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

Sammanfattning

This thesis investigates how a large, decentralized high-tech electronics manufacturer can realizemeasurable 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, tensemi-structured interviews, and an executive questionnaire to identify five capability themes and casespecificgaps in skills, infrastructure, governance, and value logic. These insights inform a PLS-SEMsurvey study (82 respondents) that operationalizes OAIR through established constructs: Staff Skills andCompetency, 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 thestrongest positive effects on OAIR, while Perceived Risk significantly undermines Trust inOrganizational AI. The study contributes an empirically grounded OAIR framework.