Technical and organizational challenges in the adoption of large language models within enterprises: A comparative study of cloud, on-premises, and hybrid environments

This study examines the technical and organizational challenges Arab enterprises face when adopting Large Language Models (LLMs) across cloud, on-premises, and hybrid deployment environments, aiming to identify how infrastructure choices influence institutional readiness and implementation success. A mixed-methods approach was employed, combining quantitative data from structured surveys with qualitative insights from expert interviews and policy analysis. Statistical techniques, including ANOVA and regression analysis, assessed relationships between deployment environments, technical barriers, governance factors, and institutional performance. Technical challenges, particularly integration complexity, cybersecurity concerns, and resource constraints, emerged as the strongest predictors of institutional performance. Organizational factors such as leadership support and governance readiness function as enabling conditions. The hybrid deployment model demonstrated context-sensitive advantages, offering flexibility and control while requiring advanced coordination. Successful LLM implementation requires alignment between infrastructure choices and organizational maturity levels, with deployment strategies tailored to institutional capabilities. Organizations should prioritize developing AI governance frameworks, investing in specialized training programs, and selecting deployment models matched to their technical and organizational readiness, particularly relevant for enterprises undergoing digital transformation in the Arab region.