AI-Driven Change Management for Digital Transformation in SMEs

Thesis Abstract

The rapid proliferation of digital technologies has accelerated the need for Small and Medium-sized Enterprises (SMEs) to undertake digital transformation, yet many struggle with managing organizational change in technological adoption, resulting in suboptimal implementation and failed initiatives. This study investigates how AI-driven change management practices influence successful digital transformation in SMEs, addressing the gap between technology potential and human-centered implementation. The aim is to determine whether AI-enabled decision support, predictive analytics, and automated change communication improve employee adoption, project outcome, and overall organizational performance during digital transformation. Specific objectives are (1) to identify AI-based change management practices that correlate with higher adoption rates and project success; (2) to quantify the impact of AI-enabled communication and resistance management on change readiness; (3) to examine the moderating effects of organizational culture, leadership style, and digital literacy on the relationship between AI-driven change management practices and transformation outcomes; and (4) to develop a practical framework for SMEs to implement AI-assisted change management during digital initiatives. A mixed-methods research design will be employed, integrating quantitative survey data with qualitative insights. The population comprises SME IT and operations leaders across manufacturing, retail, and professional services sectors in the United Kingdom, with a target frame of 2,500 firms. A stratified random sample of 400 SMEs (approximately 100 per sector) will be surveyed to obtain cross-sectional data on AI-enabled change practices, change readiness, project milestones, and performance metrics. Instrumentation includes a validated Change Management Maturity questionnaire augmented with AI capability items, the Technology Adoption and Digital Transformation Survey, and a short-form organizational culture instrument. Reliability will be assessed via Cronbach’s alpha and composite reliability, with content validity established through expert panels. In-depth interviews with 24 senior managers (6 per sector) will supplement survey data, exploring contextual factors, governance, and experiences of AI-supported change interventions. Quantitative analysis will employ structural equation modeling (SEM) to test the hypothesized pathways linking AI-driven change management practices to adoption and transformation outcomes, along with multi-group SEM to assess moderating effects of culture, leadership, and digital literacy. Regression analyses will examine the incremental variance explained by AI-enabled communication, predictive analytics for risk mitigation, and automated stakeholder engagement. Qualitative data will be analyzed using thematic analysis to identify patterns in leadership behaviors, change narratives, and adoption bottlenecks, with coding conducted independently by two researchers to ensure credibility. Triangulation will integrate quantitative and qualitative findings to validate the proposed mechanisms and refine the AI-driven change management framework. Key expected findings include (a) a positive association between AI-enabled change management practices (predictive risk flags, automated stakeholder communications, and decision-support for change initiatives) and higher change readiness, user adoption, and project success; (b) significant moderation by organizational culture supportive of experimentation and transformational leadership, and by higher levels of digital literacy; (c) the identification of specific AI tools and governance processes that minimize resistance and accelerate benefits realization in SMEs. The study will contribute to knowledge by extending change management theory to incorporate AI-enabled organizational dynamics; empirically validating a framework for AI-assisted change management in resource-constrained SME contexts; and providing a pragmatic roadmap for SMEs to balance technological capabilities with human factors during digital transformation. The main conclusion is that AI-driven change management practices, when aligned with supportive culture and capable leadership, substantially enhance the effectiveness and speed of digital transformation in SMEs. Recommendations include (i) adopting an integrated AI-enabled change management platform that combines risk analytics, stakeholder analytics, and adaptive communication; (ii) investing in targeted digital literacy and leadership development to strengthen readiness; and (iii) implementing sector-specific governance models to sustain beneficial transformation, with continuous monitoring and iteration based on AI-generated insights.