or align with SME resources and workflows, leading to underutilization of tacit and explicit knowledge. This study investigates how AI-driven KM platforms enhance decision-making, innovation, and operational efficiency in SMEs, addressing the gap in literature on scalable, cost-effective KM interventions tailored to SME contexts. The aim is to develop an evidence-based framework for deploying AI-enabled KM that improves organizational performance while remaining affordable and adaptable to diverse SME settings. Specific objectives are (1) to examine the current KM capabilities and ICT maturity levels of a representative sample of SMEs; (2) to design and implement an AI-assisted KM prototype integrating natural language processing, semantic search, automated knowledge tagging, and recommendation systems; (3) to evaluate the impact of the AI KM prototype on decision quality, innovation output, and operational efficiency; (4) to identify enablers and barriers to AI KM adoption in SMEs; and (5) to propose a practical implementation blueprint and governance mechanism for sustainable usage. The methodology adopts a mixed-methods design, combining a quasi-experimental field study with qualitative inquiry. The population comprises 320 SMEs across manufacturing, services, and retail sectors within a metropolitan region. A stratified random sample of 120 SMEs is selected, with 60 SMEs assigned to an intervention group implementing the AI-driven KM prototype and 60 to a control group maintaining existing KM practices. Data collection instruments include a pre- and post-intervention survey measuring knowledge-sharing intensity, perceived decision quality, and innovation indicators; system usage logs capturing interaction with the AI KM platform; in-depth interviews with 24 managers and KM practitioners; and organizational performance data (time-to-decision, project cycle time, and product introduction speed) obtained from enterprise records. Validity and reliability are established through pilot testing of instruments, Cronbach’s alpha checks for multi-item scales, and triangulation across survey, usage analytics, and interview data. Quantitative analysis employs difference-in-differences (DiD) to estimate the causal impact of the AI KM intervention on decision quality and operational performance, complemented by regression analyses controlling for firm size, sector, and prior ICT maturity. Mediation analysis explores whether changes in knowledge-sharing behavior and information accessibility mediate performance effects. Qualitative data are analyzed thematically using Braun and Clarke’s framework to identify patterns related to adoption, usability, governance, and contextual fit, with triangulation to quantify convergences and divergences. A conceptual model integrating technology acceptance, dynamic capability theory, and the knowledge-based view guides interpretation of results. The study also references relevant theories, including the Resource-Based View (RBV) to explain sustained competitive advantage through unique knowledge assets, and Diffusion of Innovations to understand adoption dynamics within resource-constrained SMEs. Expected findings indicate that SMEs utilizing the AI-driven KM prototype will exhibit statistically significant improvements in decision quality (p < .05), faster project cycle times (mean reduction of 18%), and higher rates of knowledge reuse and idea generation (quantified via usage metrics and novelty indicators) compared with controls. The analysis anticipates heterogeneous effects across sectors and firm sizes, with larger SMEs deriving greater efficiency gains due to greater data availability and organizational slack, while smaller SMEs gain primarily from improved information retrieval and onboarding. Qualitative insights are expected to reveal critical success factors such as robust data governance, user-centric interface design, alignment with existing processes, and management support, along with barriers including data quality issues and perceived AI opacity. The study contributes to knowledge by articulating a concrete, scalable AI KM framework tailored for SMEs, integrating technological, organizational, and governance dimensions, and providing empirically grounded guidance on kosten-, time-, and value-based deployment. Practically, it offers an implementation blueprint with module specifications, cost-benefit considerations, and a staged rollout plan, complemented by governance recommendations for data stewardship, metrics, and continuous improvement. Policy implications include recommendations for SME-differentiated ICT support and standards for AI-enabled KM interoperability. The main conclusion is that AI-driven KM, when designed with SME constraints in mind, can transform tacit and explicit knowledge into actionable insights that enhance competitive positioning, with sustained benefits contingent upon robust data governance, user engagement, and alignment with strategic objectives. Recommendations for practice include phased implementation, alignment with existing workflows, investment in data quality initiatives, and ongoing training to foster a knowledge-centric organizational culture.