AI-enabled competency mapping for SME talent management

Thesis Abstract

The rapid digitization of small and medium-sized enterprises (SMEs) has intensified the demand for precise and agile talent management systems, yet many SMEs struggle to align workforce capabilities with strategic objectives due to limited resources and fragmented competency frameworks. This study addresses the problem of inadequate, ICT-enabled competency mapping that constrains SME talent management by providing actionable, data-driven insights into employee capabilities, performance potential, and developmental pathways. The aim is to develop and validate an AI-enabled competency mapping framework that integrates formal qualifications, experiential data, and behavioral indicators to optimize recruitment, development, succession planning, and retention within SMEs. Specific objectives include (1) designing a multimodal data model for competency capture from HRIS, performance reviews, learning management systems, and manager assessments; (2) constructing an AI-driven scoring and profiling mechanism that maps individual competencies to organizational roles and strategic goals; (3) evaluating the framework’s impact on recruitment efficiency, training ROI, internal mobility, and turnover intention; and (4) identifying enablers and barriers to adoption of AI-based competency mapping in SME contexts. The study adopts a mixed-methods sequential explanatory design. The quantitative phase comprises a cross-sectional survey of 312 employees and 52 HR managers across 40 SMEs in the manufacturing and services sectors, complemented by organizational archival data for six months pre- and post-implementation of the framework. The qualitative phase involves semi-structured interviews with 20 HR practitioners and 12 line managers to elucidate contextual factors, user experience, and decision-making processes. Data collection instruments include a structured competency inventory, validated psychometric scales for perceived usefulness and ease of use (Davis, 1989), and an AI readiness questionnaire tailored for SME environments. Analytical techniques include descriptive statistics, confirmatory factor analysis to validate the competency model, and structural equation modeling to test relationships among AI capability, data quality, user acceptance, and outcomes. The qualitative data will be analyzed via thematic analysis using NVivo, with triangulation to corroborate quantitative findings. The study will also employ a model specification approach to derive an operational AI-enabled competency mapping model, integrating machine learning algorithms (random forest for feature importance, gradient boosting for predictive scoring, and k-means clustering for role-based profiles) alongside a Bayesian updating mechanism for dynamic competency tracking. Expected findings indicate that the AI-enabled framework significantly improves accuracy in role-competency alignment (p < 0.01), reduces time-to-fill positions by 18–25%, enhances targeted development planning (training ROI increases by 12–20%), and lowers early turnover risk (by 8–15%) relative to baseline SME practices. The framework is anticipated to demonstrate superior predictive validity when data quality is high and HR processes are standardized, with user acceptance moderated by perceived usefulness, data governance clarity, and change-management supports. Theoretically, the study extends human capital theory and resource-based view by evidencing how AI-driven, data-integrated competency mapping creates dynamic organizational capabilities in resource-constrained SMEs. It situates the work within the Technology-Organization-Environment (TOE) framework and the Task-Technology Fit (TTF) perspective to explain adoption and performance outcomes. The study’s contribution to knowledge is multi-fold it provides a transferable, scalable AI-enabled competency mapping model tailored to SMEs, demonstrates empirical links between integrated data analytics and talent management outcomes, and offers a pragmatic blueprint for implementing AI tools in resource-limited organizations. Methodologically, it contributes to HR analytics literature by detailing the data fusion architecture, algorithmic choices, and validation strategy for competency assessment. Practically, the research yields decision-support artifacts—including a role-competency matrix, a development roadmap, and governance guidelines for data privacy and algorithm transparency. Recommendations include building governance structures for data quality and ethics, phased deployment with pilot testing in high-need departments, and capacity-building programs to improve HR practitioners’ data literacy and trust in AI-driven decisions. The study concludes that AI-enabled competency mapping can significantly enhance SME talent management, provided that data integrity, user involvement, and organizational readiness are ensured.