Barriers and facilitators to artificial intelligence adoption in organizations: a systematic review

Abstract

Introduction: Organizational adoption of artificial intelligence depends on more than access to technical tools. Data quality, human capabilities, leadership, infrastructure, trust, and regulation influence whether an application moves from experimentation to stable, legitimate, and value-generating use. Method: A PRISMA 2020 systematic review of Scopus-indexed studies published from 2021 to 2026 was conducted. Search terms combined artificial intelligence, adoption, readiness, organization, barriers, facilitators, and drivers. Explicit eligibility criteria, structured extraction, and methodological appraisal were applied. Sixteen studies were included in the qualitative synthesis and compared across technical, human, managerial, and external dimensions. Results: Frequent facilitators included data quality and governance, senior-management support, explicit strategy, internal skills, and interdisciplinary collaboration. Barriers involved fragmented data, skill shortages, costs, technological incompatibility, resistance, regulatory uncertainty, and doubts about return on investment. Trust increased when transparency, data protection, and meaningful human oversight were present. The relative importance of these factors varied by sector, organizational size, digital maturity, and the characteristics of the proposed use case. Conclusions: Artificial intelligence adoption is a sociotechnical process requiring alignment among data, technology, people, and governance. Organizations should assess readiness before scaling, prioritize use cases with demonstrable value, and address technical, cultural, ethical, and regulatory barriers simultaneously. Readiness should therefore be treated as an ongoing organizational capability rather than a one-time technical assessment in practice.