AI-Driven Change Management for Digital Transformation in SMEs

Thesis Abstract

This study addresses the persistent gap between digital transformation initiatives and tangible organizational outcomes in small and medium-sized enterprises (SMEs) by examining how AI-driven change management practices influence the success of digital adoption. The central problem is that SMEs often deploy AI-enabled technologies without systematic change management, leading to low user adoption, misalignment with business processes, and suboptimal performance gains. The aim is to develop an evidence-based framework that integrates AI-enabled decision support, stakeholder engagement, and iterative learning to accelerate successful digital transformation in SMEs. Specific objectives are (1) to identify AI-driven change management practices associated with higher adoption rates and process improvements; (2) to quantify the impact of AI-assisted change management on operational performance, employee engagement, and innovation capability; (3) to examine how organizational culture, leadership, and data governance mediate these relationships; (4) to compare industry-specific effects across manufacturing, retail, and professional services SMEs; and (5) to formulate a practical implementation blueprint for SME managers and policy makers. The study adopts a mixed-methods design, combining quantitative analysis with qualitative insights. The population comprises SME executives and managers across three industry sectors in a defined regional economy, targeting firms with active or recent AI-driven digital transformation initiatives. A stratified random sample of 320 SMEs will be invited to participate in a structured survey, with an anticipated valid response rate yielding 250 usable responses for quantitative analysis. In-depth interviews will be conducted with 40 key informants (CEOs, CIOs, Change Leads) to contextualize survey results and explore mechanisms behind observed relationships. Data collection instruments include a validated Change Management in AI-enabled Transformations (CM-AIT) scale, operational performance indicators (throughput, cycle time, defect rate), and process alignment metrics, complemented by semi-structured interview guides. Validity and reliability will be ensured through instrument pre-testing, Cronbach’s alpha assessment (>0.70), and confirmatory factor analysis for the survey constructs. Triangulation will be employed to strengthen trustworthiness. Analytical approaches comprise descriptive statistics and multivariate regression to test hypotheses regarding the effect of AI-driven change management practices on adoption rates and performance outcomes, as well as structural equation modeling (SEM) to evaluate the mediating roles of culture, leadership style, and data governance. Market segmentation effects will be analyzed via multi-group SEM to detect sectoral differences. Thematic analysis will be applied to interview transcripts to elucidate contextual factors, barriers, and facilitators, with coding conducted by two independent researchers to ensure credibility. A residual analysis will check for model misspecification, while robustness checks will include alternative model specifications and bootstrapped confidence intervals. Key expected findings include (i) a positive association between AI-augmented change management capabilities (stakeholder engagement, iterative experimentation, and real-time performance feedback) and AI adoption rates and process improvements; (ii) significant mediation by organizational culture of learning and psychological safety, leadership in change, and data governance maturity; (iii) sector-specific variations, with manufacturing SMEs benefiting more from integration of AI-driven process standardization, and service-oriented SMEs from agile governance and customer-centric AI deployment; (iv) identification of critical enablers, such as top-management sponsorship, cross-functional teams, and continuous upskilling, that amplify benefits. The study contributes to knowledge by operationalizing a theoretically grounded framework that links AI-enabled decision support and change management practices to measurable transformation outcomes in SMEs, integrating theories of distributed leadership, technology-organization-environment (TOE) framework, and theAdaptive Change Model. It also provides practical implications, including an evidence-based implementation blueprint highlighting governance structures, change agents, governance processes, data quality requirements, and metrics for monitoring ROI. The conclusion emphasizes that AI-driven change management is essential for translating digital investments into sustained performance gains, and recommends policies that promote shared governance, accessible AI literacy programs for SME managers, and standardized metrics for digital transformation success.