Organizational AI Readiness: Identifying Capability Gaps through a mixed-method approach using SEM

Abstract

This thesis investigates how a large, decentralized high-tech electronics manufacturer can realize measurable business value from generative and agentic AI by developing Organizational AI Readiness (OAIR). Using an exploratory sequential mixed-methods design, we first conduct a scoping review, ten semi-structured interviews, and an executive questionnaire to identify five capability themes and casespecific gaps in skills, infrastructure, governance, and value logic. These insights inform a PLS-SEM survey study (82 respondents) that operationalizes OAIR through established constructs: Staff Skills and Competency, IT Infrastructure, Trust in Organizational AI, Perceived Risk, Top Management Support, and AI Strategy Alignment. The model shows that AI Strategy Alignment and IT Infrastructure have the strongest positive effects on OAIR, while Perceived Risk significantly undermines Trust in Organizational AI. The study contributes an empirically grounded OAIR framework.